diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
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 # stability-analyses
-code for simulation studies and experimental microbiome data applications in Stability manuscript
+This set of codes are used for reproducing all the simulation studies and experimental microbiome data applications in Stability manuscript.
+
+I. General code:
+	
+code_method folder: contain codes to reproduce simulation results for continuous outcomes
+
+	getStability.R: function to calculate Stability Index
+
+	cv_method.R: code for 4 selected feature selection methods with user-defined parameter grids and cross-validations for parameter tuning when applied to continuous outcomes
+
+	cv_method_binary_update.R: code for 4 selected feature selection methods with user-defined parameter grids and cross-validations for parameter tuning when applied to binary outcomes
+
+	stab_data_applications.R: function to perform hypothesis testing using bootstrap for continuous outcomes
+
+	stab_data_applications_binary.R: function to perform hypothesis testing using bootstrap for binary outcomes
+
+	bootstrap_test_compLasso_rf.R: general functions for comparing feature selection methods using hypothesis testing based on bootstrap when applied to continuous outcomes
+
+	bootstrap_test_compLasso_rf_binary.R: general functions for comparing feature selection methods using hypothesis testing based on bootstrap when applied to binary outcomes
+
+	source code for compositional lasso (continuous outcome) is available at: https://www.math.pku.edu.cn/teachers/linw/software.html
+	source code for compositional lasso (binary outcome) is available at: https://github.com/UVic-omics/Microbiome-Variable-Selection
+
+
+II. Simulation part (within simulations folder):
+
+sim_data_generation folder: contain codes to generate simulated data
+
+	sim_dat_ind_toeplitz: code to generate simulated data with Independent and Toeplitz correlation designs
+	sim_dat_block.R: code to generate simulated data with Block correlation design
+	run_sim_data.sh: bash commands for running simulation data generation code on HPC
+
+code_sim_cts folder: contain codes to reproduce simulation results for continuous outcomes 
+
+	cv_sim_apply.R: general functions for applying selected feature selection methods to simulated data when applied to continuous outcomes
+
+	1. compute Stability and MSE for different simulation scenarios
+	ind_results.R: code for comparing 3 methods (lasso, elastic net, random forests) in simulated data with Independent design and continuous outcomes
+	toe_results.R: code for comparing 3 methods (lasso, elastic net, random forests) in simulated data with Toeplitz design and continuous outcomes
+	block_results.R: code for comparing 3 methods (lasso, elastic net, random forests) in simulated data with Block design and continuous outcomes
+	CL_sim_apply.R: code for obtaining results for compositional lasso in all simulation correlation designs with continuous outcomes
+
+	2. hypothesis testing with bootstrap for selected simulation scenarios
+	boot_CL_testing.R: code for calculating bootstrapped confidence interval for compositional lasso method in simulated data with continous outcomes
+	boot_RF_testing.R: code for calculating bootstrapped confidence interval for random forests method in simulated data with continous outcomes
+
+	3. bash commands
+	run_sim_cts.sh: bash commands for running simulation code for continous outcomes on HPC
+
+
+code_sim_bin folder: contain codes to reproduce simulation results for binary outcomes
+
+	cv_sim_apply_binary_update.R: general functions for applying selected feature selection methods to simulated data when applied to binary outcomes
+
+	1. compute Stability and AUC for different simulation scenarios
+	ind_results_binary_update.R: code for comparing all 4 methods in simulated data with Independent design and binary outcomes
+	toe_results_binary_update.R: code for comparing all 4 methods in simulated data with Toeplitz design and binary outcomes
+	block_results_binary_update.R: code for comparing all 4 methods in simulated data with Block design and binary outcomes
+
+	2. hypothesis testing with bootstrap for selected simulation scenarios
+	boot_sim_binary.R: code for calculating bootstrapped confidence interval for compositional lasso and random forests methods in simulated data with binary outcomes
+
+	3. bash commands
+	run_sim_bin.sh: bash commands for running simulation code for binary outcomes on HPC
+
+notebooks_sim_cts folder: contain notebooks (R) to summarize simulation results for continuous outcome
+
+notebooks_sim_bin folder: contain notebooks (R) to summarize simulation results for binary outcome	
+
+results_summary_cts folder: contain outputs of tables from notebooks in notebooks_sim_cts folder
+
+results_summary_bin folder: contain outputs of tables from notebooks in notebooks_sim_bin folder
+
+figures_combined folder: contain figures generated for both continous and binary outcomes based on notebook 6_make_figures_combined in notebooks_sim_bin folder
+
+
+III. application part (within data_application folder):
+
+	code_cts folder: contain code for real data applications to BMI & soil datasets for continuous outcomes
+
+	code_bin folder: contain code for real data applications to BMI & soil datasets for binary outcomes
+
+	notebooks_applications folder: contain notebooks (R) to summarize microbiome application results for continuous and binary outcomes
+
+	88soils folder: contain data and application results for soil datast
+
+	BMI folder: contain data and application results for BMI datast
+
diff --git a/code_method/.DS_Store b/code_method/.DS_Store
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diff --git a/code_method/bootstrap_test_compLasso_rf.R b/code_method/bootstrap_test_compLasso_rf.R
new file mode 100755
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+##########################################################
+### hypothesis testing with bootstraps ###################
+##########################################################
+
+source('cv_method.R') 
+source('getStability.R')
+
+## set up parallel computing
+library(foreach)
+library(doParallel)
+numCores <- detectCores() - 2 
+registerDoParallel(numCores)  # use multicore, set to the number of our cores
+
+boot_stab_sim = function(num_boot=100, sim_file, method, seednum=31, ratio.training=0.8, fold.cv=10, 
+					  family='gaussian', lambda.grid=exp(seq(-4, -2, 0.2)), alpha.grid=seq(0.1, 0.9, 0.1), 
+	                  mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05){
+	
+	# load simulated data
+	load(sim_file, dat <- new.env())
+
+	idx.start = 1; idx.stop = 100 # 100 repetitions for each simulated scenario
+	rou = dat$sim_array[[1]]$rou # rou, n, p are same across all repetitions
+	n = dat$sim_array[[1]]$n
+	p = dat$sim_array[[1]]$p
+
+	## get a vector of stability index from bootstrapped data
+	stab_index = rep(0, num_boot)
+	b = 0
+	for (i in idx.start:idx.stop){
+		b = b + 1
+		print(paste('index', i, sep=':'))
+	    sub = dat$sim_array[[i]]
+
+	    # bootsrap with parallelization
+		selections = foreach (i=1:num_boot) %dopar% {
+			N = length(sub$Y) # number of samples
+			boot_ids = sample(N, size=N, replace=TRUE)
+			boot_Z = sub$Z[boot_ids, ]
+			boot_Y = sub$Y[boot_ids, ]
+
+		    ## select features from lasso/elnet
+		    if (method == 'compLasso'){
+		        result.lin = cons_lasso_cv(y=boot_Y, datx=boot_Z, seednum=i, ratio.training=ratio.training)
+		        select.lin = result.lin$coef.chosen  # since 1 represents intercept
+
+		    } else if (method == 'RF'){
+		        result.rf = randomForest_cv(y=boot_Y, datx=boot_Z, seednum=i, fold.cv=fold.cv, 
+	    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr)
+	            select.rf = result.rf$coef.chosen 
+		    }
+		}
+
+		# calculate stability index from bootstrapped data
+		stability_table = matrix(rep(0, num_boot * p), ncol=p)
+		for (j in 1:num_boot){
+		stability_table[j, selections[[j]]] = 1 
+		}
+
+		stab_index[b] = round(getStability(stability_table)$stability, 2)
+	}	
+
+	results=list(rou=rou, n=n, p=p, num_boot=num_boot, method=method, stab_index=stab_index)
+
+}
+
+
+########################################################
+## double bootstrap applied to real data application ###
+########################################################
+boot_stab_data = function(num_boot=100, data_file, method, seednum=31, ratio.training=0.8, fold.cv=10, 
+					      family='gaussian', lambda.grid=exp(seq(-4, -2, 0.2)), alpha.grid=seq(0.1, 0.9, 0.1), 
+	                      mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05){
+	
+	# load simulated data
+	set.seed(seednum)
+	load(data_file)
+	p = dim(taxa)[2]
+
+	stab_index = rep(0, num_boot)
+	MSE_list = list()
+	# first loop of bootstrap to generate num_boot bootstrapped datasets
+	for (k in 1:num_boot){
+        print(paste('num_boot', k, sep=':'))
+		N = length(y) # number of samples
+		sample_ids = seq(1, N, 1)
+
+		# bootstrapped samples
+	    boot_ids = sample(sample_ids, size=N, replace=TRUE)
+	    boot_taxa = taxa[boot_ids, ]
+	    boot_mf = y[boot_ids]
+
+	    ## second loop of bootstrap to perform variable selection
+		results = foreach (i=1:num_boot) %dopar% {
+			boot_ids_second = sample(N, size=N, replace=TRUE)
+			boot_Z = boot_taxa[boot_ids_second, ]
+			boot_Y = boot_mf[boot_ids_second]
+
+		    ## select features from lasso/elnet
+		    if (method == 'compLasso'){
+		        result.lin = cons_lasso_cv(y=boot_Y, datx=boot_Z, seednum=i, ratio.training=ratio.training)
+		        output.lin = c(result.lin$MSE, result.lin$coef.chosen) 
+
+		    } else if (method == 'RF'){
+		        result.rf = randomForest_cv(y=boot_Y, datx=boot_Z, seednum=i, fold.cv=fold.cv, 
+	    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr)
+	            output.rf = c(result.rf$MSE, result.rf$coef.chosen) 
+		    }
+		}
+
+		# reformat results (stability & MSE)
+		stability_table = matrix(rep(0, num_boot * p), ncol=p)
+		results_mse = results_chosen = list()
+		for (b in 1:num_boot){
+			results_mse[b] = results[[b]][1]
+			results_chosen[[b]] = results[[b]][-1]
+			stability_table[b, results_chosen[[b]]] = 1 
+		}
+
+		stab_index[k] = round(getStability(stability_table)$stability, 2)
+		MSE_list[[k]] = results_mse
+	}
+
+
+	results=list(num_boot=num_boot, method=method, stab_index=stab_index, MSE_list=MSE_list)
+
+}
diff --git a/code_method/bootstrap_test_compLasso_rf_binary.R b/code_method/bootstrap_test_compLasso_rf_binary.R
new file mode 100644
index 0000000..91d3664
--- /dev/null
+++ b/code_method/bootstrap_test_compLasso_rf_binary.R
@@ -0,0 +1,137 @@
+##########################################################
+### estimate correlation between stability index #########
+##########################################################
+
+source('cv_method_binary_update.R')
+source('getStability.R')
+
+## set up parallel computing
+library(foreach)
+library(doParallel)
+numCores <- detectCores() - 2 # 6 cores
+registerDoParallel(numCores)  # use multicore, set to the number of our cores
+
+########################################################
+## double bootstrap applied to simulations ###
+########################################################
+boot_stab_sim = function(num_boot=100, sim_file, method, seednum=31, 
+                         ratio.training=0.8, fold.cv=10, 
+                         family='binomial', lambda.grid=exp(seq(-4, -2, 0.2)), 
+                         alpha.grid=seq(0.1, 0.9, 0.1), 
+                         mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05,
+                         lambda.coda=seq(0.1, 0.2, 0.01), data.split=FALSE){
+	
+	# load simulated data
+	load(sim_file, dat <- new.env())
+
+	idx.start = 1; idx.stop = 100 # 100 repetitions for each simulated scenario
+	rou = dat$sim_array[[1]]$rou # rou, n, p are same across all repetitions
+	n = dat$sim_array[[1]]$n
+	p = dat$sim_array[[1]]$p
+
+	## get a vector of stability index from bootstrapped data
+	stab_index = rep(0, num_boot)
+	b = 0
+	for (i in idx.start:idx.stop){
+		b = b + 1
+		print(paste('index', i, sep=':'))
+	    sub = dat$sim_array[[i]]
+        y_binary = as.factor(ifelse(sub$Y >= median(sub$Y), 1, 0))
+
+	    # bootsrap with parallelization
+		selections = foreach (i=1:num_boot) %dopar% {
+			N = length(y_binary) # number of samples
+			boot_ids = sample(N, size=N, replace=TRUE)
+			boot_Z = sub$Z[boot_ids, ]
+			boot_Y = y_binary[boot_ids]
+            boot_X = sub$X[boot_ids, ] # for generalized compositional lasso 
+
+		    ## select features 
+		    if (method == 'GenCompLasso'){# generalized compositional lasso use X instead of Z 
+		        result.lin = gen_cons_lasso_cv(y=boot_Y, datx=boot_X, seednum=i, ratio.training=ratio.training,
+                                               lambda.coda=lambda.coda, data.split=data.split)
+		        select.lin = result.lin$coef.chosen 
+
+		    } else if (method == 'RF'){
+		        result.rf = randomForest_cv(y=boot_Y, datx=boot_Z, seednum=i, fold.cv=fold.cv, 
+	    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr)
+	            select.rf = result.rf$coef.chosen 
+		    }
+		}
+
+		# calculate stability index from bootstrapped data
+		stability_table = matrix(rep(0, num_boot * p), ncol=p)
+		for (j in 1:num_boot){
+		stability_table[j, selections[[j]]] = 1 
+		}
+
+		stab_index[b] = round(getStability(stability_table)$stability, 2)
+	}	
+
+	results=list(rou=rou, n=n, p=p, num_boot=num_boot, method=method, stab_index=stab_index)
+
+}
+
+
+########################################################
+## double bootstrap applied to real data application ###
+########################################################
+boot_stab_data = function(num_boot=100, data_file, method, seednum=31, 
+                          ratio.training=0.8, fold.cv=10, 
+					      family='binomial', lambda.grid=exp(seq(-4, -2, 0.2)), 
+                          alpha.grid=seq(0.1, 0.9, 0.1), 
+	                      mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05,
+                          lambda.coda=seq(0.1, 0.2, 0.01), data.split=FALSE){
+	
+	# load simulated data
+	set.seed(seednum)
+	load(data_file)
+	p = dim(taxa)[2]
+
+	stab_index = rep(0, num_boot)
+	ROC_list = list()
+	# first loop of bootstrap to generate num_boot bootstrapped datasets
+	for (k in 1:num_boot){
+        print(paste('num_boot', k, sep=':'))
+		N = length(y) # number of samples
+		sample_ids = seq(1, N, 1)
+
+		# bootstrapped samples
+	    boot_ids = sample(sample_ids, size=N, replace=TRUE)
+	    boot_taxa = taxa[boot_ids, ]
+	    boot_mf = y[boot_ids]
+
+	    ## second loop of bootstrap to perform variable selection
+		results = foreach (i=1:num_boot) %dopar% {
+			boot_ids_second = sample(N, size=N, replace=TRUE)
+			boot_Z = boot_taxa[boot_ids_second, ]
+			boot_Y = boot_mf[boot_ids_second]
+
+		    ## select features from lasso/elnet
+		    if (method == 'GenCompLasso'){ # use X instead of Z (log-transformed)
+		        result.lin = gen_cons_lasso_cv(y=boot_Y, datx=exp(boot_Z), seednum=i, ratio.training=ratio.training)
+		        output.lin = c(result.lin$ROC, result.lin$coef.chosen)                           
+		    } else if (method == 'RF'){
+		        result.rf = randomForest_cv(y=boot_Y, datx=boot_Z, seednum=i, fold.cv=fold.cv, 
+	    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr)
+	            output.rf = c(result.rf$ROC, result.rf$coef.chosen) 
+		    }
+		}
+
+		# reformat results (stability & ROC)
+		stability_table = matrix(rep(0, num_boot * p), ncol=p)
+		results_mse = results_chosen = list()
+		for (b in 1:num_boot){
+			results_mse[b] = results[[b]][1]
+			results_chosen[[b]] = results[[b]][-1]
+			stability_table[b, results_chosen[[b]]] = 1 
+		}
+
+		stab_index[k] = round(getStability(stability_table)$stability, 2)
+		ROC_list[[k]] = results_mse
+	}
+
+
+	results=list(num_boot=num_boot, method=method, stab_index=stab_index, ROC_list=ROC_list)
+
+}
diff --git a/code_method/code_method/.DS_Store b/code_method/code_method/.DS_Store
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diff --git a/code_method/cv_method.R b/code_method/cv_method.R
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+++ b/code_method/cv_method.R
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+###########################################
+#### methods for comparisions #############
+###########################################
+library(glmnet)
+library(caret)
+library(ranger) # faster random forest
+
+# general reference with caret on glmnet & random forest
+## http://rstudio-pubs-static.s3.amazonaws.com/251240_12a8ecea8e144fada41120ddcf52b116.html
+
+# important note
+## when training model on training set, as tuning parameters are set in final model, coefficient are chosen already
+## use test set to predict the MSE
+## as unclear how carret handle fitting and prediction, use carret only for parameter tuning in elastic net
+
+###########################################
+#### Lasso (tune lambda) ##################
+###########################################
+#' @title lasso_cv
+#' @description Does a K-fold cross-validation for Lasso.
+#' @param datx The input data matrix.
+#' @param y The response variable.
+#' @param seednum The seed number (default=31).
+#' @param family The family of linear regression models (default="gaussian").
+#' @param ratio.training The ratio of the whole data assigned for model training (default=0.8).
+#' @param fold.cv The number of folds for cross-validation.(default=10)
+#' @param lambda.grid The tuning range for the regularization parameter "lambda" of "lasso".
+#' @return A list of Lasso model output including MSE, and selected features.
+
+lasso_cv = function(datx, y, seednum=31, family=family, ratio.training=0.8, fold.cv=10, 
+					lambda.grid, lambda.choice='lambda.1se'){
+	# seednum: the seed number
+	# ratio.training: the ratio of training set (parento principle training:test=8:2)
+	# lambda.grid: possible candidate values for tuning parameter Lambda 
+	# fold.cv: n-fold cross validation
+	# lambda.choice: 'lambda.min' or 'lambda.1se' 
+
+	set.seed(seednum)
+
+	# # split data into training and tests sets
+	nn <- length(y)
+	trn <- sample(1:nn, ratio.training*nn)
+	x.train <- datx[trn, ]
+	x.test <- datx[-trn, ]
+	y.train <- y[trn]
+	y.test <- y[-trn]
+
+	# use cross-validation on training data & fit on test data
+	cv.fit <- cv.glmnet(x.train, y.train, family=family, alpha=1, lambda=lambda.grid, nfolds=fold.cv)
+	pred.fit <- predict(cv.fit, s=lambda.choice, newx=x.test, type='response')
+
+	# covariates chosen 
+	coef.fit <- predict(cv.fit, s=lambda.choice, newx=x.test, type='coefficients')
+	coef.chosen = which(coef.fit != 0) 
+    coef.chosen = coef.chosen - 1 # index for 1 representing intercept
+
+	# evaluate results
+	MSE <- mean((y.test - pred.fit)^2)
+
+
+	result = list(MSE=MSE, coef.chosen=coef.chosen)
+	return(result)
+}
+
+###########################################
+#### Elastic Net (tune lambda and alpha) ##
+###########################################
+# reference 1 (tune both parameters with caret)
+##  https://stats.stackexchange.com/questions/268885/tune-alpha-and-lambda-parameters-of-elastic-nets-in-an-optimal-way
+# reference 2 (extract final model and prediction with caret)
+## https://topepo.github.io/caret/model-training-and-tuning.html
+
+#' @title elnet_cv
+#' @description Does a K-fold cross-validation for elastic-net.
+#' @param datx The input data matrix.
+#' @param y The response variable.
+#' @param seednum The seed number (default=31).
+#' @param family The family of linear regression models (default="gaussian").
+#' @param ratio.training The ratio of the whole data assigned for model training (default=0.8).
+#' @param fold.cv The number of folds for cross-validation.(default=10)
+#' @param lambda.grid The tuning range for the regularization parameter "lambda" of "lasso".
+#' @param alpha.grid The tuning range for the elastic-net mixing parameter "alpha".
+#' @return A list of elnet model output including MSE, and selected features.
+
+elnet_cv = function(datx, y, seednum=31, alpha.grid, lambda.grid, family=family, 
+	                ratio.training=0.8, fold.cv=10){
+	# seednum: the seed number
+	# ratio.training: the ratio of training set (parento principle training:test=8:2)
+	# alpha.grid: possible candidate values for tuning parameter alpha
+	# lambda.grid: possible candidate values for tuning parameter Lambda 
+	# fold.cv: n-fold cross validation
+
+	set.seed(seednum)
+
+	# # split data into training and tests sets
+	nn <- length(y)
+	trn <- sample(1:nn, ratio.training*nn)
+	x.train <- datx[trn, ]
+	x.test <- datx[-trn, ]
+	y.train <- y[trn]
+	y.test <- y[-trn]
+
+	data.train = as.data.frame(cbind(y.train, x.train)) 
+	colnames(data.train) = c('y', paste('V', seq(1, dim(datx)[2]), sep=''))
+
+	# tune parameters with CV on training data with caret
+	trnCtrl <- trainControl(method = "cv", number = fold.cv)
+	srchGrid <- expand.grid(.alpha = alpha.grid, .lambda = lambda.grid)
+
+	my_train <- train(y ~., data.train,
+              		  method = "glmnet",
+                      tuneGrid = srchGrid,
+                      trControl = trnCtrl)
+
+	# fit the model with best tuning parameters on test data with glmnet
+	fit = glmnet(x.train, y.train, alpha=my_train$bestTune$alpha, lambda=my_train$bestTune$lambda)
+	pred.fit <- predict(fit, x.test, type='response')
+	
+	# covariates chosen 
+	coef.fit <- predict(fit, x.test, type='coefficients')
+	coef.chosen = which(coef.fit != 0) 
+    coef.chosen = coef.chosen - 1 # index for 1 representing intercept
+
+	# evaluate results
+	MSE <- mean((y.test - pred.fit)^2)
+
+
+	result = list(MSE=MSE, coef.chosen=coef.chosen)
+	return(result)
+}
+
+
+###############################################################
+#### Random Forests (tune # variables at each random split ####
+###############################################################
+# reference on regression random forst
+## https://uc-r.github.io/random_forests
+# OOB error is different from test error (see above website)
+
+#' @title randomForest_cv
+#' @description Does a K-fold cross-validation for random forests.
+#' @param datx The input data matrix.
+#' @param y The response variable.
+#' @param sim_file The path of a .RData file includes the simulation data pre-generated.
+#' @param seednum The seed number (default=31).
+#' @param fold.cv The number of folds for cross-validation.(default=10)
+#' @param ratio.training The ratio of the whole data assigned for model training (default=0.8).
+#' @param mtry.grid The tuning range for the hyperparameter "mtry", that is, number of variables to possibly split at in each node. 
+#' @param num_trees The number of trees grow in random forests.
+#' @param pval_thr The threshold for the estimated p value of RF importance scores.
+#' @param method.perm The permutation method for estimating the p value of RF importance scores.
+#' @return A list of RF model output including the "mtry" grid, selected features, MSE, OOB, and p-values of selected features.
+
+randomForest_cv = function(datx, y, seednum=31, fold.cv=5, ratio.training=0.8, mtry.grid=10, num_trees=500, 
+						   pval_thr=0.05, method.perm='altmann'){
+	# mtry: number of variables to randomly sample at each split
+	# num_trees: number of trees to grow in random forests
+	# pval_thr: threshold for permutation test
+	# note that permutation p-value can use "altmann method" for all types of data; 'Janita' for high-dimensitional data only
+	# ref on permutation methods: http://finzi.psych.upenn.edu/library/ranger/html/importance_pvalues.html
+
+	set.seed(seednum)  
+
+	# split data into training and tests sets
+	data = as.data.frame(cbind(y, datx)) 
+	colnames(data) = c('y', paste('V', seq(1, dim(datx)[2]), sep=''))
+	inTraining = createDataPartition(data$y, p = ratio.training, list=FALSE)
+	train <- data[inTraining, ]
+	test <- data[-inTraining, ]  
+
+	# tune parameter with cross validation
+	hyper.grid <- expand.grid(mtry = mtry.grid, OOB_RMSE = 0)
+	for (i in 1:nrow(hyper.grid)){
+	  model = ranger::ranger(y ~., data = train,
+	                 num.trees=500, mtry=hyper.grid$mtry[i],
+	                 seed=seednum, importance = 'permutation')
+	  hyper.grid$OOB_RMSE[i] = sqrt(model$prediction.error)
+	}
+	OOB = min(hyper.grid$OOB_RMSE) # out of bag error
+	position = which.min(hyper.grid$OOB_RMSE)
+
+	# permutation test on tuned random forst model to obtain chosen features
+	if (method.perm == 'altmann'){ # for all data types
+		rf.model <- ranger::ranger(y ~., data=test, num.trees = num_trees,
+					       mtry = hyper.grid$mtry[position], importance = 'permutation')
+	    table = as.data.frame(importance_pvalues(rf.model, method = "altmann", 
+                              formula = y ~ ., data = test)) 
+	} else if (method.perm == 'janitza'){ # for high dimensional data only
+		rf.model <- ranger::ranger(y ~., data=test, num.trees = num_trees,
+					       mtry = hyper.grid$mtry[position], importance = 'impurity_corrected')
+	    table = as.data.frame(importance_pvalues(rf.model, method = "janitza", 
+                              formula = y ~ ., data = test)) 		
+	}
+
+    coef.chosen = which(table$pvalue < pval_thr) 
+
+    # if nothing been selected
+    if (identical(coef.chosen, integer(0))){
+    	coef.chosen = 0
+    }
+
+    # obtain additional prediction error to make comparable to other methods
+    pred_rf = predict(rf.model, test)
+    MSE <- mean((test$y - pred_rf$predictions)^2)
+
+	result = list(mtry=mtry.grid[position], coef.chosen=coef.chosen, MSE=MSE, OOB=OOB, p.value=table$pvalue)
+	return(result)
+
+}
+
+
+###############################################################
+#### Compositional Lasso by Lin et al 2014 ####################
+###############################################################
+dyn.load("../../code_Lin/cvs/cdmm.dll")
+source("../../code_Lin/cvs/cdmm.R")
+
+#' @title cons_lasso_cv
+#' @description Does a K-fold cross-validation for elastic-net.
+#' @param datx The input data matrix.
+#' @param y The response variable.
+#' @param seednum The seed number (default=31).
+#' @param ratio.training The ratio of the whole data assigned for model training (default=0.8).
+#' @return A list of compLasso model output including MSE, and selected features.
+
+cons_lasso_cv = function(datx, y, seednum, ratio.training=0.8){
+	set.seed(seednum)
+	z = datx
+	n = length(y)
+
+	itrn = sample(n, ratio.training*n)
+	itst = setdiff(1:n, itrn)
+	ans <- cv.cdmm(y[itrn], z[itrn, ], refit=TRUE) # proposed method (default: 10 fold CV)
+	bet <- ans$bet; int <- ans$int
+	pe <- mean((y[itst] - int - z[itst, ] %*% bet)^2)
+
+	coef.chosen = which(bet != 0) # the first beta refer to 1st feature
+	MSE = pe
+
+	result = list(MSE=MSE, coef.chosen=coef.chosen)
+	return(result)
+}
+
+###########################################################
+#### Lasso (double cv -- no validation set) ###############
+###########################################################
+lasso_double_cv = function(datx, y, seednum=31, family=family, fold.cv=10, lambda.grid){
+	set.seed(seednum)
+
+	## double loop for cross-vlidation 
+	flds <- caret::createFolds(y, k = fold.cv, list = TRUE, returnTrain = FALSE)
+
+	## outer loop for estimating MSE
+	MSE = STAB = matrix(rep(0, fold.cv * length(lambda.grid)), nrow=fold.cv)
+	rownames(MSE) = rownames(STAB) = paste('fold', seq(1:fold.cv), sep='')
+	colnames(MSE) = colnames(STAB) = paste('lambda', seq(1:length(lambda.grid)), sep='')
+	for (b in 1:fold.cv){
+		idx.test = flds[[b]]
+		x.train = datx[-idx.test, ]
+		y.train = y[-idx.test]
+		x.test = datx[idx.test, ]
+		y.test = y[idx.test]
+
+		fit = glmnet(x.train, y.train, family=family, alpha=1, lambda=lambda.grid)
+		pred.fit <- predict(fit, newx=x.test, s=lambda.grid, type='response')
+		MSE[b, ] <- colMeans((y.test - pred.fit)^2)
+
+		## inner loup for estimating stability
+		flds.inn <- createFolds(y.train, k = fold.cv, list = TRUE, returnTrain = FALSE)
+		stab.table.inn = list()
+		for (bb in 1:fold.cv){
+		idx.test.inn = flds.inn[[bb]]
+		stab.x.train = x.train[-idx.test.inn, ]
+		stab.y.train = y.train[-idx.test.inn]
+		stab.x.test = x.train[idx.test.inn, ]
+		stab.y.test = y.train[idx.test.inn]
+
+		fit.inn = glmnet(stab.x.train, stab.y.train, family=family, alpha=1, lambda=lambda.grid)
+		coef.fit.inn <- predict(fit.inn, newx=stab.x.test, s=lambda.grid, type='coefficients')
+		coef.chosen.table = as.matrix(coef.fit.inn)
+		coef.chosen.table = coef.chosen.table[-1, ] # no need of intercept
+		coef.chosen.table[coef.chosen.table != 0] = 1 # convert to binary table
+		coef.chosen.table[coef.chosen.table == 0] = 0
+		colnames(coef.chosen.table) = paste('lambda', seq(1:length(lambda.grid)), sep='')
+		stab.table.inn[[bb]] = coef.chosen.table
+		}
+
+		# calculate stability
+		table.tmp = matrix(rep(0, dim(datx)[2] * fold.cv), nrow=fold.cv)
+		for (i in 1:length(lambda.grid)){
+			for (bb in 1:fold.cv){
+				table.tmp[bb, ] = stab.table.inn[[bb]][, i]
+			}
+			STAB[b, i] = round(getStability(table.tmp)$stability, 2)
+		}
+	}
+
+	result = list(lambda.grid=lambda.grid, MSE.list=MSE, STAB.list=STAB, MSE.value=colMeans(MSE), STAB.value=colMeans(STAB))
+	return(result)
+}
+
+
+###########################################################
+#### RF (double cv -- no validation set) ###############
+###########################################################
+randomForest_double_cv = function(datx, y, seednum=31, fold.cv=5, mtry.grid=10, num_trees=500, pval_thr=0.05){
+	set.seed(seednum)  
+
+	## double loop for cross-vlidation 
+	data = as.data.frame(cbind(y, datx)) 
+	colnames(data) = c('y', paste('V', seq(1, dim(datx)[2]), sep='')) 
+	flds <- createFolds(data$y, k = fold.cv, list = TRUE, returnTrain = FALSE)
+
+	## outer loop for estimating MSE
+	MSE = STAB = matrix(rep(0, fold.cv * length(mtry.grid)), nrow=fold.cv)
+	rownames(MSE) = rownames(STAB) = paste('fold', seq(1:fold.cv), sep='')
+	colnames(MSE) = colnames(STAB) = paste('mtry', seq(1:length(mtry.grid)), sep='')
+	for (b in 1:fold.cv){
+		idx.test = flds[[b]]
+		train <- data[-idx.test, ]
+		test <- data[idx.test, ] 		
+
+		for (mtry in mtry.grid){
+			fit = ranger(y ~., data = train, num.trees=500, mtry=mtry, seed=seednum, importance = 'permutation')
+			pred.fit <- predict(fit, test)
+			MSE[b, ] <- mean((test$y - pred.fit$predictions)^2)
+		}
+		
+		## inner loup for estimating stability
+		flds.inn <- createFolds(train$y, k = fold.cv, list = TRUE, returnTrain = FALSE)
+		stab.table.inn = list()
+		for (bb in 1:fold.cv){
+		idx.test.inn = flds.inn[[bb]]
+		stab.train <- train[-idx.test.inn, ]
+		stab.test <- train[idx.test.inn, ]
+
+			coef.chosen.table = matrix(rep(0, dim(datx)[2] * length(mtry.grid)), ncol=length(mtry.grid))
+			colnames(coef.chosen.table) = paste('mtry', seq(1:length(mtry.grid)), sep='')
+			idx = 0
+			for (mtry in mtry.grid){
+				idx = idx + 1
+				fit.inn = ranger(y ~., data = stab.train, num.trees=500, mtry=mtry, seed=seednum, importance = 'permutation')
+				table = as.data.frame(importance_pvalues(fit.inn, method = "altmann", formula = y ~ ., data = stab.train)) 
+				coef.chosen = which(table$pvalue < pval_thr)
+				coef.chosen.table[coef.chosen, idx] = 1
+				stab.table.inn[[bb]] = coef.chosen.table
+			}
+
+		}
+
+		# calculate stability
+		table.tmp = matrix(rep(0, dim(datx)[2] * fold.cv), nrow=fold.cv)
+		for (i in 1:length(mtry.grid)){
+			for (bb in 1:fold.cv){
+				table.tmp[bb, ] = stab.table.inn[[bb]][, i]
+			}
+			STAB[b, i] = round(getStability(table.tmp)$stability, 2)
+		}
+	}
+
+	result = list(mtry.grid=mtry.grid, MSE.list=MSE, STAB.list=STAB, MSE.value=colMeans(MSE), STAB.value=colMeans(STAB))
+	return(result)
+
+}
diff --git a/code_method/cv_method_binary_update.R b/code_method/cv_method_binary_update.R
new file mode 100644
index 0000000..8da826f
--- /dev/null
+++ b/code_method/cv_method_binary_update.R
@@ -0,0 +1,247 @@
+###########################################
+#### methods for binary outcome #############
+###########################################
+library(glmnet)
+library(caret)
+library(ranger) # faster random forest
+library(pROC) # calculate ROC (need to load this library to avoid error)
+
+# general reference with caret on glmnet & random forest
+## http://rstudio-pubs-static.s3.amazonaws.com/251240_12a8ecea8e144fada41120ddcf52b116.html
+
+# important note
+## when training model on training set, as tuning parameters are set in final model, coefficient are chosen already
+## use test set to predict the MSE
+## as unclear how carret handle fitting and prediction, use carret only for parameter tuning in elastic net
+
+###########################################
+#### Lasso (tune lambda) ##################
+###########################################
+lasso_cv = function(datx, y, seednum=31, family=family, ratio.training=0.8, fold.cv=10, 
+					lambda.grid, lambda.choice='lambda.1se'){
+	# seednum: the seed number
+	# ratio.training: the ratio of training set (parento principle training:test=8:2)
+	# lambda.grid: possible candidate values for tuning parameter Lambda 
+	# fold.cv: n-fold cross validation
+	# lambda.choice: 'lambda.min' or 'lambda.1se' 
+
+	set.seed(seednum)
+
+	# # split data into training and tests sets
+	nn <- length(y)
+	trn <- sample(1:nn, ratio.training*nn)
+	x.train <- datx[trn, ]
+	x.test <- datx[-trn, ]
+	y.train <- y[trn]
+	y.test <- y[-trn]
+
+	# use cross-validation on training data & fit on test data
+	cv.fit <- cv.glmnet(x.train, y.train, family=family, alpha=1, lambda=lambda.grid, nfolds=fold.cv)
+	pred.fit <- predict(cv.fit, s=lambda.choice, newx=x.test, type='class') # predict class for binomial
+
+	# covariates chosen 
+	coef.fit <- predict(cv.fit, s=lambda.choice, newx=x.test, type='coefficients')
+	coef.chosen = which(coef.fit != 0) 
+    coef.chosen = coef.chosen - 1 # index for 1 representing intercept
+
+    pred.class <- as.numeric(pred.fit)
+    ROC <- pROC::roc(y.test, pred.class)$auc # in fact should be called "AUC" value instead of ROC
+
+
+	result = list(ROC=ROC, coef.chosen=coef.chosen)
+	return(result)
+}
+
+###########################################
+#### Elastic Net (tune lambda and alpha) ##
+###########################################
+# reference 1 (tune both parameters with caret)
+##  https://stats.stackexchange.com/questions/268885/tune-alpha-and-lambda-parameters-of-elastic-nets-in-an-optimal-way
+# reference 2 (extract final model and prediction with caret)
+## https://topepo.github.io/caret/model-training-and-tuning.html
+
+
+elnet_cv = function(datx, y, seednum=31, alpha.grid, lambda.grid, family=family, 
+	                ratio.training=0.8, fold.cv=10){
+	# seednum: the seed number
+	# ratio.training: the ratio of training set (parento principle training:test=8:2)
+	# alpha.grid: possible candidate values for tuning parameter alpha
+	# lambda.grid: possible candidate values for tuning parameter Lambda 
+	# fold.cv: n-fold cross validation
+
+	set.seed(seednum)
+
+	# # split data into training and tests sets
+	nn <- length(y)
+	trn <- sample(1:nn, ratio.training*nn)
+	x.train <- datx[trn, ]
+	x.test <- datx[-trn, ]
+	y.train <- y[trn]
+	y.test <- y[-trn]
+
+	#data.train = as.data.frame(cbind(y.train, x.train)) 
+    data.train = data.frame(y.train, x.train) # avoid conversion of data types with binary response
+	colnames(data.train) = c('y', paste('V', seq(1, dim(datx)[2]), sep=''))
+
+	# tune parameters with CV on training data with caret
+	trnCtrl <- trainControl(method = "cv", number = fold.cv)
+	srchGrid <- expand.grid(.alpha = alpha.grid, .lambda = lambda.grid)
+
+    data.train$y = as.factor(data.train$y)
+	my_train <- train(y ~., data.train,
+              		  method = "glmnet", 
+                      tuneGrid = srchGrid,
+                      trControl = trnCtrl)
+
+	# fit the model with best tuning parameters on test data with glmnet
+	fit = glmnet(x.train, y.train, alpha=my_train$bestTune$alpha, 
+                 lambda=my_train$bestTune$lambda, family=family)
+	pred.fit <- predict(fit, x.test, type='class') # predict class for binomial
+	
+	# covariates chosen 
+	coef.fit <- predict(fit, x.test, type='coefficients')
+	coef.chosen = which(coef.fit != 0) 
+    coef.chosen = coef.chosen - 1 # index for 1 representing intercept
+
+	# evaluate results
+    pred.class <- as.numeric(unlist(pred.fit))
+    ROC <- pROC::roc(y.test, pred.class)$auc
+
+	result = list(ROC=ROC, coef.chosen=coef.chosen)
+    #result = list(pred.fit=pred.fit, y.test=y.test)
+	return(result)
+}
+
+
+###############################################################
+#### Random Forests (tune # variables at each random split ####
+###############################################################
+# reference on regression random forst
+## https://uc-r.github.io/random_forests
+# OOB error is different from test error (see above website)
+
+
+randomForest_cv = function(datx, y, seednum=31, fold.cv=5, ratio.training=0.8, mtry.grid=10, num_trees=500, 
+						   pval_thr=0.05, method.perm='altmann'){
+	# mtry: number of variables to randomly sample at each split
+	# num_trees: number of trees to grow in random forests
+	# pval_thr: threshold for permutation test
+	# note that permutation p-value can use "altmann method" for all types of data; 'Janita' for high-dimensitional data only
+	# ref on permutation methods: http://finzi.psych.upenn.edu/library/ranger/html/importance_pvalues.html
+
+	set.seed(seednum)  
+
+	# split data into training and tests sets
+	#data = as.data.frame(cbind(y, datx)) 
+    data = data.frame(y, datx) # avoid conversion of data types with binary response
+	colnames(data) = c('y', paste('V', seq(1, dim(datx)[2]), sep=''))
+	inTraining = createDataPartition(data$y, p = ratio.training, list=FALSE)
+	train <- data[inTraining, ]
+	test <- data[-inTraining, ]  
+
+	# tune parameter with cross validation
+	hyper.grid <- expand.grid(mtry = mtry.grid, OOB_RMSE = 0)
+	for (i in 1:nrow(hyper.grid)){
+	  model = ranger(y ~., data = train,
+	                 num.trees=500, mtry=hyper.grid$mtry[i],
+	                 seed=seednum, importance = 'permutation')
+	  hyper.grid$OOB_RMSE[i] = sqrt(model$prediction.error)
+	}
+	OOB = min(hyper.grid$OOB_RMSE) # out of bag error
+	position = which.min(hyper.grid$OOB_RMSE)
+
+	# permutation test on tuned random forst model to obtain chosen features
+	if (method.perm == 'altmann'){ # for all data types
+		rf.model <- ranger(y ~., data=test, num.trees = num_trees,
+					       mtry = hyper.grid$mtry[position], importance = 'permutation')
+	    table = as.data.frame(importance_pvalues(rf.model, method = "altmann", 
+                              formula = y ~ ., data = test)) 
+	} else if (method.perm == 'janitza'){ # for high dimensional data only
+		rf.model <- ranger(y ~., data=test, num.trees = num_trees,
+					       mtry = hyper.grid$mtry[position], importance = 'impurity_corrected')
+	    table = as.data.frame(importance_pvalues(rf.model, method = "janitza", 
+                              formula = y ~ ., data = test)) 		
+	}
+
+    coef.chosen = which(table$pvalue < pval_thr) 
+
+    # if nothing been selected
+    if (identical(coef.chosen, integer(0))){
+    	coef.chosen = 0
+    }
+
+    # predicted class: https://www.rdocumentation.org/packages/ranger/versions/0.12.1/topics/predict.ranger
+    pred_rf = predict(rf.model, test)
+    pred.class <- as.numeric(pred_rf$predictions)
+    ROC <- pROC::roc(test$y, pred.class)$auc
+
+
+	result = list(mtry=mtry.grid[position], coef.chosen=coef.chosen, ROC=ROC, OOB=OOB, p.value=table$pvalue)
+	return(result)
+
+}
+
+
+# # ###############################################################
+# # #### Generalized Compositional Lasso by Lu et al 2019 #########
+# # ###############################################################
+dir_coda  = '../code_coda/Microbiome-Variable-Selection-master/Microbiome_variable_selection_tutorial/'
+
+source(file = paste0(dir_coda, 'CoDA-Penalized-Regression/R/functions_coda_penalized_regression.R'))
+
+source(file = paste0(dir_coda, 'functions.R'))
+
+gen_cons_lasso_cv = function(datx, y, seednum=31, data.split=FALSE,
+                             ratio.training=0.8, lambda.coda=seq(0.1, 0.2, 0.01)){
+    # note that generalized compositional lasso do the X transformation within
+    # thus use sub$X instead of sub$Z for generalized compositonal lasso method
+    
+    set.seed(seednum)
+    colnames(datx) = paste('V', seq(1, dim(datx)[2]), sep='')
+    
+    if (data.split == TRUE){
+        # # split data into training and tests sets
+        nn <- length(y)
+        trn <- sample(1:nn, ratio.training*nn)
+        x.train <- datx[trn, ]
+        x.test <- datx[-trn, ]
+        y.train <- y[trn]
+        y.test <- y[-trn]
+        
+        lambda_table <- lambdaRange_codalasso(X = x.train, y = y.train, 
+                                              lambdaSeq = lambda.coda)
+        lambda_optimal <- lambda_table[which.max(lambda_table$prop.explained.dev),
+                                       'lambda']
+        codalasso_sim <- coda_logistic_lasso(X = x.test, y = y.test, 
+                                             lambda = lambda_optimal)
+        ROC <- pROC::roc(y.test, codalasso_sim$`predicted class`)$auc
+        sim.results_codalasso <- coda_lasso_wrapper(result = codalasso_sim, 
+                                                    X = x.test)
+        coef.chosen = tidyr::extract_numeric(sim.results_codalasso$varSelect)
+     }else{
+        lambda_table <- lambdaRange_codalasso(X = datx, y = y, 
+                                              lambdaSeq = lambda.coda)
+
+        # choose lambda explained most deviance (if several, choose smaller lambda)
+        lambda_optimal <- lambda_table[which.max(lambda_table$prop.explained.dev),
+                                       'lambda']
+        codalasso_sim <- coda_logistic_lasso(X = datx, y = y, 
+                                             lambda = lambda_optimal)
+        ROC <- pROC::roc(y, codalasso_sim$`predicted class`)$auc
+        sim.results_codalasso <- coda_lasso_wrapper(result = codalasso_sim, 
+                                                    X = datx)
+        coef.chosen = tidyr::extract_numeric(sim.results_codalasso$varSelect)
+     }   
+
+        result = list(ROC=ROC, coef.chosen=coef.chosen)
+        return(result)   
+}    
+    
+
+
+
+
+
+
+
+
diff --git a/code_method/getStability.R b/code_method/getStability.R
new file mode 100755
index 0000000..913a417
--- /dev/null
+++ b/code_method/getStability.R
@@ -0,0 +1,95 @@
+#' ## source code: https://github.com/nogueirs/JMLR2018/blob/master/R/getStability.R
+#' @title getStability
+#' @description Nogueira's stability measure from a binary matrix representing 
+#' feature selection results in M bootstrapped datasets using a machine-learning method.
+#' @param X A binary matrix X of size M * p, where a row represents a feature set
+#' (for one of M data sets) and a column represents the selection of a given 
+#' feature over all the M data sets.
+#' @param alpha The level of significance (e.g. if alpha=0.05, we will get 95% 
+#' confidence intervals). It's an optional argument and is set to 5% by default.
+#' @details 
+#' @return A list of stability measures is returned. The list inlcude stability, 
+#' the variance of statbility, the upper/lower bound of the (1-alpha) 
+#' confidence interval.
+#' @examples 
+#' ## extreme cases example 
+#' d = 2 # number of features
+#' M = 10 # number of bootstrap replicates
+#'
+#' ## case 1: when stability index undefined -- Z all zeros or all ones (Nogueria2018 p.13)
+#' Z_all_missed = matrix(rep(0, M*d), nrow=M) # since K_bar = 0, thus SI undefined
+#' getStability(Z_all_missed)$stability
+#'
+#' Z_all_selected = matrix(rep(1, M*d), nrow=M) # since K_bar = d = 3, thus SI underfined
+#' getStability(Z_all_selected)$stability
+#'
+#' ## case 2: when stability index reaches maximum 1 -- each column of Z either all ones or all zeros (but not only zeros or only ones)(Nogueria2018 p.13)
+#' # this was the case when we got the wrong SI almost 1 for soil datasets: since sampled dataset the same across all
+#' d = 10
+#' for (i in 1: (d-1)){
+#' 	d_ones = sample(seq(1, d, 1), i)
+#' 	Z_tmp = matrix(rep(0, M*d), nrow=M)
+#' 	Z_tmp[, d_ones] = 1 # since Sf = 0 for all features, thus SI = 1
+#' 	SI = getStability(Z_tmp)$stability
+#' 	print(i)
+#' 	print(paste('SI:', SI, sep=''))
+#' }
+#'
+#' ## case 3: when stability index near minimum 0 (appedix D: - 1/(M-1), but as M goes to infinity, minimum asymptotically 0)
+#' ## when for each column of feature, it receives same numbers of 0 and 1
+#' d_alt = rep(c(0, 1), M/2)
+#' Z_alt = matrix(rep(d_alt, d), ncol=d)
+#' getStability(Z_alt)$stability # -0.1111111, which is - 1/(M-1)
+#
+#' d_alt_2 = c(rep(0, M/2), rep(1, M/2))
+#' Z_alt_2 = matrix(rep(d_alt_2, d), ncol=d)
+#' getStability(Z_alt_2)$stability # -0.1111111, which is - 1/(M-1)
+#
+#' # when M goes to infinity
+#' M = 10000
+#' d_alt = rep(c(0, 1), M/2)
+#' Z_alt = matrix(rep(d_alt, d), ncol=d)
+#' getStability(Z_alt)$stability # -0.00010001, very close to 0
+#' @export
+getStability <- function(X, alpha=0.05) {
+## the input X is a binary matrix of size M*d where:
+## M is the number of bootstrap replicates
+## d is the total number of features
+## alpha is the level of significance (e.g. if alpha=0.05, we will get 95% confidence intervals)
+## it's an optional argument and is set to 5% by default
+### first we compute the stability
+
+M<-nrow(X)
+d<-ncol(X)
+hatPF<-colMeans(X) # selection probability of each feature 
+kbar<-sum(hatPF)
+v_rand=(kbar/d)*(1-kbar/d) # kbar is the sum of selection probability on all features; v_rand is like the variance of bernoulli dist
+stability<-1-(M/(M-1))*mean(hatPF*(1-hatPF))/v_rand ## this is the stability estimate
+
+## then we compute the variance of the estimate
+ki<-rowSums(X)
+phi_i<-rep(0,M)
+for(i in 1:M){ 
+	phi_i[i]<-(1/v_rand)*((1/d)*sum(X[i,]*hatPF)-(ki[i]*kbar)/d^2-(stability/2)*((2*kbar*ki[i])/d^2-ki[i]/d-kbar/d+1))
+}
+phi_bar=mean(phi_i)
+var_stab=(4/M^2)*sum((phi_i-phi_bar)^2) ## this is the variance of the stability estimate
+
+## then we calculate lower and upper limits of the confidence intervals
+z<-qnorm(1-alpha/2) # this is the standard normal cumulative inverse at a level 1-alpha/2
+upper<-stability+z*sqrt(var_stab) ## the upper bound of the (1-alpha) confidence interval
+lower<-stability-z*sqrt(var_stab) ## the lower bound of the (1-alpha) confidence interval
+
+return(list("stability"=stability,"variance"=var_stab,"lower"=lower,"upper"=upper))
+
+}
+
+
+
+
+
+
+
+
+
+
diff --git a/code_method/stab_data_applications.R b/code_method/stab_data_applications.R
new file mode 100755
index 0000000..403a3d8
--- /dev/null
+++ b/code_method/stab_data_applications.R
@@ -0,0 +1,73 @@
+##########################################################
+### estimate correlation between stability index #########
+##########################################################
+
+#source('cv_method.R') 
+#source('getStability.R')
+
+## set up parallel computing
+library(foreach)
+library(doParallel)
+#numCores <- detectCores() - 2 # 6 cores
+numCores <- detectCores() - 4 # for old computer
+registerDoParallel(numCores)  # use multicore, set to the number of our cores
+
+boot_stab = function(num_boot=100, dat_file, method, ratio.training=0.8, fold.cv=10, 
+	                 family='gaussian', lambda.grid=exp(seq(-4, -2, 0.2)), alpha.grid=seq(0.1, 0.9, 0.1), 
+	                 mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05, method.perm='altmann'){
+	
+	# load data
+	load(dat_file)
+
+	# bootsrap with parallelization
+	results = foreach (i=1:num_boot) %dopar% {
+		print(paste('bootnum', i, sep=":"))
+		set.seed(i) # ensure each bootstrapped data is the same across methods
+		N = length(y) # number of samples
+		sample_ids = seq(1, N, 1)
+
+	    # bootstrapped samples
+	    boot_ids = sample(sample_ids, size=N, replace=TRUE)
+	    boot_taxa = taxa[boot_ids, ]
+	    boot_mf = y[boot_ids]
+
+	    ## select features from lasso/elnet
+	    if (method == 'compLasso'){
+	        result.lin = cons_lasso_cv(y=boot_mf, datx=boot_taxa, seednum=i, ratio.training=ratio.training)
+	        output.lin = c(result.lin$MSE, result.lin$coef.chosen) 
+
+	    } else if (method == 'RF'){
+	        result.rf = randomForest_cv(y=boot_mf, datx=boot_taxa, seednum=i, fold.cv=fold.cv, 
+    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr, method.perm=method.perm)
+            output.rf = c(result.rf$MSE, result.rf$coef.chosen) 
+
+	    } else if (method == 'lasso'){
+			result.lasso = lasso_cv(y=boot_mf, datx=boot_taxa, seednum=i,family=family, lambda.choice='lambda.1se',
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    output.lasso = c(result.lasso$MSE, result.lasso$coef.chosen) 
+
+	    } else if (method == 'elnet'){
+		    result.elnet = elnet_cv(y=boot_mf, datx=boot_taxa, seednum=i,family=family, alpha.grid=alpha.grid,
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    output.elnet = c(result.elnet$MSE, result.elnet$coef.chosen) 
+	    }
+	}
+
+	# reformat results
+	p = dim(taxa)[2]
+	stability_table = matrix(rep(0, num_boot * p), ncol=p)
+	results_mse = results_chosen = list()
+	for (b in 1:num_boot){
+		results_mse[b] = results[[b]][1]
+		results_chosen[[b]] = results[[b]][-1]
+		stability_table[b, results_chosen[[b]]] = 1 
+	}
+
+	stab_index = round(getStability(stability_table)$stability, 2)
+	MSE_mean = round(mean(unlist(results_mse), na.rm=T),2)
+	MSE_se = round(FSA::se(unlist(results_mse), na.rm=T),2)
+
+	results_list=list(num_boot=num_boot, method=method, stab_index=stab_index, stab_table=stability_table,
+				 results_chosen=results_chosen, lists_mse=results_mse, MSE_mean=MSE_mean, MSE_se=MSE_se)
+
+}
diff --git a/code_method/stab_data_applications_binary.R b/code_method/stab_data_applications_binary.R
new file mode 100644
index 0000000..cc5f907
--- /dev/null
+++ b/code_method/stab_data_applications_binary.R
@@ -0,0 +1,74 @@
+##########################################################
+### estimate correlation between stability index #########
+##########################################################
+
+#source('cv_method_binary_update.R') 
+#source('getStability.R')
+
+## set up parallel computing
+library(foreach)
+library(doParallel)
+#numCores <- detectCores() - 2 # 6 cores
+numCores <- detectCores() - 4 # for old computer
+registerDoParallel(numCores)  # use multicore, set to the number of our cores
+
+boot_stab = function(num_boot=100, dat_file, method, ratio.training=0.8, fold.cv=10, 
+	                 family='binomial', lambda.grid=exp(seq(-4, -2, 0.2)), alpha.grid=seq(0.1, 0.9, 0.1), 
+	                 mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05, method.perm='altmann',
+                     lambda.coda=seq(0.1, 0.2, 0.01), data.split=FALSE){
+	
+	# load data
+	load(dat_file)
+
+	# bootsrap with parallelization
+	results = foreach (i=1:num_boot) %dopar% {
+		print(paste('bootnum', i, sep=":"))
+		set.seed(i) # ensure each bootstrapped data is the same across methods
+		N = length(y) # number of samples
+		sample_ids = seq(1, N, 1)
+
+	    # bootstrapped samples
+	    boot_ids = sample(sample_ids, size=N, replace=TRUE)
+	    boot_taxa = taxa[boot_ids, ] # log-transformed taxa relative abundance
+	    boot_mf = y[boot_ids]
+
+	    ## select features from lasso/elnet
+	    if (method == 'GenCompLasso'){ # use X instead of Z (log-transformed)
+	        result.lin = gen_cons_lasso_cv(y=boot_mf, datx=exp(boot_taxa), seednum=i, ratio.training=ratio.training)
+	        output.lin = c(result.lin$ROC, result.lin$coef.chosen) 
+
+	    } else if (method == 'RF'){
+	        result.rf = randomForest_cv(y=boot_mf, datx=boot_taxa, seednum=i, fold.cv=fold.cv, 
+    							        num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr, method.perm=method.perm)
+            output.rf = c(result.rf$ROC, result.rf$coef.chosen) 
+
+	    } else if (method == 'lasso'){
+			result.lasso = lasso_cv(y=boot_mf, datx=boot_taxa, seednum=i,family=family, lambda.choice='lambda.1se',
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    output.lasso = c(result.lasso$ROC, result.lasso$coef.chosen) 
+
+	    } else if (method == 'elnet'){
+		    result.elnet = elnet_cv(y=boot_mf, datx=boot_taxa, seednum=i,family=family, alpha.grid=alpha.grid,
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    output.elnet = c(result.elnet$ROC, result.elnet$coef.chosen) 
+	    }
+	}
+
+	# reformat results
+	p = dim(taxa)[2]
+	stability_table = matrix(rep(0, num_boot * p), ncol=p)
+	results_ROC = results_chosen = list()
+	for (b in 1:num_boot){
+		results_ROC[b] = results[[b]][1]
+		results_chosen[[b]] = results[[b]][-1]
+		stability_table[b, results_chosen[[b]]] = 1 
+	}
+
+	stab_index = round(getStability(stability_table)$stability, 2)
+	ROC_mean = round(mean(unlist(results_ROC), na.rm=T),2)
+	ROC_se = round(FSA::se(unlist(results_ROC), na.rm=T),2)
+
+	results_list=list(num_boot=num_boot, method=method, stab_index=stab_index, stab_table=stability_table,
+				 results_chosen=results_chosen, lists_ROC=results_ROC, ROC_mean=ROC_mean, ROC_se=ROC_se)
+
+}
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diff --git a/data_application/code_applications/code_bin/88soils_stab_application_binary.R b/data_application/code_applications/code_bin/88soils_stab_application_binary.R
new file mode 100644
index 0000000..db0659c
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+++ b/data_application/code_applications/code_bin/88soils_stab_application_binary.R
@@ -0,0 +1,111 @@
+#########################################################################################################
+### This is to estimate stablity & MSE using bootstrap on BMI_Lin_2014 dataset #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../../code_method/cv_method_binary_update.R')
+source('../../../code_method/getStability.R')
+source('../../../code_method/stab_data_applications_binary.R') # boot_stab()
+source('../../../code_method/bootstrap_test_compLasso_rf_binary.R') # boot_stab_data()
+
+#####################################
+##### data preparation ##############
+#####################################
+load('../../88soils/88soils_genus_table.RData') # if any error, re-process the data in HPC
+count = soil_otu
+
+## filter 1% + add pesudo count 
+x <- count[, colMeans(count > 0) >= 0.01] 
+x[x == 0] <- 0.5
+x <- x/rowSums(x) # relative abundance
+taxa <- log(x)
+print(paste('number of features:', dim(taxa)[2], sep=':'))
+
+# # metadata
+mf <- read.csv("../../88soils/88soils_modified_metadata.txt", sep='\t', row.names=1)
+y <- mf$ph[match(rownames(count), rownames(mf))]
+y <- as.factor(ifelse(y >= median(y), 1, 0)) # transform to be binary
+print(paste('number of samples:', length(y), sep=':'))
+
+# save processed data
+save(y, taxa, file='../../88soils/soils_ph_binary.RData')
+
+
+# #------------------------
+# # compare methods
+# #------------------------
+i <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+if (i == 1){
+    ####################################
+    ## Generalized compositional lasso #############
+    # ####################################
+    print('GenCompLasso')
+    out_GenCompLasso = boot_stab(num_boot = 100, method = 'GenCompLasso', lambda.grid = NULL, 
+                    dat_file = '../../88soils/soils_ph_binary.RData')
+
+    save(out_GenCompLasso, file=paste0(dir, '/soils_binary_ph_GenCompLasso.RData', sep=''))
+}else if (i == 2){
+    #############################################################
+    # Lasso with glmnet default lambda sequence #############
+    #############################################################
+    print('Lasso')
+    out_lasso = boot_stab(num_boot = 100, method = 'lasso', lambda.grid = NULL, 
+                    dat_file = '../../88soils/soils_ph_binary.RData')
+
+    save(out_lasso, file=paste0(dir, '/soils_binary_ph_lasso.RData', sep=''))
+}else if (i == 3){
+    # ##############################################################
+    # ## Elastic Net with self defined lambda (NULL not allowed) #############
+    # ##############################################################
+    print('Elnet')
+    out_elnet = boot_stab(num_boot = 100, method = 'elnet', 
+                          dat_file = '../../88soils/soils_ph_binary.RData')
+
+    save(out_elnet, file=paste0(dir, '/soils_binary_ph_elnet.RData', sep=''))
+}else if (i == 4){
+    ##############################################################
+    ## Random Forest with Altman feature selection #############
+    ##############################################################
+    print('RF')
+    out_rf = boot_stab(num_boot = 100, method = 'RF', method.perm='altmann', mtry.grid=seq(30, 60, 5), 
+                       dat_file = '../../88soils/soils_ph_binary.RData')
+
+    save(out_rf, file=paste0(dir, '/soils_binary_ph_rf.RData', sep=''))   
+}else if (i == 5){
+    ########################################################################
+    ############### double bootstrap: random forest ##################
+    # ######################################################################
+    print('double boot RF')
+    boot_rf = boot_stab_data(num_boot=100, method = 'RF',
+    	 					 data_file='../../88soils/soils_ph_binary.RData')
+    save(boot_rf, file=paste0(dir, '/soils_binary_ph_boot_rf.RData'))
+}else if (i == 6){
+    ########################################################################
+    ############### double bootstrap: generalized compositional lasso ######
+    # ######################################################################
+    print('double boot compLasso')
+    boot_compLasso = boot_stab_data(num_boot=100, method = 'GenCompLasso',
+    	 							data_file='../../88soils/soils_ph_binary.RData')
+    save(boot_compLasso, file=paste0(dir, '/soils_binary_ph_boot_compLasso.RData'))
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/data_application/code_applications/code_bin/BMI_stab_application_binary.R b/data_application/code_applications/code_bin/BMI_stab_application_binary.R
new file mode 100644
index 0000000..7f2560c
--- /dev/null
+++ b/data_application/code_applications/code_bin/BMI_stab_application_binary.R
@@ -0,0 +1,99 @@
+#########################################################################################################
+### This is to estimate stablity & MSE using bootstrap on BMI_Lin_2014 dataset #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../../code_method/cv_method_binary_update.R')
+source('../../../code_method/getStability.R')
+source('../../../code_method/stab_data_applications_binary.R') # boot_stab()
+source('../../../code_method/bootstrap_test_compLasso_rf_binary.R') # boot_stab_data()
+
+#####################################
+##### data preparation ##############
+#####################################
+count <- as.matrix(read.table("../../code_Lin/cvs/data/combo_count_tab.txt")) 
+
+# filter 1% + add pesudo count 
+depth <- sapply(strsplit(colnames(count), "\\."), length)
+x <- count[, depth == 6 & colMeans(count > 0) >= 0.01]
+x[x == 0] <- 0.5
+x <- x/rowSums(x) # relative abundance
+taxa <- log(x)
+print(paste('number of features:', dim(taxa)[2], sep=':'))
+
+# metadata
+demo <- read.delim("../../code_Lin/cvs/data/demographic.txt")
+y <- demo$bmi[match(rownames(count), demo$pid)] 
+y <- as.factor(ifelse(y >= median(y), 1, 0)) # transform to be binary
+print(paste('number of samples:', length(y), sep=':'))
+
+# save processed data
+save(y, taxa, file='../../BMI/BMI_Lin_2014_binary.RData')
+
+#------------------------
+# compare methods
+#------------------------
+i <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+if (i == 1){
+    # ####################################
+    # ## Generalized compositional lasso #############
+    # ####################################
+    print('GencompLasso')
+    out_GenCompLasso = boot_stab(num_boot = 100, method = 'GenCompLasso',
+                    dat_file = '../../BMI/BMI_Lin_2014_binary.RData')
+
+    save(out_GenCompLasso, file=paste0(dir, '/BMI_binary_GenCompLasso.RData'))
+}else if (i == 2){
+
+    ##############################################################
+    ## Lasso with glmnet default lambda sequence #############
+    ##############################################################
+    print('Lasso')
+    out_lasso = boot_stab(num_boot = 100, method = 'lasso', lambda.grid = NULL, 
+                    dat_file = '../../BMI/BMI_Lin_2014_binary.RData')
+
+    save(out_lasso, file=paste0(dir, '/BMI_binary_lasso.RData', sep=''))
+}else if (i == 3){
+    # ##############################################################
+    # ## Elastic Net with self defined lambda (NULL not allowed) #############
+    # ##############################################################
+    print('Elnet')
+    out_elnet = boot_stab(num_boot = 100, method = 'elnet', 
+                          dat_file = '../../BMI/BMI_Lin_2014_binary.RData')
+
+    save(out_elnet, file=paste0(dir, '/BMI_binary_elnet.RData', sep=''))
+}else if (i == 4){
+    # ##############################################################
+    # ## Random Forest with Altman feature selection #############
+    # ##############################################################
+    print('RF')
+    out_rf = boot_stab(num_boot = 100, method = 'RF', method.perm='altmann', 
+                           dat_file = '../../BMI/BMI_Lin_2014_binary.RData')
+
+    save(out_rf, file=paste0(dir, '/BMI_binary_rf.RData', sep=''))    
+}else if (i == 5){
+    ########################################################################
+    ############### double bootstrap: random forest ##################
+    # ######################################################################
+    print('double boot RF')
+    boot_rf = boot_stab_data(num_boot=100, method = 'RF',
+                             data_file='../../BMI/BMI_Lin_2014_binary.RData')
+    save(boot_rf, file=paste0(dir, '/BMI_binary_boot_rf.RData'))
+}else if (i == 6){
+    ########################################################################
+    ############### double bootstrap: generalized compositional lasso ######
+    # ######################################################################
+    print('double boot compLasso')
+    boot_compLasso = boot_stab_data(num_boot=100, method = 'GenCompLasso',
+    	 							data_file='../../BMI/BMI_Lin_2014_binary.RData')
+    save(boot_compLasso, file=paste0(dir, '/BMI_binary_boot_compLasso.RData'))
+}    
+
+
+
+
+
diff --git a/data_application/code_applications/code_cts/88soils_stab_application.R b/data_application/code_applications/code_cts/88soils_stab_application.R
new file mode 100755
index 0000000..d1379bb
--- /dev/null
+++ b/data_application/code_applications/code_cts/88soils_stab_application.R
@@ -0,0 +1,102 @@
+#########################################################################################################
+### This is to estimate stablity & MSE using bootstrap on BMI_Lin_2014 dataset #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../../code_method/cv_method.R')
+source('../../../code_method/getStability.R')
+source('../../../code_method/stab_data_applications.R')
+source('../../../code_method/bootstrap_test_compLasso_rf.R')
+
+#####################################
+##### data preparation ##############
+#####################################
+soil_otu = as.matrix(read.csv("../../88soils/88soils_genus_table.txt", sep='\t', row.names=1)) 
+save(soil_otu, file='../../88soils/88soils_genus_table.RData')
+
+load('../../88soils/88soils_genus_table.RData')
+count = soil_otu
+
+## filter 1% + add pesudo count 
+x <- count[, colMeans(count > 0) >= 0.01] 
+x[x == 0] <- 0.5
+x <- x/rowSums(x) # relative abundance
+taxa <- log(x)
+print(paste('number of features:', dim(taxa)[2], sep=':'))
+
+# # metadata
+mf <- read.csv("../../88soils/88soils_modified_metadata.txt", sep='\t', row.names=1)
+y <- mf$ph[match(rownames(count), rownames(mf))]
+print(paste('number of samples:', length(y), sep=':'))
+
+# save processed data
+save(y, taxa, file='../../88soils/soil_ph.RData')
+
+
+
+####################################
+## compositional lasso #############
+# ####################################
+print('compLasso')
+out_compLasso = boot_stab(num_boot = 100, method = 'compLasso',
+                dat_file = '../data_application/88soils/soils_ph.RData')
+
+save(out_compLasso, file=paste0(dir, '/soils_ph_compLasso.RData'))
+
+#############################################################
+# Lasso with glmnet default lambda sequence #############
+#############################################################
+print('Lasso')
+out_lasso = boot_stab(num_boot = 100, method = 'lasso', lambda.grid = NULL, 
+                dat_file = '../data_application/88soils/soils_ph.RData')
+
+save(out_lasso, file=paste0(dir, '/soils_ph_lasso.RData', sep=''))
+
+
+# ##############################################################
+# ## Elastic Net with self defined lambda (NULL not allowed) #############
+# ##############################################################
+print('Elnet')
+out_elnet = boot_stab(num_boot = 100, method = 'elnet', 
+                 	  dat_file = '../../88soils/soils_ph.RData')
+
+save(out_elnet, file=paste0(dir, '/soils_ph_elnet.RData', sep=''))
+
+
+##############################################################
+## Random Forest with Altman feature selection #############
+##############################################################
+print('RF')
+out_rf = boot_stab(num_boot = 100, method = 'RF', method.perm='altmann', mtry.grid=seq(30, 60, 5), 
+                   dat_file = '../../88soils/soils_ph.RData')
+
+save(out_rf, file=paste0(dir, '/soils_ph_rf.RData', sep=''))
+
+########################################################################
+############### double bootstrap: random forest ##################
+# ######################################################################
+print('double boot RF')
+boot_rf = boot_stab_data(num_boot=100, method = 'RF',
+	 					 data_file='../../88soils/soils_ph.RData')
+save(boot_rf, file=paste0(dir, '/soils_ph_boot_rf.RData'))
+
+########################################################################
+############### double bootstrap: compositional lasso ##################
+# ######################################################################
+print('double boot compLasso')
+boot_compLasso = boot_stab_data(num_boot=100, method = 'compLasso',
+	 							data_file='../../88soils/soils_ph.RData')
+save(boot_compLasso, file=paste0(dir, '/soils_ph_boot_compLasso.RData'))
+
+
+
+
+
+
+
+
+
+
diff --git a/data_application/code_applications/code_cts/BMI_stab_application.R b/data_application/code_applications/code_cts/BMI_stab_application.R
new file mode 100755
index 0000000..5514c31
--- /dev/null
+++ b/data_application/code_applications/code_cts/BMI_stab_application.R
@@ -0,0 +1,105 @@
+#########################################################################################################
+### This is to estimate stablity & MSE using bootstrap on BMI_Lin_2014 dataset #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../../code_method/cv_method.R')
+source('../../../code_method/getStability.R')
+source('../../../code_method/stab_data_applications.R')
+source('../../../code_method/bootstrap_test_compLasso_rf.R')
+
+#####################################
+##### data preparation ##############
+#####################################
+count <- as.matrix(read.table("../../code_Lin/cvs/data/combo_count_tab.txt")) 
+
+# filter 1% + add pesudo count 
+depth <- sapply(strsplit(colnames(count), "\\."), length)
+x <- count[, depth == 6 & colMeans(count > 0) >= 0.01]
+x[x == 0] <- 0.5
+x <- x/rowSums(x) # relative abundance
+taxa <- log(x)
+print(paste('number of features:', dim(taxa)[2], sep=':'))
+
+# metadata
+demo <- read.delim("../../code_Lin/cvs/data/demographic.txt")
+y <- demo$bmi[match(rownames(count), demo$pid)]
+print(paste('number of samples:', length(y), sep=':'))
+
+# save processed data
+save(y, taxa, file='../../BMI/BMI_Lin_2014.RData')
+
+# ####################################
+# ## compositional lasso #############
+# ####################################
+print('compLasso')
+out_compLasso = boot_stab(num_boot = 100, method = 'compLasso',
+                dat_file = '../../BMI/BMI_Lin_2014.RData')
+
+save(out_compLasso, file=paste0(dir, '/BMI_compLasso.RData'))
+
+##############################################################
+## Lasso with glmnet default lambda sequence #############
+##############################################################
+print('Lasso')
+out_lasso = boot_stab(num_boot = 100, method = 'lasso', lambda.grid = NULL, 
+                dat_file = '../../BMI/BMI_Lin_2014.RData')
+
+save(out_lasso, file=paste0(dir, '/BMI_lasso.RData', sep=''))
+
+
+# ##############################################################
+# ## Elastic Net with self defined lambda (NULL not allowed) #############
+# ##############################################################
+print('Elnet')
+out_elnet = boot_stab(num_boot = 100, method = 'elnet', 
+                 	  dat_file = '../../BMI/BMI_Lin_2014.RData')
+
+save(out_elnet, file=paste0(dir, '/BMI_elnet.RData', sep=''))
+
+
+# ##############################################################
+# ## Random Forest with Altman feature selection #############
+# ##############################################################
+print('RF')
+out_rf = boot_stab(num_boot = 100, method = 'RF', method.perm='altmann', 
+                       dat_file = '../../BMI/BMI_Lin_2014.RData')
+
+save(out_rf, file=paste0(dir, '/BMI_rf.RData', sep=''))
+
+
+#############################################################
+# Random Forest with Janitza feature selection #############
+#############################################################
+print("RF_JNT")
+dir = '../data_application'
+out_rf_jnt= boot_stab(num_boot = 100, method = 'RF', method.perm='janitza', 
+                      dat_file = '../data_application/BMI_Lin_2014.RData')
+
+save(out_rf_jnt, file=paste0(dir, '/BMI_rf_jnt.RData', sep=''))
+
+
+########################################################################
+############### double bootstrap: random forest ##################
+# ######################################################################
+print('double boot RF')
+boot_rf = boot_stab_data(num_boot=100, method = 'RF',
+	 					 data_file='../data_application/BMI_Lin_2014.RData')
+save(boot_rf, file=paste0(dir, '/BMI_boot_rf.RData'))
+
+########################################################################
+############### double bootstrap: compositional lasso ##################
+# ######################################################################
+print('double boot compLasso')
+boot_compLasso = boot_stab_data(num_boot=100, method = 'compLasso',
+	 							data_file='../data_application/BMI_Lin_2014.RData')
+save(boot_compLasso, file=paste0(dir, '/BMI_boot_compLasso.RData'))
+
+
+
+
+
+
diff --git a/data_application/code_applications/code_cts/run_data_applications.sh b/data_application/code_applications/code_cts/run_data_applications.sh
new file mode 100755
index 0000000..e578605
--- /dev/null
+++ b/data_application/code_applications/code_cts/run_data_applications.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+#PBS -N data_applications
+#PBS -l walltime=50:00:00
+#PBS -l nodes=1:ppn=8
+#PBS -l mem=10gb
+#PBS -V
+#PBS -j oe
+#PBS -d .
+
+set -e
+cpus=$PBS_NUM_PPN
+
+export TMPDIR=/panfs/panfs1.ucsd.edu/panscratch/$USER/Stability_2020
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+export TMPDIR=$TMPDIR/data_applications
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+#tmp=$(mktemp -d --tmpdir)
+#export TMPDIR=$tmp
+#trap "rm -r $tmp; unset TMPDIR" EXIT
+
+# do something
+source activate r-c-env
+Rscript BMI_stab_application.R $TMPDIR
+Rscript 88soils_stab_application.R $TMPDIR
+source deactivate r-c-env
+
+#mv $tmp/outdir ./outdir
diff --git a/data_application/notebooks_application/.DS_Store b/data_application/notebooks_application/.DS_Store
deleted file mode 100755
index 5008ddf..0000000
Binary files a/data_application/notebooks_application/.DS_Store and /dev/null differ
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/1.1. BMI_DataPreparation-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/1.1. BMI_DataPreparation-checkpoint.ipynb
new file mode 100755
index 0000000..2a6fe2d
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/1.1. BMI_DataPreparation-checkpoint.ipynb	
@@ -0,0 +1,875 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI dataset in Lin et al, 2014 \n",
+    "##### use unrarifed count table and retain microbes only at genus level (+ present at least one sample)\n",
+    "#### add 0.5 as pseudo count to zero counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>263</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 263\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 263\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  98 263"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria</th><th scope=col>Bacteria.Bacteroidetes</th><th scope=col>Bacteria.Cyanobacteria</th><th scope=col>Bacteria.Firmicutes</th><th scope=col>Bacteria.Fusobacteria</th><th scope=col>Bacteria.Lentisphaerae</th><th scope=col>Bacteria.OD1</th><th scope=col>Bacteria.Proteobacteria</th><th scope=col>Bacteria.Spirochaetes</th><th scope=col>Bacteria.Synergistetes</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira</th><th scope=col>Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>1   </td><td>5067</td><td>0   </td><td>4153</td><td>0   </td><td>0   </td><td>0   </td><td>534 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>0   </td><td>4659</td><td>0   </td><td>2177</td><td>0   </td><td>0   </td><td>0   </td><td>105 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>0   </td><td>4342</td><td>0   </td><td>3008</td><td>0   </td><td>2   </td><td>0   </td><td>134 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>8   </td><td>2910</td><td>0   </td><td>4147</td><td>0   </td><td>0   </td><td>0   </td><td>459 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>5   </td><td>5630</td><td>0   </td><td>4705</td><td>0   </td><td>0   </td><td>0   </td><td>214 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>5   </td><td>1868</td><td>0   </td><td>1619</td><td>0   </td><td>0   </td><td>0   </td><td> 17 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria & Bacteria.Bacteroidetes & Bacteria.Cyanobacteria & Bacteria.Firmicutes & Bacteria.Fusobacteria & Bacteria.Lentisphaerae & Bacteria.OD1 & Bacteria.Proteobacteria & Bacteria.Spirochaetes & Bacteria.Synergistetes & ... & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira & Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & 1    & 5067 & 0    & 4153 & 0    & 0    & 0    & 534  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3003 & 0    & 4659 & 0    & 2177 & 0    & 0    & 0    & 105  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3004 & 0    & 4342 & 0    & 3008 & 0    & 2    & 0    & 134  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3006 & 8    & 2910 & 0    & 4147 & 0    & 0    & 0    & 459  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3007 & 5    & 5630 & 0    & 4705 & 0    & 0    & 0    & 214  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3008 & 5    & 1868 & 0    & 1619 & 0    & 0    & 0    &  17  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria | Bacteria.Bacteroidetes | Bacteria.Cyanobacteria | Bacteria.Firmicutes | Bacteria.Fusobacteria | Bacteria.Lentisphaerae | Bacteria.OD1 | Bacteria.Proteobacteria | Bacteria.Spirochaetes | Bacteria.Synergistetes | ... | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira | Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | 1    | 5067 | 0    | 4153 | 0    | 0    | 0    | 534  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3003 | 0    | 4659 | 0    | 2177 | 0    | 0    | 0    | 105  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3004 | 0    | 4342 | 0    | 3008 | 0    | 2    | 0    | 134  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3006 | 8    | 2910 | 0    | 4147 | 0    | 0    | 0    | 459  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3007 | 5    | 5630 | 0    | 4705 | 0    | 0    | 0    | 214  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3008 | 5    | 1868 | 0    | 1619 | 0    | 0    | 0    |  17  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria Bacteria.Bacteroidetes Bacteria.Cyanobacteria\n",
+       "3001 1                       5067                   0                     \n",
+       "3003 0                       4659                   0                     \n",
+       "3004 0                       4342                   0                     \n",
+       "3006 8                       2910                   0                     \n",
+       "3007 5                       5630                   0                     \n",
+       "3008 5                       1868                   0                     \n",
+       "     Bacteria.Firmicutes Bacteria.Fusobacteria Bacteria.Lentisphaerae\n",
+       "3001 4153                0                     0                     \n",
+       "3003 2177                0                     0                     \n",
+       "3004 3008                0                     2                     \n",
+       "3006 4147                0                     0                     \n",
+       "3007 4705                0                     0                     \n",
+       "3008 1619                0                     0                     \n",
+       "     Bacteria.OD1 Bacteria.Proteobacteria Bacteria.Spirochaetes\n",
+       "3001 0            534                     0                    \n",
+       "3003 0            105                     0                    \n",
+       "3004 0            134                     0                    \n",
+       "3006 0            459                     0                    \n",
+       "3007 0            214                     0                    \n",
+       "3008 0             17                     0                    \n",
+       "     Bacteria.Synergistetes ...\n",
+       "3001 0                      ...\n",
+       "3003 0                      ...\n",
+       "3004 0                      ...\n",
+       "3006 0                      ...\n",
+       "3007 0                      ...\n",
+       "3008 0                      ...\n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 0                                                                                       \n",
+       "3003 0                                                                                       \n",
+       "3004 0                                                                                       \n",
+       "3006 0                                                                                       \n",
+       "3007 0                                                                                       \n",
+       "3008 0                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio\n",
+       "3001 0                                                                          \n",
+       "3003 0                                                                          \n",
+       "3004 0                                                                          \n",
+       "3006 0                                                                          \n",
+       "3007 0                                                                          \n",
+       "3008 0                                                                          \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 0                                                                                            \n",
+       "3003 0                                                                                            \n",
+       "3004 0                                                                                            \n",
+       "3006 0                                                                                            \n",
+       "3007 0                                                                                            \n",
+       "3008 0                                                                                            \n",
+       "     Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira\n",
+       "3001 0                                                                            \n",
+       "3003 0                                                                            \n",
+       "3004 0                                                                            \n",
+       "3006 0                                                                            \n",
+       "3007 0                                                                            \n",
+       "3008 0                                                                            \n",
+       "     Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema\n",
+       "3001 0                                                                          \n",
+       "3003 0                                                                          \n",
+       "3004 0                                                                          \n",
+       "3006 0                                                                          \n",
+       "3007 0                                                                          \n",
+       "3008 0                                                                          \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 0                                                                                           \n",
+       "3003 0                                                                                           \n",
+       "3004 0                                                                                           \n",
+       "3006 0                                                                                           \n",
+       "3007 0                                                                                           \n",
+       "3008 0                                                                                           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# unrarefied microbe count table\n",
+    "count <- as.matrix(read.table(\"../../code_Lin/cvs/data/combo_count_tab.txt\")) \n",
+    "dim(count)\n",
+    "head(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>87</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 87\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 87\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 98 87"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria</th><th scope=col>Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio</th><th scope=col>Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>2878</td><td>  0 </td><td>69  </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td> 578</td><td>180 </td><td> 0  </td><td>... </td><td>2   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>3503</td><td>143 </td><td> 0  </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>2162</td><td>  1 </td><td> 0  </td><td>... </td><td>3   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>0   </td><td>0   </td><td>0   </td><td>2   </td><td>0   </td><td>0   </td><td>0   </td><td>4453</td><td>  0 </td><td> 0  </td><td>... </td><td>9   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>0   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td> 950</td><td>237 </td><td>19  </td><td>... </td><td>4   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas & ... & Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter & Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria & Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio & Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 2878 &   0  & 69   & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3003 & 0    & 0    & 0    & 0    & 0    & 0    & 0    &  578 & 180  &  0   & ...  & 2    & 0    & 0    & 1    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3004 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 3503 & 143  &  0   & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3006 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 2162 &   1  &  0   & ...  & 3    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3007 & 0    & 0    & 0    & 2    & 0    & 0    & 0    & 4453 &   0  &  0   & ...  & 9    & 0    & 0    & 1    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3008 & 0    & 0    & 0    & 1    & 0    & 0    & 0    &  950 & 237  & 19   & ...  & 4    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas | ... | Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter | Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria | Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio | Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 2878 |   0  | 69   | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3003 | 0    | 0    | 0    | 0    | 0    | 0    | 0    |  578 | 180  |  0   | ...  | 2    | 0    | 0    | 1    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3004 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 3503 | 143  |  0   | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3006 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 2162 |   1  |  0   | ...  | 3    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3007 | 0    | 0    | 0    | 2    | 0    | 0    | 0    | 4453 |   0  |  0   | ...  | 9    | 0    | 0    | 1    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3008 | 0    | 0    | 0    | 1    | 0    | 0    | 0    |  950 | 237  | 19   | ...  | 4    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter\n",
+       "3001 0                                                                                        \n",
+       "3003 0                                                                                        \n",
+       "3004 0                                                                                        \n",
+       "3006 0                                                                                        \n",
+       "3007 0                                                                                        \n",
+       "3008 0                                                                                        \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium\n",
+       "3001 0                                                                                  \n",
+       "3003 0                                                                                  \n",
+       "3004 0                                                                                  \n",
+       "3006 0                                                                                  \n",
+       "3007 0                                                                                  \n",
+       "3008 0                                                                                  \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella\n",
+       "3001 0                                                                                    \n",
+       "3003 0                                                                                    \n",
+       "3004 0                                                                                    \n",
+       "3006 0                                                                                    \n",
+       "3007 0                                                                                    \n",
+       "3008 0                                                                                    \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella\n",
+       "3001 0                                                                                    \n",
+       "3003 0                                                                                    \n",
+       "3004 0                                                                                    \n",
+       "3006 0                                                                                    \n",
+       "3007 2                                                                                    \n",
+       "3008 1                                                                                    \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella\n",
+       "3001 0                                                                                  \n",
+       "3003 0                                                                                  \n",
+       "3004 0                                                                                  \n",
+       "3006 0                                                                                  \n",
+       "3007 0                                                                                  \n",
+       "3008 0                                                                                  \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia\n",
+       "3001 0                                                                                \n",
+       "3003 0                                                                                \n",
+       "3004 0                                                                                \n",
+       "3006 0                                                                                \n",
+       "3007 0                                                                                \n",
+       "3008 0                                                                                \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides\n",
+       "3001 2878                                                                       \n",
+       "3003  578                                                                       \n",
+       "3004 3503                                                                       \n",
+       "3006 2162                                                                       \n",
+       "3007 4453                                                                       \n",
+       "3008  950                                                                       \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella\n",
+       "3001   0                                                                            \n",
+       "3003 180                                                                            \n",
+       "3004 143                                                                            \n",
+       "3006   1                                                                            \n",
+       "3007   0                                                                            \n",
+       "3008 237                                                                            \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas\n",
+       "3001 69                                                                               \n",
+       "3003  0                                                                               \n",
+       "3004  0                                                                               \n",
+       "3006  0                                                                               \n",
+       "3007  0                                                                               \n",
+       "3008 19                                                                               \n",
+       "     ...\n",
+       "3001 ...\n",
+       "3003 ...\n",
+       "3004 ...\n",
+       "3006 ...\n",
+       "3007 ...\n",
+       "3008 ...\n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 2                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 3                                                                                      \n",
+       "3007 9                                                                                      \n",
+       "3008 4                                                                                      \n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria\n",
+       "3001 0                                                                              \n",
+       "3003 0                                                                              \n",
+       "3004 0                                                                              \n",
+       "3006 0                                                                              \n",
+       "3007 0                                                                              \n",
+       "3008 0                                                                              \n",
+       "     Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio\n",
+       "3001 0                                                                                               \n",
+       "3003 0                                                                                               \n",
+       "3004 0                                                                                               \n",
+       "3006 0                                                                                               \n",
+       "3007 0                                                                                               \n",
+       "3008 0                                                                                               \n",
+       "     Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter\n",
+       "3001 0                                                                                               \n",
+       "3003 1                                                                                               \n",
+       "3004 0                                                                                               \n",
+       "3006 0                                                                                               \n",
+       "3007 1                                                                                               \n",
+       "3008 0                                                                                               \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio\n",
+       "3001 0                                                                                          \n",
+       "3003 0                                                                                          \n",
+       "3004 0                                                                                          \n",
+       "3006 0                                                                                          \n",
+       "3007 0                                                                                          \n",
+       "3008 0                                                                                          \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 0                                                                                       \n",
+       "3003 0                                                                                       \n",
+       "3004 0                                                                                       \n",
+       "3006 0                                                                                       \n",
+       "3007 0                                                                                       \n",
+       "3008 0                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 0                                                                                            \n",
+       "3003 0                                                                                            \n",
+       "3004 0                                                                                            \n",
+       "3006 0                                                                                            \n",
+       "3007 0                                                                                            \n",
+       "3008 0                                                                                            \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 0                                                                                           \n",
+       "3003 0                                                                                           \n",
+       "3004 0                                                                                           \n",
+       "3006 0                                                                                           \n",
+       "3007 0                                                                                           \n",
+       "3008 0                                                                                           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# retain only microbes at genus level and exist at least one sample\n",
+    "depth <- sapply(strsplit(colnames(count), \"\\\\.\"), length)\n",
+    "x <- count[, depth == 6 & colSums(count != 0) >= 1] # 98 * 87\n",
+    "dim(x)\n",
+    "head(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>87</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 87\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 87\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 98 87"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria</th><th scope=col>Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio</th><th scope=col>Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-0.8429202</td><td>-9.500918 </td><td>-4.573665 </td><td>...       </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-2.2258386</td><td>-3.392456 </td><td>-9.278560 </td><td>...       </td><td>-7.892265 </td><td>-9.278560 </td><td>-9.278560 </td><td>-8.585412 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-0.4151243</td><td>-3.613655 </td><td>-9.269646 </td><td>...       </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-0.7426639</td><td>-8.421453 </td><td>-9.114600 </td><td>...       </td><td>-7.322841 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-8.257061 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-0.5488753</td><td>-9.643356 </td><td>-9.643356 </td><td>...       </td><td>-6.752984 </td><td>-9.643356 </td><td>-9.643356 </td><td>-8.950209 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-7.693937 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-0.8374753</td><td>-2.225877 </td><td>-4.749498 </td><td>...       </td><td>-6.307643 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas & ... & Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter & Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria & Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio & Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -0.8429202 & -9.500918  & -4.573665  & ...        & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918 \\\\\n",
+       "\t3003 & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -2.2258386 & -3.392456  & -9.278560  & ...        & -7.892265  & -9.278560  & -9.278560  & -8.585412  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560 \\\\\n",
+       "\t3004 & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -0.4151243 & -3.613655  & -9.269646  & ...        & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646 \\\\\n",
+       "\t3006 & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -0.7426639 & -8.421453  & -9.114600  & ...        & -7.322841  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600 \\\\\n",
+       "\t3007 & -9.643356  & -9.643356  & -9.643356  & -8.257061  & -9.643356  & -9.643356  & -9.643356  & -0.5488753 & -9.643356  & -9.643356  & ...        & -6.752984  & -9.643356  & -9.643356  & -8.950209  & -9.643356  & -9.643356  & -9.643356  & -9.643356  & -9.643356  & -9.643356 \\\\\n",
+       "\t3008 & -8.387085  & -8.387085  & -8.387085  & -7.693937  & -8.387085  & -8.387085  & -8.387085  & -0.8374753 & -2.225877  & -4.749498  & ...        & -6.307643  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085 \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas | ... | Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter | Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria | Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio | Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -0.8429202 | -9.500918  | -4.573665  | ...        | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  |\n",
+       "| 3003 | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -2.2258386 | -3.392456  | -9.278560  | ...        | -7.892265  | -9.278560  | -9.278560  | -8.585412  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  |\n",
+       "| 3004 | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -0.4151243 | -3.613655  | -9.269646  | ...        | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  |\n",
+       "| 3006 | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -0.7426639 | -8.421453  | -9.114600  | ...        | -7.322841  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  |\n",
+       "| 3007 | -9.643356  | -9.643356  | -9.643356  | -8.257061  | -9.643356  | -9.643356  | -9.643356  | -0.5488753 | -9.643356  | -9.643356  | ...        | -6.752984  | -9.643356  | -9.643356  | -8.950209  | -9.643356  | -9.643356  | -9.643356  | -9.643356  | -9.643356  | -9.643356  |\n",
+       "| 3008 | -8.387085  | -8.387085  | -8.387085  | -7.693937  | -8.387085  | -8.387085  | -8.387085  | -0.8374753 | -2.225877  | -4.749498  | ...        | -6.307643  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter\n",
+       "3001 -9.500918                                                                                \n",
+       "3003 -9.278560                                                                                \n",
+       "3004 -9.269646                                                                                \n",
+       "3006 -9.114600                                                                                \n",
+       "3007 -9.643356                                                                                \n",
+       "3008 -8.387085                                                                                \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium\n",
+       "3001 -9.500918                                                                          \n",
+       "3003 -9.278560                                                                          \n",
+       "3004 -9.269646                                                                          \n",
+       "3006 -9.114600                                                                          \n",
+       "3007 -9.643356                                                                          \n",
+       "3008 -8.387085                                                                          \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella\n",
+       "3001 -9.500918                                                                            \n",
+       "3003 -9.278560                                                                            \n",
+       "3004 -9.269646                                                                            \n",
+       "3006 -9.114600                                                                            \n",
+       "3007 -9.643356                                                                            \n",
+       "3008 -8.387085                                                                            \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella\n",
+       "3001 -9.500918                                                                            \n",
+       "3003 -9.278560                                                                            \n",
+       "3004 -9.269646                                                                            \n",
+       "3006 -9.114600                                                                            \n",
+       "3007 -8.257061                                                                            \n",
+       "3008 -7.693937                                                                            \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter\n",
+       "3001 -9.500918                                                                              \n",
+       "3003 -9.278560                                                                              \n",
+       "3004 -9.269646                                                                              \n",
+       "3006 -9.114600                                                                              \n",
+       "3007 -9.643356                                                                              \n",
+       "3008 -8.387085                                                                              \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella\n",
+       "3001 -9.500918                                                                          \n",
+       "3003 -9.278560                                                                          \n",
+       "3004 -9.269646                                                                          \n",
+       "3006 -9.114600                                                                          \n",
+       "3007 -9.643356                                                                          \n",
+       "3008 -8.387085                                                                          \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia\n",
+       "3001 -9.500918                                                                        \n",
+       "3003 -9.278560                                                                        \n",
+       "3004 -9.269646                                                                        \n",
+       "3006 -9.114600                                                                        \n",
+       "3007 -9.643356                                                                        \n",
+       "3008 -8.387085                                                                        \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides\n",
+       "3001 -0.8429202                                                                 \n",
+       "3003 -2.2258386                                                                 \n",
+       "3004 -0.4151243                                                                 \n",
+       "3006 -0.7426639                                                                 \n",
+       "3007 -0.5488753                                                                 \n",
+       "3008 -0.8374753                                                                 \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella\n",
+       "3001 -9.500918                                                                      \n",
+       "3003 -3.392456                                                                      \n",
+       "3004 -3.613655                                                                      \n",
+       "3006 -8.421453                                                                      \n",
+       "3007 -9.643356                                                                      \n",
+       "3008 -2.225877                                                                      \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas\n",
+       "3001 -4.573665                                                                        \n",
+       "3003 -9.278560                                                                        \n",
+       "3004 -9.269646                                                                        \n",
+       "3006 -9.114600                                                                        \n",
+       "3007 -9.643356                                                                        \n",
+       "3008 -4.749498                                                                        \n",
+       "     ...\n",
+       "3001 ...\n",
+       "3003 ...\n",
+       "3004 ...\n",
+       "3006 ...\n",
+       "3007 ...\n",
+       "3008 ...\n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter\n",
+       "3001 -9.500918                                                                              \n",
+       "3003 -7.892265                                                                              \n",
+       "3004 -9.269646                                                                              \n",
+       "3006 -7.322841                                                                              \n",
+       "3007 -6.752984                                                                              \n",
+       "3008 -6.307643                                                                              \n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria\n",
+       "3001 -9.500918                                                                      \n",
+       "3003 -9.278560                                                                      \n",
+       "3004 -9.269646                                                                      \n",
+       "3006 -9.114600                                                                      \n",
+       "3007 -9.643356                                                                      \n",
+       "3008 -8.387085                                                                      \n",
+       "     Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio\n",
+       "3001 -9.500918                                                                                       \n",
+       "3003 -9.278560                                                                                       \n",
+       "3004 -9.269646                                                                                       \n",
+       "3006 -9.114600                                                                                       \n",
+       "3007 -9.643356                                                                                       \n",
+       "3008 -8.387085                                                                                       \n",
+       "     Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter\n",
+       "3001 -9.500918                                                                                       \n",
+       "3003 -8.585412                                                                                       \n",
+       "3004 -9.269646                                                                                       \n",
+       "3006 -9.114600                                                                                       \n",
+       "3007 -8.950209                                                                                       \n",
+       "3008 -8.387085                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio\n",
+       "3001 -9.500918                                                                                  \n",
+       "3003 -9.278560                                                                                  \n",
+       "3004 -9.269646                                                                                  \n",
+       "3006 -9.114600                                                                                  \n",
+       "3007 -9.643356                                                                                  \n",
+       "3008 -8.387085                                                                                  \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 -9.500918                                                                               \n",
+       "3003 -9.278560                                                                               \n",
+       "3004 -9.269646                                                                               \n",
+       "3006 -9.114600                                                                               \n",
+       "3007 -9.643356                                                                               \n",
+       "3008 -8.387085                                                                               \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 -9.500918                                                                                    \n",
+       "3003 -9.278560                                                                                    \n",
+       "3004 -9.269646                                                                                    \n",
+       "3006 -9.114600                                                                                    \n",
+       "3007 -9.643356                                                                                    \n",
+       "3008 -8.387085                                                                                    \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 -9.500918                                                                     \n",
+       "3003 -9.278560                                                                     \n",
+       "3004 -9.269646                                                                     \n",
+       "3006 -9.114600                                                                     \n",
+       "3007 -9.643356                                                                     \n",
+       "3008 -8.387085                                                                     \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 -9.500918                                                                     \n",
+       "3003 -9.278560                                                                     \n",
+       "3004 -9.269646                                                                     \n",
+       "3006 -9.114600                                                                     \n",
+       "3007 -9.643356                                                                     \n",
+       "3008 -8.387085                                                                     \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 -9.500918                                                                                   \n",
+       "3003 -9.278560                                                                                   \n",
+       "3004 -9.269646                                                                                   \n",
+       "3006 -9.114600                                                                                   \n",
+       "3007 -9.643356                                                                                   \n",
+       "3008 -8.387085                                                                                   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add pesudo count 0.5\n",
+    "x[x == 0] <- 0.5\n",
+    "x <- x/rowSums(x) # relative abundance\n",
+    "taxa <- log(x)\n",
+    "dim(taxa)\n",
+    "head(taxa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>pid</th><th scope=col>visitdate</th><th scope=col>birthdate</th><th scope=col>sex1m2f</th><th scope=col>heightcm</th><th scope=col>weightkg</th><th scope=col>vdate</th><th scope=col>bdate</th><th scope=col>age</th><th scope=col>heightm</th><th scope=col>bmi</th><th scope=col>zbmius</th><th scope=col>zbmicatus</th><th scope=col>bmicat1norm2ow3ob</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>3029     </td><td>12-Apr-10</td><td>1-May-86 </td><td>1        </td><td>172.43   </td><td>83.0     </td><td>18364    </td><td> 9617    </td><td>23.94798 </td><td>1.7243   </td><td>27.91595 </td><td>NA       </td><td>         </td><td>2        </td></tr>\n",
+       "\t<tr><td>3030     </td><td>12-Apr-10</td><td>22-May-87</td><td>1        </td><td>178.87   </td><td>70.3     </td><td>18364    </td><td>10003    </td><td>22.89117 </td><td>1.7887   </td><td>21.97254 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3031     </td><td>20-Apr-10</td><td>1-Dec-82 </td><td>2        </td><td>157.60   </td><td>52.0     </td><td>18372    </td><td> 8370    </td><td>27.38398 </td><td>1.5760   </td><td>20.93586 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3032     </td><td>22-Apr-10</td><td>9-Feb-86 </td><td>1        </td><td>188.10   </td><td>89.6     </td><td>18374    </td><td> 9536    </td><td>24.19712 </td><td>1.8810   </td><td>25.32389 </td><td>NA       </td><td>         </td><td>2        </td></tr>\n",
+       "\t<tr><td>3033     </td><td>22-Apr-10</td><td>9-Apr-86 </td><td>2        </td><td>170.03   </td><td>65.2     </td><td>18374    </td><td> 9595    </td><td>24.03559 </td><td>1.7003   </td><td>22.55259 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3034     </td><td>28-Apr-10</td><td>6-Feb-86 </td><td>2        </td><td>162.16   </td><td>59.9     </td><td>18380    </td><td> 9533    </td><td>24.22177 </td><td>1.6216   </td><td>22.77925 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       " pid & visitdate & birthdate & sex1m2f & heightcm & weightkg & vdate & bdate & age & heightm & bmi & zbmius & zbmicatus & bmicat1norm2ow3ob\\\\\n",
+       "\\hline\n",
+       "\t 3029      & 12-Apr-10 & 1-May-86  & 1         & 172.43    & 83.0      & 18364     &  9617     & 23.94798  & 1.7243    & 27.91595  & NA        &           & 2        \\\\\n",
+       "\t 3030      & 12-Apr-10 & 22-May-87 & 1         & 178.87    & 70.3      & 18364     & 10003     & 22.89117  & 1.7887    & 21.97254  & NA        &           & 1        \\\\\n",
+       "\t 3031      & 20-Apr-10 & 1-Dec-82  & 2         & 157.60    & 52.0      & 18372     &  8370     & 27.38398  & 1.5760    & 20.93586  & NA        &           & 1        \\\\\n",
+       "\t 3032      & 22-Apr-10 & 9-Feb-86  & 1         & 188.10    & 89.6      & 18374     &  9536     & 24.19712  & 1.8810    & 25.32389  & NA        &           & 2        \\\\\n",
+       "\t 3033      & 22-Apr-10 & 9-Apr-86  & 2         & 170.03    & 65.2      & 18374     &  9595     & 24.03559  & 1.7003    & 22.55259  & NA        &           & 1        \\\\\n",
+       "\t 3034      & 28-Apr-10 & 6-Feb-86  & 2         & 162.16    & 59.9      & 18380     &  9533     & 24.22177  & 1.6216    & 22.77925  & NA        &           & 1        \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| pid | visitdate | birthdate | sex1m2f | heightcm | weightkg | vdate | bdate | age | heightm | bmi | zbmius | zbmicatus | bmicat1norm2ow3ob |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3029      | 12-Apr-10 | 1-May-86  | 1         | 172.43    | 83.0      | 18364     |  9617     | 23.94798  | 1.7243    | 27.91595  | NA        |           | 2         |\n",
+       "| 3030      | 12-Apr-10 | 22-May-87 | 1         | 178.87    | 70.3      | 18364     | 10003     | 22.89117  | 1.7887    | 21.97254  | NA        |           | 1         |\n",
+       "| 3031      | 20-Apr-10 | 1-Dec-82  | 2         | 157.60    | 52.0      | 18372     |  8370     | 27.38398  | 1.5760    | 20.93586  | NA        |           | 1         |\n",
+       "| 3032      | 22-Apr-10 | 9-Feb-86  | 1         | 188.10    | 89.6      | 18374     |  9536     | 24.19712  | 1.8810    | 25.32389  | NA        |           | 2         |\n",
+       "| 3033      | 22-Apr-10 | 9-Apr-86  | 2         | 170.03    | 65.2      | 18374     |  9595     | 24.03559  | 1.7003    | 22.55259  | NA        |           | 1         |\n",
+       "| 3034      | 28-Apr-10 | 6-Feb-86  | 2         | 162.16    | 59.9      | 18380     |  9533     | 24.22177  | 1.6216    | 22.77925  | NA        |           | 1         |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  pid  visitdate birthdate sex1m2f heightcm weightkg vdate bdate age     \n",
+       "1 3029 12-Apr-10 1-May-86  1       172.43   83.0     18364  9617 23.94798\n",
+       "2 3030 12-Apr-10 22-May-87 1       178.87   70.3     18364 10003 22.89117\n",
+       "3 3031 20-Apr-10 1-Dec-82  2       157.60   52.0     18372  8370 27.38398\n",
+       "4 3032 22-Apr-10 9-Feb-86  1       188.10   89.6     18374  9536 24.19712\n",
+       "5 3033 22-Apr-10 9-Apr-86  2       170.03   65.2     18374  9595 24.03559\n",
+       "6 3034 28-Apr-10 6-Feb-86  2       162.16   59.9     18380  9533 24.22177\n",
+       "  heightm bmi      zbmius zbmicatus bmicat1norm2ow3ob\n",
+       "1 1.7243  27.91595 NA               2                \n",
+       "2 1.7887  21.97254 NA               1                \n",
+       "3 1.5760  20.93586 NA               1                \n",
+       "4 1.8810  25.32389 NA               2                \n",
+       "5 1.7003  22.55259 NA               1                \n",
+       "6 1.6216  22.77925 NA               1                "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "98"
+      ],
+      "text/latex": [
+       "98"
+      ],
+      "text/markdown": [
+       "98"
+      ],
+      "text/plain": [
+       "[1] 98"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>21.6186</li>\n",
+       "\t<li>21.82244</li>\n",
+       "\t<li>20.03762</li>\n",
+       "\t<li>20.82412</li>\n",
+       "\t<li>22.66875</li>\n",
+       "\t<li>24.97552</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 21.6186\n",
+       "\\item 21.82244\n",
+       "\\item 20.03762\n",
+       "\\item 20.82412\n",
+       "\\item 22.66875\n",
+       "\\item 24.97552\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 21.6186\n",
+       "2. 21.82244\n",
+       "3. 20.03762\n",
+       "4. 20.82412\n",
+       "5. 22.66875\n",
+       "6. 24.97552\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 21.61860 21.82244 20.03762 20.82412 22.66875 24.97552"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# metadata\n",
+    "demo <- read.delim(\"../../code_Lin/cvs/data/demographic.txt\")\n",
+    "head(demo)\n",
+    "y <- demo$bmi[match(rownames(count), demo$pid)]\n",
+    "length(y)\n",
+    "head(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'matrix'"
+      ],
+      "text/latex": [
+       "'matrix'"
+      ],
+      "text/markdown": [
+       "'matrix'"
+      ],
+      "text/plain": [
+       "[1] \"matrix\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "'numeric'"
+      ],
+      "text/latex": [
+       "'numeric'"
+      ],
+      "text/markdown": [
+       "'numeric'"
+      ],
+      "text/plain": [
+       "[1] \"numeric\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check datatype\n",
+    "class(taxa); class(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save processed data\n",
+    "save(y, taxa, file='../BMI/BMI_Lin_2014.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/1.2 BMI_results_cts-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/1.2 BMI_results_cts-checkpoint.ipynb
new file mode 100755
index 0000000..3d0c385
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/1.2 BMI_results_cts-checkpoint.ipynb	
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI microbiome data application results for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### method comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../BMI/results_cts/BMI_compLasso.RData')\n",
+    "load('../BMI/results_cts/BMI_elnet.RData')\n",
+    "load('../BMI/results_cts/BMI_lasso.RData')\n",
+    "load('../BMI/results_cts/BMI_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.22</li>\n",
+       "\t<li>21.59</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.22\n",
+       "\\item 21.59\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.22\n",
+       "2. 21.59\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.22 21.59"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_compLasso$stab_index, out_compLasso$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.14</li>\n",
+       "\t<li>24.07</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.14\n",
+       "\\item 24.07\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.14\n",
+       "2. 24.07\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.14 24.07"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_lasso$stab_index, out_lasso$MSE_mean) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.23</li>\n",
+       "\t<li>25.33</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.23\n",
+       "\\item 25.33\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.23\n",
+       "2. 25.33\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.23 25.33"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_elnet$stab_index, out_elnet$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.02</li>\n",
+       "\t<li>4.99</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.02\n",
+       "\\item 4.99\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.02\n",
+       "2. 4.99\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.02 4.99"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_rf$stab_index, out_rf$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>mse</th><th scope=col>stability</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>bmi_gut  </td><td>lasso    </td><td>24.07    </td><td>0.14     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>elent    </td><td>25.33    </td><td>0.23     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>rf       </td><td> 4.99    </td><td>0.02     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>compLasso</td><td>21.59    </td><td>0.22     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " dataset & method & mse & stability\\\\\n",
+       "\\hline\n",
+       "\t bmi\\_gut & lasso     & 24.07     & 0.14     \\\\\n",
+       "\t bmi\\_gut & elent     & 25.33     & 0.23     \\\\\n",
+       "\t bmi\\_gut & rf        &  4.99     & 0.02     \\\\\n",
+       "\t bmi\\_gut & compLasso & 21.59     & 0.22     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| dataset | method | mse | stability |\n",
+       "|---|---|---|---|\n",
+       "| bmi_gut   | lasso     | 24.07     | 0.14      |\n",
+       "| bmi_gut   | elent     | 25.33     | 0.23      |\n",
+       "| bmi_gut   | rf        |  4.99     | 0.02      |\n",
+       "| bmi_gut   | compLasso | 21.59     | 0.22      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    mse   stability\n",
+       "1 bmi_gut lasso     24.07 0.14     \n",
+       "2 bmi_gut elent     25.33 0.23     \n",
+       "3 bmi_gut rf         4.99 0.02     \n",
+       "4 bmi_gut compLasso 21.59 0.22     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# combine and export results\n",
+    "bmi_gut = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(bmi_gut) = c('dataset', 'method', 'mse', 'stability')\n",
+    "bmi_gut$dataset = 'bmi_gut'\n",
+    "bmi_gut$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "bmi_gut$mse = c(out_lasso$MSE_mean, out_elnet$MSE_mean, out_rf$MSE_mean, out_compLasso$MSE_mean)\n",
+    "bmi_gut$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_compLasso$stab_index)\n",
+    "bmi_gut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../BMI/results_cts/BMI_boot_compLasso.RData')\n",
+    "load('../BMI/results_cts/BMI_boot_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2659"
+      ],
+      "text/latex": [
+       "0.2659"
+      ],
+      "text/markdown": [
+       "0.2659"
+      ],
+      "text/plain": [
+       "[1] 0.2659"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.17475</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.34</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.17475\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.34\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1747597.5%\n",
+       ":   0.34\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.17475 0.34000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "11.8026936858767"
+      ],
+      "text/latex": [
+       "11.8026936858767"
+      ],
+      "text/markdown": [
+       "11.8026936858767"
+      ],
+      "text/plain": [
+       "[1] 11.80269"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-2.09038719853993</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>41.1831613135692</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -2.09038719853993\n",
+       "\\item[97.5\\textbackslash{}\\%] 41.1831613135692\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -2.0903871985399397.5%\n",
+       ":   41.1831613135692\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     2.5%     97.5% \n",
+       "-2.090387 41.183161 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_mse = (unlist(boot_compLasso$MSE_list) - unlist(boot_rf$MSE_list)) # use all 100*100 MSEs\n",
+    "mean(diff_mse)\n",
+    "quantile(diff_mse, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/2.1.Soils_biomTableConversion (Python)-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/2.1.Soils_biomTableConversion (Python)-checkpoint.ipynb
new file mode 100755
index 0000000..16a40f2
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/2.1.Soils_biomTableConversion (Python)-checkpoint.ipynb	
@@ -0,0 +1,1571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ref: the soil dataset used in balance tree\n",
+    "# https://msystems.asm.org/content/2/1/e00162-16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import biom\n",
+    "from biom.util import biom_open\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def biom2pandas(file_biom, withTaxonomy=False, astype=int):\n",
+    "    \"\"\" Converts a biom file into a Pandas.DataFrame\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_biom : str\n",
+    "        The path to the biom file.\n",
+    "    withTaxonomy : bool\n",
+    "        If TRUE, returns a second Pandas.Series with lineage information for\n",
+    "        each feature, e.g. OTU or deblur-sequence. Default: FALSE\n",
+    "    astype : type\n",
+    "        datatype into each value of the biom table is casted. Default: int.\n",
+    "        Use e.g. float if biom table contains relative abundances instead of\n",
+    "        raw reads.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A Pandas.DataFrame holding holding numerical values from the biom file.\n",
+    "    If withTaxonomy is TRUE then a second Pandas.DataFrame is returned, holding\n",
+    "    lineage information about each feature.\n",
+    "    Raises\n",
+    "    ------\n",
+    "    IOError\n",
+    "        If file_biom cannot be read.\n",
+    "    ValueError\n",
+    "        If withTaxonomy=TRUE but biom file does not hold taxonomy information.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        table = biom.load_table(file_biom)\n",
+    "        counts = pd.DataFrame(table.matrix_data.T.todense().astype(astype),\n",
+    "                              index=table.ids(axis='sample'),\n",
+    "                              columns=table.ids(axis='observation')).T\n",
+    "        if withTaxonomy:\n",
+    "            try:\n",
+    "                md = table.metadata_to_dataframe('observation')\n",
+    "                levels = [col\n",
+    "                          for col in md.columns\n",
+    "                          if col.startswith('taxonomy_')]\n",
+    "                if levels == []:\n",
+    "                    raise ValueError(('No taxonomy information found in '\n",
+    "                                      'biom file.'))\n",
+    "                else:\n",
+    "                    taxonomy = md.apply(lambda row:\n",
+    "                                        \";\".join([row[l] for l in levels]),\n",
+    "                                        axis=1)\n",
+    "                    return counts, taxonomy\n",
+    "            except KeyError:\n",
+    "                raise ValueError(('Biom file does not have any '\n",
+    "                                  'observation metadata!'))\n",
+    "        else:\n",
+    "            return counts\n",
+    "    except IOError:\n",
+    "        raise IOError('Cannot read file \"%s\"' % file_biom)\n",
+    "\n",
+    "\n",
+    "def pandas2biom(file_biom, table, taxonomy=None, err=sys.stderr):\n",
+    "    \"\"\" Writes a Pandas.DataFrame into a biom file.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_biom: str\n",
+    "        The filename of the BIOM file to be created.\n",
+    "    table: a Pandas.DataFrame\n",
+    "        The table that should be written as BIOM.\n",
+    "    taxonomy : pandas.Series\n",
+    "        Index is taxons corresponding to table, values are lineage strings like\n",
+    "        'k__Bacteria; p__Actinobacteria'\n",
+    "    err : StringIO\n",
+    "        Stream onto which errors / warnings should be printed.\n",
+    "        Default is sys.stderr\n",
+    "    Raises\n",
+    "    ------\n",
+    "    IOError\n",
+    "        If file_biom cannot be written.\n",
+    "    TODO\n",
+    "    ----\n",
+    "        1) also store taxonomy information\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        bt = biom.Table(table.values,\n",
+    "                        observation_ids=table.index,\n",
+    "                        sample_ids=table.columns)\n",
+    "\n",
+    "        # add taxonomy metadata if provided, i.e. is not None\n",
+    "        if taxonomy is not None:\n",
+    "            if not isinstance(taxonomy, pd.core.series.Series):\n",
+    "                raise AttributeError('taxonomy must be a pandas.Series!')\n",
+    "            idx_missing_intable = set(table.index) - set(taxonomy.index)\n",
+    "            if len(idx_missing_intable) > 0:\n",
+    "                err.write(('Warning: following %i taxa are not in the '\n",
+    "                           'provided taxonomy:\\n%s\\n') % (\n",
+    "                          len(idx_missing_intable),\n",
+    "                          \", \".join(idx_missing_intable)))\n",
+    "                missing = pd.Series(\n",
+    "                    index=idx_missing_intable,\n",
+    "                    name='taxonomy',\n",
+    "                    data='k__missing_lineage_information')\n",
+    "                taxonomy = taxonomy.append(missing)\n",
+    "            idx_missing_intaxonomy = set(taxonomy.index) - set(table.index)\n",
+    "            if (len(idx_missing_intaxonomy) > 0) and err:\n",
+    "                err.write(('Warning: following %i taxa are not in the '\n",
+    "                           'provided count table, but in taxonomy:\\n%s\\n') % (\n",
+    "                          len(idx_missing_intaxonomy),\n",
+    "                          \", \".join(idx_missing_intaxonomy)))\n",
+    "\n",
+    "            t = dict()\n",
+    "            for taxon, linstr in taxonomy.iteritems():\n",
+    "                # fill missing rank annotations with rank__\n",
+    "                orig_lineage = {annot[0].lower(): annot\n",
+    "                                for annot\n",
+    "                                in (map(str.strip, linstr.split(';')))}\n",
+    "                lineage = []\n",
+    "                for rank in settings.RANKS:\n",
+    "                    rank_char = rank[0].lower()\n",
+    "                    if rank_char in orig_lineage:\n",
+    "                        lineage.append(orig_lineage[rank_char])\n",
+    "                    else:\n",
+    "                        lineage.append(rank_char+'__')\n",
+    "                t[taxon] = {'taxonomy': \";\".join(lineage)}\n",
+    "            bt.add_metadata(t, axis='observation')\n",
+    "\n",
+    "        with biom_open(file_biom, 'w') as f:\n",
+    "            bt.to_hdf5(f, \"example\")\n",
+    "    except IOError:\n",
+    "        raise IOError('Cannot write to file \"%s\"' % file_biom)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance_88soils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7396, 89)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom = biom2pandas('../88soils/238_otu_table.biom')\n",
+    "soils_biom.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.LQ1</th>\n",
+       "      <th>103.HI1</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1124701</th>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244336</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>973124</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 89 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         103.CA2  103.CO3  103.SR3  103.IE2  103.BP1  103.VC2  103.SA2  \\\n",
+       "1124701       15       14        1        8       13        7        6   \n",
+       "244336         0        0        0        1        0        0        0   \n",
+       "973124         0        0        0        0        0        0        0   \n",
+       "\n",
+       "         103.GB2  103.CO2  103.KP1  ...  103.LQ1  103.HI1  103.RT1  103.HI2  \\\n",
+       "1124701        3        2        2  ...        0        0        0        0   \n",
+       "244336         0        0        0  ...        0        0        0        0   \n",
+       "973124         0        0        1  ...        0        0        0        0   \n",
+       "\n",
+       "         103.DF1  103.CF3  103.AR1  103.TL1  103.HI4  103.BB1  \n",
+       "1124701        0        0        0        0        0        0  \n",
+       "244336         0        0        0        0        0        0  \n",
+       "973124         0        0        0        0        0        0  \n",
+       "\n",
+       "[3 rows x 89 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7396, 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa = pd.read_csv('../88soils/88soils_taxonomy.txt', sep='\\t', index_col='Feature ID')\n",
+    "soils_taxa.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Thermoleophil...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon\n",
+       "Feature ID                                                   \n",
+       "1000512     k__Bacteria;p__Actinobacteria;c__Thermoleophil...\n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...\n",
+       "1000654     k__Bacteria;p__Bacteroidetes;c__Sphingobacteri..."
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Actinobacteria</td>\n",
+       "      <td>c__Thermoleophilia</td>\n",
+       "      <td>o__Gaiellales</td>\n",
+       "      <td>f__Gaiellaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Firmicutes</td>\n",
+       "      <td>c__Bacilli</td>\n",
+       "      <td>o__Lactobacillales</td>\n",
+       "      <td>f__Streptococcaceae</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Bacteroidetes</td>\n",
+       "      <td>c__Sphingobacteriia</td>\n",
+       "      <td>o__Sphingobacteriales</td>\n",
+       "      <td>f__Sphingobacteriaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000757</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Proteobacteria</td>\n",
+       "      <td>c__Alphaproteobacteria</td>\n",
+       "      <td>o__Rhizobiales</td>\n",
+       "      <td>f__Bradyrhizobiaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Actinobacteria</td>\n",
+       "      <td>c__Actinobacteria</td>\n",
+       "      <td>o__Actinomycetales</td>\n",
+       "      <td>f__Nocardioidaceae</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      0                  1                       2  \\\n",
+       "Feature ID                                                           \n",
+       "1000512     k__Bacteria  p__Actinobacteria      c__Thermoleophilia   \n",
+       "1000547     k__Bacteria      p__Firmicutes              c__Bacilli   \n",
+       "1000654     k__Bacteria   p__Bacteroidetes     c__Sphingobacteriia   \n",
+       "1000757     k__Bacteria  p__Proteobacteria  c__Alphaproteobacteria   \n",
+       "1000876     k__Bacteria  p__Actinobacteria       c__Actinobacteria   \n",
+       "\n",
+       "                                3                       4                 5  \\\n",
+       "Feature ID                                                                    \n",
+       "1000512             o__Gaiellales          f__Gaiellaceae               g__   \n",
+       "1000547        o__Lactobacillales     f__Streptococcaceae  g__Streptococcus   \n",
+       "1000654     o__Sphingobacteriales  f__Sphingobacteriaceae               g__   \n",
+       "1000757            o__Rhizobiales    f__Bradyrhizobiaceae               g__   \n",
+       "1000876        o__Actinomycetales      f__Nocardioidaceae   g__Nocardioides   \n",
+       "\n",
+       "              6  \n",
+       "Feature ID       \n",
+       "1000512     s__  \n",
+       "1000547     s__  \n",
+       "1000654     s__  \n",
+       "1000757     s__  \n",
+       "1000876     s__  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taxa_new = soils_taxa.Taxon.str.split(pat=\";\", expand=True)\n",
+    "taxa_new.head(5)\n",
+    "# ref: https://www.geeksforgeeks.org/python-pandas-split-strings-into-two-list-columns-using-str-split/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Thermoleophil...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000757</th>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Actinobacteri...</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon  \\\n",
+       "Feature ID                                                      \n",
+       "1000512     k__Bacteria;p__Actinobacteria;c__Thermoleophil...   \n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...   \n",
+       "1000654     k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...   \n",
+       "1000757     k__Bacteria;p__Proteobacteria;c__Alphaproteoba...   \n",
+       "1000876     k__Bacteria;p__Actinobacteria;c__Actinobacteri...   \n",
+       "\n",
+       "                       Genus  \n",
+       "Feature ID                    \n",
+       "1000512                  g__  \n",
+       "1000547     g__Streptococcus  \n",
+       "1000654                  g__  \n",
+       "1000757                  g__  \n",
+       "1000876      g__Nocardioides  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa['Genus'] = taxa_new[5]\n",
+    "soils_taxa.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "g__                           5213\n",
+       "g__Rhodoplanes                 144\n",
+       "g__Bacillus                    110\n",
+       "g__Candidatus Solibacter       100\n",
+       "g__Flavobacterium               71\n",
+       "                              ... \n",
+       "g__Rhodocyclus                   1\n",
+       "g__Marinobacter                  1\n",
+       "g__Afipia                        1\n",
+       "g__Candidatus Amoebophilus       1\n",
+       "g__Desulfotomaculum              1\n",
+       "Name: Genus, Length: 335, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa.Genus.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 2)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only keep those with genus assignment\n",
+    "soils_taxa_sub = soils_taxa[soils_taxa.Genus != 'g__']\n",
+    "soils_taxa_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Actinobacteri...</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003206</th>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__Sphingomonas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon  \\\n",
+       "Feature ID                                                      \n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...   \n",
+       "1000876     k__Bacteria;p__Actinobacteria;c__Actinobacteri...   \n",
+       "1003206     k__Bacteria;p__Proteobacteria;c__Alphaproteoba...   \n",
+       "\n",
+       "                       Genus  \n",
+       "Feature ID                    \n",
+       "1000547     g__Streptococcus  \n",
+       "1000876      g__Nocardioides  \n",
+       "1003206      g__Sphingomonas  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 91)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# partition biom table \n",
+    "soils_biom.index = soils_biom.index.astype('int64') \n",
+    "soils_biom_sub = soils_biom.merge(soils_taxa_sub, how='inner', left_index=True, right_index=True)\n",
+    "soils_biom_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>244336</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...</td>\n",
+       "      <td>g__Paenibacillus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809489</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...</td>\n",
+       "      <td>g__Bacillus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533625</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__Novosphingobium</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 91 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        103.CA2  103.CO3  103.SR3  103.IE2  103.BP1  103.VC2  103.SA2  \\\n",
+       "244336        0        0        0        1        0        0        0   \n",
+       "809489        0        0        0        0        0        0        0   \n",
+       "533625        0        0        0        0        0        0        0   \n",
+       "\n",
+       "        103.GB2  103.CO2  103.KP1  ...  103.RT1  103.HI2  103.DF1  103.CF3  \\\n",
+       "244336        0        0        0  ...        0        0        0        0   \n",
+       "809489        1        0        0  ...        0        0        0        0   \n",
+       "533625        0        0        0  ...        0        0        0        0   \n",
+       "\n",
+       "        103.AR1  103.TL1  103.HI4  103.BB1  \\\n",
+       "244336        0        0        0        0   \n",
+       "809489        0        0        0        0   \n",
+       "533625        0        0        0        0   \n",
+       "\n",
+       "                                                    Taxon               Genus  \n",
+       "244336  k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...    g__Paenibacillus  \n",
+       "809489  k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...         g__Bacillus  \n",
+       "533625  k__Bacteria;p__Proteobacteria;c__Alphaproteoba...  g__Novosphingobium  \n",
+       "\n",
+       "[3 rows x 91 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soils_biom_sub.set_index('Taxon', inplace=True)\n",
+    "soils_biom_sub.drop(['Genus'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 89)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.LQ1</th>\n",
+       "      <th>103.HI1</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Taxon</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 89 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    103.CA2  103.CO3  103.SR3  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.IE2  103.BP1  103.VC2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        1        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.SA2  103.GB2  103.CO2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        1        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.KP1  ...  103.LQ1  \\\n",
+       "Taxon                                                        ...            \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0  ...        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0  ...        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0  ...        0   \n",
+       "\n",
+       "                                                    103.HI1  103.RT1  103.HI2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.DF1  103.CF3  103.AR1  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.TL1  103.HI4  103.BB1  \n",
+       "Taxon                                                                          \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0  \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0  \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0  \n",
+       "\n",
+       "[3 rows x 89 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89, 2183)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transpose the dataframe \n",
+    "soils_biom_sub_t = soils_biom_sub.T\n",
+    "soils_biom_sub_t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Taxon</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__</th>\n",
+       "      <th>k__Bacteria;p__Acidobacteria;c__Solibacteres;o__Solibacterales;f__Solibacteraceae;g__Candidatus Solibacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Nocardioidaceae;g__Nocardioides;s__</th>\n",
+       "      <th>k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptococcaceae;g__Desulfotomaculum;s__</th>\n",
+       "      <th>k__Bacteria;p__Acidobacteria;c__Acidobacteriia;o__Acidobacteriales;f__Koribacteraceae;g__Candidatus Koribacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Methylibium;s__</th>\n",
+       "      <th>...</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rhodoferax;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Dokdonella;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__Methylotenera;s__mobilis</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Streptomycetaceae;g__Streptomyces;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Luteimonas;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Planococcaceae;g__Solibacillus;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Ramlibacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Mycobacteriaceae;g__Mycobacterium;s__</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103.CA2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103.CO3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103.SR3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 2183 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__  \\\n",
+       "103.CA2                                                  0                                             \n",
+       "103.CO3                                                  0                                             \n",
+       "103.SR3                                                  0                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis  \\\n",
+       "103.CA2                                                  0                                          \n",
+       "103.CO3                                                  0                                          \n",
+       "103.SR3                                                  0                                          \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__  \\\n",
+       "103.CA2                                                  0                                                                      \n",
+       "103.CO3                                                  0                                                                      \n",
+       "103.SR3                                                  0                                                                      \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Acidobacteria;c__Solibacteres;o__Solibacterales;f__Solibacteraceae;g__Candidatus Solibacter;s__  \\\n",
+       "103.CA2                                                  0                                                                \n",
+       "103.CO3                                                  0                                                                \n",
+       "103.SR3                                                  0                                                                \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Nocardioidaceae;g__Nocardioides;s__  \\\n",
+       "103.CA2                                                  0                                                           \n",
+       "103.CO3                                                  0                                                           \n",
+       "103.SR3                                                  0                                                           \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  0                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptococcaceae;g__Desulfotomaculum;s__  \\\n",
+       "103.CA2                                                  0                                                    \n",
+       "103.CO3                                                  0                                                    \n",
+       "103.SR3                                                  0                                                    \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Acidobacteria;c__Acidobacteriia;o__Acidobacteriales;f__Koribacteraceae;g__Candidatus Koribacter;s__  \\\n",
+       "103.CA2                                                  0                                                                    \n",
+       "103.CO3                                                  0                                                                    \n",
+       "103.SR3                                                  0                                                                    \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Methylibium;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    ...  \\\n",
+       "103.CA2  ...   \n",
+       "103.CO3  ...   \n",
+       "103.SR3  ...   \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rhodoferax;s__  \\\n",
+       "103.CA2                                                  0                                                            \n",
+       "103.CO3                                                  0                                                            \n",
+       "103.SR3                                                  0                                                            \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Dokdonella;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  1                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__  \\\n",
+       "103.CA2                                                  0                                                  \n",
+       "103.CO3                                                  0                                                  \n",
+       "103.SR3                                                  0                                                  \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__Methylotenera;s__mobilis  \\\n",
+       "103.CA2                                                  0                                                                        \n",
+       "103.CO3                                                  0                                                                        \n",
+       "103.SR3                                                  0                                                                        \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Streptomycetaceae;g__Streptomyces;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Luteimonas;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  0                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Planococcaceae;g__Solibacillus;s__  \\\n",
+       "103.CA2                                                  0                                          \n",
+       "103.CO3                                                  0                                          \n",
+       "103.SR3                                                  0                                          \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Ramlibacter;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  1                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Mycobacteriaceae;g__Mycobacterium;s__  \n",
+       "103.CA2                                                  0                                                            \n",
+       "103.CO3                                                  1                                                            \n",
+       "103.SR3                                                  1                                                            \n",
+       "\n",
+       "[3 rows x 2183 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub_t.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     89.000000\n",
+       "mean     275.932584\n",
+       "std      115.508244\n",
+       "min        1.000000\n",
+       "25%      213.000000\n",
+       "50%      254.000000\n",
+       "75%      319.000000\n",
+       "max      805.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make sure that each genus exist in at least one sample\n",
+    "soils_biom_sub_t.sum(axis=1).describe() # column sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# export\n",
+    "soils_biom_sub_t.to_csv('../88soils/88soils_genus_table.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    2183.000000\n",
+       "mean       11.249656\n",
+       "std        33.869668\n",
+       "min         1.000000\n",
+       "25%         1.000000\n",
+       "50%         3.000000\n",
+       "75%         8.000000\n",
+       "max       690.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check that not rarefied\n",
+    "soils_biom_sub_t.sum(axis=0).describe() # row sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/2.2.soils_dataPreparation-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/2.2.soils_dataPreparation-checkpoint.ipynb
new file mode 100755
index 0000000..2217a77
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/2.2.soils_dataPreparation-checkpoint.ipynb
@@ -0,0 +1,1060 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/88soils_genus_table.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "count = soil_otu\n",
+    "dim(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__</th><th scope=col>k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__</th><th scope=col>...</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>103.CA2</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td></tr>\n",
+       "\t<tr><th scope=row>103.CO3</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.SR3</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>1  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.IE2</th><td>1  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>2  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.BP1</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.VC2</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>2  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Paenibacillaceae.g\\_\\_Paenibacillus.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Bacillaceae.g\\_\\_Bacillus.s\\_\\_muralis & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Sphingomonadales.f\\_\\_Sphingomonadaceae.g\\_\\_Novosphingobium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Solibacteres.o\\_\\_Solibacterales.f\\_\\_Solibacteraceae.g\\_\\_Candidatus.Solibacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Nocardioidaceae.g\\_\\_Nocardioides.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Bacteroidetes.c\\_\\_Flavobacteriia.o\\_\\_Flavobacteriales.f\\_\\_Flavobacteriaceae.g\\_\\_Flavobacterium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Clostridia.o\\_\\_Clostridiales.f\\_\\_Peptococcaceae.g\\_\\_Desulfotomaculum.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Acidobacteriia.o\\_\\_Acidobacteriales.f\\_\\_Koribacteraceae.g\\_\\_Candidatus.Koribacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Methylibium.s\\_\\_ & ... & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Rhodoferax.s\\_\\_.4 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Dokdonella.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Lactobacillales.f\\_\\_Streptococcaceae.g\\_\\_Streptococcus.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Methylophilales.f\\_\\_Methylophilaceae.g\\_\\_Methylotenera.s\\_\\_mobilis.2 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Streptomycetaceae.g\\_\\_Streptomyces.s\\_\\_.44 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Luteimonas.s\\_\\_.2 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_.142 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Planococcaceae.g\\_\\_Solibacillus.s\\_\\_.3 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Ramlibacter.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Mycobacteriaceae.g\\_\\_Mycobacterium.s\\_\\_.28\\\\\n",
+       "\\hline\n",
+       "\t103.CA2 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0  \\\\\n",
+       "\t103.CO3 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\t103.SR3 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 1   & 0   & 0   & 0   & 0   & 0   & 0   & 1   & 1  \\\\\n",
+       "\t103.IE2 & 1   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 2   & 0   & 0   & 1  \\\\\n",
+       "\t103.BP1 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\t103.VC2 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 2   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__ | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__ | k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__ | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__ | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__ | k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__ | k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__ | k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__ | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__ | ... | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2 | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28 |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 103.CA2 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   |\n",
+       "| 103.CO3 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "| 103.SR3 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 1   | 0   | 0   | 0   | 0   | 0   | 0   | 1   | 1   |\n",
+       "| 103.IE2 | 1   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 2   | 0   | 0   | 1   |\n",
+       "| 103.BP1 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "| 103.VC2 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 2   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__\n",
+       "103.CA2 0                                                                                          \n",
+       "103.CO3 0                                                                                          \n",
+       "103.SR3 0                                                                                          \n",
+       "103.IE2 1                                                                                          \n",
+       "103.BP1 0                                                                                          \n",
+       "103.VC2 0                                                                                          \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis\n",
+       "103.CA2 0                                                                                       \n",
+       "103.CO3 0                                                                                       \n",
+       "103.SR3 0                                                                                       \n",
+       "103.IE2 0                                                                                       \n",
+       "103.BP1 0                                                                                       \n",
+       "103.VC2 0                                                                                       \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__\n",
+       "103.CA2 0                                                                                                                   \n",
+       "103.CO3 0                                                                                                                   \n",
+       "103.SR3 0                                                                                                                   \n",
+       "103.IE2 0                                                                                                                   \n",
+       "103.BP1 0                                                                                                                   \n",
+       "103.VC2 0                                                                                                                   \n",
+       "        k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 0                                                                                                             \n",
+       "103.SR3 0                                                                                                             \n",
+       "103.IE2 0                                                                                                             \n",
+       "103.BP1 0                                                                                                             \n",
+       "103.VC2 0                                                                                                             \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__\n",
+       "103.CA2 0                                                                                                          \n",
+       "103.CO3 0                                                                                                          \n",
+       "103.SR3 0                                                                                                          \n",
+       "103.IE2 0                                                                                                          \n",
+       "103.BP1 0                                                                                                          \n",
+       "103.VC2 0                                                                                                          \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__\n",
+       "103.CA2 0                                                                                                        \n",
+       "103.CO3 0                                                                                                        \n",
+       "103.SR3 0                                                                                                        \n",
+       "103.IE2 0                                                                                                        \n",
+       "103.BP1 0                                                                                                        \n",
+       "103.VC2 0                                                                                                        \n",
+       "        k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__\n",
+       "103.CA2 0                                                                                                            \n",
+       "103.CO3 0                                                                                                            \n",
+       "103.SR3 0                                                                                                            \n",
+       "103.IE2 0                                                                                                            \n",
+       "103.BP1 0                                                                                                            \n",
+       "103.VC2 0                                                                                                            \n",
+       "        k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__\n",
+       "103.CA2 0                                                                                                 \n",
+       "103.CO3 0                                                                                                 \n",
+       "103.SR3 0                                                                                                 \n",
+       "103.IE2 0                                                                                                 \n",
+       "103.BP1 0                                                                                                 \n",
+       "103.VC2 0                                                                                                 \n",
+       "        k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__\n",
+       "103.CA2 0                                                                                                                 \n",
+       "103.CO3 0                                                                                                                 \n",
+       "103.SR3 0                                                                                                                 \n",
+       "103.IE2 0                                                                                                                 \n",
+       "103.BP1 0                                                                                                                 \n",
+       "103.VC2 0                                                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__\n",
+       "103.CA2 0                                                                                                          \n",
+       "103.CO3 0                                                                                                          \n",
+       "103.SR3 0                                                                                                          \n",
+       "103.IE2 0                                                                                                          \n",
+       "103.BP1 0                                                                                                          \n",
+       "103.VC2 2                                                                                                          \n",
+       "        ...\n",
+       "103.CA2 ...\n",
+       "103.CO3 ...\n",
+       "103.SR3 ...\n",
+       "103.IE2 ...\n",
+       "103.BP1 ...\n",
+       "103.VC2 ...\n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4\n",
+       "103.CA2 0                                                                                                           \n",
+       "103.CO3 0                                                                                                           \n",
+       "103.SR3 0                                                                                                           \n",
+       "103.IE2 0                                                                                                           \n",
+       "103.BP1 0                                                                                                           \n",
+       "103.VC2 0                                                                                                           \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 1                                                                                                              \n",
+       "103.IE2 0                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9\n",
+       "103.CA2 0                                                                                                 \n",
+       "103.CO3 0                                                                                                 \n",
+       "103.SR3 0                                                                                                 \n",
+       "103.IE2 0                                                                                                 \n",
+       "103.BP1 0                                                                                                 \n",
+       "103.VC2 0                                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2\n",
+       "103.CA2 0                                                                                                                       \n",
+       "103.CO3 0                                                                                                                       \n",
+       "103.SR3 0                                                                                                                       \n",
+       "103.IE2 0                                                                                                                       \n",
+       "103.BP1 0                                                                                                                       \n",
+       "103.VC2 0                                                                                                                       \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 0                                                                                                             \n",
+       "103.SR3 0                                                                                                             \n",
+       "103.IE2 0                                                                                                             \n",
+       "103.BP1 0                                                                                                             \n",
+       "103.VC2 0                                                                                                             \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 0                                                                                                              \n",
+       "103.IE2 0                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 0                                                                                                              \n",
+       "103.IE2 2                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3\n",
+       "103.CA2 0                                                                                         \n",
+       "103.CO3 0                                                                                         \n",
+       "103.SR3 0                                                                                         \n",
+       "103.IE2 0                                                                                         \n",
+       "103.BP1 0                                                                                         \n",
+       "103.VC2 0                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9\n",
+       "103.CA2 0                                                                                                            \n",
+       "103.CO3 0                                                                                                            \n",
+       "103.SR3 1                                                                                                            \n",
+       "103.IE2 0                                                                                                            \n",
+       "103.BP1 0                                                                                                            \n",
+       "103.VC2 0                                                                                                            \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 1                                                                                                             \n",
+       "103.SR3 1                                                                                                             \n",
+       "103.IE2 1                                                                                                             \n",
+       "103.BP1 1                                                                                                             \n",
+       "103.VC2 1                                                                                                             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 0.5/100]) # 0.5%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 1/100]) # 1%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>1297</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 1297\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 1297\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 1297"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 2/100]) # 2%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>576</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 576\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 576\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  89 576"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 5/100]) # 5%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__</th><th scope=col>k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__</th><th scope=col>...</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>103.CA2</th><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>...      </td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td></tr>\n",
+       "\t<tr><th scope=row>103.CO3</th><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>...      </td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.199305</td></tr>\n",
+       "\t<tr><th scope=row>103.SR3</th><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>...      </td><td>-7.942718</td><td>-7.249570</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.249570</td><td>-7.249570</td></tr>\n",
+       "\t<tr><th scope=row>103.IE2</th><td>-7.208230</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>...      </td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-6.515083</td><td>-7.901377</td><td>-7.901377</td><td>-7.208230</td></tr>\n",
+       "\t<tr><th scope=row>103.BP1</th><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>...      </td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.087991</td></tr>\n",
+       "\t<tr><th scope=row>103.VC2</th><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-6.439350</td><td>...      </td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.132498</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Paenibacillaceae.g\\_\\_Paenibacillus.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Bacillaceae.g\\_\\_Bacillus.s\\_\\_muralis & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Sphingomonadales.f\\_\\_Sphingomonadaceae.g\\_\\_Novosphingobium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Solibacteres.o\\_\\_Solibacterales.f\\_\\_Solibacteraceae.g\\_\\_Candidatus.Solibacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Nocardioidaceae.g\\_\\_Nocardioides.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Bacteroidetes.c\\_\\_Flavobacteriia.o\\_\\_Flavobacteriales.f\\_\\_Flavobacteriaceae.g\\_\\_Flavobacterium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Clostridia.o\\_\\_Clostridiales.f\\_\\_Peptococcaceae.g\\_\\_Desulfotomaculum.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Acidobacteriia.o\\_\\_Acidobacteriales.f\\_\\_Koribacteraceae.g\\_\\_Candidatus.Koribacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Methylibium.s\\_\\_ & ... & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Rhodoferax.s\\_\\_.4 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Dokdonella.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Lactobacillales.f\\_\\_Streptococcaceae.g\\_\\_Streptococcus.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Methylophilales.f\\_\\_Methylophilaceae.g\\_\\_Methylotenera.s\\_\\_mobilis.2 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Streptomycetaceae.g\\_\\_Streptomyces.s\\_\\_.44 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Luteimonas.s\\_\\_.2 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_.142 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Planococcaceae.g\\_\\_Solibacillus.s\\_\\_.3 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Ramlibacter.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Mycobacteriaceae.g\\_\\_Mycobacterium.s\\_\\_.28\\\\\n",
+       "\\hline\n",
+       "\t103.CA2 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & ...       & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436\\\\\n",
+       "\t103.CO3 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & ...       & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.199305\\\\\n",
+       "\t103.SR3 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & ...       & -7.942718 & -7.249570 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.249570 & -7.249570\\\\\n",
+       "\t103.IE2 & -7.208230 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & ...       & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -6.515083 & -7.901377 & -7.901377 & -7.208230\\\\\n",
+       "\t103.BP1 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & ...       & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.087991\\\\\n",
+       "\t103.VC2 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -6.439350 & ...       & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.132498\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__ | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__ | k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__ | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__ | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__ | k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__ | k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__ | k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__ | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__ | ... | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2 | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28 |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 103.CA2 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | ...       | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 |\n",
+       "| 103.CO3 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | ...       | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.199305 |\n",
+       "| 103.SR3 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | ...       | -7.942718 | -7.249570 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.249570 | -7.249570 |\n",
+       "| 103.IE2 | -7.208230 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | ...       | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -6.515083 | -7.901377 | -7.901377 | -7.208230 |\n",
+       "| 103.BP1 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | ...       | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.087991 |\n",
+       "| 103.VC2 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -6.439350 | ...       | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.132498 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__\n",
+       "103.CA2 -7.828436                                                                                  \n",
+       "103.CO3 -7.892452                                                                                  \n",
+       "103.SR3 -7.942718                                                                                  \n",
+       "103.IE2 -7.208230                                                                                  \n",
+       "103.BP1 -7.781139                                                                                  \n",
+       "103.VC2 -7.825645                                                                                  \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis\n",
+       "103.CA2 -7.828436                                                                               \n",
+       "103.CO3 -7.892452                                                                               \n",
+       "103.SR3 -7.942718                                                                               \n",
+       "103.IE2 -7.901377                                                                               \n",
+       "103.BP1 -7.781139                                                                               \n",
+       "103.VC2 -7.825645                                                                               \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__\n",
+       "103.CA2 -7.828436                                                                                                           \n",
+       "103.CO3 -7.892452                                                                                                           \n",
+       "103.SR3 -7.942718                                                                                                           \n",
+       "103.IE2 -7.901377                                                                                                           \n",
+       "103.BP1 -7.781139                                                                                                           \n",
+       "103.VC2 -7.825645                                                                                                           \n",
+       "        k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.892452                                                                                                     \n",
+       "103.SR3 -7.942718                                                                                                     \n",
+       "103.IE2 -7.901377                                                                                                     \n",
+       "103.BP1 -7.781139                                                                                                     \n",
+       "103.VC2 -7.825645                                                                                                     \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__\n",
+       "103.CA2 -7.828436                                                                                                  \n",
+       "103.CO3 -7.892452                                                                                                  \n",
+       "103.SR3 -7.942718                                                                                                  \n",
+       "103.IE2 -7.901377                                                                                                  \n",
+       "103.BP1 -7.781139                                                                                                  \n",
+       "103.VC2 -7.825645                                                                                                  \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__\n",
+       "103.CA2 -7.828436                                                                                                \n",
+       "103.CO3 -7.892452                                                                                                \n",
+       "103.SR3 -7.942718                                                                                                \n",
+       "103.IE2 -7.901377                                                                                                \n",
+       "103.BP1 -7.781139                                                                                                \n",
+       "103.VC2 -7.825645                                                                                                \n",
+       "        k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__\n",
+       "103.CA2 -7.828436                                                                                                    \n",
+       "103.CO3 -7.892452                                                                                                    \n",
+       "103.SR3 -7.942718                                                                                                    \n",
+       "103.IE2 -7.901377                                                                                                    \n",
+       "103.BP1 -7.781139                                                                                                    \n",
+       "103.VC2 -7.825645                                                                                                    \n",
+       "        k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__\n",
+       "103.CA2 -7.828436                                                                                         \n",
+       "103.CO3 -7.892452                                                                                         \n",
+       "103.SR3 -7.942718                                                                                         \n",
+       "103.IE2 -7.901377                                                                                         \n",
+       "103.BP1 -7.781139                                                                                         \n",
+       "103.VC2 -7.825645                                                                                         \n",
+       "        k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__\n",
+       "103.CA2 -7.828436                                                                                                         \n",
+       "103.CO3 -7.892452                                                                                                         \n",
+       "103.SR3 -7.942718                                                                                                         \n",
+       "103.IE2 -7.901377                                                                                                         \n",
+       "103.BP1 -7.781139                                                                                                         \n",
+       "103.VC2 -7.825645                                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__\n",
+       "103.CA2 -7.828436                                                                                                  \n",
+       "103.CO3 -7.892452                                                                                                  \n",
+       "103.SR3 -7.942718                                                                                                  \n",
+       "103.IE2 -7.901377                                                                                                  \n",
+       "103.BP1 -7.781139                                                                                                  \n",
+       "103.VC2 -6.439350                                                                                                  \n",
+       "        ...\n",
+       "103.CA2 ...\n",
+       "103.CO3 ...\n",
+       "103.SR3 ...\n",
+       "103.IE2 ...\n",
+       "103.BP1 ...\n",
+       "103.VC2 ...\n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4\n",
+       "103.CA2 -7.828436                                                                                                   \n",
+       "103.CO3 -7.892452                                                                                                   \n",
+       "103.SR3 -7.942718                                                                                                   \n",
+       "103.IE2 -7.901377                                                                                                   \n",
+       "103.BP1 -7.781139                                                                                                   \n",
+       "103.VC2 -7.825645                                                                                                   \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.249570                                                                                                      \n",
+       "103.IE2 -7.901377                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9\n",
+       "103.CA2 -7.828436                                                                                         \n",
+       "103.CO3 -7.892452                                                                                         \n",
+       "103.SR3 -7.942718                                                                                         \n",
+       "103.IE2 -7.901377                                                                                         \n",
+       "103.BP1 -7.781139                                                                                         \n",
+       "103.VC2 -7.825645                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2\n",
+       "103.CA2 -7.828436                                                                                                               \n",
+       "103.CO3 -7.892452                                                                                                               \n",
+       "103.SR3 -7.942718                                                                                                               \n",
+       "103.IE2 -7.901377                                                                                                               \n",
+       "103.BP1 -7.781139                                                                                                               \n",
+       "103.VC2 -7.825645                                                                                                               \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.892452                                                                                                     \n",
+       "103.SR3 -7.942718                                                                                                     \n",
+       "103.IE2 -7.901377                                                                                                     \n",
+       "103.BP1 -7.781139                                                                                                     \n",
+       "103.VC2 -7.825645                                                                                                     \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.942718                                                                                                      \n",
+       "103.IE2 -7.901377                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.942718                                                                                                      \n",
+       "103.IE2 -6.515083                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3\n",
+       "103.CA2 -7.828436                                                                                 \n",
+       "103.CO3 -7.892452                                                                                 \n",
+       "103.SR3 -7.942718                                                                                 \n",
+       "103.IE2 -7.901377                                                                                 \n",
+       "103.BP1 -7.781139                                                                                 \n",
+       "103.VC2 -7.825645                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9\n",
+       "103.CA2 -7.828436                                                                                                    \n",
+       "103.CO3 -7.892452                                                                                                    \n",
+       "103.SR3 -7.249570                                                                                                    \n",
+       "103.IE2 -7.901377                                                                                                    \n",
+       "103.BP1 -7.781139                                                                                                    \n",
+       "103.VC2 -7.825645                                                                                                    \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.199305                                                                                                     \n",
+       "103.SR3 -7.249570                                                                                                     \n",
+       "103.IE2 -7.208230                                                                                                     \n",
+       "103.BP1 -7.087991                                                                                                     \n",
+       "103.VC2 -7.132498                                                                                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add pesudo count 0.5\n",
+    "x = count # preprossed done already\n",
+    "x[x == 0] <- 0.5\n",
+    "x <- x/rowSums(x) # relative abundance\n",
+    "taxa <- log(x)\n",
+    "dim(taxa)\n",
+    "head(taxa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>'BarcodeSequence'</li>\n",
+       "\t<li>'LinkerPrimerSequence'</li>\n",
+       "\t<li>'barcode_read_group_tag'</li>\n",
+       "\t<li>'dna_extracted_prep'</li>\n",
+       "\t<li>'experiment_alias'</li>\n",
+       "\t<li>'experiment_center'</li>\n",
+       "\t<li>'experiment_design_description'</li>\n",
+       "\t<li>'experiment_title'</li>\n",
+       "\t<li>'instrument_name'</li>\n",
+       "\t<li>'key_seq'</li>\n",
+       "\t<li>'library_construction_protocol'</li>\n",
+       "\t<li>'linker'</li>\n",
+       "\t<li>'pcr_primers'</li>\n",
+       "\t<li>'physical_specimen_remaining_prep'</li>\n",
+       "\t<li>'platform'</li>\n",
+       "\t<li>'pool_member_name'</li>\n",
+       "\t<li>'pool_proportion'</li>\n",
+       "\t<li>'primer_read_group_tag'</li>\n",
+       "\t<li>'region'</li>\n",
+       "\t<li>'run_alias'</li>\n",
+       "\t<li>'run_center'</li>\n",
+       "\t<li>'run_date'</li>\n",
+       "\t<li>'run_prefix'</li>\n",
+       "\t<li>'samp_size'</li>\n",
+       "\t<li>'sample_center'</li>\n",
+       "\t<li>'sample_type_prep'</li>\n",
+       "\t<li>'sequencing_meth'</li>\n",
+       "\t<li>'study_center'</li>\n",
+       "\t<li>'study_ref'</li>\n",
+       "\t<li>'target_gene'</li>\n",
+       "\t<li>'target_subfragment'</li>\n",
+       "\t<li>'altitude'</li>\n",
+       "\t<li>'annual_season_precpt'</li>\n",
+       "\t<li>'annual_season_temp'</li>\n",
+       "\t<li>'anonymized_name'</li>\n",
+       "\t<li>'assigned_from_geo'</li>\n",
+       "\t<li>'carb_nitro_ratio'</li>\n",
+       "\t<li>'cmin_rate'</li>\n",
+       "\t<li>'collection_date'</li>\n",
+       "\t<li>'common_name'</li>\n",
+       "\t<li>'country'</li>\n",
+       "\t<li>'depth'</li>\n",
+       "\t<li>'dna_extracted'</li>\n",
+       "\t<li>'elevation'</li>\n",
+       "\t<li>'env_biome'</li>\n",
+       "\t<li>'env_feature'</li>\n",
+       "\t<li>'env_matter'</li>\n",
+       "\t<li>'host_subject_id'</li>\n",
+       "\t<li>'latitude'</li>\n",
+       "\t<li>'longitude'</li>\n",
+       "\t<li>'ph'</li>\n",
+       "\t<li>'physical_specimen_remaining'</li>\n",
+       "\t<li>'project_name'</li>\n",
+       "\t<li>'public'</li>\n",
+       "\t<li>'sample_type'</li>\n",
+       "\t<li>'silt_clay'</li>\n",
+       "\t<li>'soil_moisture_deficit'</li>\n",
+       "\t<li>'soil_type'</li>\n",
+       "\t<li>'specific_location'</li>\n",
+       "\t<li>'taxon_id'</li>\n",
+       "\t<li>'texture'</li>\n",
+       "\t<li>'title'</li>\n",
+       "\t<li>'tot_org_carb'</li>\n",
+       "\t<li>'tot_org_nitro'</li>\n",
+       "\t<li>'Description'</li>\n",
+       "\t<li>'ph2'</li>\n",
+       "\t<li>'ph3'</li>\n",
+       "\t<li>'ph4'</li>\n",
+       "\t<li>'ph_rounded'</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'BarcodeSequence'\n",
+       "\\item 'LinkerPrimerSequence'\n",
+       "\\item 'barcode\\_read\\_group\\_tag'\n",
+       "\\item 'dna\\_extracted\\_prep'\n",
+       "\\item 'experiment\\_alias'\n",
+       "\\item 'experiment\\_center'\n",
+       "\\item 'experiment\\_design\\_description'\n",
+       "\\item 'experiment\\_title'\n",
+       "\\item 'instrument\\_name'\n",
+       "\\item 'key\\_seq'\n",
+       "\\item 'library\\_construction\\_protocol'\n",
+       "\\item 'linker'\n",
+       "\\item 'pcr\\_primers'\n",
+       "\\item 'physical\\_specimen\\_remaining\\_prep'\n",
+       "\\item 'platform'\n",
+       "\\item 'pool\\_member\\_name'\n",
+       "\\item 'pool\\_proportion'\n",
+       "\\item 'primer\\_read\\_group\\_tag'\n",
+       "\\item 'region'\n",
+       "\\item 'run\\_alias'\n",
+       "\\item 'run\\_center'\n",
+       "\\item 'run\\_date'\n",
+       "\\item 'run\\_prefix'\n",
+       "\\item 'samp\\_size'\n",
+       "\\item 'sample\\_center'\n",
+       "\\item 'sample\\_type\\_prep'\n",
+       "\\item 'sequencing\\_meth'\n",
+       "\\item 'study\\_center'\n",
+       "\\item 'study\\_ref'\n",
+       "\\item 'target\\_gene'\n",
+       "\\item 'target\\_subfragment'\n",
+       "\\item 'altitude'\n",
+       "\\item 'annual\\_season\\_precpt'\n",
+       "\\item 'annual\\_season\\_temp'\n",
+       "\\item 'anonymized\\_name'\n",
+       "\\item 'assigned\\_from\\_geo'\n",
+       "\\item 'carb\\_nitro\\_ratio'\n",
+       "\\item 'cmin\\_rate'\n",
+       "\\item 'collection\\_date'\n",
+       "\\item 'common\\_name'\n",
+       "\\item 'country'\n",
+       "\\item 'depth'\n",
+       "\\item 'dna\\_extracted'\n",
+       "\\item 'elevation'\n",
+       "\\item 'env\\_biome'\n",
+       "\\item 'env\\_feature'\n",
+       "\\item 'env\\_matter'\n",
+       "\\item 'host\\_subject\\_id'\n",
+       "\\item 'latitude'\n",
+       "\\item 'longitude'\n",
+       "\\item 'ph'\n",
+       "\\item 'physical\\_specimen\\_remaining'\n",
+       "\\item 'project\\_name'\n",
+       "\\item 'public'\n",
+       "\\item 'sample\\_type'\n",
+       "\\item 'silt\\_clay'\n",
+       "\\item 'soil\\_moisture\\_deficit'\n",
+       "\\item 'soil\\_type'\n",
+       "\\item 'specific\\_location'\n",
+       "\\item 'taxon\\_id'\n",
+       "\\item 'texture'\n",
+       "\\item 'title'\n",
+       "\\item 'tot\\_org\\_carb'\n",
+       "\\item 'tot\\_org\\_nitro'\n",
+       "\\item 'Description'\n",
+       "\\item 'ph2'\n",
+       "\\item 'ph3'\n",
+       "\\item 'ph4'\n",
+       "\\item 'ph\\_rounded'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'BarcodeSequence'\n",
+       "2. 'LinkerPrimerSequence'\n",
+       "3. 'barcode_read_group_tag'\n",
+       "4. 'dna_extracted_prep'\n",
+       "5. 'experiment_alias'\n",
+       "6. 'experiment_center'\n",
+       "7. 'experiment_design_description'\n",
+       "8. 'experiment_title'\n",
+       "9. 'instrument_name'\n",
+       "10. 'key_seq'\n",
+       "11. 'library_construction_protocol'\n",
+       "12. 'linker'\n",
+       "13. 'pcr_primers'\n",
+       "14. 'physical_specimen_remaining_prep'\n",
+       "15. 'platform'\n",
+       "16. 'pool_member_name'\n",
+       "17. 'pool_proportion'\n",
+       "18. 'primer_read_group_tag'\n",
+       "19. 'region'\n",
+       "20. 'run_alias'\n",
+       "21. 'run_center'\n",
+       "22. 'run_date'\n",
+       "23. 'run_prefix'\n",
+       "24. 'samp_size'\n",
+       "25. 'sample_center'\n",
+       "26. 'sample_type_prep'\n",
+       "27. 'sequencing_meth'\n",
+       "28. 'study_center'\n",
+       "29. 'study_ref'\n",
+       "30. 'target_gene'\n",
+       "31. 'target_subfragment'\n",
+       "32. 'altitude'\n",
+       "33. 'annual_season_precpt'\n",
+       "34. 'annual_season_temp'\n",
+       "35. 'anonymized_name'\n",
+       "36. 'assigned_from_geo'\n",
+       "37. 'carb_nitro_ratio'\n",
+       "38. 'cmin_rate'\n",
+       "39. 'collection_date'\n",
+       "40. 'common_name'\n",
+       "41. 'country'\n",
+       "42. 'depth'\n",
+       "43. 'dna_extracted'\n",
+       "44. 'elevation'\n",
+       "45. 'env_biome'\n",
+       "46. 'env_feature'\n",
+       "47. 'env_matter'\n",
+       "48. 'host_subject_id'\n",
+       "49. 'latitude'\n",
+       "50. 'longitude'\n",
+       "51. 'ph'\n",
+       "52. 'physical_specimen_remaining'\n",
+       "53. 'project_name'\n",
+       "54. 'public'\n",
+       "55. 'sample_type'\n",
+       "56. 'silt_clay'\n",
+       "57. 'soil_moisture_deficit'\n",
+       "58. 'soil_type'\n",
+       "59. 'specific_location'\n",
+       "60. 'taxon_id'\n",
+       "61. 'texture'\n",
+       "62. 'title'\n",
+       "63. 'tot_org_carb'\n",
+       "64. 'tot_org_nitro'\n",
+       "65. 'Description'\n",
+       "66. 'ph2'\n",
+       "67. 'ph3'\n",
+       "68. 'ph4'\n",
+       "69. 'ph_rounded'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"BarcodeSequence\"                  \"LinkerPrimerSequence\"            \n",
+       " [3] \"barcode_read_group_tag\"           \"dna_extracted_prep\"              \n",
+       " [5] \"experiment_alias\"                 \"experiment_center\"               \n",
+       " [7] \"experiment_design_description\"    \"experiment_title\"                \n",
+       " [9] \"instrument_name\"                  \"key_seq\"                         \n",
+       "[11] \"library_construction_protocol\"    \"linker\"                          \n",
+       "[13] \"pcr_primers\"                      \"physical_specimen_remaining_prep\"\n",
+       "[15] \"platform\"                         \"pool_member_name\"                \n",
+       "[17] \"pool_proportion\"                  \"primer_read_group_tag\"           \n",
+       "[19] \"region\"                           \"run_alias\"                       \n",
+       "[21] \"run_center\"                       \"run_date\"                        \n",
+       "[23] \"run_prefix\"                       \"samp_size\"                       \n",
+       "[25] \"sample_center\"                    \"sample_type_prep\"                \n",
+       "[27] \"sequencing_meth\"                  \"study_center\"                    \n",
+       "[29] \"study_ref\"                        \"target_gene\"                     \n",
+       "[31] \"target_subfragment\"               \"altitude\"                        \n",
+       "[33] \"annual_season_precpt\"             \"annual_season_temp\"              \n",
+       "[35] \"anonymized_name\"                  \"assigned_from_geo\"               \n",
+       "[37] \"carb_nitro_ratio\"                 \"cmin_rate\"                       \n",
+       "[39] \"collection_date\"                  \"common_name\"                     \n",
+       "[41] \"country\"                          \"depth\"                           \n",
+       "[43] \"dna_extracted\"                    \"elevation\"                       \n",
+       "[45] \"env_biome\"                        \"env_feature\"                     \n",
+       "[47] \"env_matter\"                       \"host_subject_id\"                 \n",
+       "[49] \"latitude\"                         \"longitude\"                       \n",
+       "[51] \"ph\"                               \"physical_specimen_remaining\"     \n",
+       "[53] \"project_name\"                     \"public\"                          \n",
+       "[55] \"sample_type\"                      \"silt_clay\"                       \n",
+       "[57] \"soil_moisture_deficit\"            \"soil_type\"                       \n",
+       "[59] \"specific_location\"                \"taxon_id\"                        \n",
+       "[61] \"texture\"                          \"title\"                           \n",
+       "[63] \"tot_org_carb\"                     \"tot_org_nitro\"                   \n",
+       "[65] \"Description\"                      \"ph2\"                             \n",
+       "[67] \"ph3\"                              \"ph4\"                             \n",
+       "[69] \"ph_rounded\"                      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colnames(demo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "89"
+      ],
+      "text/latex": [
+       "89"
+      ],
+      "text/markdown": [
+       "89"
+      ],
+      "text/plain": [
+       "[1] 89"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>8.02</li>\n",
+       "\t<li>6.02</li>\n",
+       "\t<li>6.95</li>\n",
+       "\t<li>5.52</li>\n",
+       "\t<li>7.53</li>\n",
+       "\t<li>5.99</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 8.02\n",
+       "\\item 6.02\n",
+       "\\item 6.95\n",
+       "\\item 5.52\n",
+       "\\item 7.53\n",
+       "\\item 5.99\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 8.02\n",
+       "2. 6.02\n",
+       "3. 6.95\n",
+       "4. 5.52\n",
+       "5. 7.53\n",
+       "6. 5.99\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 8.02 6.02 6.95 5.52 7.53 5.99"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# metadata\n",
+    "mf <- read.csv(\"../88soils/88soils_modified_metadata.txt\", sep='\\t', row.names=1)\n",
+    "y <- mf$ph[match(rownames(count), rownames(mf))]\n",
+    "length(y)\n",
+    "head(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'matrix'"
+      ],
+      "text/latex": [
+       "'matrix'"
+      ],
+      "text/markdown": [
+       "'matrix'"
+      ],
+      "text/plain": [
+       "[1] \"matrix\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "'numeric'"
+      ],
+      "text/latex": [
+       "'numeric'"
+      ],
+      "text/markdown": [
+       "'numeric'"
+      ],
+      "text/plain": [
+       "[1] \"numeric\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check datatype\n",
+    "class(taxa); class(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save processed data\n",
+    "save(y, taxa, file='../88soils/soils_ph.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/2.3 soils_results_cts-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/2.3 soils_results_cts-checkpoint.ipynb
new file mode 100755
index 0000000..34fdcc6
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/2.3 soils_results_cts-checkpoint.ipynb	
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Soil microbiome data application results for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### method comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/results_cts/soils_ph_compLasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_elnet.RData')\n",
+    "load('../88soils/results_cts/soils_ph_lasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.39</li>\n",
+       "\t<li>0.46</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.39\n",
+       "\\item 0.46\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.39\n",
+       "2. 0.46\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.39 0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_compLasso$stab_index, out_compLasso$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.31</li>\n",
+       "\t<li>0.34</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.31\n",
+       "\\item 0.34\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.31\n",
+       "2. 0.34\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.31 0.34"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_lasso$stab_index, out_lasso$MSE_mean) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.16</li>\n",
+       "\t<li>0.23</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.16\n",
+       "\\item 0.23\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.16\n",
+       "2. 0.23\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.16 0.23"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_elnet$stab_index, out_elnet$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.04</li>\n",
+       "\t<li>0.26</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.04\n",
+       "\\item 0.26\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.04\n",
+       "2. 0.26\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.04 0.26"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_rf$stab_index, out_rf$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>mse</th><th scope=col>stability</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>soil_88  </td><td>lasso    </td><td>0.34     </td><td>0.31     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>elent    </td><td>0.23     </td><td>0.16     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>rf       </td><td>0.26     </td><td>0.04     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>compLasso</td><td>0.46     </td><td>0.39     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " dataset & method & mse & stability\\\\\n",
+       "\\hline\n",
+       "\t soil\\_88 & lasso     & 0.34      & 0.31     \\\\\n",
+       "\t soil\\_88 & elent     & 0.23      & 0.16     \\\\\n",
+       "\t soil\\_88 & rf        & 0.26      & 0.04     \\\\\n",
+       "\t soil\\_88 & compLasso & 0.46      & 0.39     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| dataset | method | mse | stability |\n",
+       "|---|---|---|---|\n",
+       "| soil_88   | lasso     | 0.34      | 0.31      |\n",
+       "| soil_88   | elent     | 0.23      | 0.16      |\n",
+       "| soil_88   | rf        | 0.26      | 0.04      |\n",
+       "| soil_88   | compLasso | 0.46      | 0.39      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    mse  stability\n",
+       "1 soil_88 lasso     0.34 0.31     \n",
+       "2 soil_88 elent     0.23 0.16     \n",
+       "3 soil_88 rf        0.26 0.04     \n",
+       "4 soil_88 compLasso 0.46 0.39     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# combine and export results\n",
+    "soil_88 = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(soil_88) = c('dataset', 'method', 'mse', 'stability')\n",
+    "soil_88$dataset = 'soil_88'\n",
+    "soil_88$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "soil_88$mse = c(out_lasso$MSE_mean, out_elnet$MSE_mean, out_rf$MSE_mean, out_compLasso$MSE_mean)\n",
+    "soil_88$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_compLasso$stab_index)\n",
+    "soil_88"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/results_cts/soils_ph_boot_compLasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_boot_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.3595"
+      ],
+      "text/latex": [
+       "0.3595"
+      ],
+      "text/markdown": [
+       "0.3595"
+      ],
+      "text/plain": [
+       "[1] 0.3595"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.28</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.44</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.28\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.44\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.2897.5%\n",
+       ":   0.44\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.28  0.44 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.0811150164524993"
+      ],
+      "text/latex": [
+       "0.0811150164524993"
+      ],
+      "text/markdown": [
+       "0.0811150164524993"
+      ],
+      "text/plain": [
+       "[1] 0.08111502"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.283912044397921</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.945808111396662</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.283912044397921\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.945808111396662\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.28391204439792197.5%\n",
+       ":   0.945808111396662\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.2839120  0.9458081 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_mse = (unlist(boot_compLasso$MSE_list) - unlist(boot_rf$MSE_list)) # use all 100*100 MSEs\n",
+    "mean(diff_mse)\n",
+    "quantile(diff_mse, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/.ipynb_checkpoints/3_applications_bin_results-checkpoint.ipynb b/data_application/notebooks_application/.ipynb_checkpoints/3_applications_bin_results-checkpoint.ipynb
new file mode 100644
index 0000000..fa46c90
--- /dev/null
+++ b/data_application/notebooks_application/.ipynb_checkpoints/3_applications_bin_results-checkpoint.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results for real microbiome data applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/data_applications/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/BMI_binary_GenCompLasso.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>bmi_gut</td><td>lasso    </td><td>0.63</td><td>0.14</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>elent    </td><td>0.78</td><td>0.19</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>rf       </td><td>1.00</td><td>0.01</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>compLasso</td><td>0.85</td><td>0.29</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t bmi\\_gut & lasso     & 0.63 & 0.14\\\\\n",
+       "\t bmi\\_gut & elent     & 0.78 & 0.19\\\\\n",
+       "\t bmi\\_gut & rf        & 1.00 & 0.01\\\\\n",
+       "\t bmi\\_gut & compLasso & 0.85 & 0.29\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| bmi_gut | lasso     | 0.63 | 0.14 |\n",
+       "| bmi_gut | elent     | 0.78 | 0.19 |\n",
+       "| bmi_gut | rf        | 1.00 | 0.01 |\n",
+       "| bmi_gut | compLasso | 0.85 | 0.29 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 bmi_gut lasso     0.63 0.14     \n",
+       "2 bmi_gut elent     0.78 0.19     \n",
+       "3 bmi_gut rf        1.00 0.01     \n",
+       "4 bmi_gut compLasso 0.85 0.29     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmi_gut = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(bmi_gut) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "bmi_gut$dataset = 'bmi_gut'\n",
+    "bmi_gut$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "bmi_gut$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "bmi_gut$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "bmi_gut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/BMI_binary_boot_rf.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2997"
+      ],
+      "text/latex": [
+       "0.2997"
+      ],
+      "text/markdown": [
+       "0.2997"
+      ],
+      "text/plain": [
+       "[1] 0.2997"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.11</dd><dt>97.5%</dt><dd>0.41525</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.11\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.41525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1197.5%\n",
+       ":   0.41525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.11000 0.41525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0942798529453605"
+      ],
+      "text/latex": [
+       "-0.0942798529453605"
+      ],
+      "text/markdown": [
+       "-0.0942798529453605"
+      ],
+      "text/plain": [
+       "[1] -0.09427985"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.19020132947925</dd><dt>97.5%</dt><dd>-0.020967051775781</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.19020132947925\n",
+       "\\item[97.5\\textbackslash{}\\%] -0.020967051775781\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.1902013294792597.5%\n",
+       ":   -0.020967051775781\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.19020133 -0.02096705 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "### CI doesn't contain zero: compLasso is significantly different from RF based on ROC\n",
+    "### although very close to zero (now ROC has similar scale as Stability, Stability still better differentiation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 88 soils dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/soils_binary_ph_GenCompLasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>soil_88</td><td>lasso    </td><td>0.90</td><td>0.28</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>elent    </td><td>0.94</td><td>0.32</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>rf       </td><td>1.00</td><td>0.03</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>compLasso</td><td>0.96</td><td>0.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t soil\\_88 & lasso     & 0.90 & 0.28\\\\\n",
+       "\t soil\\_88 & elent     & 0.94 & 0.32\\\\\n",
+       "\t soil\\_88 & rf        & 1.00 & 0.03\\\\\n",
+       "\t soil\\_88 & compLasso & 0.96 & 0.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| soil_88 | lasso     | 0.90 | 0.28 |\n",
+       "| soil_88 | elent     | 0.94 | 0.32 |\n",
+       "| soil_88 | rf        | 1.00 | 0.03 |\n",
+       "| soil_88 | compLasso | 0.96 | 0.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 soil_88 lasso     0.90 0.28     \n",
+       "2 soil_88 elent     0.94 0.32     \n",
+       "3 soil_88 rf        1.00 0.03     \n",
+       "4 soil_88 compLasso 0.96 0.46     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "soil_88 = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(soil_88) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "soil_88$dataset = 'soil_88'\n",
+    "soil_88$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "soil_88$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "soil_88$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "soil_88"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_rf.RData'))\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.4323"
+      ],
+      "text/latex": [
+       "0.4323"
+      ],
+      "text/markdown": [
+       "0.4323"
+      ],
+      "text/plain": [
+       "[1] 0.4323"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.37</dd><dt>97.5%</dt><dd>0.5</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.37\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.5\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.3797.5%\n",
+       ":   0.5\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.37  0.50 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0247120820917325"
+      ],
+      "text/latex": [
+       "-0.0247120820917325"
+      ],
+      "text/markdown": [
+       "-0.0247120820917325"
+      ],
+      "text/plain": [
+       "[1] -0.02471208"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.0838574423480084</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.0838574423480084\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.083857442348008497.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.08385744  0.00000000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on ROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
+       "-0.24949 -0.04206 -0.02250 -0.02471  0.00000  0.03769 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(diff_ROC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/1.1. BMI_DataPreparation.ipynb b/data_application/notebooks_application/1.1. BMI_DataPreparation.ipynb
new file mode 100755
index 0000000..2a6fe2d
--- /dev/null
+++ b/data_application/notebooks_application/1.1. BMI_DataPreparation.ipynb	
@@ -0,0 +1,875 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI dataset in Lin et al, 2014 \n",
+    "##### use unrarifed count table and retain microbes only at genus level (+ present at least one sample)\n",
+    "#### add 0.5 as pseudo count to zero counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>263</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 263\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 263\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  98 263"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria</th><th scope=col>Bacteria.Bacteroidetes</th><th scope=col>Bacteria.Cyanobacteria</th><th scope=col>Bacteria.Firmicutes</th><th scope=col>Bacteria.Fusobacteria</th><th scope=col>Bacteria.Lentisphaerae</th><th scope=col>Bacteria.OD1</th><th scope=col>Bacteria.Proteobacteria</th><th scope=col>Bacteria.Spirochaetes</th><th scope=col>Bacteria.Synergistetes</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira</th><th scope=col>Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>1   </td><td>5067</td><td>0   </td><td>4153</td><td>0   </td><td>0   </td><td>0   </td><td>534 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>0   </td><td>4659</td><td>0   </td><td>2177</td><td>0   </td><td>0   </td><td>0   </td><td>105 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>0   </td><td>4342</td><td>0   </td><td>3008</td><td>0   </td><td>2   </td><td>0   </td><td>134 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>8   </td><td>2910</td><td>0   </td><td>4147</td><td>0   </td><td>0   </td><td>0   </td><td>459 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>5   </td><td>5630</td><td>0   </td><td>4705</td><td>0   </td><td>0   </td><td>0   </td><td>214 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>5   </td><td>1868</td><td>0   </td><td>1619</td><td>0   </td><td>0   </td><td>0   </td><td> 17 </td><td>0   </td><td>0   </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria & Bacteria.Bacteroidetes & Bacteria.Cyanobacteria & Bacteria.Firmicutes & Bacteria.Fusobacteria & Bacteria.Lentisphaerae & Bacteria.OD1 & Bacteria.Proteobacteria & Bacteria.Spirochaetes & Bacteria.Synergistetes & ... & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira & Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & 1    & 5067 & 0    & 4153 & 0    & 0    & 0    & 534  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3003 & 0    & 4659 & 0    & 2177 & 0    & 0    & 0    & 105  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3004 & 0    & 4342 & 0    & 3008 & 0    & 2    & 0    & 134  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3006 & 8    & 2910 & 0    & 4147 & 0    & 0    & 0    & 459  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3007 & 5    & 5630 & 0    & 4705 & 0    & 0    & 0    & 214  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3008 & 5    & 1868 & 0    & 1619 & 0    & 0    & 0    &  17  & 0    & 0    & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria | Bacteria.Bacteroidetes | Bacteria.Cyanobacteria | Bacteria.Firmicutes | Bacteria.Fusobacteria | Bacteria.Lentisphaerae | Bacteria.OD1 | Bacteria.Proteobacteria | Bacteria.Spirochaetes | Bacteria.Synergistetes | ... | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira | Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | 1    | 5067 | 0    | 4153 | 0    | 0    | 0    | 534  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3003 | 0    | 4659 | 0    | 2177 | 0    | 0    | 0    | 105  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3004 | 0    | 4342 | 0    | 3008 | 0    | 2    | 0    | 134  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3006 | 8    | 2910 | 0    | 4147 | 0    | 0    | 0    | 459  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3007 | 5    | 5630 | 0    | 4705 | 0    | 0    | 0    | 214  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3008 | 5    | 1868 | 0    | 1619 | 0    | 0    | 0    |  17  | 0    | 0    | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria Bacteria.Bacteroidetes Bacteria.Cyanobacteria\n",
+       "3001 1                       5067                   0                     \n",
+       "3003 0                       4659                   0                     \n",
+       "3004 0                       4342                   0                     \n",
+       "3006 8                       2910                   0                     \n",
+       "3007 5                       5630                   0                     \n",
+       "3008 5                       1868                   0                     \n",
+       "     Bacteria.Firmicutes Bacteria.Fusobacteria Bacteria.Lentisphaerae\n",
+       "3001 4153                0                     0                     \n",
+       "3003 2177                0                     0                     \n",
+       "3004 3008                0                     2                     \n",
+       "3006 4147                0                     0                     \n",
+       "3007 4705                0                     0                     \n",
+       "3008 1619                0                     0                     \n",
+       "     Bacteria.OD1 Bacteria.Proteobacteria Bacteria.Spirochaetes\n",
+       "3001 0            534                     0                    \n",
+       "3003 0            105                     0                    \n",
+       "3004 0            134                     0                    \n",
+       "3006 0            459                     0                    \n",
+       "3007 0            214                     0                    \n",
+       "3008 0             17                     0                    \n",
+       "     Bacteria.Synergistetes ...\n",
+       "3001 0                      ...\n",
+       "3003 0                      ...\n",
+       "3004 0                      ...\n",
+       "3006 0                      ...\n",
+       "3007 0                      ...\n",
+       "3008 0                      ...\n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Moraxellaceae.Enhydrobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 0                                                                                       \n",
+       "3003 0                                                                                       \n",
+       "3004 0                                                                                       \n",
+       "3006 0                                                                                       \n",
+       "3007 0                                                                                       \n",
+       "3008 0                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Vibrionales.Vibrionaceae.Vibrio\n",
+       "3001 0                                                                          \n",
+       "3003 0                                                                          \n",
+       "3004 0                                                                          \n",
+       "3006 0                                                                          \n",
+       "3007 0                                                                          \n",
+       "3008 0                                                                          \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Lysobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 0                                                                                            \n",
+       "3003 0                                                                                            \n",
+       "3004 0                                                                                            \n",
+       "3006 0                                                                                            \n",
+       "3007 0                                                                                            \n",
+       "3008 0                                                                                            \n",
+       "     Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Brachyspiraceae.Brachyspira\n",
+       "3001 0                                                                            \n",
+       "3003 0                                                                            \n",
+       "3004 0                                                                            \n",
+       "3006 0                                                                            \n",
+       "3007 0                                                                            \n",
+       "3008 0                                                                            \n",
+       "     Bacteria.Spirochaetes.Spirochaetes.Spirochaetales.Spirochaetaceae.Treponema\n",
+       "3001 0                                                                          \n",
+       "3003 0                                                                          \n",
+       "3004 0                                                                          \n",
+       "3006 0                                                                          \n",
+       "3007 0                                                                          \n",
+       "3008 0                                                                          \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 0                                                                                           \n",
+       "3003 0                                                                                           \n",
+       "3004 0                                                                                           \n",
+       "3006 0                                                                                           \n",
+       "3007 0                                                                                           \n",
+       "3008 0                                                                                           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# unrarefied microbe count table\n",
+    "count <- as.matrix(read.table(\"../../code_Lin/cvs/data/combo_count_tab.txt\")) \n",
+    "dim(count)\n",
+    "head(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>87</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 87\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 87\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 98 87"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria</th><th scope=col>Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio</th><th scope=col>Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>2878</td><td>  0 </td><td>69  </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td> 578</td><td>180 </td><td> 0  </td><td>... </td><td>2   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>3503</td><td>143 </td><td> 0  </td><td>... </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>2162</td><td>  1 </td><td> 0  </td><td>... </td><td>3   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>0   </td><td>0   </td><td>0   </td><td>2   </td><td>0   </td><td>0   </td><td>0   </td><td>4453</td><td>  0 </td><td> 0  </td><td>... </td><td>9   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>0   </td><td>0   </td><td>0   </td><td>1   </td><td>0   </td><td>0   </td><td>0   </td><td> 950</td><td>237 </td><td>19  </td><td>... </td><td>4   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td><td>0   </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas & ... & Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter & Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria & Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio & Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 2878 &   0  & 69   & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3003 & 0    & 0    & 0    & 0    & 0    & 0    & 0    &  578 & 180  &  0   & ...  & 2    & 0    & 0    & 1    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3004 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 3503 & 143  &  0   & ...  & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3006 & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 2162 &   1  &  0   & ...  & 3    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3007 & 0    & 0    & 0    & 2    & 0    & 0    & 0    & 4453 &   0  &  0   & ...  & 9    & 0    & 0    & 1    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\t3008 & 0    & 0    & 0    & 1    & 0    & 0    & 0    &  950 & 237  & 19   & ...  & 4    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0    & 0   \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas | ... | Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter | Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria | Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio | Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 2878 |   0  | 69   | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3003 | 0    | 0    | 0    | 0    | 0    | 0    | 0    |  578 | 180  |  0   | ...  | 2    | 0    | 0    | 1    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3004 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 3503 | 143  |  0   | ...  | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3006 | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 2162 |   1  |  0   | ...  | 3    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3007 | 0    | 0    | 0    | 2    | 0    | 0    | 0    | 4453 |   0  |  0   | ...  | 9    | 0    | 0    | 1    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "| 3008 | 0    | 0    | 0    | 1    | 0    | 0    | 0    |  950 | 237  | 19   | ...  | 4    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    | 0    |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter\n",
+       "3001 0                                                                                        \n",
+       "3003 0                                                                                        \n",
+       "3004 0                                                                                        \n",
+       "3006 0                                                                                        \n",
+       "3007 0                                                                                        \n",
+       "3008 0                                                                                        \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium\n",
+       "3001 0                                                                                  \n",
+       "3003 0                                                                                  \n",
+       "3004 0                                                                                  \n",
+       "3006 0                                                                                  \n",
+       "3007 0                                                                                  \n",
+       "3008 0                                                                                  \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella\n",
+       "3001 0                                                                                    \n",
+       "3003 0                                                                                    \n",
+       "3004 0                                                                                    \n",
+       "3006 0                                                                                    \n",
+       "3007 0                                                                                    \n",
+       "3008 0                                                                                    \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella\n",
+       "3001 0                                                                                    \n",
+       "3003 0                                                                                    \n",
+       "3004 0                                                                                    \n",
+       "3006 0                                                                                    \n",
+       "3007 2                                                                                    \n",
+       "3008 1                                                                                    \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter\n",
+       "3001 0                                                                                      \n",
+       "3003 0                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 0                                                                                      \n",
+       "3007 0                                                                                      \n",
+       "3008 0                                                                                      \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella\n",
+       "3001 0                                                                                  \n",
+       "3003 0                                                                                  \n",
+       "3004 0                                                                                  \n",
+       "3006 0                                                                                  \n",
+       "3007 0                                                                                  \n",
+       "3008 0                                                                                  \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia\n",
+       "3001 0                                                                                \n",
+       "3003 0                                                                                \n",
+       "3004 0                                                                                \n",
+       "3006 0                                                                                \n",
+       "3007 0                                                                                \n",
+       "3008 0                                                                                \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides\n",
+       "3001 2878                                                                       \n",
+       "3003  578                                                                       \n",
+       "3004 3503                                                                       \n",
+       "3006 2162                                                                       \n",
+       "3007 4453                                                                       \n",
+       "3008  950                                                                       \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella\n",
+       "3001   0                                                                            \n",
+       "3003 180                                                                            \n",
+       "3004 143                                                                            \n",
+       "3006   1                                                                            \n",
+       "3007   0                                                                            \n",
+       "3008 237                                                                            \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas\n",
+       "3001 69                                                                               \n",
+       "3003  0                                                                               \n",
+       "3004  0                                                                               \n",
+       "3006  0                                                                               \n",
+       "3007  0                                                                               \n",
+       "3008 19                                                                               \n",
+       "     ...\n",
+       "3001 ...\n",
+       "3003 ...\n",
+       "3004 ...\n",
+       "3006 ...\n",
+       "3007 ...\n",
+       "3008 ...\n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter\n",
+       "3001 0                                                                                      \n",
+       "3003 2                                                                                      \n",
+       "3004 0                                                                                      \n",
+       "3006 3                                                                                      \n",
+       "3007 9                                                                                      \n",
+       "3008 4                                                                                      \n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria\n",
+       "3001 0                                                                              \n",
+       "3003 0                                                                              \n",
+       "3004 0                                                                              \n",
+       "3006 0                                                                              \n",
+       "3007 0                                                                              \n",
+       "3008 0                                                                              \n",
+       "     Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio\n",
+       "3001 0                                                                                               \n",
+       "3003 0                                                                                               \n",
+       "3004 0                                                                                               \n",
+       "3006 0                                                                                               \n",
+       "3007 0                                                                                               \n",
+       "3008 0                                                                                               \n",
+       "     Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter\n",
+       "3001 0                                                                                               \n",
+       "3003 1                                                                                               \n",
+       "3004 0                                                                                               \n",
+       "3006 0                                                                                               \n",
+       "3007 1                                                                                               \n",
+       "3008 0                                                                                               \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio\n",
+       "3001 0                                                                                          \n",
+       "3003 0                                                                                          \n",
+       "3004 0                                                                                          \n",
+       "3006 0                                                                                          \n",
+       "3007 0                                                                                          \n",
+       "3008 0                                                                                          \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 0                                                                                       \n",
+       "3003 0                                                                                       \n",
+       "3004 0                                                                                       \n",
+       "3006 0                                                                                       \n",
+       "3007 0                                                                                       \n",
+       "3008 0                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 0                                                                                            \n",
+       "3003 0                                                                                            \n",
+       "3004 0                                                                                            \n",
+       "3006 0                                                                                            \n",
+       "3007 0                                                                                            \n",
+       "3008 0                                                                                            \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 0                                                                             \n",
+       "3003 0                                                                             \n",
+       "3004 0                                                                             \n",
+       "3006 0                                                                             \n",
+       "3007 0                                                                             \n",
+       "3008 0                                                                             \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 0                                                                                           \n",
+       "3003 0                                                                                           \n",
+       "3004 0                                                                                           \n",
+       "3006 0                                                                                           \n",
+       "3007 0                                                                                           \n",
+       "3008 0                                                                                           "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# retain only microbes at genus level and exist at least one sample\n",
+    "depth <- sapply(strsplit(colnames(count), \"\\\\.\"), length)\n",
+    "x <- count[, depth == 6 & colSums(count != 0) >= 1] # 98 * 87\n",
+    "dim(x)\n",
+    "head(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>98</li>\n",
+       "\t<li>87</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 98\n",
+       "\\item 87\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 98\n",
+       "2. 87\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 98 87"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella</th><th scope=col>Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella</th><th scope=col>Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas</th><th scope=col>...</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter</th><th scope=col>Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria</th><th scope=col>Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio</th><th scope=col>Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas</th><th scope=col>Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus</th><th scope=col>Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter</th><th scope=col>Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3001</th><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-0.8429202</td><td>-9.500918 </td><td>-4.573665 </td><td>...       </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td><td>-9.500918 </td></tr>\n",
+       "\t<tr><th scope=row>3003</th><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-2.2258386</td><td>-3.392456 </td><td>-9.278560 </td><td>...       </td><td>-7.892265 </td><td>-9.278560 </td><td>-9.278560 </td><td>-8.585412 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td><td>-9.278560 </td></tr>\n",
+       "\t<tr><th scope=row>3004</th><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-0.4151243</td><td>-3.613655 </td><td>-9.269646 </td><td>...       </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td><td>-9.269646 </td></tr>\n",
+       "\t<tr><th scope=row>3006</th><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-0.7426639</td><td>-8.421453 </td><td>-9.114600 </td><td>...       </td><td>-7.322841 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td><td>-9.114600 </td></tr>\n",
+       "\t<tr><th scope=row>3007</th><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-8.257061 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-0.5488753</td><td>-9.643356 </td><td>-9.643356 </td><td>...       </td><td>-6.752984 </td><td>-9.643356 </td><td>-9.643356 </td><td>-8.950209 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td><td>-9.643356 </td></tr>\n",
+       "\t<tr><th scope=row>3008</th><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-7.693937 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-0.8374753</td><td>-2.225877 </td><td>-4.749498 </td><td>...       </td><td>-6.307643 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td><td>-8.387085 </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella & Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella & Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas & ... & Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter & Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria & Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio & Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter & Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio & Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas & Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus & Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter & Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\\\\\n",
+       "\\hline\n",
+       "\t3001 & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -0.8429202 & -9.500918  & -4.573665  & ...        & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918  & -9.500918 \\\\\n",
+       "\t3003 & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -2.2258386 & -3.392456  & -9.278560  & ...        & -7.892265  & -9.278560  & -9.278560  & -8.585412  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560  & -9.278560 \\\\\n",
+       "\t3004 & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -0.4151243 & -3.613655  & -9.269646  & ...        & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646  & -9.269646 \\\\\n",
+       "\t3006 & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -0.7426639 & -8.421453  & -9.114600  & ...        & -7.322841  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600  & -9.114600 \\\\\n",
+       "\t3007 & -9.643356  & -9.643356  & -9.643356  & -8.257061  & -9.643356  & -9.643356  & -9.643356  & -0.5488753 & -9.643356  & -9.643356  & ...        & -6.752984  & -9.643356  & -9.643356  & -8.950209  & -9.643356  & -9.643356  & -9.643356  & -9.643356  & -9.643356  & -9.643356 \\\\\n",
+       "\t3008 & -8.387085  & -8.387085  & -8.387085  & -7.693937  & -8.387085  & -8.387085  & -8.387085  & -0.8374753 & -2.225877  & -4.749498  & ...        & -6.307643  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085  & -8.387085 \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella | Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella | Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas | ... | Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter | Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria | Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio | Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter | Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio | Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas | Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus | Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter | Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3001 | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -0.8429202 | -9.500918  | -4.573665  | ...        | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  | -9.500918  |\n",
+       "| 3003 | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -2.2258386 | -3.392456  | -9.278560  | ...        | -7.892265  | -9.278560  | -9.278560  | -8.585412  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  | -9.278560  |\n",
+       "| 3004 | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -0.4151243 | -3.613655  | -9.269646  | ...        | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  | -9.269646  |\n",
+       "| 3006 | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -0.7426639 | -8.421453  | -9.114600  | ...        | -7.322841  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  | -9.114600  |\n",
+       "| 3007 | -9.643356  | -9.643356  | -9.643356  | -8.257061  | -9.643356  | -9.643356  | -9.643356  | -0.5488753 | -9.643356  | -9.643356  | ...        | -6.752984  | -9.643356  | -9.643356  | -8.950209  | -9.643356  | -9.643356  | -9.643356  | -9.643356  | -9.643356  | -9.643356  |\n",
+       "| 3008 | -8.387085  | -8.387085  | -8.387085  | -7.693937  | -8.387085  | -8.387085  | -8.387085  | -0.8374753 | -2.225877  | -4.749498  | ...        | -6.307643  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  | -8.387085  |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Asaccharobacter\n",
+       "3001 -9.500918                                                                                \n",
+       "3003 -9.278560                                                                                \n",
+       "3004 -9.269646                                                                                \n",
+       "3006 -9.114600                                                                                \n",
+       "3007 -9.643356                                                                                \n",
+       "3008 -8.387085                                                                                \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Atopobium\n",
+       "3001 -9.500918                                                                          \n",
+       "3003 -9.278560                                                                          \n",
+       "3004 -9.269646                                                                          \n",
+       "3006 -9.114600                                                                          \n",
+       "3007 -9.643356                                                                          \n",
+       "3008 -8.387085                                                                          \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Collinsella\n",
+       "3001 -9.500918                                                                            \n",
+       "3003 -9.278560                                                                            \n",
+       "3004 -9.269646                                                                            \n",
+       "3006 -9.114600                                                                            \n",
+       "3007 -9.643356                                                                            \n",
+       "3008 -8.387085                                                                            \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Eggerthella\n",
+       "3001 -9.500918                                                                            \n",
+       "3003 -9.278560                                                                            \n",
+       "3004 -9.269646                                                                            \n",
+       "3006 -9.114600                                                                            \n",
+       "3007 -8.257061                                                                            \n",
+       "3008 -7.693937                                                                            \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Gordonibacter\n",
+       "3001 -9.500918                                                                              \n",
+       "3003 -9.278560                                                                              \n",
+       "3004 -9.269646                                                                              \n",
+       "3006 -9.114600                                                                              \n",
+       "3007 -9.643356                                                                              \n",
+       "3008 -8.387085                                                                              \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Olsenella\n",
+       "3001 -9.500918                                                                          \n",
+       "3003 -9.278560                                                                          \n",
+       "3004 -9.269646                                                                          \n",
+       "3006 -9.114600                                                                          \n",
+       "3007 -9.643356                                                                          \n",
+       "3008 -8.387085                                                                          \n",
+       "     Bacteria.Actinobacteria.Actinobacteria.Coriobacteriales.Coriobacteriaceae.Slackia\n",
+       "3001 -9.500918                                                                        \n",
+       "3003 -9.278560                                                                        \n",
+       "3004 -9.269646                                                                        \n",
+       "3006 -9.114600                                                                        \n",
+       "3007 -9.643356                                                                        \n",
+       "3008 -8.387085                                                                        \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Bacteroidaceae.Bacteroides\n",
+       "3001 -0.8429202                                                                 \n",
+       "3003 -2.2258386                                                                 \n",
+       "3004 -0.4151243                                                                 \n",
+       "3006 -0.7426639                                                                 \n",
+       "3007 -0.5488753                                                                 \n",
+       "3008 -0.8374753                                                                 \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Barnesiella\n",
+       "3001 -9.500918                                                                      \n",
+       "3003 -3.392456                                                                      \n",
+       "3004 -3.613655                                                                      \n",
+       "3006 -8.421453                                                                      \n",
+       "3007 -9.643356                                                                      \n",
+       "3008 -2.225877                                                                      \n",
+       "     Bacteria.Bacteroidetes.Bacteroidia.Bacteroidales.Porphyromonadaceae.Butyricimonas\n",
+       "3001 -4.573665                                                                        \n",
+       "3003 -9.278560                                                                        \n",
+       "3004 -9.269646                                                                        \n",
+       "3006 -9.114600                                                                        \n",
+       "3007 -9.643356                                                                        \n",
+       "3008 -4.749498                                                                        \n",
+       "     ...\n",
+       "3001 ...\n",
+       "3003 ...\n",
+       "3004 ...\n",
+       "3006 ...\n",
+       "3007 ...\n",
+       "3008 ...\n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Burkholderiales.Oxalobacteraceae.Oxalobacter\n",
+       "3001 -9.500918                                                                              \n",
+       "3003 -7.892265                                                                              \n",
+       "3004 -9.269646                                                                              \n",
+       "3006 -7.322841                                                                              \n",
+       "3007 -6.752984                                                                              \n",
+       "3008 -6.307643                                                                              \n",
+       "     Bacteria.Proteobacteria.Betaproteobacteria.Neisseriales.Neisseriaceae.Neisseria\n",
+       "3001 -9.500918                                                                      \n",
+       "3003 -9.278560                                                                      \n",
+       "3004 -9.269646                                                                      \n",
+       "3006 -9.114600                                                                      \n",
+       "3007 -9.643356                                                                      \n",
+       "3008 -8.387085                                                                      \n",
+       "     Bacteria.Proteobacteria.Deltaproteobacteria.Desulfovibrionales.Desulfovibrionaceae.Desulfovibrio\n",
+       "3001 -9.500918                                                                                       \n",
+       "3003 -9.278560                                                                                       \n",
+       "3004 -9.269646                                                                                       \n",
+       "3006 -9.114600                                                                                       \n",
+       "3007 -9.643356                                                                                       \n",
+       "3008 -8.387085                                                                                       \n",
+       "     Bacteria.Proteobacteria.Epsilonproteobacteria.Campylobacterales.Campylobacteraceae.Campylobacter\n",
+       "3001 -9.500918                                                                                       \n",
+       "3003 -8.585412                                                                                       \n",
+       "3004 -9.269646                                                                                       \n",
+       "3006 -9.114600                                                                                       \n",
+       "3007 -8.950209                                                                                       \n",
+       "3008 -8.387085                                                                                       \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Aeromonadales.Succinivibrionaceae.Succinivibrio\n",
+       "3001 -9.500918                                                                                  \n",
+       "3003 -9.278560                                                                                  \n",
+       "3004 -9.269646                                                                                  \n",
+       "3006 -9.114600                                                                                  \n",
+       "3007 -9.643356                                                                                  \n",
+       "3008 -8.387085                                                                                  \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Pseudomonadales.Pseudomonadaceae.Pseudomonas\n",
+       "3001 -9.500918                                                                               \n",
+       "3003 -9.278560                                                                               \n",
+       "3004 -9.269646                                                                               \n",
+       "3006 -9.114600                                                                               \n",
+       "3007 -9.643356                                                                               \n",
+       "3008 -8.387085                                                                               \n",
+       "     Bacteria.Proteobacteria.Gammaproteobacteria.Xanthomonadales.Xanthomonadaceae.Stenotrophomonas\n",
+       "3001 -9.500918                                                                                    \n",
+       "3003 -9.278560                                                                                    \n",
+       "3004 -9.269646                                                                                    \n",
+       "3006 -9.114600                                                                                    \n",
+       "3007 -9.643356                                                                                    \n",
+       "3008 -8.387085                                                                                    \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Cloacibacillus\n",
+       "3001 -9.500918                                                                     \n",
+       "3003 -9.278560                                                                     \n",
+       "3004 -9.269646                                                                     \n",
+       "3006 -9.114600                                                                     \n",
+       "3007 -9.643356                                                                     \n",
+       "3008 -8.387085                                                                     \n",
+       "     Bacteria.Synergistetes.Synergistia.Synergistales.Synergistaceae.Pyramidobacter\n",
+       "3001 -9.500918                                                                     \n",
+       "3003 -9.278560                                                                     \n",
+       "3004 -9.269646                                                                     \n",
+       "3006 -9.114600                                                                     \n",
+       "3007 -9.643356                                                                     \n",
+       "3008 -8.387085                                                                     \n",
+       "     Bacteria.Verrucomicrobia.Verrucomicrobiae.Verrucomicrobiales.Verrucomicrobiaceae.Akkermansia\n",
+       "3001 -9.500918                                                                                   \n",
+       "3003 -9.278560                                                                                   \n",
+       "3004 -9.269646                                                                                   \n",
+       "3006 -9.114600                                                                                   \n",
+       "3007 -9.643356                                                                                   \n",
+       "3008 -8.387085                                                                                   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add pesudo count 0.5\n",
+    "x[x == 0] <- 0.5\n",
+    "x <- x/rowSums(x) # relative abundance\n",
+    "taxa <- log(x)\n",
+    "dim(taxa)\n",
+    "head(taxa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>pid</th><th scope=col>visitdate</th><th scope=col>birthdate</th><th scope=col>sex1m2f</th><th scope=col>heightcm</th><th scope=col>weightkg</th><th scope=col>vdate</th><th scope=col>bdate</th><th scope=col>age</th><th scope=col>heightm</th><th scope=col>bmi</th><th scope=col>zbmius</th><th scope=col>zbmicatus</th><th scope=col>bmicat1norm2ow3ob</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>3029     </td><td>12-Apr-10</td><td>1-May-86 </td><td>1        </td><td>172.43   </td><td>83.0     </td><td>18364    </td><td> 9617    </td><td>23.94798 </td><td>1.7243   </td><td>27.91595 </td><td>NA       </td><td>         </td><td>2        </td></tr>\n",
+       "\t<tr><td>3030     </td><td>12-Apr-10</td><td>22-May-87</td><td>1        </td><td>178.87   </td><td>70.3     </td><td>18364    </td><td>10003    </td><td>22.89117 </td><td>1.7887   </td><td>21.97254 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3031     </td><td>20-Apr-10</td><td>1-Dec-82 </td><td>2        </td><td>157.60   </td><td>52.0     </td><td>18372    </td><td> 8370    </td><td>27.38398 </td><td>1.5760   </td><td>20.93586 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3032     </td><td>22-Apr-10</td><td>9-Feb-86 </td><td>1        </td><td>188.10   </td><td>89.6     </td><td>18374    </td><td> 9536    </td><td>24.19712 </td><td>1.8810   </td><td>25.32389 </td><td>NA       </td><td>         </td><td>2        </td></tr>\n",
+       "\t<tr><td>3033     </td><td>22-Apr-10</td><td>9-Apr-86 </td><td>2        </td><td>170.03   </td><td>65.2     </td><td>18374    </td><td> 9595    </td><td>24.03559 </td><td>1.7003   </td><td>22.55259 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "\t<tr><td>3034     </td><td>28-Apr-10</td><td>6-Feb-86 </td><td>2        </td><td>162.16   </td><td>59.9     </td><td>18380    </td><td> 9533    </td><td>24.22177 </td><td>1.6216   </td><td>22.77925 </td><td>NA       </td><td>         </td><td>1        </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       " pid & visitdate & birthdate & sex1m2f & heightcm & weightkg & vdate & bdate & age & heightm & bmi & zbmius & zbmicatus & bmicat1norm2ow3ob\\\\\n",
+       "\\hline\n",
+       "\t 3029      & 12-Apr-10 & 1-May-86  & 1         & 172.43    & 83.0      & 18364     &  9617     & 23.94798  & 1.7243    & 27.91595  & NA        &           & 2        \\\\\n",
+       "\t 3030      & 12-Apr-10 & 22-May-87 & 1         & 178.87    & 70.3      & 18364     & 10003     & 22.89117  & 1.7887    & 21.97254  & NA        &           & 1        \\\\\n",
+       "\t 3031      & 20-Apr-10 & 1-Dec-82  & 2         & 157.60    & 52.0      & 18372     &  8370     & 27.38398  & 1.5760    & 20.93586  & NA        &           & 1        \\\\\n",
+       "\t 3032      & 22-Apr-10 & 9-Feb-86  & 1         & 188.10    & 89.6      & 18374     &  9536     & 24.19712  & 1.8810    & 25.32389  & NA        &           & 2        \\\\\n",
+       "\t 3033      & 22-Apr-10 & 9-Apr-86  & 2         & 170.03    & 65.2      & 18374     &  9595     & 24.03559  & 1.7003    & 22.55259  & NA        &           & 1        \\\\\n",
+       "\t 3034      & 28-Apr-10 & 6-Feb-86  & 2         & 162.16    & 59.9      & 18380     &  9533     & 24.22177  & 1.6216    & 22.77925  & NA        &           & 1        \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| pid | visitdate | birthdate | sex1m2f | heightcm | weightkg | vdate | bdate | age | heightm | bmi | zbmius | zbmicatus | bmicat1norm2ow3ob |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3029      | 12-Apr-10 | 1-May-86  | 1         | 172.43    | 83.0      | 18364     |  9617     | 23.94798  | 1.7243    | 27.91595  | NA        |           | 2         |\n",
+       "| 3030      | 12-Apr-10 | 22-May-87 | 1         | 178.87    | 70.3      | 18364     | 10003     | 22.89117  | 1.7887    | 21.97254  | NA        |           | 1         |\n",
+       "| 3031      | 20-Apr-10 | 1-Dec-82  | 2         | 157.60    | 52.0      | 18372     |  8370     | 27.38398  | 1.5760    | 20.93586  | NA        |           | 1         |\n",
+       "| 3032      | 22-Apr-10 | 9-Feb-86  | 1         | 188.10    | 89.6      | 18374     |  9536     | 24.19712  | 1.8810    | 25.32389  | NA        |           | 2         |\n",
+       "| 3033      | 22-Apr-10 | 9-Apr-86  | 2         | 170.03    | 65.2      | 18374     |  9595     | 24.03559  | 1.7003    | 22.55259  | NA        |           | 1         |\n",
+       "| 3034      | 28-Apr-10 | 6-Feb-86  | 2         | 162.16    | 59.9      | 18380     |  9533     | 24.22177  | 1.6216    | 22.77925  | NA        |           | 1         |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  pid  visitdate birthdate sex1m2f heightcm weightkg vdate bdate age     \n",
+       "1 3029 12-Apr-10 1-May-86  1       172.43   83.0     18364  9617 23.94798\n",
+       "2 3030 12-Apr-10 22-May-87 1       178.87   70.3     18364 10003 22.89117\n",
+       "3 3031 20-Apr-10 1-Dec-82  2       157.60   52.0     18372  8370 27.38398\n",
+       "4 3032 22-Apr-10 9-Feb-86  1       188.10   89.6     18374  9536 24.19712\n",
+       "5 3033 22-Apr-10 9-Apr-86  2       170.03   65.2     18374  9595 24.03559\n",
+       "6 3034 28-Apr-10 6-Feb-86  2       162.16   59.9     18380  9533 24.22177\n",
+       "  heightm bmi      zbmius zbmicatus bmicat1norm2ow3ob\n",
+       "1 1.7243  27.91595 NA               2                \n",
+       "2 1.7887  21.97254 NA               1                \n",
+       "3 1.5760  20.93586 NA               1                \n",
+       "4 1.8810  25.32389 NA               2                \n",
+       "5 1.7003  22.55259 NA               1                \n",
+       "6 1.6216  22.77925 NA               1                "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "98"
+      ],
+      "text/latex": [
+       "98"
+      ],
+      "text/markdown": [
+       "98"
+      ],
+      "text/plain": [
+       "[1] 98"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>21.6186</li>\n",
+       "\t<li>21.82244</li>\n",
+       "\t<li>20.03762</li>\n",
+       "\t<li>20.82412</li>\n",
+       "\t<li>22.66875</li>\n",
+       "\t<li>24.97552</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 21.6186\n",
+       "\\item 21.82244\n",
+       "\\item 20.03762\n",
+       "\\item 20.82412\n",
+       "\\item 22.66875\n",
+       "\\item 24.97552\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 21.6186\n",
+       "2. 21.82244\n",
+       "3. 20.03762\n",
+       "4. 20.82412\n",
+       "5. 22.66875\n",
+       "6. 24.97552\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 21.61860 21.82244 20.03762 20.82412 22.66875 24.97552"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# metadata\n",
+    "demo <- read.delim(\"../../code_Lin/cvs/data/demographic.txt\")\n",
+    "head(demo)\n",
+    "y <- demo$bmi[match(rownames(count), demo$pid)]\n",
+    "length(y)\n",
+    "head(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'matrix'"
+      ],
+      "text/latex": [
+       "'matrix'"
+      ],
+      "text/markdown": [
+       "'matrix'"
+      ],
+      "text/plain": [
+       "[1] \"matrix\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "'numeric'"
+      ],
+      "text/latex": [
+       "'numeric'"
+      ],
+      "text/markdown": [
+       "'numeric'"
+      ],
+      "text/plain": [
+       "[1] \"numeric\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check datatype\n",
+    "class(taxa); class(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save processed data\n",
+    "save(y, taxa, file='../BMI/BMI_Lin_2014.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/1.2 BMI_results_cts.ipynb b/data_application/notebooks_application/1.2 BMI_results_cts.ipynb
new file mode 100755
index 0000000..3d0c385
--- /dev/null
+++ b/data_application/notebooks_application/1.2 BMI_results_cts.ipynb	
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI microbiome data application results for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### method comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../BMI/results_cts/BMI_compLasso.RData')\n",
+    "load('../BMI/results_cts/BMI_elnet.RData')\n",
+    "load('../BMI/results_cts/BMI_lasso.RData')\n",
+    "load('../BMI/results_cts/BMI_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.22</li>\n",
+       "\t<li>21.59</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.22\n",
+       "\\item 21.59\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.22\n",
+       "2. 21.59\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.22 21.59"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_compLasso$stab_index, out_compLasso$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.14</li>\n",
+       "\t<li>24.07</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.14\n",
+       "\\item 24.07\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.14\n",
+       "2. 24.07\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.14 24.07"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_lasso$stab_index, out_lasso$MSE_mean) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.23</li>\n",
+       "\t<li>25.33</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.23\n",
+       "\\item 25.33\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.23\n",
+       "2. 25.33\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  0.23 25.33"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_elnet$stab_index, out_elnet$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.02</li>\n",
+       "\t<li>4.99</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.02\n",
+       "\\item 4.99\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.02\n",
+       "2. 4.99\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.02 4.99"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_rf$stab_index, out_rf$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>mse</th><th scope=col>stability</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>bmi_gut  </td><td>lasso    </td><td>24.07    </td><td>0.14     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>elent    </td><td>25.33    </td><td>0.23     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>rf       </td><td> 4.99    </td><td>0.02     </td></tr>\n",
+       "\t<tr><td>bmi_gut  </td><td>compLasso</td><td>21.59    </td><td>0.22     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " dataset & method & mse & stability\\\\\n",
+       "\\hline\n",
+       "\t bmi\\_gut & lasso     & 24.07     & 0.14     \\\\\n",
+       "\t bmi\\_gut & elent     & 25.33     & 0.23     \\\\\n",
+       "\t bmi\\_gut & rf        &  4.99     & 0.02     \\\\\n",
+       "\t bmi\\_gut & compLasso & 21.59     & 0.22     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| dataset | method | mse | stability |\n",
+       "|---|---|---|---|\n",
+       "| bmi_gut   | lasso     | 24.07     | 0.14      |\n",
+       "| bmi_gut   | elent     | 25.33     | 0.23      |\n",
+       "| bmi_gut   | rf        |  4.99     | 0.02      |\n",
+       "| bmi_gut   | compLasso | 21.59     | 0.22      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    mse   stability\n",
+       "1 bmi_gut lasso     24.07 0.14     \n",
+       "2 bmi_gut elent     25.33 0.23     \n",
+       "3 bmi_gut rf         4.99 0.02     \n",
+       "4 bmi_gut compLasso 21.59 0.22     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# combine and export results\n",
+    "bmi_gut = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(bmi_gut) = c('dataset', 'method', 'mse', 'stability')\n",
+    "bmi_gut$dataset = 'bmi_gut'\n",
+    "bmi_gut$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "bmi_gut$mse = c(out_lasso$MSE_mean, out_elnet$MSE_mean, out_rf$MSE_mean, out_compLasso$MSE_mean)\n",
+    "bmi_gut$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_compLasso$stab_index)\n",
+    "bmi_gut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../BMI/results_cts/BMI_boot_compLasso.RData')\n",
+    "load('../BMI/results_cts/BMI_boot_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2659"
+      ],
+      "text/latex": [
+       "0.2659"
+      ],
+      "text/markdown": [
+       "0.2659"
+      ],
+      "text/plain": [
+       "[1] 0.2659"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.17475</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.34</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.17475\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.34\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1747597.5%\n",
+       ":   0.34\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.17475 0.34000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "11.8026936858767"
+      ],
+      "text/latex": [
+       "11.8026936858767"
+      ],
+      "text/markdown": [
+       "11.8026936858767"
+      ],
+      "text/plain": [
+       "[1] 11.80269"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-2.09038719853993</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>41.1831613135692</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -2.09038719853993\n",
+       "\\item[97.5\\textbackslash{}\\%] 41.1831613135692\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -2.0903871985399397.5%\n",
+       ":   41.1831613135692\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     2.5%     97.5% \n",
+       "-2.090387 41.183161 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_mse = (unlist(boot_compLasso$MSE_list) - unlist(boot_rf$MSE_list)) # use all 100*100 MSEs\n",
+    "mean(diff_mse)\n",
+    "quantile(diff_mse, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/2.1.Soils_biomTableConversion (Python).ipynb b/data_application/notebooks_application/2.1.Soils_biomTableConversion (Python).ipynb
new file mode 100755
index 0000000..16a40f2
--- /dev/null
+++ b/data_application/notebooks_application/2.1.Soils_biomTableConversion (Python).ipynb	
@@ -0,0 +1,1571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ref: the soil dataset used in balance tree\n",
+    "# https://msystems.asm.org/content/2/1/e00162-16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import biom\n",
+    "from biom.util import biom_open\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def biom2pandas(file_biom, withTaxonomy=False, astype=int):\n",
+    "    \"\"\" Converts a biom file into a Pandas.DataFrame\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_biom : str\n",
+    "        The path to the biom file.\n",
+    "    withTaxonomy : bool\n",
+    "        If TRUE, returns a second Pandas.Series with lineage information for\n",
+    "        each feature, e.g. OTU or deblur-sequence. Default: FALSE\n",
+    "    astype : type\n",
+    "        datatype into each value of the biom table is casted. Default: int.\n",
+    "        Use e.g. float if biom table contains relative abundances instead of\n",
+    "        raw reads.\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A Pandas.DataFrame holding holding numerical values from the biom file.\n",
+    "    If withTaxonomy is TRUE then a second Pandas.DataFrame is returned, holding\n",
+    "    lineage information about each feature.\n",
+    "    Raises\n",
+    "    ------\n",
+    "    IOError\n",
+    "        If file_biom cannot be read.\n",
+    "    ValueError\n",
+    "        If withTaxonomy=TRUE but biom file does not hold taxonomy information.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        table = biom.load_table(file_biom)\n",
+    "        counts = pd.DataFrame(table.matrix_data.T.todense().astype(astype),\n",
+    "                              index=table.ids(axis='sample'),\n",
+    "                              columns=table.ids(axis='observation')).T\n",
+    "        if withTaxonomy:\n",
+    "            try:\n",
+    "                md = table.metadata_to_dataframe('observation')\n",
+    "                levels = [col\n",
+    "                          for col in md.columns\n",
+    "                          if col.startswith('taxonomy_')]\n",
+    "                if levels == []:\n",
+    "                    raise ValueError(('No taxonomy information found in '\n",
+    "                                      'biom file.'))\n",
+    "                else:\n",
+    "                    taxonomy = md.apply(lambda row:\n",
+    "                                        \";\".join([row[l] for l in levels]),\n",
+    "                                        axis=1)\n",
+    "                    return counts, taxonomy\n",
+    "            except KeyError:\n",
+    "                raise ValueError(('Biom file does not have any '\n",
+    "                                  'observation metadata!'))\n",
+    "        else:\n",
+    "            return counts\n",
+    "    except IOError:\n",
+    "        raise IOError('Cannot read file \"%s\"' % file_biom)\n",
+    "\n",
+    "\n",
+    "def pandas2biom(file_biom, table, taxonomy=None, err=sys.stderr):\n",
+    "    \"\"\" Writes a Pandas.DataFrame into a biom file.\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    file_biom: str\n",
+    "        The filename of the BIOM file to be created.\n",
+    "    table: a Pandas.DataFrame\n",
+    "        The table that should be written as BIOM.\n",
+    "    taxonomy : pandas.Series\n",
+    "        Index is taxons corresponding to table, values are lineage strings like\n",
+    "        'k__Bacteria; p__Actinobacteria'\n",
+    "    err : StringIO\n",
+    "        Stream onto which errors / warnings should be printed.\n",
+    "        Default is sys.stderr\n",
+    "    Raises\n",
+    "    ------\n",
+    "    IOError\n",
+    "        If file_biom cannot be written.\n",
+    "    TODO\n",
+    "    ----\n",
+    "        1) also store taxonomy information\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        bt = biom.Table(table.values,\n",
+    "                        observation_ids=table.index,\n",
+    "                        sample_ids=table.columns)\n",
+    "\n",
+    "        # add taxonomy metadata if provided, i.e. is not None\n",
+    "        if taxonomy is not None:\n",
+    "            if not isinstance(taxonomy, pd.core.series.Series):\n",
+    "                raise AttributeError('taxonomy must be a pandas.Series!')\n",
+    "            idx_missing_intable = set(table.index) - set(taxonomy.index)\n",
+    "            if len(idx_missing_intable) > 0:\n",
+    "                err.write(('Warning: following %i taxa are not in the '\n",
+    "                           'provided taxonomy:\\n%s\\n') % (\n",
+    "                          len(idx_missing_intable),\n",
+    "                          \", \".join(idx_missing_intable)))\n",
+    "                missing = pd.Series(\n",
+    "                    index=idx_missing_intable,\n",
+    "                    name='taxonomy',\n",
+    "                    data='k__missing_lineage_information')\n",
+    "                taxonomy = taxonomy.append(missing)\n",
+    "            idx_missing_intaxonomy = set(taxonomy.index) - set(table.index)\n",
+    "            if (len(idx_missing_intaxonomy) > 0) and err:\n",
+    "                err.write(('Warning: following %i taxa are not in the '\n",
+    "                           'provided count table, but in taxonomy:\\n%s\\n') % (\n",
+    "                          len(idx_missing_intaxonomy),\n",
+    "                          \", \".join(idx_missing_intaxonomy)))\n",
+    "\n",
+    "            t = dict()\n",
+    "            for taxon, linstr in taxonomy.iteritems():\n",
+    "                # fill missing rank annotations with rank__\n",
+    "                orig_lineage = {annot[0].lower(): annot\n",
+    "                                for annot\n",
+    "                                in (map(str.strip, linstr.split(';')))}\n",
+    "                lineage = []\n",
+    "                for rank in settings.RANKS:\n",
+    "                    rank_char = rank[0].lower()\n",
+    "                    if rank_char in orig_lineage:\n",
+    "                        lineage.append(orig_lineage[rank_char])\n",
+    "                    else:\n",
+    "                        lineage.append(rank_char+'__')\n",
+    "                t[taxon] = {'taxonomy': \";\".join(lineage)}\n",
+    "            bt.add_metadata(t, axis='observation')\n",
+    "\n",
+    "        with biom_open(file_biom, 'w') as f:\n",
+    "            bt.to_hdf5(f, \"example\")\n",
+    "    except IOError:\n",
+    "        raise IOError('Cannot write to file \"%s\"' % file_biom)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Balance_88soils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7396, 89)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom = biom2pandas('../88soils/238_otu_table.biom')\n",
+    "soils_biom.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.LQ1</th>\n",
+       "      <th>103.HI1</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1124701</th>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244336</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>973124</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 89 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         103.CA2  103.CO3  103.SR3  103.IE2  103.BP1  103.VC2  103.SA2  \\\n",
+       "1124701       15       14        1        8       13        7        6   \n",
+       "244336         0        0        0        1        0        0        0   \n",
+       "973124         0        0        0        0        0        0        0   \n",
+       "\n",
+       "         103.GB2  103.CO2  103.KP1  ...  103.LQ1  103.HI1  103.RT1  103.HI2  \\\n",
+       "1124701        3        2        2  ...        0        0        0        0   \n",
+       "244336         0        0        0  ...        0        0        0        0   \n",
+       "973124         0        0        1  ...        0        0        0        0   \n",
+       "\n",
+       "         103.DF1  103.CF3  103.AR1  103.TL1  103.HI4  103.BB1  \n",
+       "1124701        0        0        0        0        0        0  \n",
+       "244336         0        0        0        0        0        0  \n",
+       "973124         0        0        0        0        0        0  \n",
+       "\n",
+       "[3 rows x 89 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7396, 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa = pd.read_csv('../88soils/88soils_taxonomy.txt', sep='\\t', index_col='Feature ID')\n",
+    "soils_taxa.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Thermoleophil...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon\n",
+       "Feature ID                                                   \n",
+       "1000512     k__Bacteria;p__Actinobacteria;c__Thermoleophil...\n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...\n",
+       "1000654     k__Bacteria;p__Bacteroidetes;c__Sphingobacteri..."
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Actinobacteria</td>\n",
+       "      <td>c__Thermoleophilia</td>\n",
+       "      <td>o__Gaiellales</td>\n",
+       "      <td>f__Gaiellaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Firmicutes</td>\n",
+       "      <td>c__Bacilli</td>\n",
+       "      <td>o__Lactobacillales</td>\n",
+       "      <td>f__Streptococcaceae</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Bacteroidetes</td>\n",
+       "      <td>c__Sphingobacteriia</td>\n",
+       "      <td>o__Sphingobacteriales</td>\n",
+       "      <td>f__Sphingobacteriaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000757</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Proteobacteria</td>\n",
+       "      <td>c__Alphaproteobacteria</td>\n",
+       "      <td>o__Rhizobiales</td>\n",
+       "      <td>f__Bradyrhizobiaceae</td>\n",
+       "      <td>g__</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria</td>\n",
+       "      <td>p__Actinobacteria</td>\n",
+       "      <td>c__Actinobacteria</td>\n",
+       "      <td>o__Actinomycetales</td>\n",
+       "      <td>f__Nocardioidaceae</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "      <td>s__</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      0                  1                       2  \\\n",
+       "Feature ID                                                           \n",
+       "1000512     k__Bacteria  p__Actinobacteria      c__Thermoleophilia   \n",
+       "1000547     k__Bacteria      p__Firmicutes              c__Bacilli   \n",
+       "1000654     k__Bacteria   p__Bacteroidetes     c__Sphingobacteriia   \n",
+       "1000757     k__Bacteria  p__Proteobacteria  c__Alphaproteobacteria   \n",
+       "1000876     k__Bacteria  p__Actinobacteria       c__Actinobacteria   \n",
+       "\n",
+       "                                3                       4                 5  \\\n",
+       "Feature ID                                                                    \n",
+       "1000512             o__Gaiellales          f__Gaiellaceae               g__   \n",
+       "1000547        o__Lactobacillales     f__Streptococcaceae  g__Streptococcus   \n",
+       "1000654     o__Sphingobacteriales  f__Sphingobacteriaceae               g__   \n",
+       "1000757            o__Rhizobiales    f__Bradyrhizobiaceae               g__   \n",
+       "1000876        o__Actinomycetales      f__Nocardioidaceae   g__Nocardioides   \n",
+       "\n",
+       "              6  \n",
+       "Feature ID       \n",
+       "1000512     s__  \n",
+       "1000547     s__  \n",
+       "1000654     s__  \n",
+       "1000757     s__  \n",
+       "1000876     s__  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taxa_new = soils_taxa.Taxon.str.split(pat=\";\", expand=True)\n",
+    "taxa_new.head(5)\n",
+    "# ref: https://www.geeksforgeeks.org/python-pandas-split-strings-into-two-list-columns-using-str-split/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000512</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Thermoleophil...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000654</th>\n",
+       "      <td>k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000757</th>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Actinobacteri...</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon  \\\n",
+       "Feature ID                                                      \n",
+       "1000512     k__Bacteria;p__Actinobacteria;c__Thermoleophil...   \n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...   \n",
+       "1000654     k__Bacteria;p__Bacteroidetes;c__Sphingobacteri...   \n",
+       "1000757     k__Bacteria;p__Proteobacteria;c__Alphaproteoba...   \n",
+       "1000876     k__Bacteria;p__Actinobacteria;c__Actinobacteri...   \n",
+       "\n",
+       "                       Genus  \n",
+       "Feature ID                    \n",
+       "1000512                  g__  \n",
+       "1000547     g__Streptococcus  \n",
+       "1000654                  g__  \n",
+       "1000757                  g__  \n",
+       "1000876      g__Nocardioides  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa['Genus'] = taxa_new[5]\n",
+    "soils_taxa.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "g__                           5213\n",
+       "g__Rhodoplanes                 144\n",
+       "g__Bacillus                    110\n",
+       "g__Candidatus Solibacter       100\n",
+       "g__Flavobacterium               71\n",
+       "                              ... \n",
+       "g__Rhodocyclus                   1\n",
+       "g__Marinobacter                  1\n",
+       "g__Afipia                        1\n",
+       "g__Candidatus Amoebophilus       1\n",
+       "g__Desulfotomaculum              1\n",
+       "Name: Genus, Length: 335, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa.Genus.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 2)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# only keep those with genus assignment\n",
+    "soils_taxa_sub = soils_taxa[soils_taxa.Genus != 'g__']\n",
+    "soils_taxa_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Feature ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1000547</th>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...</td>\n",
+       "      <td>g__Streptococcus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1000876</th>\n",
+       "      <td>k__Bacteria;p__Actinobacteria;c__Actinobacteri...</td>\n",
+       "      <td>g__Nocardioides</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003206</th>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__Sphingomonas</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                        Taxon  \\\n",
+       "Feature ID                                                      \n",
+       "1000547     k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactob...   \n",
+       "1000876     k__Bacteria;p__Actinobacteria;c__Actinobacteri...   \n",
+       "1003206     k__Bacteria;p__Proteobacteria;c__Alphaproteoba...   \n",
+       "\n",
+       "                       Genus  \n",
+       "Feature ID                    \n",
+       "1000547     g__Streptococcus  \n",
+       "1000876      g__Nocardioides  \n",
+       "1003206      g__Sphingomonas  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_taxa_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 91)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# partition biom table \n",
+    "soils_biom.index = soils_biom.index.astype('int64') \n",
+    "soils_biom_sub = soils_biom.merge(soils_taxa_sub, how='inner', left_index=True, right_index=True)\n",
+    "soils_biom_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "      <th>Taxon</th>\n",
+       "      <th>Genus</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>244336</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...</td>\n",
+       "      <td>g__Paenibacillus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>809489</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...</td>\n",
+       "      <td>g__Bacillus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>533625</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>k__Bacteria;p__Proteobacteria;c__Alphaproteoba...</td>\n",
+       "      <td>g__Novosphingobium</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 91 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        103.CA2  103.CO3  103.SR3  103.IE2  103.BP1  103.VC2  103.SA2  \\\n",
+       "244336        0        0        0        1        0        0        0   \n",
+       "809489        0        0        0        0        0        0        0   \n",
+       "533625        0        0        0        0        0        0        0   \n",
+       "\n",
+       "        103.GB2  103.CO2  103.KP1  ...  103.RT1  103.HI2  103.DF1  103.CF3  \\\n",
+       "244336        0        0        0  ...        0        0        0        0   \n",
+       "809489        1        0        0  ...        0        0        0        0   \n",
+       "533625        0        0        0  ...        0        0        0        0   \n",
+       "\n",
+       "        103.AR1  103.TL1  103.HI4  103.BB1  \\\n",
+       "244336        0        0        0        0   \n",
+       "809489        0        0        0        0   \n",
+       "533625        0        0        0        0   \n",
+       "\n",
+       "                                                    Taxon               Genus  \n",
+       "244336  k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...    g__Paenibacillus  \n",
+       "809489  k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacill...         g__Bacillus  \n",
+       "533625  k__Bacteria;p__Proteobacteria;c__Alphaproteoba...  g__Novosphingobium  \n",
+       "\n",
+       "[3 rows x 91 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soils_biom_sub.set_index('Taxon', inplace=True)\n",
+    "soils_biom_sub.drop(['Genus'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2183, 89)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>103.CA2</th>\n",
+       "      <th>103.CO3</th>\n",
+       "      <th>103.SR3</th>\n",
+       "      <th>103.IE2</th>\n",
+       "      <th>103.BP1</th>\n",
+       "      <th>103.VC2</th>\n",
+       "      <th>103.SA2</th>\n",
+       "      <th>103.GB2</th>\n",
+       "      <th>103.CO2</th>\n",
+       "      <th>103.KP1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>103.LQ1</th>\n",
+       "      <th>103.HI1</th>\n",
+       "      <th>103.RT1</th>\n",
+       "      <th>103.HI2</th>\n",
+       "      <th>103.DF1</th>\n",
+       "      <th>103.CF3</th>\n",
+       "      <th>103.AR1</th>\n",
+       "      <th>103.TL1</th>\n",
+       "      <th>103.HI4</th>\n",
+       "      <th>103.BB1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Taxon</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 89 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    103.CA2  103.CO3  103.SR3  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.IE2  103.BP1  103.VC2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        1        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.SA2  103.GB2  103.CO2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        1        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.KP1  ...  103.LQ1  \\\n",
+       "Taxon                                                        ...            \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0  ...        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0  ...        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0  ...        0   \n",
+       "\n",
+       "                                                    103.HI1  103.RT1  103.HI2  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.DF1  103.CF3  103.AR1  \\\n",
+       "Taxon                                                                           \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0   \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0   \n",
+       "\n",
+       "                                                    103.TL1  103.HI4  103.BB1  \n",
+       "Taxon                                                                          \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0  \n",
+       "k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacilla...        0        0        0  \n",
+       "k__Bacteria;p__Proteobacteria;c__Alphaproteobac...        0        0        0  \n",
+       "\n",
+       "[3 rows x 89 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(89, 2183)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# transpose the dataframe \n",
+    "soils_biom_sub_t = soils_biom_sub.T\n",
+    "soils_biom_sub_t.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Taxon</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__</th>\n",
+       "      <th>k__Bacteria;p__Acidobacteria;c__Solibacteres;o__Solibacterales;f__Solibacteraceae;g__Candidatus Solibacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Nocardioidaceae;g__Nocardioides;s__</th>\n",
+       "      <th>k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptococcaceae;g__Desulfotomaculum;s__</th>\n",
+       "      <th>k__Bacteria;p__Acidobacteria;c__Acidobacteriia;o__Acidobacteriales;f__Koribacteraceae;g__Candidatus Koribacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Methylibium;s__</th>\n",
+       "      <th>...</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rhodoferax;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Dokdonella;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__Methylotenera;s__mobilis</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Streptomycetaceae;g__Streptomyces;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Luteimonas;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__</th>\n",
+       "      <th>k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Planococcaceae;g__Solibacillus;s__</th>\n",
+       "      <th>k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Ramlibacter;s__</th>\n",
+       "      <th>k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Mycobacteriaceae;g__Mycobacterium;s__</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103.CA2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103.CO3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103.SR3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 2183 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Paenibacillaceae;g__Paenibacillus;s__  \\\n",
+       "103.CA2                                                  0                                             \n",
+       "103.CO3                                                  0                                             \n",
+       "103.SR3                                                  0                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__muralis  \\\n",
+       "103.CA2                                                  0                                          \n",
+       "103.CO3                                                  0                                          \n",
+       "103.SR3                                                  0                                          \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Sphingomonadales;f__Sphingomonadaceae;g__Novosphingobium;s__  \\\n",
+       "103.CA2                                                  0                                                                      \n",
+       "103.CO3                                                  0                                                                      \n",
+       "103.SR3                                                  0                                                                      \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Acidobacteria;c__Solibacteres;o__Solibacterales;f__Solibacteraceae;g__Candidatus Solibacter;s__  \\\n",
+       "103.CA2                                                  0                                                                \n",
+       "103.CO3                                                  0                                                                \n",
+       "103.SR3                                                  0                                                                \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Nocardioidaceae;g__Nocardioides;s__  \\\n",
+       "103.CA2                                                  0                                                           \n",
+       "103.CO3                                                  0                                                           \n",
+       "103.SR3                                                  0                                                           \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Bacteroidetes;c__Flavobacteriia;o__Flavobacteriales;f__Flavobacteriaceae;g__Flavobacterium;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  0                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Peptococcaceae;g__Desulfotomaculum;s__  \\\n",
+       "103.CA2                                                  0                                                    \n",
+       "103.CO3                                                  0                                                    \n",
+       "103.SR3                                                  0                                                    \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Acidobacteria;c__Acidobacteriia;o__Acidobacteriales;f__Koribacteraceae;g__Candidatus Koribacter;s__  \\\n",
+       "103.CA2                                                  0                                                                    \n",
+       "103.CO3                                                  0                                                                    \n",
+       "103.SR3                                                  0                                                                    \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Methylibium;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    ...  \\\n",
+       "103.CA2  ...   \n",
+       "103.CO3  ...   \n",
+       "103.SR3  ...   \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Rhodoferax;s__  \\\n",
+       "103.CA2                                                  0                                                            \n",
+       "103.CO3                                                  0                                                            \n",
+       "103.SR3                                                  0                                                            \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Dokdonella;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  1                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus;s__  \\\n",
+       "103.CA2                                                  0                                                  \n",
+       "103.CO3                                                  0                                                  \n",
+       "103.SR3                                                  0                                                  \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Methylophilales;f__Methylophilaceae;g__Methylotenera;s__mobilis  \\\n",
+       "103.CA2                                                  0                                                                        \n",
+       "103.CO3                                                  0                                                                        \n",
+       "103.SR3                                                  0                                                                        \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Streptomycetaceae;g__Streptomyces;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales;f__Xanthomonadaceae;g__Luteimonas;s__  \\\n",
+       "103.CA2                                                  0                                                               \n",
+       "103.CO3                                                  0                                                               \n",
+       "103.SR3                                                  0                                                               \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Alphaproteobacteria;o__Rhizobiales;f__Hyphomicrobiaceae;g__Rhodoplanes;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  0                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Planococcaceae;g__Solibacillus;s__  \\\n",
+       "103.CA2                                                  0                                          \n",
+       "103.CO3                                                  0                                          \n",
+       "103.SR3                                                  0                                          \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Ramlibacter;s__  \\\n",
+       "103.CA2                                                  0                                                             \n",
+       "103.CO3                                                  0                                                             \n",
+       "103.SR3                                                  1                                                             \n",
+       "\n",
+       "Taxon    k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales;f__Mycobacteriaceae;g__Mycobacterium;s__  \n",
+       "103.CA2                                                  0                                                            \n",
+       "103.CO3                                                  1                                                            \n",
+       "103.SR3                                                  1                                                            \n",
+       "\n",
+       "[3 rows x 2183 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soils_biom_sub_t.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     89.000000\n",
+       "mean     275.932584\n",
+       "std      115.508244\n",
+       "min        1.000000\n",
+       "25%      213.000000\n",
+       "50%      254.000000\n",
+       "75%      319.000000\n",
+       "max      805.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make sure that each genus exist in at least one sample\n",
+    "soils_biom_sub_t.sum(axis=1).describe() # column sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# export\n",
+    "soils_biom_sub_t.to_csv('../88soils/88soils_genus_table.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    2183.000000\n",
+       "mean       11.249656\n",
+       "std        33.869668\n",
+       "min         1.000000\n",
+       "25%         1.000000\n",
+       "50%         3.000000\n",
+       "75%         8.000000\n",
+       "max       690.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check that not rarefied\n",
+    "soils_biom_sub_t.sum(axis=0).describe() # row sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data_application/notebooks_application/2.2.soils_dataPreparation.ipynb b/data_application/notebooks_application/2.2.soils_dataPreparation.ipynb
new file mode 100755
index 0000000..2217a77
--- /dev/null
+++ b/data_application/notebooks_application/2.2.soils_dataPreparation.ipynb
@@ -0,0 +1,1060 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/88soils_genus_table.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "count = soil_otu\n",
+    "dim(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__</th><th scope=col>k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__</th><th scope=col>...</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>103.CA2</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td></tr>\n",
+       "\t<tr><th scope=row>103.CO3</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.SR3</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>1  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.IE2</th><td>1  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>2  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.BP1</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "\t<tr><th scope=row>103.VC2</th><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>2  </td><td>...</td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>0  </td><td>1  </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Paenibacillaceae.g\\_\\_Paenibacillus.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Bacillaceae.g\\_\\_Bacillus.s\\_\\_muralis & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Sphingomonadales.f\\_\\_Sphingomonadaceae.g\\_\\_Novosphingobium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Solibacteres.o\\_\\_Solibacterales.f\\_\\_Solibacteraceae.g\\_\\_Candidatus.Solibacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Nocardioidaceae.g\\_\\_Nocardioides.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Bacteroidetes.c\\_\\_Flavobacteriia.o\\_\\_Flavobacteriales.f\\_\\_Flavobacteriaceae.g\\_\\_Flavobacterium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Clostridia.o\\_\\_Clostridiales.f\\_\\_Peptococcaceae.g\\_\\_Desulfotomaculum.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Acidobacteriia.o\\_\\_Acidobacteriales.f\\_\\_Koribacteraceae.g\\_\\_Candidatus.Koribacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Methylibium.s\\_\\_ & ... & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Rhodoferax.s\\_\\_.4 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Dokdonella.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Lactobacillales.f\\_\\_Streptococcaceae.g\\_\\_Streptococcus.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Methylophilales.f\\_\\_Methylophilaceae.g\\_\\_Methylotenera.s\\_\\_mobilis.2 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Streptomycetaceae.g\\_\\_Streptomyces.s\\_\\_.44 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Luteimonas.s\\_\\_.2 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_.142 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Planococcaceae.g\\_\\_Solibacillus.s\\_\\_.3 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Ramlibacter.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Mycobacteriaceae.g\\_\\_Mycobacterium.s\\_\\_.28\\\\\n",
+       "\\hline\n",
+       "\t103.CA2 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0  \\\\\n",
+       "\t103.CO3 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\t103.SR3 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 1   & 0   & 0   & 0   & 0   & 0   & 0   & 1   & 1  \\\\\n",
+       "\t103.IE2 & 1   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 2   & 0   & 0   & 1  \\\\\n",
+       "\t103.BP1 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\t103.VC2 & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 2   & ... & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 0   & 1  \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__ | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__ | k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__ | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__ | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__ | k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__ | k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__ | k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__ | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__ | ... | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2 | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28 |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 103.CA2 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   |\n",
+       "| 103.CO3 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "| 103.SR3 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 1   | 0   | 0   | 0   | 0   | 0   | 0   | 1   | 1   |\n",
+       "| 103.IE2 | 1   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 2   | 0   | 0   | 1   |\n",
+       "| 103.BP1 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "| 103.VC2 | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 2   | ... | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 0   | 1   |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__\n",
+       "103.CA2 0                                                                                          \n",
+       "103.CO3 0                                                                                          \n",
+       "103.SR3 0                                                                                          \n",
+       "103.IE2 1                                                                                          \n",
+       "103.BP1 0                                                                                          \n",
+       "103.VC2 0                                                                                          \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis\n",
+       "103.CA2 0                                                                                       \n",
+       "103.CO3 0                                                                                       \n",
+       "103.SR3 0                                                                                       \n",
+       "103.IE2 0                                                                                       \n",
+       "103.BP1 0                                                                                       \n",
+       "103.VC2 0                                                                                       \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__\n",
+       "103.CA2 0                                                                                                                   \n",
+       "103.CO3 0                                                                                                                   \n",
+       "103.SR3 0                                                                                                                   \n",
+       "103.IE2 0                                                                                                                   \n",
+       "103.BP1 0                                                                                                                   \n",
+       "103.VC2 0                                                                                                                   \n",
+       "        k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 0                                                                                                             \n",
+       "103.SR3 0                                                                                                             \n",
+       "103.IE2 0                                                                                                             \n",
+       "103.BP1 0                                                                                                             \n",
+       "103.VC2 0                                                                                                             \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__\n",
+       "103.CA2 0                                                                                                          \n",
+       "103.CO3 0                                                                                                          \n",
+       "103.SR3 0                                                                                                          \n",
+       "103.IE2 0                                                                                                          \n",
+       "103.BP1 0                                                                                                          \n",
+       "103.VC2 0                                                                                                          \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__\n",
+       "103.CA2 0                                                                                                        \n",
+       "103.CO3 0                                                                                                        \n",
+       "103.SR3 0                                                                                                        \n",
+       "103.IE2 0                                                                                                        \n",
+       "103.BP1 0                                                                                                        \n",
+       "103.VC2 0                                                                                                        \n",
+       "        k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__\n",
+       "103.CA2 0                                                                                                            \n",
+       "103.CO3 0                                                                                                            \n",
+       "103.SR3 0                                                                                                            \n",
+       "103.IE2 0                                                                                                            \n",
+       "103.BP1 0                                                                                                            \n",
+       "103.VC2 0                                                                                                            \n",
+       "        k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__\n",
+       "103.CA2 0                                                                                                 \n",
+       "103.CO3 0                                                                                                 \n",
+       "103.SR3 0                                                                                                 \n",
+       "103.IE2 0                                                                                                 \n",
+       "103.BP1 0                                                                                                 \n",
+       "103.VC2 0                                                                                                 \n",
+       "        k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__\n",
+       "103.CA2 0                                                                                                                 \n",
+       "103.CO3 0                                                                                                                 \n",
+       "103.SR3 0                                                                                                                 \n",
+       "103.IE2 0                                                                                                                 \n",
+       "103.BP1 0                                                                                                                 \n",
+       "103.VC2 0                                                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__\n",
+       "103.CA2 0                                                                                                          \n",
+       "103.CO3 0                                                                                                          \n",
+       "103.SR3 0                                                                                                          \n",
+       "103.IE2 0                                                                                                          \n",
+       "103.BP1 0                                                                                                          \n",
+       "103.VC2 2                                                                                                          \n",
+       "        ...\n",
+       "103.CA2 ...\n",
+       "103.CO3 ...\n",
+       "103.SR3 ...\n",
+       "103.IE2 ...\n",
+       "103.BP1 ...\n",
+       "103.VC2 ...\n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4\n",
+       "103.CA2 0                                                                                                           \n",
+       "103.CO3 0                                                                                                           \n",
+       "103.SR3 0                                                                                                           \n",
+       "103.IE2 0                                                                                                           \n",
+       "103.BP1 0                                                                                                           \n",
+       "103.VC2 0                                                                                                           \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 1                                                                                                              \n",
+       "103.IE2 0                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9\n",
+       "103.CA2 0                                                                                                 \n",
+       "103.CO3 0                                                                                                 \n",
+       "103.SR3 0                                                                                                 \n",
+       "103.IE2 0                                                                                                 \n",
+       "103.BP1 0                                                                                                 \n",
+       "103.VC2 0                                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2\n",
+       "103.CA2 0                                                                                                                       \n",
+       "103.CO3 0                                                                                                                       \n",
+       "103.SR3 0                                                                                                                       \n",
+       "103.IE2 0                                                                                                                       \n",
+       "103.BP1 0                                                                                                                       \n",
+       "103.VC2 0                                                                                                                       \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 0                                                                                                             \n",
+       "103.SR3 0                                                                                                             \n",
+       "103.IE2 0                                                                                                             \n",
+       "103.BP1 0                                                                                                             \n",
+       "103.VC2 0                                                                                                             \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 0                                                                                                              \n",
+       "103.IE2 0                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142\n",
+       "103.CA2 0                                                                                                              \n",
+       "103.CO3 0                                                                                                              \n",
+       "103.SR3 0                                                                                                              \n",
+       "103.IE2 2                                                                                                              \n",
+       "103.BP1 0                                                                                                              \n",
+       "103.VC2 0                                                                                                              \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3\n",
+       "103.CA2 0                                                                                         \n",
+       "103.CO3 0                                                                                         \n",
+       "103.SR3 0                                                                                         \n",
+       "103.IE2 0                                                                                         \n",
+       "103.BP1 0                                                                                         \n",
+       "103.VC2 0                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9\n",
+       "103.CA2 0                                                                                                            \n",
+       "103.CO3 0                                                                                                            \n",
+       "103.SR3 1                                                                                                            \n",
+       "103.IE2 0                                                                                                            \n",
+       "103.BP1 0                                                                                                            \n",
+       "103.VC2 0                                                                                                            \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28\n",
+       "103.CA2 0                                                                                                             \n",
+       "103.CO3 1                                                                                                             \n",
+       "103.SR3 1                                                                                                             \n",
+       "103.IE2 1                                                                                                             \n",
+       "103.BP1 1                                                                                                             \n",
+       "103.VC2 1                                                                                                             "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 0.5/100]) # 0.5%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 1/100]) # 1%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>1297</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 1297\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 1297\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 1297"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 2/100]) # 2%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>576</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 576\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 576\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]  89 576"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(count[, colMeans(count > 0) >= 5/100]) # 5%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>89</li>\n",
+       "\t<li>2183</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 89\n",
+       "\\item 2183\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 89\n",
+       "2. 2183\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1]   89 2183"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__</th><th scope=col>k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__</th><th scope=col>k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__</th><th scope=col>k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__</th><th scope=col>...</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142</th><th scope=col>k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3</th><th scope=col>k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9</th><th scope=col>k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>103.CA2</th><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>...      </td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td><td>-7.828436</td></tr>\n",
+       "\t<tr><th scope=row>103.CO3</th><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>...      </td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.892452</td><td>-7.199305</td></tr>\n",
+       "\t<tr><th scope=row>103.SR3</th><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>...      </td><td>-7.942718</td><td>-7.249570</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.942718</td><td>-7.249570</td><td>-7.249570</td></tr>\n",
+       "\t<tr><th scope=row>103.IE2</th><td>-7.208230</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>...      </td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-7.901377</td><td>-6.515083</td><td>-7.901377</td><td>-7.901377</td><td>-7.208230</td></tr>\n",
+       "\t<tr><th scope=row>103.BP1</th><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>...      </td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.781139</td><td>-7.087991</td></tr>\n",
+       "\t<tr><th scope=row>103.VC2</th><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-6.439350</td><td>...      </td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.825645</td><td>-7.132498</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
+       "  & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Paenibacillaceae.g\\_\\_Paenibacillus.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Bacillaceae.g\\_\\_Bacillus.s\\_\\_muralis & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Sphingomonadales.f\\_\\_Sphingomonadaceae.g\\_\\_Novosphingobium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Solibacteres.o\\_\\_Solibacterales.f\\_\\_Solibacteraceae.g\\_\\_Candidatus.Solibacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Nocardioidaceae.g\\_\\_Nocardioides.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Bacteroidetes.c\\_\\_Flavobacteriia.o\\_\\_Flavobacteriales.f\\_\\_Flavobacteriaceae.g\\_\\_Flavobacterium.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Clostridia.o\\_\\_Clostridiales.f\\_\\_Peptococcaceae.g\\_\\_Desulfotomaculum.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Acidobacteria.c\\_\\_Acidobacteriia.o\\_\\_Acidobacteriales.f\\_\\_Koribacteraceae.g\\_\\_Candidatus.Koribacter.s\\_\\_ & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Methylibium.s\\_\\_ & ... & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Rhodoferax.s\\_\\_.4 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Dokdonella.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Lactobacillales.f\\_\\_Streptococcaceae.g\\_\\_Streptococcus.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Methylophilales.f\\_\\_Methylophilaceae.g\\_\\_Methylotenera.s\\_\\_mobilis.2 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Streptomycetaceae.g\\_\\_Streptomyces.s\\_\\_.44 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Gammaproteobacteria.o\\_\\_Xanthomonadales.f\\_\\_Xanthomonadaceae.g\\_\\_Luteimonas.s\\_\\_.2 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Alphaproteobacteria.o\\_\\_Rhizobiales.f\\_\\_Hyphomicrobiaceae.g\\_\\_Rhodoplanes.s\\_\\_.142 & k\\_\\_Bacteria.p\\_\\_Firmicutes.c\\_\\_Bacilli.o\\_\\_Bacillales.f\\_\\_Planococcaceae.g\\_\\_Solibacillus.s\\_\\_.3 & k\\_\\_Bacteria.p\\_\\_Proteobacteria.c\\_\\_Betaproteobacteria.o\\_\\_Burkholderiales.f\\_\\_Comamonadaceae.g\\_\\_Ramlibacter.s\\_\\_.9 & k\\_\\_Bacteria.p\\_\\_Actinobacteria.c\\_\\_Actinobacteria.o\\_\\_Actinomycetales.f\\_\\_Mycobacteriaceae.g\\_\\_Mycobacterium.s\\_\\_.28\\\\\n",
+       "\\hline\n",
+       "\t103.CA2 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & ...       & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436 & -7.828436\\\\\n",
+       "\t103.CO3 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & ...       & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.892452 & -7.199305\\\\\n",
+       "\t103.SR3 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & ...       & -7.942718 & -7.249570 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.942718 & -7.249570 & -7.249570\\\\\n",
+       "\t103.IE2 & -7.208230 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & ...       & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -7.901377 & -6.515083 & -7.901377 & -7.901377 & -7.208230\\\\\n",
+       "\t103.BP1 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & ...       & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.781139 & -7.087991\\\\\n",
+       "\t103.VC2 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -6.439350 & ...       & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.825645 & -7.132498\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__ | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__ | k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__ | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__ | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__ | k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__ | k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__ | k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__ | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__ | ... | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44 | k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2 | k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142 | k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3 | k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9 | k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28 |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 103.CA2 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | ...       | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 | -7.828436 |\n",
+       "| 103.CO3 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | ...       | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.892452 | -7.199305 |\n",
+       "| 103.SR3 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | ...       | -7.942718 | -7.249570 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.942718 | -7.249570 | -7.249570 |\n",
+       "| 103.IE2 | -7.208230 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | ...       | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -7.901377 | -6.515083 | -7.901377 | -7.901377 | -7.208230 |\n",
+       "| 103.BP1 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | ...       | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.781139 | -7.087991 |\n",
+       "| 103.VC2 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -6.439350 | ...       | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.825645 | -7.132498 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Paenibacillaceae.g__Paenibacillus.s__\n",
+       "103.CA2 -7.828436                                                                                  \n",
+       "103.CO3 -7.892452                                                                                  \n",
+       "103.SR3 -7.942718                                                                                  \n",
+       "103.IE2 -7.208230                                                                                  \n",
+       "103.BP1 -7.781139                                                                                  \n",
+       "103.VC2 -7.825645                                                                                  \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Bacillaceae.g__Bacillus.s__muralis\n",
+       "103.CA2 -7.828436                                                                               \n",
+       "103.CO3 -7.892452                                                                               \n",
+       "103.SR3 -7.942718                                                                               \n",
+       "103.IE2 -7.901377                                                                               \n",
+       "103.BP1 -7.781139                                                                               \n",
+       "103.VC2 -7.825645                                                                               \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Sphingomonadales.f__Sphingomonadaceae.g__Novosphingobium.s__\n",
+       "103.CA2 -7.828436                                                                                                           \n",
+       "103.CO3 -7.892452                                                                                                           \n",
+       "103.SR3 -7.942718                                                                                                           \n",
+       "103.IE2 -7.901377                                                                                                           \n",
+       "103.BP1 -7.781139                                                                                                           \n",
+       "103.VC2 -7.825645                                                                                                           \n",
+       "        k__Bacteria.p__Acidobacteria.c__Solibacteres.o__Solibacterales.f__Solibacteraceae.g__Candidatus.Solibacter.s__\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.892452                                                                                                     \n",
+       "103.SR3 -7.942718                                                                                                     \n",
+       "103.IE2 -7.901377                                                                                                     \n",
+       "103.BP1 -7.781139                                                                                                     \n",
+       "103.VC2 -7.825645                                                                                                     \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__\n",
+       "103.CA2 -7.828436                                                                                                  \n",
+       "103.CO3 -7.892452                                                                                                  \n",
+       "103.SR3 -7.942718                                                                                                  \n",
+       "103.IE2 -7.901377                                                                                                  \n",
+       "103.BP1 -7.781139                                                                                                  \n",
+       "103.VC2 -7.825645                                                                                                  \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Nocardioidaceae.g__Nocardioides.s__\n",
+       "103.CA2 -7.828436                                                                                                \n",
+       "103.CO3 -7.892452                                                                                                \n",
+       "103.SR3 -7.942718                                                                                                \n",
+       "103.IE2 -7.901377                                                                                                \n",
+       "103.BP1 -7.781139                                                                                                \n",
+       "103.VC2 -7.825645                                                                                                \n",
+       "        k__Bacteria.p__Bacteroidetes.c__Flavobacteriia.o__Flavobacteriales.f__Flavobacteriaceae.g__Flavobacterium.s__\n",
+       "103.CA2 -7.828436                                                                                                    \n",
+       "103.CO3 -7.892452                                                                                                    \n",
+       "103.SR3 -7.942718                                                                                                    \n",
+       "103.IE2 -7.901377                                                                                                    \n",
+       "103.BP1 -7.781139                                                                                                    \n",
+       "103.VC2 -7.825645                                                                                                    \n",
+       "        k__Bacteria.p__Firmicutes.c__Clostridia.o__Clostridiales.f__Peptococcaceae.g__Desulfotomaculum.s__\n",
+       "103.CA2 -7.828436                                                                                         \n",
+       "103.CO3 -7.892452                                                                                         \n",
+       "103.SR3 -7.942718                                                                                         \n",
+       "103.IE2 -7.901377                                                                                         \n",
+       "103.BP1 -7.781139                                                                                         \n",
+       "103.VC2 -7.825645                                                                                         \n",
+       "        k__Bacteria.p__Acidobacteria.c__Acidobacteriia.o__Acidobacteriales.f__Koribacteraceae.g__Candidatus.Koribacter.s__\n",
+       "103.CA2 -7.828436                                                                                                         \n",
+       "103.CO3 -7.892452                                                                                                         \n",
+       "103.SR3 -7.942718                                                                                                         \n",
+       "103.IE2 -7.901377                                                                                                         \n",
+       "103.BP1 -7.781139                                                                                                         \n",
+       "103.VC2 -7.825645                                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Methylibium.s__\n",
+       "103.CA2 -7.828436                                                                                                  \n",
+       "103.CO3 -7.892452                                                                                                  \n",
+       "103.SR3 -7.942718                                                                                                  \n",
+       "103.IE2 -7.901377                                                                                                  \n",
+       "103.BP1 -7.781139                                                                                                  \n",
+       "103.VC2 -6.439350                                                                                                  \n",
+       "        ...\n",
+       "103.CA2 ...\n",
+       "103.CO3 ...\n",
+       "103.SR3 ...\n",
+       "103.IE2 ...\n",
+       "103.BP1 ...\n",
+       "103.VC2 ...\n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Rhodoferax.s__.4\n",
+       "103.CA2 -7.828436                                                                                                   \n",
+       "103.CO3 -7.892452                                                                                                   \n",
+       "103.SR3 -7.942718                                                                                                   \n",
+       "103.IE2 -7.901377                                                                                                   \n",
+       "103.BP1 -7.781139                                                                                                   \n",
+       "103.VC2 -7.825645                                                                                                   \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Dokdonella.s__.9\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.249570                                                                                                      \n",
+       "103.IE2 -7.901377                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Lactobacillales.f__Streptococcaceae.g__Streptococcus.s__.9\n",
+       "103.CA2 -7.828436                                                                                         \n",
+       "103.CO3 -7.892452                                                                                         \n",
+       "103.SR3 -7.942718                                                                                         \n",
+       "103.IE2 -7.901377                                                                                         \n",
+       "103.BP1 -7.781139                                                                                         \n",
+       "103.VC2 -7.825645                                                                                         \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Methylophilales.f__Methylophilaceae.g__Methylotenera.s__mobilis.2\n",
+       "103.CA2 -7.828436                                                                                                               \n",
+       "103.CO3 -7.892452                                                                                                               \n",
+       "103.SR3 -7.942718                                                                                                               \n",
+       "103.IE2 -7.901377                                                                                                               \n",
+       "103.BP1 -7.781139                                                                                                               \n",
+       "103.VC2 -7.825645                                                                                                               \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Streptomycetaceae.g__Streptomyces.s__.44\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.892452                                                                                                     \n",
+       "103.SR3 -7.942718                                                                                                     \n",
+       "103.IE2 -7.901377                                                                                                     \n",
+       "103.BP1 -7.781139                                                                                                     \n",
+       "103.VC2 -7.825645                                                                                                     \n",
+       "        k__Bacteria.p__Proteobacteria.c__Gammaproteobacteria.o__Xanthomonadales.f__Xanthomonadaceae.g__Luteimonas.s__.2\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.942718                                                                                                      \n",
+       "103.IE2 -7.901377                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Proteobacteria.c__Alphaproteobacteria.o__Rhizobiales.f__Hyphomicrobiaceae.g__Rhodoplanes.s__.142\n",
+       "103.CA2 -7.828436                                                                                                      \n",
+       "103.CO3 -7.892452                                                                                                      \n",
+       "103.SR3 -7.942718                                                                                                      \n",
+       "103.IE2 -6.515083                                                                                                      \n",
+       "103.BP1 -7.781139                                                                                                      \n",
+       "103.VC2 -7.825645                                                                                                      \n",
+       "        k__Bacteria.p__Firmicutes.c__Bacilli.o__Bacillales.f__Planococcaceae.g__Solibacillus.s__.3\n",
+       "103.CA2 -7.828436                                                                                 \n",
+       "103.CO3 -7.892452                                                                                 \n",
+       "103.SR3 -7.942718                                                                                 \n",
+       "103.IE2 -7.901377                                                                                 \n",
+       "103.BP1 -7.781139                                                                                 \n",
+       "103.VC2 -7.825645                                                                                 \n",
+       "        k__Bacteria.p__Proteobacteria.c__Betaproteobacteria.o__Burkholderiales.f__Comamonadaceae.g__Ramlibacter.s__.9\n",
+       "103.CA2 -7.828436                                                                                                    \n",
+       "103.CO3 -7.892452                                                                                                    \n",
+       "103.SR3 -7.249570                                                                                                    \n",
+       "103.IE2 -7.901377                                                                                                    \n",
+       "103.BP1 -7.781139                                                                                                    \n",
+       "103.VC2 -7.825645                                                                                                    \n",
+       "        k__Bacteria.p__Actinobacteria.c__Actinobacteria.o__Actinomycetales.f__Mycobacteriaceae.g__Mycobacterium.s__.28\n",
+       "103.CA2 -7.828436                                                                                                     \n",
+       "103.CO3 -7.199305                                                                                                     \n",
+       "103.SR3 -7.249570                                                                                                     \n",
+       "103.IE2 -7.208230                                                                                                     \n",
+       "103.BP1 -7.087991                                                                                                     \n",
+       "103.VC2 -7.132498                                                                                                     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# add pesudo count 0.5\n",
+    "x = count # preprossed done already\n",
+    "x[x == 0] <- 0.5\n",
+    "x <- x/rowSums(x) # relative abundance\n",
+    "taxa <- log(x)\n",
+    "dim(taxa)\n",
+    "head(taxa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>'BarcodeSequence'</li>\n",
+       "\t<li>'LinkerPrimerSequence'</li>\n",
+       "\t<li>'barcode_read_group_tag'</li>\n",
+       "\t<li>'dna_extracted_prep'</li>\n",
+       "\t<li>'experiment_alias'</li>\n",
+       "\t<li>'experiment_center'</li>\n",
+       "\t<li>'experiment_design_description'</li>\n",
+       "\t<li>'experiment_title'</li>\n",
+       "\t<li>'instrument_name'</li>\n",
+       "\t<li>'key_seq'</li>\n",
+       "\t<li>'library_construction_protocol'</li>\n",
+       "\t<li>'linker'</li>\n",
+       "\t<li>'pcr_primers'</li>\n",
+       "\t<li>'physical_specimen_remaining_prep'</li>\n",
+       "\t<li>'platform'</li>\n",
+       "\t<li>'pool_member_name'</li>\n",
+       "\t<li>'pool_proportion'</li>\n",
+       "\t<li>'primer_read_group_tag'</li>\n",
+       "\t<li>'region'</li>\n",
+       "\t<li>'run_alias'</li>\n",
+       "\t<li>'run_center'</li>\n",
+       "\t<li>'run_date'</li>\n",
+       "\t<li>'run_prefix'</li>\n",
+       "\t<li>'samp_size'</li>\n",
+       "\t<li>'sample_center'</li>\n",
+       "\t<li>'sample_type_prep'</li>\n",
+       "\t<li>'sequencing_meth'</li>\n",
+       "\t<li>'study_center'</li>\n",
+       "\t<li>'study_ref'</li>\n",
+       "\t<li>'target_gene'</li>\n",
+       "\t<li>'target_subfragment'</li>\n",
+       "\t<li>'altitude'</li>\n",
+       "\t<li>'annual_season_precpt'</li>\n",
+       "\t<li>'annual_season_temp'</li>\n",
+       "\t<li>'anonymized_name'</li>\n",
+       "\t<li>'assigned_from_geo'</li>\n",
+       "\t<li>'carb_nitro_ratio'</li>\n",
+       "\t<li>'cmin_rate'</li>\n",
+       "\t<li>'collection_date'</li>\n",
+       "\t<li>'common_name'</li>\n",
+       "\t<li>'country'</li>\n",
+       "\t<li>'depth'</li>\n",
+       "\t<li>'dna_extracted'</li>\n",
+       "\t<li>'elevation'</li>\n",
+       "\t<li>'env_biome'</li>\n",
+       "\t<li>'env_feature'</li>\n",
+       "\t<li>'env_matter'</li>\n",
+       "\t<li>'host_subject_id'</li>\n",
+       "\t<li>'latitude'</li>\n",
+       "\t<li>'longitude'</li>\n",
+       "\t<li>'ph'</li>\n",
+       "\t<li>'physical_specimen_remaining'</li>\n",
+       "\t<li>'project_name'</li>\n",
+       "\t<li>'public'</li>\n",
+       "\t<li>'sample_type'</li>\n",
+       "\t<li>'silt_clay'</li>\n",
+       "\t<li>'soil_moisture_deficit'</li>\n",
+       "\t<li>'soil_type'</li>\n",
+       "\t<li>'specific_location'</li>\n",
+       "\t<li>'taxon_id'</li>\n",
+       "\t<li>'texture'</li>\n",
+       "\t<li>'title'</li>\n",
+       "\t<li>'tot_org_carb'</li>\n",
+       "\t<li>'tot_org_nitro'</li>\n",
+       "\t<li>'Description'</li>\n",
+       "\t<li>'ph2'</li>\n",
+       "\t<li>'ph3'</li>\n",
+       "\t<li>'ph4'</li>\n",
+       "\t<li>'ph_rounded'</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'BarcodeSequence'\n",
+       "\\item 'LinkerPrimerSequence'\n",
+       "\\item 'barcode\\_read\\_group\\_tag'\n",
+       "\\item 'dna\\_extracted\\_prep'\n",
+       "\\item 'experiment\\_alias'\n",
+       "\\item 'experiment\\_center'\n",
+       "\\item 'experiment\\_design\\_description'\n",
+       "\\item 'experiment\\_title'\n",
+       "\\item 'instrument\\_name'\n",
+       "\\item 'key\\_seq'\n",
+       "\\item 'library\\_construction\\_protocol'\n",
+       "\\item 'linker'\n",
+       "\\item 'pcr\\_primers'\n",
+       "\\item 'physical\\_specimen\\_remaining\\_prep'\n",
+       "\\item 'platform'\n",
+       "\\item 'pool\\_member\\_name'\n",
+       "\\item 'pool\\_proportion'\n",
+       "\\item 'primer\\_read\\_group\\_tag'\n",
+       "\\item 'region'\n",
+       "\\item 'run\\_alias'\n",
+       "\\item 'run\\_center'\n",
+       "\\item 'run\\_date'\n",
+       "\\item 'run\\_prefix'\n",
+       "\\item 'samp\\_size'\n",
+       "\\item 'sample\\_center'\n",
+       "\\item 'sample\\_type\\_prep'\n",
+       "\\item 'sequencing\\_meth'\n",
+       "\\item 'study\\_center'\n",
+       "\\item 'study\\_ref'\n",
+       "\\item 'target\\_gene'\n",
+       "\\item 'target\\_subfragment'\n",
+       "\\item 'altitude'\n",
+       "\\item 'annual\\_season\\_precpt'\n",
+       "\\item 'annual\\_season\\_temp'\n",
+       "\\item 'anonymized\\_name'\n",
+       "\\item 'assigned\\_from\\_geo'\n",
+       "\\item 'carb\\_nitro\\_ratio'\n",
+       "\\item 'cmin\\_rate'\n",
+       "\\item 'collection\\_date'\n",
+       "\\item 'common\\_name'\n",
+       "\\item 'country'\n",
+       "\\item 'depth'\n",
+       "\\item 'dna\\_extracted'\n",
+       "\\item 'elevation'\n",
+       "\\item 'env\\_biome'\n",
+       "\\item 'env\\_feature'\n",
+       "\\item 'env\\_matter'\n",
+       "\\item 'host\\_subject\\_id'\n",
+       "\\item 'latitude'\n",
+       "\\item 'longitude'\n",
+       "\\item 'ph'\n",
+       "\\item 'physical\\_specimen\\_remaining'\n",
+       "\\item 'project\\_name'\n",
+       "\\item 'public'\n",
+       "\\item 'sample\\_type'\n",
+       "\\item 'silt\\_clay'\n",
+       "\\item 'soil\\_moisture\\_deficit'\n",
+       "\\item 'soil\\_type'\n",
+       "\\item 'specific\\_location'\n",
+       "\\item 'taxon\\_id'\n",
+       "\\item 'texture'\n",
+       "\\item 'title'\n",
+       "\\item 'tot\\_org\\_carb'\n",
+       "\\item 'tot\\_org\\_nitro'\n",
+       "\\item 'Description'\n",
+       "\\item 'ph2'\n",
+       "\\item 'ph3'\n",
+       "\\item 'ph4'\n",
+       "\\item 'ph\\_rounded'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'BarcodeSequence'\n",
+       "2. 'LinkerPrimerSequence'\n",
+       "3. 'barcode_read_group_tag'\n",
+       "4. 'dna_extracted_prep'\n",
+       "5. 'experiment_alias'\n",
+       "6. 'experiment_center'\n",
+       "7. 'experiment_design_description'\n",
+       "8. 'experiment_title'\n",
+       "9. 'instrument_name'\n",
+       "10. 'key_seq'\n",
+       "11. 'library_construction_protocol'\n",
+       "12. 'linker'\n",
+       "13. 'pcr_primers'\n",
+       "14. 'physical_specimen_remaining_prep'\n",
+       "15. 'platform'\n",
+       "16. 'pool_member_name'\n",
+       "17. 'pool_proportion'\n",
+       "18. 'primer_read_group_tag'\n",
+       "19. 'region'\n",
+       "20. 'run_alias'\n",
+       "21. 'run_center'\n",
+       "22. 'run_date'\n",
+       "23. 'run_prefix'\n",
+       "24. 'samp_size'\n",
+       "25. 'sample_center'\n",
+       "26. 'sample_type_prep'\n",
+       "27. 'sequencing_meth'\n",
+       "28. 'study_center'\n",
+       "29. 'study_ref'\n",
+       "30. 'target_gene'\n",
+       "31. 'target_subfragment'\n",
+       "32. 'altitude'\n",
+       "33. 'annual_season_precpt'\n",
+       "34. 'annual_season_temp'\n",
+       "35. 'anonymized_name'\n",
+       "36. 'assigned_from_geo'\n",
+       "37. 'carb_nitro_ratio'\n",
+       "38. 'cmin_rate'\n",
+       "39. 'collection_date'\n",
+       "40. 'common_name'\n",
+       "41. 'country'\n",
+       "42. 'depth'\n",
+       "43. 'dna_extracted'\n",
+       "44. 'elevation'\n",
+       "45. 'env_biome'\n",
+       "46. 'env_feature'\n",
+       "47. 'env_matter'\n",
+       "48. 'host_subject_id'\n",
+       "49. 'latitude'\n",
+       "50. 'longitude'\n",
+       "51. 'ph'\n",
+       "52. 'physical_specimen_remaining'\n",
+       "53. 'project_name'\n",
+       "54. 'public'\n",
+       "55. 'sample_type'\n",
+       "56. 'silt_clay'\n",
+       "57. 'soil_moisture_deficit'\n",
+       "58. 'soil_type'\n",
+       "59. 'specific_location'\n",
+       "60. 'taxon_id'\n",
+       "61. 'texture'\n",
+       "62. 'title'\n",
+       "63. 'tot_org_carb'\n",
+       "64. 'tot_org_nitro'\n",
+       "65. 'Description'\n",
+       "66. 'ph2'\n",
+       "67. 'ph3'\n",
+       "68. 'ph4'\n",
+       "69. 'ph_rounded'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"BarcodeSequence\"                  \"LinkerPrimerSequence\"            \n",
+       " [3] \"barcode_read_group_tag\"           \"dna_extracted_prep\"              \n",
+       " [5] \"experiment_alias\"                 \"experiment_center\"               \n",
+       " [7] \"experiment_design_description\"    \"experiment_title\"                \n",
+       " [9] \"instrument_name\"                  \"key_seq\"                         \n",
+       "[11] \"library_construction_protocol\"    \"linker\"                          \n",
+       "[13] \"pcr_primers\"                      \"physical_specimen_remaining_prep\"\n",
+       "[15] \"platform\"                         \"pool_member_name\"                \n",
+       "[17] \"pool_proportion\"                  \"primer_read_group_tag\"           \n",
+       "[19] \"region\"                           \"run_alias\"                       \n",
+       "[21] \"run_center\"                       \"run_date\"                        \n",
+       "[23] \"run_prefix\"                       \"samp_size\"                       \n",
+       "[25] \"sample_center\"                    \"sample_type_prep\"                \n",
+       "[27] \"sequencing_meth\"                  \"study_center\"                    \n",
+       "[29] \"study_ref\"                        \"target_gene\"                     \n",
+       "[31] \"target_subfragment\"               \"altitude\"                        \n",
+       "[33] \"annual_season_precpt\"             \"annual_season_temp\"              \n",
+       "[35] \"anonymized_name\"                  \"assigned_from_geo\"               \n",
+       "[37] \"carb_nitro_ratio\"                 \"cmin_rate\"                       \n",
+       "[39] \"collection_date\"                  \"common_name\"                     \n",
+       "[41] \"country\"                          \"depth\"                           \n",
+       "[43] \"dna_extracted\"                    \"elevation\"                       \n",
+       "[45] \"env_biome\"                        \"env_feature\"                     \n",
+       "[47] \"env_matter\"                       \"host_subject_id\"                 \n",
+       "[49] \"latitude\"                         \"longitude\"                       \n",
+       "[51] \"ph\"                               \"physical_specimen_remaining\"     \n",
+       "[53] \"project_name\"                     \"public\"                          \n",
+       "[55] \"sample_type\"                      \"silt_clay\"                       \n",
+       "[57] \"soil_moisture_deficit\"            \"soil_type\"                       \n",
+       "[59] \"specific_location\"                \"taxon_id\"                        \n",
+       "[61] \"texture\"                          \"title\"                           \n",
+       "[63] \"tot_org_carb\"                     \"tot_org_nitro\"                   \n",
+       "[65] \"Description\"                      \"ph2\"                             \n",
+       "[67] \"ph3\"                              \"ph4\"                             \n",
+       "[69] \"ph_rounded\"                      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colnames(demo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "89"
+      ],
+      "text/latex": [
+       "89"
+      ],
+      "text/markdown": [
+       "89"
+      ],
+      "text/plain": [
+       "[1] 89"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>8.02</li>\n",
+       "\t<li>6.02</li>\n",
+       "\t<li>6.95</li>\n",
+       "\t<li>5.52</li>\n",
+       "\t<li>7.53</li>\n",
+       "\t<li>5.99</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 8.02\n",
+       "\\item 6.02\n",
+       "\\item 6.95\n",
+       "\\item 5.52\n",
+       "\\item 7.53\n",
+       "\\item 5.99\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 8.02\n",
+       "2. 6.02\n",
+       "3. 6.95\n",
+       "4. 5.52\n",
+       "5. 7.53\n",
+       "6. 5.99\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 8.02 6.02 6.95 5.52 7.53 5.99"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# metadata\n",
+    "mf <- read.csv(\"../88soils/88soils_modified_metadata.txt\", sep='\\t', row.names=1)\n",
+    "y <- mf$ph[match(rownames(count), rownames(mf))]\n",
+    "length(y)\n",
+    "head(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'matrix'"
+      ],
+      "text/latex": [
+       "'matrix'"
+      ],
+      "text/markdown": [
+       "'matrix'"
+      ],
+      "text/plain": [
+       "[1] \"matrix\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "'numeric'"
+      ],
+      "text/latex": [
+       "'numeric'"
+      ],
+      "text/markdown": [
+       "'numeric'"
+      ],
+      "text/plain": [
+       "[1] \"numeric\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check datatype\n",
+    "class(taxa); class(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save processed data\n",
+    "save(y, taxa, file='../88soils/soils_ph.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/2.3 soils_results_cts.ipynb b/data_application/notebooks_application/2.3 soils_results_cts.ipynb
new file mode 100755
index 0000000..34fdcc6
--- /dev/null
+++ b/data_application/notebooks_application/2.3 soils_results_cts.ipynb	
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Soil microbiome data application results for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### method comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/results_cts/soils_ph_compLasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_elnet.RData')\n",
+    "load('../88soils/results_cts/soils_ph_lasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.39</li>\n",
+       "\t<li>0.46</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.39\n",
+       "\\item 0.46\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.39\n",
+       "2. 0.46\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.39 0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_compLasso$stab_index, out_compLasso$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.31</li>\n",
+       "\t<li>0.34</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.31\n",
+       "\\item 0.34\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.31\n",
+       "2. 0.34\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.31 0.34"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_lasso$stab_index, out_lasso$MSE_mean) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.16</li>\n",
+       "\t<li>0.23</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.16\n",
+       "\\item 0.23\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.16\n",
+       "2. 0.23\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.16 0.23"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_elnet$stab_index, out_elnet$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>0.04</li>\n",
+       "\t<li>0.26</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 0.04\n",
+       "\\item 0.26\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 0.04\n",
+       "2. 0.26\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 0.04 0.26"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c(out_rf$stab_index, out_rf$MSE_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>mse</th><th scope=col>stability</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>soil_88  </td><td>lasso    </td><td>0.34     </td><td>0.31     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>elent    </td><td>0.23     </td><td>0.16     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>rf       </td><td>0.26     </td><td>0.04     </td></tr>\n",
+       "\t<tr><td>soil_88  </td><td>compLasso</td><td>0.46     </td><td>0.39     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " dataset & method & mse & stability\\\\\n",
+       "\\hline\n",
+       "\t soil\\_88 & lasso     & 0.34      & 0.31     \\\\\n",
+       "\t soil\\_88 & elent     & 0.23      & 0.16     \\\\\n",
+       "\t soil\\_88 & rf        & 0.26      & 0.04     \\\\\n",
+       "\t soil\\_88 & compLasso & 0.46      & 0.39     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| dataset | method | mse | stability |\n",
+       "|---|---|---|---|\n",
+       "| soil_88   | lasso     | 0.34      | 0.31      |\n",
+       "| soil_88   | elent     | 0.23      | 0.16      |\n",
+       "| soil_88   | rf        | 0.26      | 0.04      |\n",
+       "| soil_88   | compLasso | 0.46      | 0.39      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    mse  stability\n",
+       "1 soil_88 lasso     0.34 0.31     \n",
+       "2 soil_88 elent     0.23 0.16     \n",
+       "3 soil_88 rf        0.26 0.04     \n",
+       "4 soil_88 compLasso 0.46 0.39     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# combine and export results\n",
+    "soil_88 = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(soil_88) = c('dataset', 'method', 'mse', 'stability')\n",
+    "soil_88$dataset = 'soil_88'\n",
+    "soil_88$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "soil_88$mse = c(out_lasso$MSE_mean, out_elnet$MSE_mean, out_rf$MSE_mean, out_compLasso$MSE_mean)\n",
+    "soil_88$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_compLasso$stab_index)\n",
+    "soil_88"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load('../88soils/results_cts/soils_ph_boot_compLasso.RData')\n",
+    "load('../88soils/results_cts/soils_ph_boot_rf.RData')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.3595"
+      ],
+      "text/latex": [
+       "0.3595"
+      ],
+      "text/markdown": [
+       "0.3595"
+      ],
+      "text/plain": [
+       "[1] 0.3595"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.28</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.44</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.28\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.44\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.2897.5%\n",
+       ":   0.44\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.28  0.44 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.0811150164524993"
+      ],
+      "text/latex": [
+       "0.0811150164524993"
+      ],
+      "text/markdown": [
+       "0.0811150164524993"
+      ],
+      "text/plain": [
+       "[1] 0.08111502"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.283912044397921</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.945808111396662</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.283912044397921\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.945808111396662\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.28391204439792197.5%\n",
+       ":   0.945808111396662\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.2839120  0.9458081 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_mse = (unlist(boot_compLasso$MSE_list) - unlist(boot_rf$MSE_list)) # use all 100*100 MSEs\n",
+    "mean(diff_mse)\n",
+    "quantile(diff_mse, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on MSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data_application/notebooks_application/3_applications_bin_results.ipynb b/data_application/notebooks_application/3_applications_bin_results.ipynb
new file mode 100644
index 0000000..fa46c90
--- /dev/null
+++ b/data_application/notebooks_application/3_applications_bin_results.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results for real microbiome data applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/data_applications/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/BMI_binary_GenCompLasso.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>bmi_gut</td><td>lasso    </td><td>0.63</td><td>0.14</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>elent    </td><td>0.78</td><td>0.19</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>rf       </td><td>1.00</td><td>0.01</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>compLasso</td><td>0.85</td><td>0.29</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t bmi\\_gut & lasso     & 0.63 & 0.14\\\\\n",
+       "\t bmi\\_gut & elent     & 0.78 & 0.19\\\\\n",
+       "\t bmi\\_gut & rf        & 1.00 & 0.01\\\\\n",
+       "\t bmi\\_gut & compLasso & 0.85 & 0.29\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| bmi_gut | lasso     | 0.63 | 0.14 |\n",
+       "| bmi_gut | elent     | 0.78 | 0.19 |\n",
+       "| bmi_gut | rf        | 1.00 | 0.01 |\n",
+       "| bmi_gut | compLasso | 0.85 | 0.29 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 bmi_gut lasso     0.63 0.14     \n",
+       "2 bmi_gut elent     0.78 0.19     \n",
+       "3 bmi_gut rf        1.00 0.01     \n",
+       "4 bmi_gut compLasso 0.85 0.29     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmi_gut = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(bmi_gut) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "bmi_gut$dataset = 'bmi_gut'\n",
+    "bmi_gut$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "bmi_gut$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "bmi_gut$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "bmi_gut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/BMI_binary_boot_rf.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2997"
+      ],
+      "text/latex": [
+       "0.2997"
+      ],
+      "text/markdown": [
+       "0.2997"
+      ],
+      "text/plain": [
+       "[1] 0.2997"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.11</dd><dt>97.5%</dt><dd>0.41525</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.11\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.41525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1197.5%\n",
+       ":   0.41525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.11000 0.41525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0942798529453605"
+      ],
+      "text/latex": [
+       "-0.0942798529453605"
+      ],
+      "text/markdown": [
+       "-0.0942798529453605"
+      ],
+      "text/plain": [
+       "[1] -0.09427985"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.19020132947925</dd><dt>97.5%</dt><dd>-0.020967051775781</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.19020132947925\n",
+       "\\item[97.5\\textbackslash{}\\%] -0.020967051775781\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.1902013294792597.5%\n",
+       ":   -0.020967051775781\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.19020133 -0.02096705 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "### CI doesn't contain zero: compLasso is significantly different from RF based on ROC\n",
+    "### although very close to zero (now ROC has similar scale as Stability, Stability still better differentiation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 88 soils dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/soils_binary_ph_GenCompLasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>soil_88</td><td>lasso    </td><td>0.90</td><td>0.28</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>elent    </td><td>0.94</td><td>0.32</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>rf       </td><td>1.00</td><td>0.03</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>compLasso</td><td>0.96</td><td>0.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t soil\\_88 & lasso     & 0.90 & 0.28\\\\\n",
+       "\t soil\\_88 & elent     & 0.94 & 0.32\\\\\n",
+       "\t soil\\_88 & rf        & 1.00 & 0.03\\\\\n",
+       "\t soil\\_88 & compLasso & 0.96 & 0.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| soil_88 | lasso     | 0.90 | 0.28 |\n",
+       "| soil_88 | elent     | 0.94 | 0.32 |\n",
+       "| soil_88 | rf        | 1.00 | 0.03 |\n",
+       "| soil_88 | compLasso | 0.96 | 0.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 soil_88 lasso     0.90 0.28     \n",
+       "2 soil_88 elent     0.94 0.32     \n",
+       "3 soil_88 rf        1.00 0.03     \n",
+       "4 soil_88 compLasso 0.96 0.46     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "soil_88 = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(soil_88) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "soil_88$dataset = 'soil_88'\n",
+    "soil_88$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "soil_88$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "soil_88$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "soil_88"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_rf.RData'))\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.4323"
+      ],
+      "text/latex": [
+       "0.4323"
+      ],
+      "text/markdown": [
+       "0.4323"
+      ],
+      "text/plain": [
+       "[1] 0.4323"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.37</dd><dt>97.5%</dt><dd>0.5</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.37\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.5\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.3797.5%\n",
+       ":   0.5\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.37  0.50 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0247120820917325"
+      ],
+      "text/latex": [
+       "-0.0247120820917325"
+      ],
+      "text/markdown": [
+       "-0.0247120820917325"
+      ],
+      "text/plain": [
+       "[1] -0.02471208"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.0838574423480084</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.0838574423480084\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.083857442348008497.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.08385744  0.00000000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on ROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
+       "-0.24949 -0.04206 -0.02250 -0.02471  0.00000  0.03769 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(diff_ROC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/ms_writing_figs/.DS_Store b/ms_writing_figs/.DS_Store
deleted file mode 100755
index 5008ddf..0000000
Binary files a/ms_writing_figs/.DS_Store and /dev/null differ
diff --git a/simulations/.DS_Store b/simulations/.DS_Store
deleted file mode 100755
index 33cb786..0000000
Binary files a/simulations/.DS_Store and /dev/null differ
diff --git a/simulations/code_sim_bin/.Rhistory b/simulations/code_sim_bin/.Rhistory
new file mode 100644
index 0000000..b24e8d4
--- /dev/null
+++ b/simulations/code_sim_bin/.Rhistory
@@ -0,0 +1,58 @@
+6000.02/200.89
+512.18+438.53+430.93+403.07+161.12+94.60+72.56+24.66+0.60
+2138.25-1480.6
+85.02+243.51
+10+85.53+55.85+26.47+7.92+107.19+33.69
+110+85.02+250
+2138.25-143.32
+2188.21
+2188.21/201.16
+2188.21/201.1
+2188.21/201.2
+175.21
+175.21/87.6
+2806.70+1245+444.69
+7479.45-1121.20-4496.39
+5000-1625*2
+813.98+1750
+2940*2+2560
+4500+1500+1000
+1440*12
+x <- "hello xxx other stuff"
+x
+sub(" xxx.*", "", x)
+x <- "hello (xxx) other stuff"
+x
+sub(" (.*", "", x)
+sub(" (*", "", x)
+x.split(' ')
+fake_variable <- ' Country name is (FR)'
+modified_fake_variable <- stringr::str_extract(string = fake_variable,
+pattern = "(?<=\\().*(?=\\))")
+modified_fake_variable
+modified_fake_variable <- stringr::str_extract(string = fake_variable,
+pattern = "(?>=\\()")
+modified_fake_variable
+sub("\\|.*", "", x)
+x
+sub("\\(.*", "", x)
+5.5*3+6*2
+174.21/87.11
+174.21/87
+761.62/12
+setwd("~/Documents/Stability/github_code_history/stability-analyses/data_application/code_applications/code_cts")
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('../../code_method/stab_data_applications.R')
+source('../../code_method/bootstrap_test_compLasso_rf.R')
+#####################################
+##### data preparation ##############
+#####################################
+load('../../88soils/88soils_genus_table.RData')
+source('../../../code_method/cv_method.R')
+load('../../88soils/88soils_genus_table.RData')
+setwd("~/Documents/Stability/github_code_history/stability-analyses/simulations/code_sim_bin")
+source('../../code_method/cv_method_binary_update.R')
+source('../../../code_method/cv_method_binary_update.R')
+source('../../../code_method/getStability.R')
+source('../../code_method/getStability.R')
diff --git a/simulations/code_sim_bin/block_results_binary_update.R b/simulations/code_sim_bin/block_results_binary_update.R
new file mode 100644
index 0000000..90817d8
--- /dev/null
+++ b/simulations/code_sim_bin/block_results_binary_update.R
@@ -0,0 +1,102 @@
+#####################################################################################
+### run all methods on simulated data with block correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+library(FSA)
+source('../../code_method/getStability.R')
+source('../../code_method/cv_method_binary_update.R')
+source('cv_sim_apply_binary_update.R')
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+## correlation strength
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+#------------------------
+# compare methods
+#------------------------
+i <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+###################
+#### Lasso ########
+###################
+print('Lasso')
+file_name = files[[i]]
+results_block_lasso = sim_evaluate_cv(sim_file=file_name, method='lasso', family='binomial')
+
+# when i = 73, sim_idx = 4, ROC cannot be calculated as y.test has all 0s
+# thus change seed number to avoid this extreme example
+# results_block_lasso = sim_evaluate_cv(sim_file=file_name, method='lasso', 
+#                                      family='binomial', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/block_Lasso_binary_', i, '.RData'), results_block_lasso, file_name)
+
+
+#########################
+#### Elastic Net ########
+#########################
+print('Elnet')
+file_name = files[[i]]
+results_block_elnet= sim_evaluate_cv(sim_file=file_name, method='elnet', family='binomial')
+
+when i = 73, sim_idx = 4, ROC cannot be calculated as y.test has all 0s
+thus change seed number to avoid this extreme example
+results_block_elnet= sim_evaluate_cv(sim_file=file_name, method='elnet', 
+                                     family='binomial', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/block_Elnet_binary_', i, '.RData'), results_block_elnet, file_name)
+    
+############################
+#### Random Forests ########
+############################
+print('Random Forests')
+file_name = files[[i]]
+results_block_rf = sim_evaluate_cv(sim_file=file_name, method='RF')
+
+when i = 73, sim_idx = 4, ROC cannot be calculated as y.test has all 0s
+thus change seed number to avoid this extreme example
+results_block_rf = sim_evaluate_cv(sim_file=file_name, method='RF', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/block_RF_binary_', i, '.RData'), results_block_rf, file_name)
+
+# #########################################################
+# #### Generalized compositional Lasso ########
+# #########################################################
+print('Generalized Compositional Lasso')
+file_name = files[[i]]
+results_block_GenCompLasso = sim_evaluate_cv(sim_file=file_name,
+                                              method='GenCompLasso')
+
+save(file=paste0(dir, '/binary_update/block_GenCompLasso_binary_', i, '.RData'),
+     results_block_GenCompLasso, file_name)
+
+results_block_GenCompLasso_dataSplit = sim_evaluate_cv(sim_file=file_name,
+                                                      method='GenCompLasso',
+                                                      data.split=TRUE)
+
+# when i = 73, sim_idx = 4, ROC cannot be calculated as y.test has all 0s
+# thus change seed number to avoid this extreme example
+# results_block_GenCompLasso_dataSplit = sim_evaluate_cv(sim_file=file_name,
+#                                                       method='GenCompLasso',
+#                                                       data.split=TRUE, seednum=5231)
+
diff --git a/simulations/code_sim_bin/boot_sim_binary.R b/simulations/code_sim_bin/boot_sim_binary.R
new file mode 100644
index 0000000..92167e0
--- /dev/null
+++ b/simulations/code_sim_bin/boot_sim_binary.R
@@ -0,0 +1,46 @@
+#-----------------------------------------------------------------------------------------------
+# hypothesis testing based on boostrapped confidence interval in selected simulation scenarios
+#-----------------------------------------------------------------------------------------------
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+library(FSA)
+source('../../code_method/cv_method_binary_update.R')
+source('../../code_method/getStability.R')
+source('../../code_method/bootstrap_test_compLasso_rf_binary.R')
+
+M <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+if (M == 1){
+    print('toeplitz: random forests')
+    toe_rf = boot_stab_sim(num_boot=100, sim_file= paste0(dir, '/sim_toeplitz_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'RF', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+    save(toe_rf, file=paste0(dir, '/binary_update/boot_toe_RF_binary.RData'))
+}else if (M == 2){
+    print('toeplitz: generalized compositional lasso')
+    toe_genCompLasso = boot_stab_sim(num_boot=100, sim_file= paste0(dir, '/sim_toeplitz_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'GenCompLasso', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   lambda.coda=seq(0.1, 0.2, 0.01))
+    save(toe_genCompLasso, file=paste0(dir, '/binary_update/boot_toe_genCompLasso_binary.RData'))
+}else if (M == 3){
+    print('block: random forests')
+    block_rf = boot_stab_sim(num_boot=100, sim_file= paste0(dir, '/sim_block_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'RF', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+
+    save(block_rf, file=paste0(dir, '/binary_update/boot_block_RF_binary.RData'))
+}else if (M == 4){
+    print('block: generalized compositional lasso')
+    block_genCompLasso = boot_stab_sim(num_boot=100, sim_file= paste0(dir, '/sim_block_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'GenCompLasso', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   lambda.coda=seq(0.1, 0.2, 0.01))
+    save(block_genCompLasso, file=paste0(dir, '/binary_update/boot_block_genCompLasso_binary.RData'))
+}    
+
+
+
+
+
+
diff --git a/simulations/code_sim_bin/cv_sim_apply_binary_update.R b/simulations/code_sim_bin/cv_sim_apply_binary_update.R
new file mode 100644
index 0000000..855c352
--- /dev/null
+++ b/simulations/code_sim_bin/cv_sim_apply_binary_update.R
@@ -0,0 +1,82 @@
+#######################################################################################
+### apply different feature selection methods to simulated data #######################
+#######################################################################################
+
+# library(FSA) # for se()
+# source('cv_method_binary_update.R') 
+# source('getStability.R')
+
+sim_evaluate_cv = function(sim_file, method, seednum=31, ratio.training=0.8,
+                           fold.cv=10, family='binomial',
+                           lambda.grid=exp(seq(-4, -2, 0.2)), 
+                           alpha.grid=seq(0.1, 0.9, 0.1), 
+	                       mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05,
+                           method.perm='altmann', 
+                           lambda.coda=seq(0.1, 0.2, 0.01), data.split=FALSE){
+	# load simulated data
+	load(sim_file, dat <- new.env())
+
+	idx.start = 1; idx.stop = 100 # 100 repetitions for each simulated scenario
+	rou = dat$sim_array[[1]]$rou # rou, n, p are same across all repetitions
+	n = dat$sim_array[[1]]$n
+	p = dat$sim_array[[1]]$p
+
+	# evaluating different methods	
+	fp = fn = roc = NULL
+	stability.table = matrix(rep(0, (idx.stop - idx.start + 1) * p), ncol=p)
+	colnames(stability.table) = paste('V', seq(1:p), sep='')
+
+	for (i in idx.start:idx.stop){ 
+        print(paste0('simulation idx: ', i))
+	    sub = dat$sim_array[[i]]
+	    coef = sub$beta
+	    coef.true = which(coef != 0)
+	    y_binary = as.factor(ifelse(sub$Y >= median(sub$Y), 1, 0))
+	    
+		if (method == 'lasso'){
+			result = lasso_cv(y=y_binary, datx=sub$Z, seednum=seednum,family=family, lambda.choice='lambda.1se',
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    select.features = result$coef.chosen 
+		} else if (method == 'elnet'){
+		    result = elnet_cv(y=y_binary, datx=sub$Z, seednum=seednum,family=family, alpha.grid=alpha.grid,
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    select.features = result$coef.chosen 
+		} else if (method == 'RF'){
+		    result = randomForest_cv(y=y_binary, datx=sub$Z, seednum=seednum, fold.cv=fold.cv, 
+		                             num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr, method.perm=method.perm)
+		    select.features = result$coef.chosen 
+		} else if (method == 'GenCompLasso'){
+            # note that generalized compositonal lasso do X to Z transformation within
+			result = gen_cons_lasso_cv (y=y_binary, datx=sub$X, seednum=seednum,
+                                        data.split=data.split,
+                                        ratio.training=ratio.training,
+                                        lambda.coda=lambda.coda)
+			select.features = result$coef.chosen
+		}
+
+	    # false positives: shouldn't be chosen but chosen
+	    fp = c(fp, length(setdiff(select.features, coef.true)))
+
+	    # false negatives: # should be chosen yet not
+	    fn = c(fn, length(setdiff(coef.true, select.features)))
+
+	    # classification error
+	    roc = c(roc, result$ROC)
+
+	    # stability table
+	    stability.table[i, select.features] = 1
+	}
+
+	# store results
+	FP = paste(mean(fp), '(', round(se(fp),2), ')')
+	FN = paste(mean(fn), '(', round(se(fn),2), ')')
+	ROC = paste(round(mean(roc, na.rm=T),2), '(', round(se(roc, na.rm=T),2), ')')
+	Stab = round(getStability(stability.table)$stability, 2)
+
+	results=list(rou=rou, n=n, p=p, FP=FP, FN=FN, ROC=ROC, Stab=Stab, 
+		         Stab.table=stability.table, FP.list=fp, FN.list=fn, ROC.list=roc)
+
+}
+
+
+
diff --git a/simulations/code_sim_bin/ind_results_binary_update.R b/simulations/code_sim_bin/ind_results_binary_update.R
new file mode 100644
index 0000000..478b295
--- /dev/null
+++ b/simulations/code_sim_bin/ind_results_binary_update.R
@@ -0,0 +1,72 @@
+#####################################################################################
+### run all methods on simulated data with independent correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+#dir = '../sim_data'
+
+library(FSA)
+source('../../code_method/cv_method_binary_update.R')
+source('../../code_method/getStability.R')
+source('cv_sim_apply_binary_update.R')
+
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+files = NULL
+for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))
+}
+
+#------------------------
+# compare methods
+#------------------------
+i <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+# ###################
+# #### Lasso ########
+# ###################
+print('Lasso')
+file_name = files[[i]]
+results_ind_lasso = sim_evaluate_cv(sim_file=file_name, method='lasso', family='binomial')
+
+save(file=paste0(dir, '/binary_update/ind_Lasso_binary_', i, '.RData'), results_ind_lasso, file_name)
+
+# # #########################
+# # #### Elastic Net ########
+# # #########################
+print('Elnet')
+file_name = files[[i]]
+results_ind_elnet = sim_evaluate_cv(sim_file=file_name, method='elnet', family='binomial')
+
+save(file=paste0(dir, '/binary_update/ind_Elnet_binary_', i, '.RData'), results_ind_elnet, file_name)
+
+# ############################
+# #### Random Forests ########
+# ############################
+print('Random Forests')
+file_name = files[[i]]
+results_ind_rf = sim_evaluate_cv(sim_file=file_name, method='RF')
+save(file=paste0(dir, '/binary_update/ind_RF_binary_', i, '.RData'), results_ind_rf, file_name)
+
+# #########################################################
+# #### Generalized compositional Lasso ########
+# #########################################################
+print('Generalized Compositional Lasso')
+file_name = files[[i]]
+results_ind_GenCompLasso = sim_evaluate_cv (sim_file=file_name, method='GenCompLasso')
+
+save(file=paste0(dir, '/binary_update/ind_GenCompLasso_binary_', i, '.RData'),
+     results_ind_GenCompLasso, file_name)
+
diff --git a/simulations/code_sim_bin/run_sim_bin.sh b/simulations/code_sim_bin/run_sim_bin.sh
new file mode 100644
index 0000000..f7327c8
--- /dev/null
+++ b/simulations/code_sim_bin/run_sim_bin.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+#PBS -N sim_stab
+#PBS -l walltime=500:00:00
+#PBS -l nodes=1:ppn=10
+#PBS -l mem=20gb
+#PBS -V
+#PBS -j oe
+#PBS -d .
+#PBS -t 1-4%4
+#PBS -o messages_outputs/
+#PBS -e messages_errors/
+
+set -e
+cpus=$PBS_NUM_PPN
+
+export TMPDIR=/panfs/panfs1.ucsd.edu/panscratch/$USER/Stability_2020
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+export TMPDIR=$TMPDIR/sim_data
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+#tmp=$(mktemp -d --tmpdir)
+#export TMPDIR=$tmp
+#trap "rm -r $tmp; unset TMPDIR" EXIT
+
+# do something
+source activate r-env
+Rscript ind_results_binary_update.R $TMPDIR # t = 1-16%10
+Rscript toe_results_binary_update.R $TMPDIR # t = 1-80%10
+Rscript block_results_binary_update.R $TMPDIR # t = 1-80%10
+Rscript boot_sim_binary.R $TMPDIR # t = 1-4%4
+source deactivate r-env
+
+#mv $tmp/outdir ./outdir
diff --git a/simulations/code_sim_bin/toe_results_binary_update.R b/simulations/code_sim_bin/toe_results_binary_update.R
new file mode 100644
index 0000000..1a4986d
--- /dev/null
+++ b/simulations/code_sim_bin/toe_results_binary_update.R
@@ -0,0 +1,100 @@
+#####################################################################################
+### run all methods on simulated data with Toeplitz correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+library(FSA)
+source('../../code_method/cv_method_binary_update.R')
+source('../../code_method/getStability.R')
+source('cv_sim_apply_binary_update.R')
+
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+## correlation strength
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+
+#------------------------
+# compare methods
+#------------------------
+i <- as.numeric(Sys.getenv("PBS_ARRAYID"))
+
+# ###################
+# #### Lasso ########
+# ###################
+print('Lasso')
+file_name = files[[i]]
+results_toe_lasso = sim_evaluate_cv(sim_file=file_name, method='lasso', family='binomial')
+
+when i = 61, sim_idx = 16, ROC cannot be calculated as y.test has all 0s
+thus change seed number to avoid this extreme example
+results_toe_lasso = sim_evaluate_cv(sim_file=file_name, method='lasso', family='binomial', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/toe_Lasso_binary_', i, '.RData'), results_toe_lasso, file_name)
+
+# # #########################
+# # #### Elastic Net ########
+# # #########################
+print('Elnet')
+file_name = files[[i]]
+results_toe_elnet = sim_evaluate_cv(sim_file=file_name, method='elnet', family='binomial')
+
+when i = 61, sim_idx = 16, ROC cannot be calculated as y.test has all 0s
+thus change seed number to avoid this extreme example
+results_toe_elnet = sim_evaluate_cv(sim_file=file_name, method='elnet', family='binomial', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/toe_Elnet_binary_', i, '.RData'), results_toe_elnet, file_name)
+
+# ############################
+# #### Random Forests ########
+# ############################
+print('Random Forests')
+file_name = files[[i]]
+results_toe_rf = sim_evaluate_cv(sim_file=file_name, method='RF')
+
+when i = 61, sim_idx = 16, ROC cannot be calculated as y.test has all 0s
+thus change seed number to avoid this extreme example
+results_toe_rf = sim_evaluate_cv(sim_file=file_name, method='RF', seednum=5231)
+
+save(file=paste0(dir, '/binary_update/toe_RF_binary_', i, '.RData'), results_toe_rf, file_name)
+
+# #########################################################
+# #### Generalized compositional Lasso ########
+# #########################################################
+print('Generalized Compositional Lasso')
+file_name = files[[i]]
+results_toe_GenCompLasso = sim_evaluate_cv(sim_file=file_name, method='GenCompLasso')
+
+save(file=paste0(dir, '/binary_update/toe_GenCompLasso_binary_', i, '.RData'),
+     results_toe_GenCompLasso, file_name)
+
+results_toe_GenCompLasso_dataSplit = sim_evaluate_cv(sim_file=file_name,
+                                                     method='GenCompLasso',
+                                                     data.split=TRUE)
+
+# when i = 61, sim_idx = 16, ROC cannot be calculated as y.test has all 0s
+# thus change seed number to avoid this extreme example
+# results_toe_GenCompLasso_dataSplit = sim_evaluate_cv(sim_file=file_name,
+#                                                      method='GenCompLasso',
+#                                                      data.split=TRUE, seednum=5231)
+
diff --git a/simulations/code_sim_cts/CL_sim_apply.R b/simulations/code_sim_cts/CL_sim_apply.R
new file mode 100755
index 0000000..5f909ec
--- /dev/null
+++ b/simulations/code_sim_cts/CL_sim_apply.R
@@ -0,0 +1,87 @@
+#####################################################################
+#### Compositional Lasso on simulation results ######################
+#####################################################################
+library(FSA) # for se()
+source('../../code_method/cv_method.R') 
+source('../../code_method/getStability.R')
+source('cv_sim_apply.R')
+
+dir = '../sim_data'
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+###########################################
+#### Independent simulations ##############
+###########################################
+files = NULL
+for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))
+}
+
+results_ind_compLasso = NULL
+for (i in 1:length(files)){ # parallel computing not working
+	print(i)
+	results_ind_compLasso[[i]] = sim_evaluate_cv(sim_file=files[i], method='compLasso')
+}
+save(file=paste0(dir, '/independent_compLasso.RData'), results_ind_compLasso)
+
+###########################################
+#### Toeplitz simulations ##############
+###########################################
+## correlation strength
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+results_toe_compLasso = NULL
+for (i in 1:length(files)){ # parallel computing not working
+	print(i)
+	results_toe_compLasso[[i]] = sim_evaluate_cv(sim_file=files[i], method='compLasso')
+}
+save(file=paste0(dir, '/toe_compLasso.RData'), results_toe_compLasso)
+
+###########################################
+#### Block simulations ####################
+###########################################
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+results_block_compLasso = NULL
+for (i in 1:length(files)){ # parallel computing not working
+	print(i)
+	results_block_compLasso[[i]] = sim_evaluate_cv(sim_file=files[i], method='compLasso')
+}
+save(file=paste0(dir, '/block_compLasso.RData'), results_block_compLasso)
+
+
+
+
+
+
+
+
+
diff --git a/simulations/code_sim_cts/block_results.R b/simulations/code_sim_cts/block_results.R
new file mode 100755
index 0000000..22c8818
--- /dev/null
+++ b/simulations/code_sim_cts/block_results.R
@@ -0,0 +1,75 @@
+#####################################################################################
+### run all methods on simulated data with block correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('cv_sim_apply.R')
+
+
+library(FSA)
+library(foreach)
+library(doParallel)
+numCores <- detectCores() - 2 
+registerDoParallel(numCores)  
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+## correlation strength
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+
+##################
+### Lasso ########
+##################
+print('Lasso')
+results_block_lasso = foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='lasso')
+}
+
+save(file=paste0(dir, '/block_Lasso.RData'), results_block_lasso)
+
+
+#########################
+#### Elastic Net ########
+#########################
+print('Elnet')
+results_block_elnet= foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='elnet')
+}
+
+save(file=paste0(dir, '/block_Elnet.RData'), results_block_elnet)
+
+############################
+#### Random Forests ########
+############################
+print('Random Forests')
+results_block_rf = foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='RF')
+}
+
+save(file=paste0(dir, '/block_RF.RData'), results_block_rf)
+
diff --git a/simulations/code_sim_cts/boot_CL_testing.R b/simulations/code_sim_cts/boot_CL_testing.R
new file mode 100755
index 0000000..494efbf
--- /dev/null
+++ b/simulations/code_sim_cts/boot_CL_testing.R
@@ -0,0 +1,31 @@
+#########################################################################################################
+### This is to use bootstrap for testing on compositional lasso #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('../../code_method/bootstrap_test_compLasso_rf.R')
+
+dir = '../sim_data'
+
+toe_lin = boot_stab(num_boot=100, sim_file= paste0(dir, '/sim_toeplitz_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'compLasso', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+
+save(toe_lin, file=paste0(dir, '/boot_toe_compLasso.RData'))
+
+block_lin = boot_stab(num_boot=100, sim_file= paste0(dir, '/sim_block_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'compLasso', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+
+save(block_lin, file=paste0(dir, '/boot_block_compLasso.RData'))
+
+
+
+
+
+
diff --git a/simulations/code_sim_cts/boot_RF_testing.R b/simulations/code_sim_cts/boot_RF_testing.R
new file mode 100755
index 0000000..c2fc990
--- /dev/null
+++ b/simulations/code_sim_cts/boot_RF_testing.R
@@ -0,0 +1,32 @@
+#########################################################################################################
+### This is to use bootstrap for testing on random forests #################
+#########################################################################################################
+
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('../../code_method/bootstrap_test_compLasso_rf.R')
+
+# the function boot_stab is probably upddated to be boot_stab_sim() now
+
+toe_rf = boot_stab(num_boot=100, sim_file= paste0(dir, '/sim_toeplitz_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'RF', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+
+save(toe_rf, file=paste0(dir, '/boot_toe_RF.RData'))
+
+block_rf = boot_stab(num_boot=100, sim_file= paste0(dir, '/sim_block_corr0.5P_1000_N_100.RData', sep=''), 
+				   method= 'RF', seednum=31, ratio.training=0.8, fold.cv=10, 
+				   mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05)
+
+save(block_rf, file=paste0(dir, '/boot_block_RF.RData'))
+
+
+
+
+
+
+
diff --git a/simulations/code_sim_cts/cv_sim_apply.R b/simulations/code_sim_cts/cv_sim_apply.R
new file mode 100755
index 0000000..aa717e8
--- /dev/null
+++ b/simulations/code_sim_cts/cv_sim_apply.R
@@ -0,0 +1,91 @@
+#######################################################################################
+### apply different feature selection methods to simulated data #######################
+#######################################################################################
+
+#' library(FSA) # for se()
+#' source('cv_method.R') 
+#' source('getStability.R')
+#' @title sim_evaluate_cv
+#' @description 
+#' @param sim_file The path of a .RData file includes the simulation data pre-generated.
+#' @param method The machine learning algorithms to be used. This should be one of "lasso", "elnet", "RF", and "compLosso". 
+#' @param seednum The seed number (default=31).
+#' @param ratio.training The ratio of the whole data assigned for model training (default=0.8).
+#' @param fold.cv The number of folds for cross-validation.(default=10)
+#' @param family The family of linear regression models (only related to "lasso" or "elnet").
+#' @param lambda.grid The tuning range for the regularization parameter "lambda" of "lasso" or "elnet".
+#' @param alpha.grid The tuning range for the elastic-net mixing parameter "alpha".
+#' @param mtry.grid The tuning range for the "RF" hyperparameter "mtry", that is, number of variables to possibly split at in each node. 
+#' @param num_trees The number of decision trees built in the RF.
+#' @param pval_thr The threshold for the estimated p value of RF importance scores.
+#' @param method.perm The permutation method for estimating the p value of RF importance scores.
+#' @details 
+
+sim_evaluate_cv = function(sim_file, method, seednum=31, ratio.training=0.8, fold.cv=10, family='gaussian',
+	                       lambda.grid=exp(seq(-4, -2, 0.2)), alpha.grid=seq(0.1, 0.9, 0.1), 
+	                       mtry.grid=seq(5, 25, 5), num_trees = 500, pval_thr = 0.05, method.perm='altmann'){
+	# load simulated data
+	load(sim_file, dat <- new.env())
+
+	idx.start = 1; idx.stop = 100 # 100 repetitions for each simulated scenario
+	rou = dat$sim_array[[1]]$rou # rou, n, p are same across all repetitions
+	n = dat$sim_array[[1]]$n
+	p = dat$sim_array[[1]]$p
+
+	# evaluating different methods	
+	fp = fn = mse = OOB_rf = NULL
+	stability.table = matrix(rep(0, (idx.stop - idx.start + 1) * p), ncol=p)
+	colnames(stability.table) = paste('V', seq(1:p), sep='')
+
+	for (i in idx.start:idx.stop){ 
+	    sub = dat$sim_array[[i]]
+	    coef = sub$beta
+	    coef.true = which(coef != 0)
+	    
+		if (method == 'lasso'){
+			result = lasso_cv(y=sub$Y, datx=sub$Z, seednum=seednum,family=family, lambda.choice='lambda.1se',
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    select.features = result$coef.chosen 
+		} else if (method == 'elnet'){
+		    result = elnet_cv(y=sub$Y, datx=sub$Z, seednum=seednum,family=family, alpha.grid=alpha.grid,
+		                            ratio.training=ratio.training, fold.cv=fold.cv, lambda.grid=lambda.grid)
+		    select.features = result$coef.chosen 
+		} else if (method == 'RF'){
+		    result = randomForest_cv(y=sub$Y, datx=sub$Z, seednum=seednum, fold.cv=fold.cv, 
+		                             num_trees=num_trees, mtry.grid = mtry.grid, pval_thr=pval_thr, method.perm=method.perm)
+		    select.features = result$coef.chosen 
+		} else if (method == 'compLasso'){
+			result = cons_lasso_cv(datx=sub$Z, y=sub$Y, seednum=seednum, ratio.training=ratio.training)
+			select.features = result$coef.chosen
+		}
+
+	    # false positives: shouldn't be chosen but chosen
+	    fp = c(fp, length(setdiff(select.features, coef.true)))
+
+	    # false negatives: # should be chosen yet not
+	    fn = c(fn, length(setdiff(coef.true, select.features)))
+
+	    # prediction error
+	    mse = c(mse, result$MSE)
+
+	    if (method == 'RF'){
+	    	OOB_rf = c(OOB_rf, result$OOB)
+	    }
+
+	    # stability table
+	    stability.table[i, select.features] = 1
+	}
+
+	# store results
+	FP = paste(mean(fp), '(', round(se(fp),2), ')') # FPR?
+	FN = paste(mean(fn), '(', round(se(fn),2), ')') # FNR?
+	MSE = paste(round(mean(mse, na.rm=T),2), '(', round(se(mse, na.rm=T),2), ')')
+	Stab = round(getStability(stability.table)$stability, 2)
+
+	results=list(rou=rou, n=n, p=p, FP=FP, FN=FN, MSE=MSE, Stab=Stab, 
+		         Stab.table=stability.table, FP.list=fp, FN.list=fn, MSE.list=mse, OOB.list=OOB_rf)
+
+}
+
+
+
diff --git a/simulations/code_sim_cts/ind_results.R b/simulations/code_sim_cts/ind_results.R
new file mode 100755
index 0000000..0544fbd
--- /dev/null
+++ b/simulations/code_sim_cts/ind_results.R
@@ -0,0 +1,68 @@
+#####################################################################################
+### run all methods on simulated data with independent correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('cv_sim_apply.R')
+
+
+library(FSA)
+library(foreach)
+library(doParallel)
+numCores <- detectCores() - 2 
+registerDoParallel(numCores)  
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+files = NULL
+for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))
+}
+
+##################
+### Lasso ########
+##################
+print('Lasso')
+results_ind_lasso = foreach(i = iter(files)) %dopar%{
+   sim_evaluate_cv(sim_file=i, method='lasso')
+}
+
+save(file=paste0(dir, '/independent_Lasso.RData'), results_ind_lasso)
+
+
+#########################
+#### Elastic Net ########
+#########################
+print('Elnet')
+results_ind_elnet= foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='elnet')
+}
+
+save(file=paste0(dir, '/independent_Elnet.RData'), results_ind_elnet)
+
+############################
+#### Random Forests ########
+############################
+print('RF')
+results_ind_rf = foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='RF')
+}
+
+save(file=paste0(dir, '/independent_RF.RData'), results_ind_rf)
+
diff --git a/simulations/code_sim_cts/run_sim_cts.sh b/simulations/code_sim_cts/run_sim_cts.sh
new file mode 100755
index 0000000..af06b77
--- /dev/null
+++ b/simulations/code_sim_cts/run_sim_cts.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+
+#PBS -N ind_cv
+#PBS -l walltime=500:00:00
+#PBS -l nodes=1:ppn=10
+#PBS -l mem=50gb
+#PBS -V
+#PBS -j oe
+#PBS -d .
+
+set -e
+cpus=$PBS_NUM_PPN
+
+export TMPDIR=/panfs/panfs1.ucsd.edu/panscratch/$USER/Stability_2020
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+export TMPDIR=$TMPDIR/sim_data
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+#tmp=$(mktemp -d --tmpdir)
+#export TMPDIR=$tmp
+#trap "rm -r $tmp; unset TMPDIR" EXIT
+
+# do something
+source activate r-env
+Rscript ind_results.R $TMPDIR
+Rscript block_results.R $TMPDIR
+Rscript toe_results.R $TMPDIR
+Rscript boot_RF_testing.R $TMPDIR
+source deactivate r-env
+
+#mv $tmp/outdir ./outdir
diff --git a/simulations/code_sim_cts/toe_results.R b/simulations/code_sim_cts/toe_results.R
new file mode 100755
index 0000000..b582db8
--- /dev/null
+++ b/simulations/code_sim_cts/toe_results.R
@@ -0,0 +1,73 @@
+#####################################################################################
+### run all methods on simulated data with Toeplitz correlation ##################
+#####################################################################################
+args = commandArgs(trailingOnly=TRUE)
+print(args)
+dir = args[1]
+
+source('../../code_method/cv_method.R')
+source('../../code_method/getStability.R')
+source('cv_sim_apply.R')
+
+
+library(FSA)
+library(foreach)
+library(doParallel)
+numCores <- detectCores() - 2 
+registerDoParallel(numCores)  
+
+dim.list = list()
+size = c(50, 100, 500, 1000)
+idx = 0
+for (P in size){
+    for (N in size){
+        idx = idx + 1
+        dim.list[[idx]] = c(P=P, N=N)
+    }
+}
+
+## correlation strength
+rou.list = seq(0.1, 0.9, 0.2)
+
+files = NULL
+for (rou in rou.list){
+  for (dim in dim.list){
+    p = dim[1]
+    n = dim[2]
+    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))
+  }
+}
+
+
+##################
+### Lasso ########
+##################
+print('Lasso')
+results_toe_lasso = foreach(i = iter(files)) %dopar%{
+   print(i)
+   sim_evaluate_cv(sim_file=i, method='lasso')
+}
+
+save(file=paste0(dir, '/toe_Lasso.RData'), results_toe_lasso)
+
+
+#########################
+#### Elastic Net ########
+#########################
+print('Elnet')
+results_toe_elnet= foreach(i = iter(files)) %dopar%{
+   sim_evaluate_cv(sim_file=i, method='elnet')
+}
+
+save(file=paste0(dir, '/toe_Elnet.RData'), results_toe_elnet)
+
+############################
+#### Random Forests ########
+############################
+print('Random Forests')
+results_toe_rf = foreach(i = iter(files)) %dopar%{
+   sim_evaluate_cv(sim_file=i, method='RF')
+}
+
+save(file=paste0(dir, '/toe_RF.RData'), results_toe_rf)
+
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diff --git a/simulations/figures_sim/.DS_Store b/simulations/figures_sim/.DS_Store
deleted file mode 100755
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diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.1_sim_ind_lasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.1_sim_ind_lasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..7532ddd
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.1_sim_ind_lasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,683 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>3.69 ( 0.25 )</td><td>2.5 ( 0.12 )</td><td>0.74 ( 0.01 )</td><td>0.33</td><td> 6.19</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>7.16 ( 0.37 )</td><td>0.64 ( 0.08 )</td><td>0.85 ( 0.01 )</td><td>0.35</td><td>11.52</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>5.21 ( 0.21 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.53</td><td>10.21</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>2.25 ( 0.11 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.8</td><td> 7.25</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>5.13 ( 0.34 )</td><td>2.93 ( 0.1 )</td><td>0.73 ( 0.01 )</td><td>0.25</td><td> 7.20</td><td>0.50</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>8.22 ( 0.44 )</td><td>1.03 ( 0.09 )</td><td>0.83 ( 0.01 )</td><td>0.32</td><td>12.19</td><td>0.54</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 3.69 ( 0.25 ) & 2.5 ( 0.12 ) & 0.74 ( 0.01 ) & 0.33 &  6.19 & 0.37\\\\\n",
+       "\t2 & 100 & 50 & 0 & 7.16 ( 0.37 ) & 0.64 ( 0.08 ) & 0.85 ( 0.01 ) & 0.35 & 11.52 & 0.48\\\\\n",
+       "\t3 & 500 & 50 & 0 & 5.21 ( 0.21 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.53 & 10.21 & 0.39\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 2.25 ( 0.11 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.8 &  7.25 & 0.15\\\\\n",
+       "\t5 & 50 & 100 & 0 & 5.13 ( 0.34 ) & 2.93 ( 0.1 ) & 0.73 ( 0.01 ) & 0.25 &  7.20 & 0.50\\\\\n",
+       "\t6 & 100 & 100 & 0 & 8.22 ( 0.44 ) & 1.03 ( 0.09 ) & 0.83 ( 0.01 ) & 0.32 & 12.19 & 0.54\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 3.69 ( 0.25 ) | 2.5 ( 0.12 ) | 0.74 ( 0.01 ) | 0.33 |  6.19 | 0.37 |\n",
+       "| 2 | 100 | 50 | 0 | 7.16 ( 0.37 ) | 0.64 ( 0.08 ) | 0.85 ( 0.01 ) | 0.35 | 11.52 | 0.48 |\n",
+       "| 3 | 500 | 50 | 0 | 5.21 ( 0.21 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.53 | 10.21 | 0.39 |\n",
+       "| 4 | 1000 | 50 | 0 | 2.25 ( 0.11 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.8 |  7.25 | 0.15 |\n",
+       "| 5 | 50 | 100 | 0 | 5.13 ( 0.34 ) | 2.93 ( 0.1 ) | 0.73 ( 0.01 ) | 0.25 |  7.20 | 0.50 |\n",
+       "| 6 | 100 | 100 | 0 | 8.22 ( 0.44 ) | 1.03 ( 0.09 ) | 0.83 ( 0.01 ) | 0.32 | 12.19 | 0.54 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0   3.69 ( 0.25 ) 2.5 ( 0.12 )  0.74 ( 0.01 ) 0.33  6.19      0.37\n",
+       "2 100  50  0   7.16 ( 0.37 ) 0.64 ( 0.08 ) 0.85 ( 0.01 ) 0.35 11.52      0.48\n",
+       "3 500  50  0   5.21 ( 0.21 ) 0 ( 0 )       0.93 ( 0 )    0.53 10.21      0.39\n",
+       "4 1000 50  0   2.25 ( 0.11 ) 0 ( 0 )       0.93 ( 0 )    0.8   7.25      0.15\n",
+       "5 50   100 0   5.13 ( 0.34 ) 2.93 ( 0.1 )  0.73 ( 0.01 ) 0.25  7.20      0.50\n",
+       "6 100  100 0   8.22 ( 0.44 ) 1.03 ( 0.09 ) 0.83 ( 0.01 ) 0.32 12.19      0.54"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>23.83 ( 0.69 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.2</td><td>28.83</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>11.38 ( 0.41 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.36</td><td>16.38</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>9.77 ( 0.24 )</td><td>4.03 ( 0.08 )</td><td>0.67 ( 0.01 )</td><td>0.09</td><td>10.74</td><td>0.81</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>11.91 ( 0.8 )</td><td>2.32 ( 0.1 )</td><td>0.78 ( 0.01 )</td><td>0.2</td><td>14.59</td><td>0.66</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>30.84 ( 0.92 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.16</td><td>35.84</td><td>0.82</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>19.96 ( 0.61 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.24</td><td>24.96</td><td>0.74</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 23.83 ( 0.69 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.2 & 28.83 & 0.78\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 11.38 ( 0.41 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.36 & 16.38 & 0.60\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 9.77 ( 0.24 ) & 4.03 ( 0.08 ) & 0.67 ( 0.01 ) & 0.09 & 10.74 & 0.81\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 11.91 ( 0.8 ) & 2.32 ( 0.1 ) & 0.78 ( 0.01 ) & 0.2 & 14.59 & 0.66\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 30.84 ( 0.92 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.16 & 35.84 & 0.82\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 19.96 ( 0.61 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.24 & 24.96 & 0.74\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 23.83 ( 0.69 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.2 | 28.83 | 0.78 |\n",
+       "| 12 | 1000 | 500 | 0 | 11.38 ( 0.41 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.36 | 16.38 | 0.60 |\n",
+       "| 13 | 50 | 1000 | 0 | 9.77 ( 0.24 ) | 4.03 ( 0.08 ) | 0.67 ( 0.01 ) | 0.09 | 10.74 | 0.81 |\n",
+       "| 14 | 100 | 1000 | 0 | 11.91 ( 0.8 ) | 2.32 ( 0.1 ) | 0.78 ( 0.01 ) | 0.2 | 14.59 | 0.66 |\n",
+       "| 15 | 500 | 1000 | 0 | 30.84 ( 0.92 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.16 | 35.84 | 0.82 |\n",
+       "| 16 | 1000 | 1000 | 0 | 19.96 ( 0.61 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.24 | 24.96 | 0.74 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "11 500  500  0   23.83 ( 0.69 ) 0 ( 0 )       0.92 ( 0 )    0.2  28.83     \n",
+       "12 1000 500  0   11.38 ( 0.41 ) 0 ( 0 )       0.93 ( 0 )    0.36 16.38     \n",
+       "13 50   1000 0   9.77 ( 0.24 )  4.03 ( 0.08 ) 0.67 ( 0.01 ) 0.09 10.74     \n",
+       "14 100  1000 0   11.91 ( 0.8 )  2.32 ( 0.1 )  0.78 ( 0.01 ) 0.2  14.59     \n",
+       "15 500  1000 0   30.84 ( 0.92 ) 0 ( 0 )       0.91 ( 0 )    0.16 35.84     \n",
+       "16 1000 1000 0   19.96 ( 0.61 ) 0 ( 0 )       0.93 ( 0 )    0.24 24.96     \n",
+       "   FDR \n",
+       "11 0.78\n",
+       "12 0.60\n",
+       "13 0.81\n",
+       "14 0.66\n",
+       "15 0.82\n",
+       "16 0.74"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.33</td><td>0.74 ( 0.01 )</td><td>3.69 ( 0.25 )</td><td>2.5 ( 0.12 ) </td><td> 6.19</td><td>0.37</td><td>0.74</td><td>3.69</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.35</td><td>0.85 ( 0.01 )</td><td>7.16 ( 0.37 )</td><td>0.64 ( 0.08 )</td><td>11.52</td><td>0.48</td><td>0.85</td><td>7.16</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.53</td><td>0.93 ( 0 )   </td><td>5.21 ( 0.21 )</td><td>0 ( 0 )      </td><td>10.21</td><td>0.39</td><td>0.93</td><td>5.21</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.80</td><td>0.93 ( 0 )   </td><td>2.25 ( 0.11 )</td><td>0 ( 0 )      </td><td> 7.25</td><td>0.15</td><td>0.93</td><td>2.25</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.25</td><td>0.73 ( 0.01 )</td><td>5.13 ( 0.34 )</td><td>2.93 ( 0.1 ) </td><td> 7.20</td><td>0.5 </td><td>0.73</td><td>5.13</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.32</td><td>0.83 ( 0.01 )</td><td>8.22 ( 0.44 )</td><td>1.03 ( 0.09 )</td><td>12.19</td><td>0.54</td><td>0.83</td><td>8.22</td><td>1.03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.33 & 0.74 ( 0.01 ) & 3.69 ( 0.25 ) & 2.5 ( 0.12 )  &  6.19 & 0.37 & 0.74 & 3.69 & 2.50\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.35 & 0.85 ( 0.01 ) & 7.16 ( 0.37 ) & 0.64 ( 0.08 ) & 11.52 & 0.48 & 0.85 & 7.16 & 0.64\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.53 & 0.93 ( 0 )    & 5.21 ( 0.21 ) & 0 ( 0 )       & 10.21 & 0.39 & 0.93 & 5.21 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.80 & 0.93 ( 0 )    & 2.25 ( 0.11 ) & 0 ( 0 )       &  7.25 & 0.15 & 0.93 & 2.25 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.25 & 0.73 ( 0.01 ) & 5.13 ( 0.34 ) & 2.93 ( 0.1 )  &  7.20 & 0.5  & 0.73 & 5.13 & 2.93\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.32 & 0.83 ( 0.01 ) & 8.22 ( 0.44 ) & 1.03 ( 0.09 ) & 12.19 & 0.54 & 0.83 & 8.22 & 1.03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.33 | 0.74 ( 0.01 ) | 3.69 ( 0.25 ) | 2.5 ( 0.12 )  |  6.19 | 0.37 | 0.74 | 3.69 | 2.50 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.35 | 0.85 ( 0.01 ) | 7.16 ( 0.37 ) | 0.64 ( 0.08 ) | 11.52 | 0.48 | 0.85 | 7.16 | 0.64 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.53 | 0.93 ( 0 )    | 5.21 ( 0.21 ) | 0 ( 0 )       | 10.21 | 0.39 | 0.93 | 5.21 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.80 | 0.93 ( 0 )    | 2.25 ( 0.11 ) | 0 ( 0 )       |  7.25 | 0.15 | 0.93 | 2.25 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.25 | 0.73 ( 0.01 ) | 5.13 ( 0.34 ) | 2.93 ( 0.1 )  |  7.20 | 0.5  | 0.73 | 5.13 | 2.93 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.32 | 0.83 ( 0.01 ) | 8.22 ( 0.44 ) | 1.03 ( 0.09 ) | 12.19 | 0.54 | 0.83 | 8.22 | 1.03 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0    1.00  0.33 0.74 ( 0.01 ) 3.69 ( 0.25 ) 2.5 ( 0.12 )   6.19     \n",
+       "2  100  50 0    0.50  0.35 0.85 ( 0.01 ) 7.16 ( 0.37 ) 0.64 ( 0.08 ) 11.52     \n",
+       "3  500  50 0    0.10  0.53 0.93 ( 0 )    5.21 ( 0.21 ) 0 ( 0 )       10.21     \n",
+       "4 1000  50 0    0.05  0.80 0.93 ( 0 )    2.25 ( 0.11 ) 0 ( 0 )        7.25     \n",
+       "5   50 100 0    2.00  0.25 0.73 ( 0.01 ) 5.13 ( 0.34 ) 2.93 ( 0.1 )   7.20     \n",
+       "6  100 100 0    1.00  0.32 0.83 ( 0.01 ) 8.22 ( 0.44 ) 1.03 ( 0.09 ) 12.19     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.37 0.74     3.69    2.50   \n",
+       "2 0.48 0.85     7.16    0.64   \n",
+       "3 0.39 0.93     5.21    0.00   \n",
+       "4 0.15 0.93     2.25    0.00   \n",
+       "5 0.5  0.73     5.13    2.93   \n",
+       "6 0.54 0.83     8.22    1.03   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.20</td><td>0.92 ( 0 )   </td><td>23.83 ( 0.69 )</td><td>0 ( 0 )      </td><td>28.83</td><td>0.78</td><td>0.92</td><td>23.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.38 ( 0.41 )</td><td>0 ( 0 )      </td><td>16.38</td><td>0.6 </td><td>0.93</td><td>11.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.09</td><td>0.67 ( 0.01 )</td><td>9.77 ( 0.24 ) </td><td>4.03 ( 0.08 )</td><td>10.74</td><td>0.81</td><td>0.67</td><td> 9.77</td><td>4.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>11.91 ( 0.8 ) </td><td>2.32 ( 0.1 ) </td><td>14.59</td><td>0.66</td><td>0.78</td><td>11.91</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.16</td><td>0.91 ( 0 )   </td><td>30.84 ( 0.92 )</td><td>0 ( 0 )      </td><td>35.84</td><td>0.82</td><td>0.91</td><td>30.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.24</td><td>0.93 ( 0 )   </td><td>19.96 ( 0.61 )</td><td>0 ( 0 )      </td><td>24.96</td><td>0.74</td><td>0.93</td><td>19.96</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.20 & 0.92 ( 0 )    & 23.83 ( 0.69 ) & 0 ( 0 )       & 28.83 & 0.78 & 0.92 & 23.83 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.36 & 0.93 ( 0 )    & 11.38 ( 0.41 ) & 0 ( 0 )       & 16.38 & 0.6  & 0.93 & 11.38 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.09 & 0.67 ( 0.01 ) & 9.77 ( 0.24 )  & 4.03 ( 0.08 ) & 10.74 & 0.81 & 0.67 &  9.77 & 4.03\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.20 & 0.78 ( 0.01 ) & 11.91 ( 0.8 )  & 2.32 ( 0.1 )  & 14.59 & 0.66 & 0.78 & 11.91 & 2.32\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.16 & 0.91 ( 0 )    & 30.84 ( 0.92 ) & 0 ( 0 )       & 35.84 & 0.82 & 0.91 & 30.84 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.24 & 0.93 ( 0 )    & 19.96 ( 0.61 ) & 0 ( 0 )       & 24.96 & 0.74 & 0.93 & 19.96 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.20 | 0.92 ( 0 )    | 23.83 ( 0.69 ) | 0 ( 0 )       | 28.83 | 0.78 | 0.92 | 23.83 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.36 | 0.93 ( 0 )    | 11.38 ( 0.41 ) | 0 ( 0 )       | 16.38 | 0.6  | 0.93 | 11.38 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.09 | 0.67 ( 0.01 ) | 9.77 ( 0.24 )  | 4.03 ( 0.08 ) | 10.74 | 0.81 | 0.67 |  9.77 | 4.03 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.20 | 0.78 ( 0.01 ) | 11.91 ( 0.8 )  | 2.32 ( 0.1 )  | 14.59 | 0.66 | 0.78 | 11.91 | 2.32 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.16 | 0.91 ( 0 )    | 30.84 ( 0.92 ) | 0 ( 0 )       | 35.84 | 0.82 | 0.91 | 30.84 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.24 | 0.93 ( 0 )    | 19.96 ( 0.61 ) | 0 ( 0 )       | 24.96 | 0.74 | 0.93 | 19.96 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "11  500  500 0     1.0  0.20 0.92 ( 0 )    23.83 ( 0.69 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.5  0.36 0.93 ( 0 )    11.38 ( 0.41 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.09 0.67 ( 0.01 ) 9.77 ( 0.24 )  4.03 ( 0.08 )\n",
+       "14  100 1000 0    10.0  0.20 0.78 ( 0.01 ) 11.91 ( 0.8 )  2.32 ( 0.1 ) \n",
+       "15  500 1000 0     2.0  0.16 0.91 ( 0 )    30.84 ( 0.92 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.0  0.24 0.93 ( 0 )    19.96 ( 0.61 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11 28.83      0.78 0.92     23.83   0.00   \n",
+       "12 16.38      0.6  0.93     11.38   0.00   \n",
+       "13 10.74      0.81 0.67      9.77   4.03   \n",
+       "14 14.59      0.66 0.78     11.91   2.32   \n",
+       "15 35.84      0.82 0.91     30.84   0.00   \n",
+       "16 24.96      0.74 0.93     19.96   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.33</td><td>0.74 ( 0.01 )</td><td>3.69 ( 0.25 ) </td><td>2.5 ( 0.12 ) </td><td> 6.19</td><td>0.37</td><td>0.74</td><td> 3.69</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.35</td><td>0.85 ( 0.01 )</td><td>7.16 ( 0.37 ) </td><td>0.64 ( 0.08 )</td><td>11.52</td><td>0.48</td><td>0.85</td><td> 7.16</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.53</td><td>0.93 ( 0 )   </td><td>5.21 ( 0.21 ) </td><td>0 ( 0 )      </td><td>10.21</td><td>0.39</td><td>0.93</td><td> 5.21</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.80</td><td>0.93 ( 0 )   </td><td>2.25 ( 0.11 ) </td><td>0 ( 0 )      </td><td> 7.25</td><td>0.15</td><td>0.93</td><td> 2.25</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.25</td><td>0.73 ( 0.01 )</td><td>5.13 ( 0.34 ) </td><td>2.93 ( 0.1 ) </td><td> 7.20</td><td>0.5 </td><td>0.73</td><td> 5.13</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.32</td><td>0.83 ( 0.01 )</td><td>8.22 ( 0.44 ) </td><td>1.03 ( 0.09 )</td><td>12.19</td><td>0.54</td><td>0.83</td><td> 8.22</td><td>1.03</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.40</td><td>0.92 ( 0 )   </td><td>8.89 ( 0.29 ) </td><td>0 ( 0 )      </td><td>13.89</td><td>0.55</td><td>0.92</td><td> 8.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.69</td><td>0.93 ( 0 )   </td><td>3.53 ( 0.16 ) </td><td>0 ( 0 )      </td><td> 8.53</td><td>0.27</td><td>0.93</td><td> 3.53</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.13</td><td>0.7 ( 0.01 ) </td><td>8.05 ( 0.28 ) </td><td>3.74 ( 0.09 )</td><td> 9.31</td><td>0.74</td><td>0.70</td><td> 8.05</td><td>3.74</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.24</td><td>0.79 ( 0.01 )</td><td>11.14 ( 0.64 )</td><td>1.71 ( 0.1 ) </td><td>14.43</td><td>0.63</td><td>0.79</td><td>11.14</td><td>1.71</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.20</td><td>0.92 ( 0 )   </td><td>23.83 ( 0.69 )</td><td>0 ( 0 )      </td><td>28.83</td><td>0.78</td><td>0.92</td><td>23.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.38 ( 0.41 )</td><td>0 ( 0 )      </td><td>16.38</td><td>0.6 </td><td>0.93</td><td>11.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.09</td><td>0.67 ( 0.01 )</td><td>9.77 ( 0.24 ) </td><td>4.03 ( 0.08 )</td><td>10.74</td><td>0.81</td><td>0.67</td><td> 9.77</td><td>4.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>11.91 ( 0.8 ) </td><td>2.32 ( 0.1 ) </td><td>14.59</td><td>0.66</td><td>0.78</td><td>11.91</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.16</td><td>0.91 ( 0 )   </td><td>30.84 ( 0.92 )</td><td>0 ( 0 )      </td><td>35.84</td><td>0.82</td><td>0.91</td><td>30.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.24</td><td>0.93 ( 0 )   </td><td>19.96 ( 0.61 )</td><td>0 ( 0 )      </td><td>24.96</td><td>0.74</td><td>0.93</td><td>19.96</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.33 & 0.74 ( 0.01 ) & 3.69 ( 0.25 )  & 2.5 ( 0.12 )  &  6.19 & 0.37 & 0.74 &  3.69 & 2.50\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.35 & 0.85 ( 0.01 ) & 7.16 ( 0.37 )  & 0.64 ( 0.08 ) & 11.52 & 0.48 & 0.85 &  7.16 & 0.64\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.53 & 0.93 ( 0 )    & 5.21 ( 0.21 )  & 0 ( 0 )       & 10.21 & 0.39 & 0.93 &  5.21 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.80 & 0.93 ( 0 )    & 2.25 ( 0.11 )  & 0 ( 0 )       &  7.25 & 0.15 & 0.93 &  2.25 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.25 & 0.73 ( 0.01 ) & 5.13 ( 0.34 )  & 2.93 ( 0.1 )  &  7.20 & 0.5  & 0.73 &  5.13 & 2.93\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.32 & 0.83 ( 0.01 ) & 8.22 ( 0.44 )  & 1.03 ( 0.09 ) & 12.19 & 0.54 & 0.83 &  8.22 & 1.03\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.40 & 0.92 ( 0 )    & 8.89 ( 0.29 )  & 0 ( 0 )       & 13.89 & 0.55 & 0.92 &  8.89 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.69 & 0.93 ( 0 )    & 3.53 ( 0.16 )  & 0 ( 0 )       &  8.53 & 0.27 & 0.93 &  3.53 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.13 & 0.7 ( 0.01 )  & 8.05 ( 0.28 )  & 3.74 ( 0.09 ) &  9.31 & 0.74 & 0.70 &  8.05 & 3.74\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.24 & 0.79 ( 0.01 ) & 11.14 ( 0.64 ) & 1.71 ( 0.1 )  & 14.43 & 0.63 & 0.79 & 11.14 & 1.71\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.20 & 0.92 ( 0 )    & 23.83 ( 0.69 ) & 0 ( 0 )       & 28.83 & 0.78 & 0.92 & 23.83 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.36 & 0.93 ( 0 )    & 11.38 ( 0.41 ) & 0 ( 0 )       & 16.38 & 0.6  & 0.93 & 11.38 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.09 & 0.67 ( 0.01 ) & 9.77 ( 0.24 )  & 4.03 ( 0.08 ) & 10.74 & 0.81 & 0.67 &  9.77 & 4.03\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.20 & 0.78 ( 0.01 ) & 11.91 ( 0.8 )  & 2.32 ( 0.1 )  & 14.59 & 0.66 & 0.78 & 11.91 & 2.32\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.16 & 0.91 ( 0 )    & 30.84 ( 0.92 ) & 0 ( 0 )       & 35.84 & 0.82 & 0.91 & 30.84 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.24 & 0.93 ( 0 )    & 19.96 ( 0.61 ) & 0 ( 0 )       & 24.96 & 0.74 & 0.93 & 19.96 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.33 | 0.74 ( 0.01 ) | 3.69 ( 0.25 )  | 2.5 ( 0.12 )  |  6.19 | 0.37 | 0.74 |  3.69 | 2.50 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.35 | 0.85 ( 0.01 ) | 7.16 ( 0.37 )  | 0.64 ( 0.08 ) | 11.52 | 0.48 | 0.85 |  7.16 | 0.64 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.53 | 0.93 ( 0 )    | 5.21 ( 0.21 )  | 0 ( 0 )       | 10.21 | 0.39 | 0.93 |  5.21 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.80 | 0.93 ( 0 )    | 2.25 ( 0.11 )  | 0 ( 0 )       |  7.25 | 0.15 | 0.93 |  2.25 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.25 | 0.73 ( 0.01 ) | 5.13 ( 0.34 )  | 2.93 ( 0.1 )  |  7.20 | 0.5  | 0.73 |  5.13 | 2.93 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.32 | 0.83 ( 0.01 ) | 8.22 ( 0.44 )  | 1.03 ( 0.09 ) | 12.19 | 0.54 | 0.83 |  8.22 | 1.03 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.40 | 0.92 ( 0 )    | 8.89 ( 0.29 )  | 0 ( 0 )       | 13.89 | 0.55 | 0.92 |  8.89 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.69 | 0.93 ( 0 )    | 3.53 ( 0.16 )  | 0 ( 0 )       |  8.53 | 0.27 | 0.93 |  3.53 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.13 | 0.7 ( 0.01 )  | 8.05 ( 0.28 )  | 3.74 ( 0.09 ) |  9.31 | 0.74 | 0.70 |  8.05 | 3.74 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.24 | 0.79 ( 0.01 ) | 11.14 ( 0.64 ) | 1.71 ( 0.1 )  | 14.43 | 0.63 | 0.79 | 11.14 | 1.71 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.20 | 0.92 ( 0 )    | 23.83 ( 0.69 ) | 0 ( 0 )       | 28.83 | 0.78 | 0.92 | 23.83 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.36 | 0.93 ( 0 )    | 11.38 ( 0.41 ) | 0 ( 0 )       | 16.38 | 0.6  | 0.93 | 11.38 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.09 | 0.67 ( 0.01 ) | 9.77 ( 0.24 )  | 4.03 ( 0.08 ) | 10.74 | 0.81 | 0.67 |  9.77 | 4.03 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.20 | 0.78 ( 0.01 ) | 11.91 ( 0.8 )  | 2.32 ( 0.1 )  | 14.59 | 0.66 | 0.78 | 11.91 | 2.32 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.16 | 0.91 ( 0 )    | 30.84 ( 0.92 ) | 0 ( 0 )       | 35.84 | 0.82 | 0.91 | 30.84 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.24 | 0.93 ( 0 )    | 19.96 ( 0.61 ) | 0 ( 0 )       | 24.96 | 0.74 | 0.93 | 19.96 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0     1.00 0.33 0.74 ( 0.01 ) 3.69 ( 0.25 )  2.5 ( 0.12 ) \n",
+       "2   100   50 0     0.50 0.35 0.85 ( 0.01 ) 7.16 ( 0.37 )  0.64 ( 0.08 )\n",
+       "3   500   50 0     0.10 0.53 0.93 ( 0 )    5.21 ( 0.21 )  0 ( 0 )      \n",
+       "4  1000   50 0     0.05 0.80 0.93 ( 0 )    2.25 ( 0.11 )  0 ( 0 )      \n",
+       "5    50  100 0     2.00 0.25 0.73 ( 0.01 ) 5.13 ( 0.34 )  2.93 ( 0.1 ) \n",
+       "6   100  100 0     1.00 0.32 0.83 ( 0.01 ) 8.22 ( 0.44 )  1.03 ( 0.09 )\n",
+       "7   500  100 0     0.20 0.40 0.92 ( 0 )    8.89 ( 0.29 )  0 ( 0 )      \n",
+       "8  1000  100 0     0.10 0.69 0.93 ( 0 )    3.53 ( 0.16 )  0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.13 0.7 ( 0.01 )  8.05 ( 0.28 )  3.74 ( 0.09 )\n",
+       "10  100  500 0     5.00 0.24 0.79 ( 0.01 ) 11.14 ( 0.64 ) 1.71 ( 0.1 ) \n",
+       "11  500  500 0     1.00 0.20 0.92 ( 0 )    23.83 ( 0.69 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.50 0.36 0.93 ( 0 )    11.38 ( 0.41 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.09 0.67 ( 0.01 ) 9.77 ( 0.24 )  4.03 ( 0.08 )\n",
+       "14  100 1000 0    10.00 0.20 0.78 ( 0.01 ) 11.91 ( 0.8 )  2.32 ( 0.1 ) \n",
+       "15  500 1000 0     2.00 0.16 0.91 ( 0 )    30.84 ( 0.92 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.00 0.24 0.93 ( 0 )    19.96 ( 0.61 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   6.19      0.37 0.74      3.69   2.50   \n",
+       "2  11.52      0.48 0.85      7.16   0.64   \n",
+       "3  10.21      0.39 0.93      5.21   0.00   \n",
+       "4   7.25      0.15 0.93      2.25   0.00   \n",
+       "5   7.20      0.5  0.73      5.13   2.93   \n",
+       "6  12.19      0.54 0.83      8.22   1.03   \n",
+       "7  13.89      0.55 0.92      8.89   0.00   \n",
+       "8   8.53      0.27 0.93      3.53   0.00   \n",
+       "9   9.31      0.74 0.70      8.05   3.74   \n",
+       "10 14.43      0.63 0.79     11.14   1.71   \n",
+       "11 28.83      0.78 0.92     23.83   0.00   \n",
+       "12 16.38      0.6  0.93     11.38   0.00   \n",
+       "13 10.74      0.81 0.67      9.77   4.03   \n",
+       "14 14.59      0.66 0.78     11.91   2.32   \n",
+       "15 35.84      0.82 0.91     30.84   0.00   \n",
+       "16 24.96      0.74 0.93     19.96   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_lasso_binary.txt', \n",
+    "            sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.2_sim_ind_elnet_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.2_sim_ind_elnet_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..da8ff21
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.2_sim_ind_elnet_binary_update-checkpoint.ipynb
@@ -0,0 +1,680 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>0.76 ( 0.01 )</td><td>0.15</td><td>15.62</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>0.84 ( 0.01 )</td><td>0.23</td><td>15.98</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>8.74 ( 0.72 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.36</td><td>13.74</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>7.56 ( 0.72 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.41</td><td>12.56</td><td>0.40</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>18.92 ( 1.7 )</td><td>1.81 ( 0.12 )</td><td>0.72 ( 0.01 )</td><td>0.1</td><td>22.11</td><td>0.72</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>12.48 ( 0.9 )</td><td>0.96 ( 0.09 )</td><td>0.82 ( 0.01 )</td><td>0.22</td><td>16.52</td><td>0.58</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 0.76 ( 0.01 ) & 0.15 & 15.62 & 0.61\\\\\n",
+       "\t2 & 100 & 50 & 0 & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 0.84 ( 0.01 ) & 0.23 & 15.98 & 0.56\\\\\n",
+       "\t3 & 500 & 50 & 0 & 8.74 ( 0.72 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.36 & 13.74 & 0.45\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 7.56 ( 0.72 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.41 & 12.56 & 0.40\\\\\n",
+       "\t5 & 50 & 100 & 0 & 18.92 ( 1.7 ) & 1.81 ( 0.12 ) & 0.72 ( 0.01 ) & 0.1 & 22.11 & 0.72\\\\\n",
+       "\t6 & 100 & 100 & 0 & 12.48 ( 0.9 ) & 0.96 ( 0.09 ) & 0.82 ( 0.01 ) & 0.22 & 16.52 & 0.58\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 0.76 ( 0.01 ) | 0.15 | 15.62 | 0.61 |\n",
+       "| 2 | 100 | 50 | 0 | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 0.84 ( 0.01 ) | 0.23 | 15.98 | 0.56 |\n",
+       "| 3 | 500 | 50 | 0 | 8.74 ( 0.72 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.36 | 13.74 | 0.45 |\n",
+       "| 4 | 1000 | 50 | 0 | 7.56 ( 0.72 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.41 | 12.56 | 0.40 |\n",
+       "| 5 | 50 | 100 | 0 | 18.92 ( 1.7 ) | 1.81 ( 0.12 ) | 0.72 ( 0.01 ) | 0.1 | 22.11 | 0.72 |\n",
+       "| 6 | 100 | 100 | 0 | 12.48 ( 0.9 ) | 0.96 ( 0.09 ) | 0.82 ( 0.01 ) | 0.22 | 16.52 | 0.58 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0   12.04 ( 0.89 ) 1.42 ( 0.11 ) 0.76 ( 0.01 ) 0.15 15.62      0.61\n",
+       "2 100  50  0   11.44 ( 0.76 ) 0.46 ( 0.07 ) 0.84 ( 0.01 ) 0.23 15.98      0.56\n",
+       "3 500  50  0   8.74 ( 0.72 )  0 ( 0 )       0.92 ( 0 )    0.36 13.74      0.45\n",
+       "4 1000 50  0   7.56 ( 0.72 )  0 ( 0 )       0.93 ( 0 )    0.41 12.56      0.40\n",
+       "5 50   100 0   18.92 ( 1.7 )  1.81 ( 0.12 ) 0.72 ( 0.01 ) 0.1  22.11      0.72\n",
+       "6 100  100 0   12.48 ( 0.9 )  0.96 ( 0.09 ) 0.82 ( 0.01 ) 0.22 16.52      0.58"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>16.79 ( 1.47 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.27</td><td>21.79</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>11.47 ( 1.14 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.36</td><td>16.47</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>0.67 ( 0.01 )</td><td>0.02</td><td>74.99</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )</td><td>0.76 ( 0.01 )</td><td>0.09</td><td>32.95</td><td>0.77</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>19.47 ( 1.76 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.24</td><td>24.47</td><td>0.62</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>18.47 ( 1.76 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.25</td><td>23.47</td><td>0.58</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 16.79 ( 1.47 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.27 & 21.79 & 0.61\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 11.47 ( 1.14 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.36 & 16.47 & 0.48\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 0.67 ( 0.01 ) & 0.02 & 74.99 & 0.91\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 29.95 ( 4.05 ) & 2 ( 0.1 ) & 0.76 ( 0.01 ) & 0.09 & 32.95 & 0.77\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 19.47 ( 1.76 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.24 & 24.47 & 0.62\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 18.47 ( 1.76 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.25 & 23.47 & 0.58\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 16.79 ( 1.47 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.27 | 21.79 | 0.61 |\n",
+       "| 12 | 1000 | 500 | 0 | 11.47 ( 1.14 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.36 | 16.47 | 0.48 |\n",
+       "| 13 | 50 | 1000 | 0 | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 0.67 ( 0.01 ) | 0.02 | 74.99 | 0.91 |\n",
+       "| 14 | 100 | 1000 | 0 | 29.95 ( 4.05 ) | 2 ( 0.1 ) | 0.76 ( 0.01 ) | 0.09 | 32.95 | 0.77 |\n",
+       "| 15 | 500 | 1000 | 0 | 19.47 ( 1.76 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.24 | 24.47 | 0.62 |\n",
+       "| 16 | 1000 | 1000 | 0 | 18.47 ( 1.76 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.25 | 23.47 | 0.58 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "11 500  500  0   16.79 ( 1.47 ) 0 ( 0 )       0.92 ( 0 )    0.27 21.79     \n",
+       "12 1000 500  0   11.47 ( 1.14 ) 0 ( 0 )       0.93 ( 0 )    0.36 16.47     \n",
+       "13 50   1000 0   72.88 ( 8.55 ) 2.89 ( 0.13 ) 0.67 ( 0.01 ) 0.02 74.99     \n",
+       "14 100  1000 0   29.95 ( 4.05 ) 2 ( 0.1 )     0.76 ( 0.01 ) 0.09 32.95     \n",
+       "15 500  1000 0   19.47 ( 1.76 ) 0 ( 0 )       0.91 ( 0 )    0.24 24.47     \n",
+       "16 1000 1000 0   18.47 ( 1.76 ) 0 ( 0 )       0.93 ( 0 )    0.25 23.47     \n",
+       "   FDR \n",
+       "11 0.61\n",
+       "12 0.48\n",
+       "13 0.91\n",
+       "14 0.77\n",
+       "15 0.62\n",
+       "16 0.58"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.15</td><td>0.76 ( 0.01 )</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>15.62</td><td>0.61</td><td>0.76</td><td>12.04</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.23</td><td>0.84 ( 0.01 )</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>15.98</td><td>0.56</td><td>0.84</td><td>11.44</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.36</td><td>0.92 ( 0 )   </td><td>8.74 ( 0.72 ) </td><td>0 ( 0 )      </td><td>13.74</td><td>0.45</td><td>0.92</td><td> 8.74</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.41</td><td>0.93 ( 0 )   </td><td>7.56 ( 0.72 ) </td><td>0 ( 0 )      </td><td>12.56</td><td>0.4 </td><td>0.93</td><td> 7.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.10</td><td>0.72 ( 0.01 )</td><td>18.92 ( 1.7 ) </td><td>1.81 ( 0.12 )</td><td>22.11</td><td>0.72</td><td>0.72</td><td>18.92</td><td>1.81</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.22</td><td>0.82 ( 0.01 )</td><td>12.48 ( 0.9 ) </td><td>0.96 ( 0.09 )</td><td>16.52</td><td>0.58</td><td>0.82</td><td>12.48</td><td>0.96</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.15 & 0.76 ( 0.01 ) & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 15.62 & 0.61 & 0.76 & 12.04 & 1.42\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.23 & 0.84 ( 0.01 ) & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 15.98 & 0.56 & 0.84 & 11.44 & 0.46\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.36 & 0.92 ( 0 )    & 8.74 ( 0.72 )  & 0 ( 0 )       & 13.74 & 0.45 & 0.92 &  8.74 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.41 & 0.93 ( 0 )    & 7.56 ( 0.72 )  & 0 ( 0 )       & 12.56 & 0.4  & 0.93 &  7.56 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.10 & 0.72 ( 0.01 ) & 18.92 ( 1.7 )  & 1.81 ( 0.12 ) & 22.11 & 0.72 & 0.72 & 18.92 & 1.81\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.22 & 0.82 ( 0.01 ) & 12.48 ( 0.9 )  & 0.96 ( 0.09 ) & 16.52 & 0.58 & 0.82 & 12.48 & 0.96\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.15 | 0.76 ( 0.01 ) | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 15.62 | 0.61 | 0.76 | 12.04 | 1.42 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.23 | 0.84 ( 0.01 ) | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 15.98 | 0.56 | 0.84 | 11.44 | 0.46 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.36 | 0.92 ( 0 )    | 8.74 ( 0.72 )  | 0 ( 0 )       | 13.74 | 0.45 | 0.92 |  8.74 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.41 | 0.93 ( 0 )    | 7.56 ( 0.72 )  | 0 ( 0 )       | 12.56 | 0.4  | 0.93 |  7.56 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.10 | 0.72 ( 0.01 ) | 18.92 ( 1.7 )  | 1.81 ( 0.12 ) | 22.11 | 0.72 | 0.72 | 18.92 | 1.81 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.22 | 0.82 ( 0.01 ) | 12.48 ( 0.9 )  | 0.96 ( 0.09 ) | 16.52 | 0.58 | 0.82 | 12.48 | 0.96 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0    1.00  0.15 0.76 ( 0.01 ) 12.04 ( 0.89 ) 1.42 ( 0.11 )\n",
+       "2  100  50 0    0.50  0.23 0.84 ( 0.01 ) 11.44 ( 0.76 ) 0.46 ( 0.07 )\n",
+       "3  500  50 0    0.10  0.36 0.92 ( 0 )    8.74 ( 0.72 )  0 ( 0 )      \n",
+       "4 1000  50 0    0.05  0.41 0.93 ( 0 )    7.56 ( 0.72 )  0 ( 0 )      \n",
+       "5   50 100 0    2.00  0.10 0.72 ( 0.01 ) 18.92 ( 1.7 )  1.81 ( 0.12 )\n",
+       "6  100 100 0    1.00  0.22 0.82 ( 0.01 ) 12.48 ( 0.9 )  0.96 ( 0.09 )\n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 15.62      0.61 0.76     12.04   1.42   \n",
+       "2 15.98      0.56 0.84     11.44   0.46   \n",
+       "3 13.74      0.45 0.92      8.74   0.00   \n",
+       "4 12.56      0.4  0.93      7.56   0.00   \n",
+       "5 22.11      0.72 0.72     18.92   1.81   \n",
+       "6 16.52      0.58 0.82     12.48   0.96   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.27</td><td>0.92 ( 0 )   </td><td>16.79 ( 1.47 )</td><td>0 ( 0 )      </td><td>21.79</td><td>0.61</td><td>0.92</td><td>16.79</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.47 ( 1.14 )</td><td>0 ( 0 )      </td><td>16.47</td><td>0.48</td><td>0.93</td><td>11.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>74.99</td><td>0.91</td><td>0.67</td><td>72.88</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.09</td><td>0.76 ( 0.01 )</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )    </td><td>32.95</td><td>0.77</td><td>0.76</td><td>29.95</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.24</td><td>0.91 ( 0 )   </td><td>19.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>24.47</td><td>0.62</td><td>0.91</td><td>19.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.25</td><td>0.93 ( 0 )   </td><td>18.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>23.47</td><td>0.58</td><td>0.93</td><td>18.47</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.27 & 0.92 ( 0 )    & 16.79 ( 1.47 ) & 0 ( 0 )       & 21.79 & 0.61 & 0.92 & 16.79 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.36 & 0.93 ( 0 )    & 11.47 ( 1.14 ) & 0 ( 0 )       & 16.47 & 0.48 & 0.93 & 11.47 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.02 & 0.67 ( 0.01 ) & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 74.99 & 0.91 & 0.67 & 72.88 & 2.89\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.09 & 0.76 ( 0.01 ) & 29.95 ( 4.05 ) & 2 ( 0.1 )     & 32.95 & 0.77 & 0.76 & 29.95 & 2.00\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.24 & 0.91 ( 0 )    & 19.47 ( 1.76 ) & 0 ( 0 )       & 24.47 & 0.62 & 0.91 & 19.47 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.25 & 0.93 ( 0 )    & 18.47 ( 1.76 ) & 0 ( 0 )       & 23.47 & 0.58 & 0.93 & 18.47 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.27 | 0.92 ( 0 )    | 16.79 ( 1.47 ) | 0 ( 0 )       | 21.79 | 0.61 | 0.92 | 16.79 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.36 | 0.93 ( 0 )    | 11.47 ( 1.14 ) | 0 ( 0 )       | 16.47 | 0.48 | 0.93 | 11.47 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.02 | 0.67 ( 0.01 ) | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 74.99 | 0.91 | 0.67 | 72.88 | 2.89 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.09 | 0.76 ( 0.01 ) | 29.95 ( 4.05 ) | 2 ( 0.1 )     | 32.95 | 0.77 | 0.76 | 29.95 | 2.00 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.24 | 0.91 ( 0 )    | 19.47 ( 1.76 ) | 0 ( 0 )       | 24.47 | 0.62 | 0.91 | 19.47 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.25 | 0.93 ( 0 )    | 18.47 ( 1.76 ) | 0 ( 0 )       | 23.47 | 0.58 | 0.93 | 18.47 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "11  500  500 0     1.0  0.27 0.92 ( 0 )    16.79 ( 1.47 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.5  0.36 0.93 ( 0 )    11.47 ( 1.14 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.02 0.67 ( 0.01 ) 72.88 ( 8.55 ) 2.89 ( 0.13 )\n",
+       "14  100 1000 0    10.0  0.09 0.76 ( 0.01 ) 29.95 ( 4.05 ) 2 ( 0.1 )    \n",
+       "15  500 1000 0     2.0  0.24 0.91 ( 0 )    19.47 ( 1.76 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.0  0.25 0.93 ( 0 )    18.47 ( 1.76 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11 21.79      0.61 0.92     16.79   0.00   \n",
+       "12 16.47      0.48 0.93     11.47   0.00   \n",
+       "13 74.99      0.91 0.67     72.88   2.89   \n",
+       "14 32.95      0.77 0.76     29.95   2.00   \n",
+       "15 24.47      0.62 0.91     19.47   0.00   \n",
+       "16 23.47      0.58 0.93     18.47   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.15</td><td>0.76 ( 0.01 )</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>15.62</td><td>0.61</td><td>0.76</td><td>12.04</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.23</td><td>0.84 ( 0.01 )</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>15.98</td><td>0.56</td><td>0.84</td><td>11.44</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.36</td><td>0.92 ( 0 )   </td><td>8.74 ( 0.72 ) </td><td>0 ( 0 )      </td><td>13.74</td><td>0.45</td><td>0.92</td><td> 8.74</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.41</td><td>0.93 ( 0 )   </td><td>7.56 ( 0.72 ) </td><td>0 ( 0 )      </td><td>12.56</td><td>0.4 </td><td>0.93</td><td> 7.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.10</td><td>0.72 ( 0.01 )</td><td>18.92 ( 1.7 ) </td><td>1.81 ( 0.12 )</td><td>22.11</td><td>0.72</td><td>0.72</td><td>18.92</td><td>1.81</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.22</td><td>0.82 ( 0.01 )</td><td>12.48 ( 0.9 ) </td><td>0.96 ( 0.09 )</td><td>16.52</td><td>0.58</td><td>0.82</td><td>12.48</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.32</td><td>0.92 ( 0 )   </td><td>11.78 ( 0.99 )</td><td>0 ( 0 )      </td><td>16.78</td><td>0.52</td><td>0.92</td><td>11.78</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.34</td><td>0.93 ( 0 )   </td><td>10.71 ( 1.03 )</td><td>0 ( 0 )      </td><td>15.71</td><td>0.47</td><td>0.93</td><td>10.71</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.03</td><td>0.68 ( 0.01 )</td><td>57.17 ( 5.72 )</td><td>2.27 ( 0.13 )</td><td>59.90</td><td>0.88</td><td>0.68</td><td>57.17</td><td>2.27</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>20.84 ( 2.08 )</td><td>1.49 ( 0.1 ) </td><td>24.35</td><td>0.69</td><td>0.77</td><td>20.84</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.27</td><td>0.92 ( 0 )   </td><td>16.79 ( 1.47 )</td><td>0 ( 0 )      </td><td>21.79</td><td>0.61</td><td>0.92</td><td>16.79</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.47 ( 1.14 )</td><td>0 ( 0 )      </td><td>16.47</td><td>0.48</td><td>0.93</td><td>11.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>74.99</td><td>0.91</td><td>0.67</td><td>72.88</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.09</td><td>0.76 ( 0.01 )</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )    </td><td>32.95</td><td>0.77</td><td>0.76</td><td>29.95</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.24</td><td>0.91 ( 0 )   </td><td>19.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>24.47</td><td>0.62</td><td>0.91</td><td>19.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.25</td><td>0.93 ( 0 )   </td><td>18.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>23.47</td><td>0.58</td><td>0.93</td><td>18.47</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.15 & 0.76 ( 0.01 ) & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 15.62 & 0.61 & 0.76 & 12.04 & 1.42\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.23 & 0.84 ( 0.01 ) & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 15.98 & 0.56 & 0.84 & 11.44 & 0.46\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.36 & 0.92 ( 0 )    & 8.74 ( 0.72 )  & 0 ( 0 )       & 13.74 & 0.45 & 0.92 &  8.74 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.41 & 0.93 ( 0 )    & 7.56 ( 0.72 )  & 0 ( 0 )       & 12.56 & 0.4  & 0.93 &  7.56 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.10 & 0.72 ( 0.01 ) & 18.92 ( 1.7 )  & 1.81 ( 0.12 ) & 22.11 & 0.72 & 0.72 & 18.92 & 1.81\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.22 & 0.82 ( 0.01 ) & 12.48 ( 0.9 )  & 0.96 ( 0.09 ) & 16.52 & 0.58 & 0.82 & 12.48 & 0.96\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.32 & 0.92 ( 0 )    & 11.78 ( 0.99 ) & 0 ( 0 )       & 16.78 & 0.52 & 0.92 & 11.78 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.34 & 0.93 ( 0 )    & 10.71 ( 1.03 ) & 0 ( 0 )       & 15.71 & 0.47 & 0.93 & 10.71 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.03 & 0.68 ( 0.01 ) & 57.17 ( 5.72 ) & 2.27 ( 0.13 ) & 59.90 & 0.88 & 0.68 & 57.17 & 2.27\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.15 & 0.77 ( 0.01 ) & 20.84 ( 2.08 ) & 1.49 ( 0.1 )  & 24.35 & 0.69 & 0.77 & 20.84 & 1.49\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.27 & 0.92 ( 0 )    & 16.79 ( 1.47 ) & 0 ( 0 )       & 21.79 & 0.61 & 0.92 & 16.79 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.36 & 0.93 ( 0 )    & 11.47 ( 1.14 ) & 0 ( 0 )       & 16.47 & 0.48 & 0.93 & 11.47 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.02 & 0.67 ( 0.01 ) & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 74.99 & 0.91 & 0.67 & 72.88 & 2.89\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.09 & 0.76 ( 0.01 ) & 29.95 ( 4.05 ) & 2 ( 0.1 )     & 32.95 & 0.77 & 0.76 & 29.95 & 2.00\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.24 & 0.91 ( 0 )    & 19.47 ( 1.76 ) & 0 ( 0 )       & 24.47 & 0.62 & 0.91 & 19.47 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.25 & 0.93 ( 0 )    & 18.47 ( 1.76 ) & 0 ( 0 )       & 23.47 & 0.58 & 0.93 & 18.47 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.15 | 0.76 ( 0.01 ) | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 15.62 | 0.61 | 0.76 | 12.04 | 1.42 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.23 | 0.84 ( 0.01 ) | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 15.98 | 0.56 | 0.84 | 11.44 | 0.46 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.36 | 0.92 ( 0 )    | 8.74 ( 0.72 )  | 0 ( 0 )       | 13.74 | 0.45 | 0.92 |  8.74 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.41 | 0.93 ( 0 )    | 7.56 ( 0.72 )  | 0 ( 0 )       | 12.56 | 0.4  | 0.93 |  7.56 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.10 | 0.72 ( 0.01 ) | 18.92 ( 1.7 )  | 1.81 ( 0.12 ) | 22.11 | 0.72 | 0.72 | 18.92 | 1.81 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.22 | 0.82 ( 0.01 ) | 12.48 ( 0.9 )  | 0.96 ( 0.09 ) | 16.52 | 0.58 | 0.82 | 12.48 | 0.96 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.32 | 0.92 ( 0 )    | 11.78 ( 0.99 ) | 0 ( 0 )       | 16.78 | 0.52 | 0.92 | 11.78 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.34 | 0.93 ( 0 )    | 10.71 ( 1.03 ) | 0 ( 0 )       | 15.71 | 0.47 | 0.93 | 10.71 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.03 | 0.68 ( 0.01 ) | 57.17 ( 5.72 ) | 2.27 ( 0.13 ) | 59.90 | 0.88 | 0.68 | 57.17 | 2.27 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.15 | 0.77 ( 0.01 ) | 20.84 ( 2.08 ) | 1.49 ( 0.1 )  | 24.35 | 0.69 | 0.77 | 20.84 | 1.49 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.27 | 0.92 ( 0 )    | 16.79 ( 1.47 ) | 0 ( 0 )       | 21.79 | 0.61 | 0.92 | 16.79 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.36 | 0.93 ( 0 )    | 11.47 ( 1.14 ) | 0 ( 0 )       | 16.47 | 0.48 | 0.93 | 11.47 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.02 | 0.67 ( 0.01 ) | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 74.99 | 0.91 | 0.67 | 72.88 | 2.89 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.09 | 0.76 ( 0.01 ) | 29.95 ( 4.05 ) | 2 ( 0.1 )     | 32.95 | 0.77 | 0.76 | 29.95 | 2.00 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.24 | 0.91 ( 0 )    | 19.47 ( 1.76 ) | 0 ( 0 )       | 24.47 | 0.62 | 0.91 | 19.47 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.25 | 0.93 ( 0 )    | 18.47 ( 1.76 ) | 0 ( 0 )       | 23.47 | 0.58 | 0.93 | 18.47 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0     1.00 0.15 0.76 ( 0.01 ) 12.04 ( 0.89 ) 1.42 ( 0.11 )\n",
+       "2   100   50 0     0.50 0.23 0.84 ( 0.01 ) 11.44 ( 0.76 ) 0.46 ( 0.07 )\n",
+       "3   500   50 0     0.10 0.36 0.92 ( 0 )    8.74 ( 0.72 )  0 ( 0 )      \n",
+       "4  1000   50 0     0.05 0.41 0.93 ( 0 )    7.56 ( 0.72 )  0 ( 0 )      \n",
+       "5    50  100 0     2.00 0.10 0.72 ( 0.01 ) 18.92 ( 1.7 )  1.81 ( 0.12 )\n",
+       "6   100  100 0     1.00 0.22 0.82 ( 0.01 ) 12.48 ( 0.9 )  0.96 ( 0.09 )\n",
+       "7   500  100 0     0.20 0.32 0.92 ( 0 )    11.78 ( 0.99 ) 0 ( 0 )      \n",
+       "8  1000  100 0     0.10 0.34 0.93 ( 0 )    10.71 ( 1.03 ) 0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.03 0.68 ( 0.01 ) 57.17 ( 5.72 ) 2.27 ( 0.13 )\n",
+       "10  100  500 0     5.00 0.15 0.77 ( 0.01 ) 20.84 ( 2.08 ) 1.49 ( 0.1 ) \n",
+       "11  500  500 0     1.00 0.27 0.92 ( 0 )    16.79 ( 1.47 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.50 0.36 0.93 ( 0 )    11.47 ( 1.14 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.02 0.67 ( 0.01 ) 72.88 ( 8.55 ) 2.89 ( 0.13 )\n",
+       "14  100 1000 0    10.00 0.09 0.76 ( 0.01 ) 29.95 ( 4.05 ) 2 ( 0.1 )    \n",
+       "15  500 1000 0     2.00 0.24 0.91 ( 0 )    19.47 ( 1.76 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.00 0.25 0.93 ( 0 )    18.47 ( 1.76 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1  15.62      0.61 0.76     12.04   1.42   \n",
+       "2  15.98      0.56 0.84     11.44   0.46   \n",
+       "3  13.74      0.45 0.92      8.74   0.00   \n",
+       "4  12.56      0.4  0.93      7.56   0.00   \n",
+       "5  22.11      0.72 0.72     18.92   1.81   \n",
+       "6  16.52      0.58 0.82     12.48   0.96   \n",
+       "7  16.78      0.52 0.92     11.78   0.00   \n",
+       "8  15.71      0.47 0.93     10.71   0.00   \n",
+       "9  59.90      0.88 0.68     57.17   2.27   \n",
+       "10 24.35      0.69 0.77     20.84   1.49   \n",
+       "11 21.79      0.61 0.92     16.79   0.00   \n",
+       "12 16.47      0.48 0.93     11.47   0.00   \n",
+       "13 74.99      0.91 0.67     72.88   2.89   \n",
+       "14 32.95      0.77 0.76     29.95   2.00   \n",
+       "15 24.47      0.62 0.91     19.47   0.00   \n",
+       "16 23.47      0.58 0.93     18.47   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.3_sim_ind_rf_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.3_sim_ind_rf_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..6dfa2c6
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.3_sim_ind_rf_binary_update-checkpoint.ipynb
@@ -0,0 +1,694 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>1.97 ( 0.13 )</td><td>4.62 ( 0.08 )</td><td>1 ( 0 )</td><td>0.1</td><td>3.34</td><td>0.55</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>1.26 ( 0.12 )</td><td>2.19 ( 0.08 )</td><td>1 ( 0 )</td><td>0.54</td><td>5.07</td><td>0.22</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>0.86 ( 0.09 )</td><td>1.12 ( 0.08 )</td><td>1 ( 0 )</td><td>0.73</td><td>5.74</td><td>0.13</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>4.29 ( 0.17 )</td><td>4.79 ( 0.09 )</td><td>1 ( 0 )</td><td>0.05</td><td>5.50</td><td>0.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0 & 1.97 ( 0.13 ) & 4.62 ( 0.08 ) & 1 ( 0 ) & 0.1 & 3.34 & 0.55\\\\\n",
+       "\t3 & 500 & 50 & 0 & 1.26 ( 0.12 ) & 2.19 ( 0.08 ) & 1 ( 0 ) & 0.54 & 5.07 & 0.22\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 0.86 ( 0.09 ) & 1.12 ( 0.08 ) & 1 ( 0 ) & 0.73 & 5.74 & 0.13\\\\\n",
+       "\t5 & 50 & 100 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0 & 4.29 ( 0.17 ) & 4.79 ( 0.09 ) & 1 ( 0 ) & 0.05 & 5.50 & 0.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0 | 1.97 ( 0.13 ) | 4.62 ( 0.08 ) | 1 ( 0 ) | 0.1 | 3.34 | 0.55 |\n",
+       "| 3 | 500 | 50 | 0 | 1.26 ( 0.12 ) | 2.19 ( 0.08 ) | 1 ( 0 ) | 0.54 | 5.07 | 0.22 |\n",
+       "| 4 | 1000 | 50 | 0 | 0.86 ( 0.09 ) | 1.12 ( 0.08 ) | 1 ( 0 ) | 0.73 | 5.74 | 0.13 |\n",
+       "| 5 | 50 | 100 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0 | 4.29 ( 0.17 ) | 4.79 ( 0.09 ) | 1 ( 0 ) | 0.05 | 5.50 | 0.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0   1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0   1.97 ( 0.13 ) 4.62 ( 0.08 ) 1 ( 0 ) 0.1  3.34       0.55\n",
+       "3 500  50  0   1.26 ( 0.12 ) 2.19 ( 0.08 ) 1 ( 0 ) 0.54 5.07       0.22\n",
+       "4 1000 50  0   0.86 ( 0.09 ) 1.12 ( 0.08 ) 1 ( 0 ) 0.73 5.74       0.13\n",
+       "5 50   100 0   1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0   4.29 ( 0.17 ) 4.79 ( 0.09 ) 1 ( 0 ) 0.05 5.50       0.77"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 )</td><td>1 ( 0 )</td><td>0.09</td><td>26.74</td><td>0.86</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>1 ( 0 )</td><td>0.14</td><td>27.38</td><td>0.83</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>1 ( 0 )</td><td>0.01</td><td>47.90</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )</td><td>1 ( 0 )</td><td>0.04</td><td>51.32</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>1 ( 0 )</td><td>0.07</td><td>51.68</td><td>0.91</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 23.08 ( 0.51 ) & 2.34 ( 0.1 ) & 1 ( 0 ) & 0.09 & 26.74 & 0.86\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 1 ( 0 ) & 0.14 & 27.38 & 0.83\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 1 ( 0 ) & 0.01 & 47.90 & 0.98\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 47.82 ( 0.66 ) & 2.5 ( 0.1 ) & 1 ( 0 ) & 0.04 & 51.32 & 0.93\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 1 ( 0 ) & 0.07 & 51.68 & 0.91\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 23.08 ( 0.51 ) | 2.34 ( 0.1 ) | 1 ( 0 ) | 0.09 | 26.74 | 0.86 |\n",
+       "| 12 | 1000 | 500 | 0 | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 1 ( 0 ) | 0.14 | 27.38 | 0.83 |\n",
+       "| 13 | 50 | 1000 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 14 | 100 | 1000 | 0 | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 1 ( 0 ) | 0.01 | 47.90 | 0.98 |\n",
+       "| 15 | 500 | 1000 | 0 | 47.82 ( 0.66 ) | 2.5 ( 0.1 ) | 1 ( 0 ) | 0.04 | 51.32 | 0.93 |\n",
+       "| 16 | 1000 | 1000 | 0 | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 1 ( 0 ) | 0.07 | 51.68 | 0.91 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "11 500  500  0   23.08 ( 0.51 ) 2.34 ( 0.1 )  1 ( 0 ) 0.09 26.74      0.86\n",
+       "12 1000 500  0   22.72 ( 0.46 ) 1.34 ( 0.08 ) 1 ( 0 ) 0.14 27.38      0.83\n",
+       "13 50   1000 0   1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "14 100  1000 0   46.76 ( 0.67 ) 4.86 ( 0.09 ) 1 ( 0 ) 0.01 47.90      0.98\n",
+       "15 500  1000 0   47.82 ( 0.66 ) 2.5 ( 0.1 )   1 ( 0 ) 0.04 51.32      0.93\n",
+       "16 1000 1000 0   47.14 ( 0.77 ) 1.46 ( 0.08 ) 1 ( 0 ) 0.07 51.68      0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0     1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0     2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0    10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0    20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.97 ( 0.13 )</td><td>4.62 ( 0.08 )</td><td>3.34</td><td>0.55</td><td>1</td><td>1.97</td><td>4.62</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.54</td><td>1 ( 0 )</td><td>1.26 ( 0.12 )</td><td>2.19 ( 0.08 )</td><td>5.07</td><td>0.22</td><td>1</td><td>1.26</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.73</td><td>1 ( 0 )</td><td>0.86 ( 0.09 )</td><td>1.12 ( 0.08 )</td><td>5.74</td><td>0.13</td><td>1</td><td>0.86</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.29 ( 0.17 )</td><td>4.79 ( 0.09 )</td><td>5.50</td><td>0.77</td><td>1</td><td>4.29</td><td>4.79</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.10 & 1 ( 0 ) & 1.97 ( 0.13 ) & 4.62 ( 0.08 ) & 3.34 & 0.55 & 1 & 1.97 & 4.62\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.54 & 1 ( 0 ) & 1.26 ( 0.12 ) & 2.19 ( 0.08 ) & 5.07 & 0.22 & 1 & 1.26 & 2.19\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.73 & 1 ( 0 ) & 0.86 ( 0.09 ) & 1.12 ( 0.08 ) & 5.74 & 0.13 & 1 & 0.86 & 1.12\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.05 & 1 ( 0 ) & 4.29 ( 0.17 ) & 4.79 ( 0.09 ) & 5.50 & 0.77 & 1 & 4.29 & 4.79\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.10 | 1 ( 0 ) | 1.97 ( 0.13 ) | 4.62 ( 0.08 ) | 3.34 | 0.55 | 1 | 1.97 | 4.62 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.54 | 1 ( 0 ) | 1.26 ( 0.12 ) | 2.19 ( 0.08 ) | 5.07 | 0.22 | 1 | 1.26 | 2.19 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.73 | 1 ( 0 ) | 0.86 ( 0.09 ) | 1.12 ( 0.08 ) | 5.74 | 0.13 | 1 | 0.86 | 1.12 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.05 | 1 ( 0 ) | 4.29 ( 0.17 ) | 4.79 ( 0.09 ) | 5.50 | 0.77 | 1 | 4.29 | 4.79 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0    1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0    0.50  0.10 1 ( 0 ) 1.97 ( 0.13 ) 4.62 ( 0.08 ) 3.34       0.55\n",
+       "3  500  50 0    0.10  0.54 1 ( 0 ) 1.26 ( 0.12 ) 2.19 ( 0.08 ) 5.07       0.22\n",
+       "4 1000  50 0    0.05  0.73 1 ( 0 ) 0.86 ( 0.09 ) 1.12 ( 0.08 ) 5.74       0.13\n",
+       "5   50 100 0    2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0    1.00  0.05 1 ( 0 ) 4.29 ( 0.17 ) 4.79 ( 0.09 ) 5.50       0.77\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        1.97    4.62   \n",
+       "3 1        1.26    2.19   \n",
+       "4 1        0.86    1.12   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        4.29    4.79   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.09</td><td>1 ( 0 )</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 ) </td><td>26.74</td><td>0.86</td><td>1</td><td>23.08</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.14</td><td>1 ( 0 )</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>27.38</td><td>0.83</td><td>1</td><td>22.72</td><td>1.34</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.01</td><td>1 ( 0 )</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>47.90</td><td>0.98</td><td>1</td><td>46.76</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )  </td><td>51.32</td><td>0.93</td><td>1</td><td>47.82</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>51.68</td><td>0.91</td><td>1</td><td>47.14</td><td>1.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.09 & 1 ( 0 ) & 23.08 ( 0.51 ) & 2.34 ( 0.1 )  & 26.74 & 0.86 & 1 & 23.08 & 2.34\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.14 & 1 ( 0 ) & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 27.38 & 0.83 & 1 & 22.72 & 1.34\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.01 & 1 ( 0 ) & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 47.90 & 0.98 & 1 & 46.76 & 4.86\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.04 & 1 ( 0 ) & 47.82 ( 0.66 ) & 2.5 ( 0.1 )   & 51.32 & 0.93 & 1 & 47.82 & 2.50\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.07 & 1 ( 0 ) & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 51.68 & 0.91 & 1 & 47.14 & 1.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.09 | 1 ( 0 ) | 23.08 ( 0.51 ) | 2.34 ( 0.1 )  | 26.74 | 0.86 | 1 | 23.08 | 2.34 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.14 | 1 ( 0 ) | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 27.38 | 0.83 | 1 | 22.72 | 1.34 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.01 | 1 ( 0 ) | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 47.90 | 0.98 | 1 | 46.76 | 4.86 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.04 | 1 ( 0 ) | 47.82 ( 0.66 ) | 2.5 ( 0.1 )   | 51.32 | 0.93 | 1 | 47.82 | 2.50 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.07 | 1 ( 0 ) | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 51.68 | 0.91 | 1 | 47.14 | 1.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "11  500  500 0     1.0  0.09 1 ( 0 ) 23.08 ( 0.51 ) 2.34 ( 0.1 )  26.74     \n",
+       "12 1000  500 0     0.5  0.14 1 ( 0 ) 22.72 ( 0.46 ) 1.34 ( 0.08 ) 27.38     \n",
+       "13   50 1000 0    20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0    10.0  0.01 1 ( 0 ) 46.76 ( 0.67 ) 4.86 ( 0.09 ) 47.90     \n",
+       "15  500 1000 0     2.0  0.04 1 ( 0 ) 47.82 ( 0.66 ) 2.5 ( 0.1 )   51.32     \n",
+       "16 1000 1000 0     1.0  0.07 1 ( 0 ) 47.14 ( 0.77 ) 1.46 ( 0.08 ) 51.68     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "11 0.86 1        23.08   2.34   \n",
+       "12 0.83 1        22.72   1.34   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        46.76   4.86   \n",
+       "15 0.93 1        47.82   2.50   \n",
+       "16 0.91 1        47.14   1.46   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.97 ( 0.13 ) </td><td>4.62 ( 0.08 )</td><td> 3.34</td><td>0.55</td><td>1</td><td> 1.97</td><td>4.62</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.54</td><td>1 ( 0 )</td><td>1.26 ( 0.12 ) </td><td>2.19 ( 0.08 )</td><td> 5.07</td><td>0.22</td><td>1</td><td> 1.26</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.73</td><td>1 ( 0 )</td><td>0.86 ( 0.09 ) </td><td>1.12 ( 0.08 )</td><td> 5.74</td><td>0.13</td><td>1</td><td> 0.86</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.29 ( 0.17 ) </td><td>4.79 ( 0.09 )</td><td> 5.50</td><td>0.77</td><td>1</td><td> 4.29</td><td>4.79</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.40</td><td>1 ( 0 )</td><td>3.42 ( 0.16 ) </td><td>2.01 ( 0.09 )</td><td> 7.41</td><td>0.44</td><td>1</td><td> 3.42</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.49</td><td>1 ( 0 )</td><td>3.39 ( 0.19 ) </td><td>1.14 ( 0.07 )</td><td> 8.25</td><td>0.38</td><td>1</td><td> 3.39</td><td>1.14</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.68 ( 0.48 )</td><td>4.5 ( 0.09 ) </td><td>26.18</td><td>0.94</td><td>1</td><td>24.68</td><td>4.50</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.09</td><td>1 ( 0 )</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 ) </td><td>26.74</td><td>0.86</td><td>1</td><td>23.08</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>27.38</td><td>0.83</td><td>1</td><td>22.72</td><td>1.34</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>47.90</td><td>0.98</td><td>1</td><td>46.76</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )  </td><td>51.32</td><td>0.93</td><td>1</td><td>47.82</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>51.68</td><td>0.91</td><td>1</td><td>47.14</td><td>1.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.10 & 1 ( 0 ) & 1.97 ( 0.13 )  & 4.62 ( 0.08 ) &  3.34 & 0.55 & 1 &  1.97 & 4.62\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.54 & 1 ( 0 ) & 1.26 ( 0.12 )  & 2.19 ( 0.08 ) &  5.07 & 0.22 & 1 &  1.26 & 2.19\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.73 & 1 ( 0 ) & 0.86 ( 0.09 )  & 1.12 ( 0.08 ) &  5.74 & 0.13 & 1 &  0.86 & 1.12\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.05 & 1 ( 0 ) & 4.29 ( 0.17 )  & 4.79 ( 0.09 ) &  5.50 & 0.77 & 1 &  4.29 & 4.79\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.40 & 1 ( 0 ) & 3.42 ( 0.16 )  & 2.01 ( 0.09 ) &  7.41 & 0.44 & 1 &  3.42 & 2.01\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.49 & 1 ( 0 ) & 3.39 ( 0.19 )  & 1.14 ( 0.07 ) &  8.25 & 0.38 & 1 &  3.39 & 1.14\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.01 & 1 ( 0 ) & 24.68 ( 0.48 ) & 4.5 ( 0.09 )  & 26.18 & 0.94 & 1 & 24.68 & 4.50\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.09 & 1 ( 0 ) & 23.08 ( 0.51 ) & 2.34 ( 0.1 )  & 26.74 & 0.86 & 1 & 23.08 & 2.34\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.14 & 1 ( 0 ) & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 27.38 & 0.83 & 1 & 22.72 & 1.34\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.01 & 1 ( 0 ) & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 47.90 & 0.98 & 1 & 46.76 & 4.86\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.04 & 1 ( 0 ) & 47.82 ( 0.66 ) & 2.5 ( 0.1 )   & 51.32 & 0.93 & 1 & 47.82 & 2.50\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.07 & 1 ( 0 ) & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 51.68 & 0.91 & 1 & 47.14 & 1.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.10 | 1 ( 0 ) | 1.97 ( 0.13 )  | 4.62 ( 0.08 ) |  3.34 | 0.55 | 1 |  1.97 | 4.62 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.54 | 1 ( 0 ) | 1.26 ( 0.12 )  | 2.19 ( 0.08 ) |  5.07 | 0.22 | 1 |  1.26 | 2.19 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.73 | 1 ( 0 ) | 0.86 ( 0.09 )  | 1.12 ( 0.08 ) |  5.74 | 0.13 | 1 |  0.86 | 1.12 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.05 | 1 ( 0 ) | 4.29 ( 0.17 )  | 4.79 ( 0.09 ) |  5.50 | 0.77 | 1 |  4.29 | 4.79 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.40 | 1 ( 0 ) | 3.42 ( 0.16 )  | 2.01 ( 0.09 ) |  7.41 | 0.44 | 1 |  3.42 | 2.01 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.49 | 1 ( 0 ) | 3.39 ( 0.19 )  | 1.14 ( 0.07 ) |  8.25 | 0.38 | 1 |  3.39 | 1.14 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.01 | 1 ( 0 ) | 24.68 ( 0.48 ) | 4.5 ( 0.09 )  | 26.18 | 0.94 | 1 | 24.68 | 4.50 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.09 | 1 ( 0 ) | 23.08 ( 0.51 ) | 2.34 ( 0.1 )  | 26.74 | 0.86 | 1 | 23.08 | 2.34 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.14 | 1 ( 0 ) | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 27.38 | 0.83 | 1 | 22.72 | 1.34 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.01 | 1 ( 0 ) | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 47.90 | 0.98 | 1 | 46.76 | 4.86 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.04 | 1 ( 0 ) | 47.82 ( 0.66 ) | 2.5 ( 0.1 )   | 51.32 | 0.93 | 1 | 47.82 | 2.50 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.07 | 1 ( 0 ) | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 51.68 | 0.91 | 1 | 47.14 | 1.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0     1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0     0.50 0.10 1 ( 0 ) 1.97 ( 0.13 )  4.62 ( 0.08 )  3.34     \n",
+       "3   500   50 0     0.10 0.54 1 ( 0 ) 1.26 ( 0.12 )  2.19 ( 0.08 )  5.07     \n",
+       "4  1000   50 0     0.05 0.73 1 ( 0 ) 0.86 ( 0.09 )  1.12 ( 0.08 )  5.74     \n",
+       "5    50  100 0     2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0     1.00 0.05 1 ( 0 ) 4.29 ( 0.17 )  4.79 ( 0.09 )  5.50     \n",
+       "7   500  100 0     0.20 0.40 1 ( 0 ) 3.42 ( 0.16 )  2.01 ( 0.09 )  7.41     \n",
+       "8  1000  100 0     0.10 0.49 1 ( 0 ) 3.39 ( 0.19 )  1.14 ( 0.07 )  8.25     \n",
+       "9    50  500 0    10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0     5.00 0.01 1 ( 0 ) 24.68 ( 0.48 ) 4.5 ( 0.09 )  26.18     \n",
+       "11  500  500 0     1.00 0.09 1 ( 0 ) 23.08 ( 0.51 ) 2.34 ( 0.1 )  26.74     \n",
+       "12 1000  500 0     0.50 0.14 1 ( 0 ) 22.72 ( 0.46 ) 1.34 ( 0.08 ) 27.38     \n",
+       "13   50 1000 0    20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0    10.00 0.01 1 ( 0 ) 46.76 ( 0.67 ) 4.86 ( 0.09 ) 47.90     \n",
+       "15  500 1000 0     2.00 0.04 1 ( 0 ) 47.82 ( 0.66 ) 2.5 ( 0.1 )   51.32     \n",
+       "16 1000 1000 0     1.00 0.07 1 ( 0 ) 47.14 ( 0.77 ) 1.46 ( 0.08 ) 51.68     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.55 1         1.97   4.62   \n",
+       "3  0.22 1         1.26   2.19   \n",
+       "4  0.13 1         0.86   1.12   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.77 1         4.29   4.79   \n",
+       "7  0.44 1         3.42   2.01   \n",
+       "8  0.38 1         3.39   1.14   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.94 1        24.68   4.50   \n",
+       "11 0.86 1        23.08   2.34   \n",
+       "12 0.83 1        22.72   1.34   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        46.76   4.86   \n",
+       "15 0.93 1        47.82   2.50   \n",
+       "16 0.91 1        47.14   1.46   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.4_sim_ind_compLasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.4_sim_ind_compLasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..e765396
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/0.4_sim_ind_compLasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>16.38 ( 0.96 )</td><td>0.61 ( 0.06 )</td><td>0.98 ( 0 )</td><td>0.13</td><td>21.77</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>7.94 ( 0.54 )</td><td>0.34 ( 0.06 )</td><td>0.93 ( 0 )</td><td>0.32</td><td>13.60</td><td>0.54</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>2.67 ( 0.6 )</td><td>0.1 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.63</td><td> 8.57</td><td>0.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>1.61 ( 0.48 )</td><td>0.04 ( 0.02 )</td><td>0.91 ( 0 )</td><td>0.75</td><td> 7.57</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>39.18 ( 2.14 )</td><td>0.63 ( 0.07 )</td><td>0.99 ( 0 )</td><td>0.06</td><td>44.55</td><td>0.85</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>16.29 ( 1.16 )</td><td>0.47 ( 0.06 )</td><td>0.94 ( 0 )</td><td>0.2</td><td>21.82</td><td>0.70</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 16.38 ( 0.96 ) & 0.61 ( 0.06 ) & 0.98 ( 0 ) & 0.13 & 21.77 & 0.71\\\\\n",
+       "\t2 & 100 & 50 & 0 & 7.94 ( 0.54 ) & 0.34 ( 0.06 ) & 0.93 ( 0 ) & 0.32 & 13.60 & 0.54\\\\\n",
+       "\t3 & 500 & 50 & 0 & 2.67 ( 0.6 ) & 0.1 ( 0.03 ) & 0.91 ( 0 ) & 0.63 &  8.57 & 0.19\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 1.61 ( 0.48 ) & 0.04 ( 0.02 ) & 0.91 ( 0 ) & 0.75 &  7.57 & 0.09\\\\\n",
+       "\t5 & 50 & 100 & 0 & 39.18 ( 2.14 ) & 0.63 ( 0.07 ) & 0.99 ( 0 ) & 0.06 & 44.55 & 0.85\\\\\n",
+       "\t6 & 100 & 100 & 0 & 16.29 ( 1.16 ) & 0.47 ( 0.06 ) & 0.94 ( 0 ) & 0.2 & 21.82 & 0.70\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 16.38 ( 0.96 ) | 0.61 ( 0.06 ) | 0.98 ( 0 ) | 0.13 | 21.77 | 0.71 |\n",
+       "| 2 | 100 | 50 | 0 | 7.94 ( 0.54 ) | 0.34 ( 0.06 ) | 0.93 ( 0 ) | 0.32 | 13.60 | 0.54 |\n",
+       "| 3 | 500 | 50 | 0 | 2.67 ( 0.6 ) | 0.1 ( 0.03 ) | 0.91 ( 0 ) | 0.63 |  8.57 | 0.19 |\n",
+       "| 4 | 1000 | 50 | 0 | 1.61 ( 0.48 ) | 0.04 ( 0.02 ) | 0.91 ( 0 ) | 0.75 |  7.57 | 0.09 |\n",
+       "| 5 | 50 | 100 | 0 | 39.18 ( 2.14 ) | 0.63 ( 0.07 ) | 0.99 ( 0 ) | 0.06 | 44.55 | 0.85 |\n",
+       "| 6 | 100 | 100 | 0 | 16.29 ( 1.16 ) | 0.47 ( 0.06 ) | 0.94 ( 0 ) | 0.2 | 21.82 | 0.70 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC        Stab num_select FDR \n",
+       "1 50   50  0   16.38 ( 0.96 ) 0.61 ( 0.06 ) 0.98 ( 0 ) 0.13 21.77      0.71\n",
+       "2 100  50  0   7.94 ( 0.54 )  0.34 ( 0.06 ) 0.93 ( 0 ) 0.32 13.60      0.54\n",
+       "3 500  50  0   2.67 ( 0.6 )   0.1 ( 0.03 )  0.91 ( 0 ) 0.63  8.57      0.19\n",
+       "4 1000 50  0   1.61 ( 0.48 )  0.04 ( 0.02 ) 0.91 ( 0 ) 0.75  7.57      0.09\n",
+       "5 50   100 0   39.18 ( 2.14 ) 0.63 ( 0.07 ) 0.99 ( 0 ) 0.06 44.55      0.85\n",
+       "6 100  100 0   16.29 ( 1.16 ) 0.47 ( 0.06 ) 0.94 ( 0 ) 0.2  21.82      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>8.32 ( 1.15 )</td><td>0.1 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.43</td><td> 14.22</td><td>0.41</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>4.63 ( 1.57 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.6</td><td> 10.63</td><td>0.21</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>1 ( 0 )</td><td>0.01</td><td>411.59</td><td>0.99</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>1 ( 0 )</td><td>0.02</td><td>288.75</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>9.4 ( 1.07 )</td><td>0.07 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.4</td><td> 15.33</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>1.91 ( 0.29 )</td><td>0.01 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.82</td><td>  7.90</td><td>0.18</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 8.32 ( 1.15 ) & 0.1 ( 0.03 ) & 0.91 ( 0 ) & 0.43 &  14.22 & 0.41\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 4.63 ( 1.57 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.6 &  10.63 & 0.21\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 1 ( 0 ) & 0.01 & 411.59 & 0.99\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 1 ( 0 ) & 0.02 & 288.75 & 0.98\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 9.4 ( 1.07 ) & 0.07 ( 0.03 ) & 0.91 ( 0 ) & 0.4 &  15.33 & 0.47\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 1.91 ( 0.29 ) & 0.01 ( 0.01 ) & 0.91 ( 0 ) & 0.82 &   7.90 & 0.18\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 8.32 ( 1.15 ) | 0.1 ( 0.03 ) | 0.91 ( 0 ) | 0.43 |  14.22 | 0.41 |\n",
+       "| 12 | 1000 | 500 | 0 | 4.63 ( 1.57 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.6 |  10.63 | 0.21 |\n",
+       "| 13 | 50 | 1000 | 0 | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 1 ( 0 ) | 0.01 | 411.59 | 0.99 |\n",
+       "| 14 | 100 | 1000 | 0 | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 1 ( 0 ) | 0.02 | 288.75 | 0.98 |\n",
+       "| 15 | 500 | 1000 | 0 | 9.4 ( 1.07 ) | 0.07 ( 0.03 ) | 0.91 ( 0 ) | 0.4 |  15.33 | 0.47 |\n",
+       "| 16 | 1000 | 1000 | 0 | 1.91 ( 0.29 ) | 0.01 ( 0.01 ) | 0.91 ( 0 ) | 0.82 |   7.90 | 0.18 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP              FN            ROC        Stab num_select FDR \n",
+       "11 500  500  0   8.32 ( 1.15 )   0.1 ( 0.03 )  0.91 ( 0 ) 0.43  14.22     0.41\n",
+       "12 1000 500  0   4.63 ( 1.57 )   0 ( 0 )       0.91 ( 0 ) 0.6   10.63     0.21\n",
+       "13 50   1000 0   406.32 ( 7.33 ) 0.73 ( 0.07 ) 1 ( 0 )    0.01 411.59     0.99\n",
+       "14 100  1000 0   283.24 ( 7.31 ) 0.49 ( 0.06 ) 1 ( 0 )    0.02 288.75     0.98\n",
+       "15 500  1000 0   9.4 ( 1.07 )    0.07 ( 0.03 ) 0.91 ( 0 ) 0.4   15.33     0.47\n",
+       "16 1000 1000 0   1.91 ( 0.29 )   0.01 ( 0.01 ) 0.91 ( 0 ) 0.82   7.90     0.18"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "# result.table_toe$ROC_mean = as.numeric(substr(result.table_toe$ROC, start=1, stop=4))\n",
+    "# result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "# result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "# result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.13</td><td>0.98 ( 0 )</td><td>16.38 ( 0.96 )</td><td>0.61 ( 0.06 )</td><td>21.77</td><td>0.71</td><td>0.98</td><td>16.38</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.32</td><td>0.93 ( 0 )</td><td>7.94 ( 0.54 ) </td><td>0.34 ( 0.06 )</td><td>13.60</td><td>0.54</td><td>0.93</td><td> 7.94</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.63</td><td>0.91 ( 0 )</td><td>2.67 ( 0.6 )  </td><td>0.1 ( 0.03 ) </td><td> 8.57</td><td>0.19</td><td>0.91</td><td> 2.67</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.75</td><td>0.91 ( 0 )</td><td>1.61 ( 0.48 ) </td><td>0.04 ( 0.02 )</td><td> 7.57</td><td>0.09</td><td>0.91</td><td> 1.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>39.18 ( 2.14 )</td><td>0.63 ( 0.07 )</td><td>44.55</td><td>0.85</td><td>0.99</td><td>39.18</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.29 ( 1.16 )</td><td>0.47 ( 0.06 )</td><td>21.82</td><td>0.7 </td><td>0.94</td><td>16.29</td><td>0.47</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.13 & 0.98 ( 0 ) & 16.38 ( 0.96 ) & 0.61 ( 0.06 ) & 21.77 & 0.71 & 0.98 & 16.38 & 0.61\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.32 & 0.93 ( 0 ) & 7.94 ( 0.54 )  & 0.34 ( 0.06 ) & 13.60 & 0.54 & 0.93 &  7.94 & 0.34\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.63 & 0.91 ( 0 ) & 2.67 ( 0.6 )   & 0.1 ( 0.03 )  &  8.57 & 0.19 & 0.91 &  2.67 & 0.10\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.75 & 0.91 ( 0 ) & 1.61 ( 0.48 )  & 0.04 ( 0.02 ) &  7.57 & 0.09 & 0.91 &  1.61 & 0.04\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.06 & 0.99 ( 0 ) & 39.18 ( 2.14 ) & 0.63 ( 0.07 ) & 44.55 & 0.85 & 0.99 & 39.18 & 0.63\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.20 & 0.94 ( 0 ) & 16.29 ( 1.16 ) & 0.47 ( 0.06 ) & 21.82 & 0.7  & 0.94 & 16.29 & 0.47\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.13 | 0.98 ( 0 ) | 16.38 ( 0.96 ) | 0.61 ( 0.06 ) | 21.77 | 0.71 | 0.98 | 16.38 | 0.61 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.32 | 0.93 ( 0 ) | 7.94 ( 0.54 )  | 0.34 ( 0.06 ) | 13.60 | 0.54 | 0.93 |  7.94 | 0.34 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.63 | 0.91 ( 0 ) | 2.67 ( 0.6 )   | 0.1 ( 0.03 )  |  8.57 | 0.19 | 0.91 |  2.67 | 0.10 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.75 | 0.91 ( 0 ) | 1.61 ( 0.48 )  | 0.04 ( 0.02 ) |  7.57 | 0.09 | 0.91 |  1.61 | 0.04 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.06 | 0.99 ( 0 ) | 39.18 ( 2.14 ) | 0.63 ( 0.07 ) | 44.55 | 0.85 | 0.99 | 39.18 | 0.63 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.20 | 0.94 ( 0 ) | 16.29 ( 1.16 ) | 0.47 ( 0.06 ) | 21.82 | 0.7  | 0.94 | 16.29 | 0.47 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC        FP             FN            num_select\n",
+       "1   50  50 0    1.00  0.13 0.98 ( 0 ) 16.38 ( 0.96 ) 0.61 ( 0.06 ) 21.77     \n",
+       "2  100  50 0    0.50  0.32 0.93 ( 0 ) 7.94 ( 0.54 )  0.34 ( 0.06 ) 13.60     \n",
+       "3  500  50 0    0.10  0.63 0.91 ( 0 ) 2.67 ( 0.6 )   0.1 ( 0.03 )   8.57     \n",
+       "4 1000  50 0    0.05  0.75 0.91 ( 0 ) 1.61 ( 0.48 )  0.04 ( 0.02 )  7.57     \n",
+       "5   50 100 0    2.00  0.06 0.99 ( 0 ) 39.18 ( 2.14 ) 0.63 ( 0.07 ) 44.55     \n",
+       "6  100 100 0    1.00  0.20 0.94 ( 0 ) 16.29 ( 1.16 ) 0.47 ( 0.06 ) 21.82     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.71 0.98     16.38   0.61   \n",
+       "2 0.54 0.93      7.94   0.34   \n",
+       "3 0.19 0.91      2.67   0.10   \n",
+       "4 0.09 0.91      1.61   0.04   \n",
+       "5 0.85 0.99     39.18   0.63   \n",
+       "6 0.7  0.94     16.29   0.47   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.43</td><td>0.91 ( 0 )</td><td>8.32 ( 1.15 )  </td><td>0.1 ( 0.03 ) </td><td> 14.22</td><td>0.41</td><td>0.91</td><td>  8.32</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.60</td><td>0.91 ( 0 )</td><td>4.63 ( 1.57 )  </td><td>0 ( 0 )      </td><td> 10.63</td><td>0.21</td><td>0.91</td><td>  4.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>411.59</td><td>0.99</td><td>1.00</td><td>406.32</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.02</td><td>1 ( 0 )   </td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>288.75</td><td>0.98</td><td>1.00</td><td>283.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.40</td><td>0.91 ( 0 )</td><td>9.4 ( 1.07 )   </td><td>0.07 ( 0.03 )</td><td> 15.33</td><td>0.47</td><td>0.91</td><td>  9.40</td><td>0.07</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.82</td><td>0.91 ( 0 )</td><td>1.91 ( 0.29 )  </td><td>0.01 ( 0.01 )</td><td>  7.90</td><td>0.18</td><td>0.91</td><td>  1.91</td><td>0.01</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.43 & 0.91 ( 0 ) & 8.32 ( 1.15 )   & 0.1 ( 0.03 )  &  14.22 & 0.41 & 0.91 &   8.32 & 0.10\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.60 & 0.91 ( 0 ) & 4.63 ( 1.57 )   & 0 ( 0 )       &  10.63 & 0.21 & 0.91 &   4.63 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.01 & 1 ( 0 )    & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 411.59 & 0.99 & 1.00 & 406.32 & 0.73\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.02 & 1 ( 0 )    & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 288.75 & 0.98 & 1.00 & 283.24 & 0.49\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.40 & 0.91 ( 0 ) & 9.4 ( 1.07 )    & 0.07 ( 0.03 ) &  15.33 & 0.47 & 0.91 &   9.40 & 0.07\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.82 & 0.91 ( 0 ) & 1.91 ( 0.29 )   & 0.01 ( 0.01 ) &   7.90 & 0.18 & 0.91 &   1.91 & 0.01\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.43 | 0.91 ( 0 ) | 8.32 ( 1.15 )   | 0.1 ( 0.03 )  |  14.22 | 0.41 | 0.91 |   8.32 | 0.10 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.60 | 0.91 ( 0 ) | 4.63 ( 1.57 )   | 0 ( 0 )       |  10.63 | 0.21 | 0.91 |   4.63 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.01 | 1 ( 0 )    | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 411.59 | 0.99 | 1.00 | 406.32 | 0.73 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.02 | 1 ( 0 )    | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 288.75 | 0.98 | 1.00 | 283.24 | 0.49 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.40 | 0.91 ( 0 ) | 9.4 ( 1.07 )    | 0.07 ( 0.03 ) |  15.33 | 0.47 | 0.91 |   9.40 | 0.07 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.82 | 0.91 ( 0 ) | 1.91 ( 0.29 )   | 0.01 ( 0.01 ) |   7.90 | 0.18 | 0.91 |   1.91 | 0.01 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "11  500  500 0     1.0  0.43 0.91 ( 0 ) 8.32 ( 1.15 )   0.1 ( 0.03 ) \n",
+       "12 1000  500 0     0.5  0.60 0.91 ( 0 ) 4.63 ( 1.57 )   0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.01 1 ( 0 )    406.32 ( 7.33 ) 0.73 ( 0.07 )\n",
+       "14  100 1000 0    10.0  0.02 1 ( 0 )    283.24 ( 7.31 ) 0.49 ( 0.06 )\n",
+       "15  500 1000 0     2.0  0.40 0.91 ( 0 ) 9.4 ( 1.07 )    0.07 ( 0.03 )\n",
+       "16 1000 1000 0     1.0  0.82 0.91 ( 0 ) 1.91 ( 0.29 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11  14.22     0.41 0.91       8.32  0.10   \n",
+       "12  10.63     0.21 0.91       4.63  0.00   \n",
+       "13 411.59     0.99 1.00     406.32  0.73   \n",
+       "14 288.75     0.98 1.00     283.24  0.49   \n",
+       "15  15.33     0.47 0.91       9.40  0.07   \n",
+       "16   7.90     0.18 0.91       1.91  0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.13</td><td>0.98 ( 0 )</td><td>16.38 ( 0.96 ) </td><td>0.61 ( 0.06 )</td><td> 21.77</td><td>0.71</td><td>0.98</td><td> 16.38</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.32</td><td>0.93 ( 0 )</td><td>7.94 ( 0.54 )  </td><td>0.34 ( 0.06 )</td><td> 13.60</td><td>0.54</td><td>0.93</td><td>  7.94</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.63</td><td>0.91 ( 0 )</td><td>2.67 ( 0.6 )   </td><td>0.1 ( 0.03 ) </td><td>  8.57</td><td>0.19</td><td>0.91</td><td>  2.67</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.75</td><td>0.91 ( 0 )</td><td>1.61 ( 0.48 )  </td><td>0.04 ( 0.02 )</td><td>  7.57</td><td>0.09</td><td>0.91</td><td>  1.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>39.18 ( 2.14 ) </td><td>0.63 ( 0.07 )</td><td> 44.55</td><td>0.85</td><td>0.99</td><td> 39.18</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.29 ( 1.16 ) </td><td>0.47 ( 0.06 )</td><td> 21.82</td><td>0.7 </td><td>0.94</td><td> 16.29</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.47</td><td>0.91 ( 0 )</td><td>5.63 ( 0.84 )  </td><td>0.13 ( 0.03 )</td><td> 11.50</td><td>0.3 </td><td>0.91</td><td>  5.63</td><td>0.13</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.43</td><td>0.91 ( 0 )</td><td>6.87 ( 1.8 )   </td><td>0 ( 0 )      </td><td> 12.87</td><td>0.17</td><td>0.91</td><td>  6.87</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>251.02 ( 6.8 ) </td><td>0.36 ( 0.06 )</td><td>256.66</td><td>0.98</td><td>1.00</td><td>251.02</td><td>0.36</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.03</td><td>1 ( 0 )   </td><td>134.32 ( 6.63 )</td><td>0.33 ( 0.05 )</td><td>139.99</td><td>0.95</td><td>1.00</td><td>134.32</td><td>0.33</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.43</td><td>0.91 ( 0 )</td><td>8.32 ( 1.15 )  </td><td>0.1 ( 0.03 ) </td><td> 14.22</td><td>0.41</td><td>0.91</td><td>  8.32</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.60</td><td>0.91 ( 0 )</td><td>4.63 ( 1.57 )  </td><td>0 ( 0 )      </td><td> 10.63</td><td>0.21</td><td>0.91</td><td>  4.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>411.59</td><td>0.99</td><td>1.00</td><td>406.32</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>288.75</td><td>0.98</td><td>1.00</td><td>283.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.40</td><td>0.91 ( 0 )</td><td>9.4 ( 1.07 )   </td><td>0.07 ( 0.03 )</td><td> 15.33</td><td>0.47</td><td>0.91</td><td>  9.40</td><td>0.07</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.82</td><td>0.91 ( 0 )</td><td>1.91 ( 0.29 )  </td><td>0.01 ( 0.01 )</td><td>  7.90</td><td>0.18</td><td>0.91</td><td>  1.91</td><td>0.01</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.13 & 0.98 ( 0 ) & 16.38 ( 0.96 )  & 0.61 ( 0.06 ) &  21.77 & 0.71 & 0.98 &  16.38 & 0.61\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.32 & 0.93 ( 0 ) & 7.94 ( 0.54 )   & 0.34 ( 0.06 ) &  13.60 & 0.54 & 0.93 &   7.94 & 0.34\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.63 & 0.91 ( 0 ) & 2.67 ( 0.6 )    & 0.1 ( 0.03 )  &   8.57 & 0.19 & 0.91 &   2.67 & 0.10\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.75 & 0.91 ( 0 ) & 1.61 ( 0.48 )   & 0.04 ( 0.02 ) &   7.57 & 0.09 & 0.91 &   1.61 & 0.04\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.06 & 0.99 ( 0 ) & 39.18 ( 2.14 )  & 0.63 ( 0.07 ) &  44.55 & 0.85 & 0.99 &  39.18 & 0.63\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.20 & 0.94 ( 0 ) & 16.29 ( 1.16 )  & 0.47 ( 0.06 ) &  21.82 & 0.7  & 0.94 &  16.29 & 0.47\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.47 & 0.91 ( 0 ) & 5.63 ( 0.84 )   & 0.13 ( 0.03 ) &  11.50 & 0.3  & 0.91 &   5.63 & 0.13\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.43 & 0.91 ( 0 ) & 6.87 ( 1.8 )    & 0 ( 0 )       &  12.87 & 0.17 & 0.91 &   6.87 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.01 & 1 ( 0 )    & 251.02 ( 6.8 )  & 0.36 ( 0.06 ) & 256.66 & 0.98 & 1.00 & 251.02 & 0.36\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.03 & 1 ( 0 )    & 134.32 ( 6.63 ) & 0.33 ( 0.05 ) & 139.99 & 0.95 & 1.00 & 134.32 & 0.33\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.43 & 0.91 ( 0 ) & 8.32 ( 1.15 )   & 0.1 ( 0.03 )  &  14.22 & 0.41 & 0.91 &   8.32 & 0.10\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.60 & 0.91 ( 0 ) & 4.63 ( 1.57 )   & 0 ( 0 )       &  10.63 & 0.21 & 0.91 &   4.63 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.01 & 1 ( 0 )    & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 411.59 & 0.99 & 1.00 & 406.32 & 0.73\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.02 & 1 ( 0 )    & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 288.75 & 0.98 & 1.00 & 283.24 & 0.49\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.40 & 0.91 ( 0 ) & 9.4 ( 1.07 )    & 0.07 ( 0.03 ) &  15.33 & 0.47 & 0.91 &   9.40 & 0.07\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.82 & 0.91 ( 0 ) & 1.91 ( 0.29 )   & 0.01 ( 0.01 ) &   7.90 & 0.18 & 0.91 &   1.91 & 0.01\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.13 | 0.98 ( 0 ) | 16.38 ( 0.96 )  | 0.61 ( 0.06 ) |  21.77 | 0.71 | 0.98 |  16.38 | 0.61 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.32 | 0.93 ( 0 ) | 7.94 ( 0.54 )   | 0.34 ( 0.06 ) |  13.60 | 0.54 | 0.93 |   7.94 | 0.34 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.63 | 0.91 ( 0 ) | 2.67 ( 0.6 )    | 0.1 ( 0.03 )  |   8.57 | 0.19 | 0.91 |   2.67 | 0.10 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.75 | 0.91 ( 0 ) | 1.61 ( 0.48 )   | 0.04 ( 0.02 ) |   7.57 | 0.09 | 0.91 |   1.61 | 0.04 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.06 | 0.99 ( 0 ) | 39.18 ( 2.14 )  | 0.63 ( 0.07 ) |  44.55 | 0.85 | 0.99 |  39.18 | 0.63 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.20 | 0.94 ( 0 ) | 16.29 ( 1.16 )  | 0.47 ( 0.06 ) |  21.82 | 0.7  | 0.94 |  16.29 | 0.47 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.47 | 0.91 ( 0 ) | 5.63 ( 0.84 )   | 0.13 ( 0.03 ) |  11.50 | 0.3  | 0.91 |   5.63 | 0.13 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.43 | 0.91 ( 0 ) | 6.87 ( 1.8 )    | 0 ( 0 )       |  12.87 | 0.17 | 0.91 |   6.87 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.01 | 1 ( 0 )    | 251.02 ( 6.8 )  | 0.36 ( 0.06 ) | 256.66 | 0.98 | 1.00 | 251.02 | 0.36 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.03 | 1 ( 0 )    | 134.32 ( 6.63 ) | 0.33 ( 0.05 ) | 139.99 | 0.95 | 1.00 | 134.32 | 0.33 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.43 | 0.91 ( 0 ) | 8.32 ( 1.15 )   | 0.1 ( 0.03 )  |  14.22 | 0.41 | 0.91 |   8.32 | 0.10 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.60 | 0.91 ( 0 ) | 4.63 ( 1.57 )   | 0 ( 0 )       |  10.63 | 0.21 | 0.91 |   4.63 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.01 | 1 ( 0 )    | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 411.59 | 0.99 | 1.00 | 406.32 | 0.73 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.02 | 1 ( 0 )    | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 288.75 | 0.98 | 1.00 | 283.24 | 0.49 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.40 | 0.91 ( 0 ) | 9.4 ( 1.07 )    | 0.07 ( 0.03 ) |  15.33 | 0.47 | 0.91 |   9.40 | 0.07 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.82 | 0.91 ( 0 ) | 1.91 ( 0.29 )   | 0.01 ( 0.01 ) |   7.90 | 0.18 | 0.91 |   1.91 | 0.01 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "1    50   50 0     1.00 0.13 0.98 ( 0 ) 16.38 ( 0.96 )  0.61 ( 0.06 )\n",
+       "2   100   50 0     0.50 0.32 0.93 ( 0 ) 7.94 ( 0.54 )   0.34 ( 0.06 )\n",
+       "3   500   50 0     0.10 0.63 0.91 ( 0 ) 2.67 ( 0.6 )    0.1 ( 0.03 ) \n",
+       "4  1000   50 0     0.05 0.75 0.91 ( 0 ) 1.61 ( 0.48 )   0.04 ( 0.02 )\n",
+       "5    50  100 0     2.00 0.06 0.99 ( 0 ) 39.18 ( 2.14 )  0.63 ( 0.07 )\n",
+       "6   100  100 0     1.00 0.20 0.94 ( 0 ) 16.29 ( 1.16 )  0.47 ( 0.06 )\n",
+       "7   500  100 0     0.20 0.47 0.91 ( 0 ) 5.63 ( 0.84 )   0.13 ( 0.03 )\n",
+       "8  1000  100 0     0.10 0.43 0.91 ( 0 ) 6.87 ( 1.8 )    0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.01 1 ( 0 )    251.02 ( 6.8 )  0.36 ( 0.06 )\n",
+       "10  100  500 0     5.00 0.03 1 ( 0 )    134.32 ( 6.63 ) 0.33 ( 0.05 )\n",
+       "11  500  500 0     1.00 0.43 0.91 ( 0 ) 8.32 ( 1.15 )   0.1 ( 0.03 ) \n",
+       "12 1000  500 0     0.50 0.60 0.91 ( 0 ) 4.63 ( 1.57 )   0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.01 1 ( 0 )    406.32 ( 7.33 ) 0.73 ( 0.07 )\n",
+       "14  100 1000 0    10.00 0.02 1 ( 0 )    283.24 ( 7.31 ) 0.49 ( 0.06 )\n",
+       "15  500 1000 0     2.00 0.40 0.91 ( 0 ) 9.4 ( 1.07 )    0.07 ( 0.03 )\n",
+       "16 1000 1000 0     1.00 0.82 0.91 ( 0 ) 1.91 ( 0.29 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   21.77     0.71 0.98      16.38  0.61   \n",
+       "2   13.60     0.54 0.93       7.94  0.34   \n",
+       "3    8.57     0.19 0.91       2.67  0.10   \n",
+       "4    7.57     0.09 0.91       1.61  0.04   \n",
+       "5   44.55     0.85 0.99      39.18  0.63   \n",
+       "6   21.82     0.7  0.94      16.29  0.47   \n",
+       "7   11.50     0.3  0.91       5.63  0.13   \n",
+       "8   12.87     0.17 0.91       6.87  0.00   \n",
+       "9  256.66     0.98 1.00     251.02  0.36   \n",
+       "10 139.99     0.95 1.00     134.32  0.33   \n",
+       "11  14.22     0.41 0.91       8.32  0.10   \n",
+       "12  10.63     0.21 0.91       4.63  0.00   \n",
+       "13 411.59     0.99 1.00     406.32  0.73   \n",
+       "14 288.75     0.98 1.00     283.24  0.49   \n",
+       "15  15.33     0.47 0.91       9.40  0.07   \n",
+       "16   7.90     0.18 0.91       1.91  0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_updated/sim_ind_GencompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.1_sim_toe_lasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.1_sim_toe_lasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..339fd90
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.1_sim_toe_lasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
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+      "[1] \"indx: 19\"\n",
+      "[1] \"indx: 20\"\n",
+      "[1] \"indx: 21\"\n",
+      "[1] \"indx: 22\"\n",
+      "[1] \"indx: 23\"\n",
+      "[1] \"indx: 24\"\n",
+      "[1] \"indx: 25\"\n",
+      "[1] \"indx: 26\"\n",
+      "[1] \"indx: 27\"\n",
+      "[1] \"indx: 28\"\n",
+      "[1] \"indx: 29\"\n",
+      "[1] \"indx: 30\"\n",
+      "[1] \"indx: 31\"\n",
+      "[1] \"indx: 32\"\n",
+      "[1] \"indx: 33\"\n",
+      "[1] \"indx: 34\"\n",
+      "[1] \"indx: 35\"\n",
+      "[1] \"indx: 36\"\n",
+      "[1] \"indx: 37\"\n",
+      "[1] \"indx: 38\"\n",
+      "[1] \"indx: 39\"\n",
+      "[1] \"indx: 40\"\n",
+      "[1] \"indx: 41\"\n",
+      "[1] \"indx: 42\"\n",
+      "[1] \"indx: 43\"\n",
+      "[1] \"indx: 44\"\n",
+      "[1] \"indx: 45\"\n",
+      "[1] \"indx: 46\"\n",
+      "[1] \"indx: 47\"\n",
+      "[1] \"indx: 48\"\n",
+      "[1] \"indx: 49\"\n",
+      "[1] \"indx: 50\"\n",
+      "[1] \"indx: 51\"\n",
+      "[1] \"indx: 52\"\n",
+      "[1] \"indx: 53\"\n",
+      "[1] \"indx: 54\"\n",
+      "[1] \"indx: 55\"\n",
+      "[1] \"indx: 56\"\n",
+      "[1] \"indx: 57\"\n",
+      "[1] \"indx: 58\"\n",
+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>3.65 ( 0.23 )</td><td>2.83 ( 0.11 )</td><td>0.78 ( 0.01 )</td><td>0.32</td><td> 5.82</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>7.35 ( 0.34 )</td><td>0.68 ( 0.09 )</td><td>0.85 ( 0.01 )</td><td>0.34</td><td>11.67</td><td>0.50</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>4.99 ( 0.22 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.55</td><td> 9.99</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>2.42 ( 0.12 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.78</td><td> 7.42</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>4.65 ( 0.29 )</td><td>2.93 ( 0.12 )</td><td>0.74 ( 0.01 )</td><td>0.26</td><td> 6.72</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>8.11 ( 0.41 )</td><td>0.86 ( 0.09 )</td><td>0.85 ( 0.01 )</td><td>0.33</td><td>12.25</td><td>0.52</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 3.65 ( 0.23 ) & 2.83 ( 0.11 ) & 0.78 ( 0.01 ) & 0.32 &  5.82 & 0.39\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 7.35 ( 0.34 ) & 0.68 ( 0.09 ) & 0.85 ( 0.01 ) & 0.34 & 11.67 & 0.50\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 4.99 ( 0.22 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.55 &  9.99 & 0.37\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 2.42 ( 0.12 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.78 &  7.42 & 0.17\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 4.65 ( 0.29 ) & 2.93 ( 0.12 ) & 0.74 ( 0.01 ) & 0.26 &  6.72 & 0.48\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 8.11 ( 0.41 ) & 0.86 ( 0.09 ) & 0.85 ( 0.01 ) & 0.33 & 12.25 & 0.52\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 3.65 ( 0.23 ) | 2.83 ( 0.11 ) | 0.78 ( 0.01 ) | 0.32 |  5.82 | 0.39 |\n",
+       "| 2 | 100 | 50 | 0.1 | 7.35 ( 0.34 ) | 0.68 ( 0.09 ) | 0.85 ( 0.01 ) | 0.34 | 11.67 | 0.50 |\n",
+       "| 3 | 500 | 50 | 0.1 | 4.99 ( 0.22 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.55 |  9.99 | 0.37 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 2.42 ( 0.12 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.78 |  7.42 | 0.17 |\n",
+       "| 5 | 50 | 100 | 0.1 | 4.65 ( 0.29 ) | 2.93 ( 0.12 ) | 0.74 ( 0.01 ) | 0.26 |  6.72 | 0.48 |\n",
+       "| 6 | 100 | 100 | 0.1 | 8.11 ( 0.41 ) | 0.86 ( 0.09 ) | 0.85 ( 0.01 ) | 0.33 | 12.25 | 0.52 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 3.65 ( 0.23 ) 2.83 ( 0.11 ) 0.78 ( 0.01 ) 0.32  5.82      0.39\n",
+       "2 100  50  0.1 7.35 ( 0.34 ) 0.68 ( 0.09 ) 0.85 ( 0.01 ) 0.34 11.67      0.50\n",
+       "3 500  50  0.1 4.99 ( 0.22 ) 0 ( 0 )       0.92 ( 0 )    0.55  9.99      0.37\n",
+       "4 1000 50  0.1 2.42 ( 0.12 ) 0 ( 0 )       0.93 ( 0 )    0.78  7.42      0.17\n",
+       "5 50   100 0.1 4.65 ( 0.29 ) 2.93 ( 0.12 ) 0.74 ( 0.01 ) 0.26  6.72      0.48\n",
+       "6 100  100 0.1 8.11 ( 0.41 ) 0.86 ( 0.09 ) 0.85 ( 0.01 ) 0.33 12.25      0.52"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 )</td><td>0.81 ( 0 )</td><td>0.19</td><td>19.51</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>0.83 ( 0 )</td><td>0.31</td><td>16.54</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>8.02 ( 0.28 )</td><td>4.9 ( 0.07 )</td><td>0.66 ( 0.01 )</td><td>0.08</td><td> 8.12</td><td>0.85</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>5.85 ( 0.58 )</td><td>4.37 ( 0.07 )</td><td>0.69 ( 0.01 )</td><td>0.18</td><td> 6.48</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>18.7 ( 0.93 )</td><td>1.88 ( 0.1 )</td><td>0.8 ( 0 )</td><td>0.16</td><td>21.82</td><td>0.77</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>0.83 ( 0 )</td><td>0.21</td><td>24.66</td><td>0.75</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 15.93 ( 0.73 ) & 1.42 ( 0.1 ) & 0.81 ( 0 ) & 0.19 & 19.51 & 0.73\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 0.83 ( 0 ) & 0.31 & 16.54 & 0.63\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 8.02 ( 0.28 ) & 4.9 ( 0.07 ) & 0.66 ( 0.01 ) & 0.08 &  8.12 & 0.85\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 5.85 ( 0.58 ) & 4.37 ( 0.07 ) & 0.69 ( 0.01 ) & 0.18 &  6.48 & 0.61\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 18.7 ( 0.93 ) & 1.88 ( 0.1 ) & 0.8 ( 0 ) & 0.16 & 21.82 & 0.77\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 0.83 ( 0 ) & 0.21 & 24.66 & 0.75\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 15.93 ( 0.73 ) | 1.42 ( 0.1 ) | 0.81 ( 0 ) | 0.19 | 19.51 | 0.73 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 0.83 ( 0 ) | 0.31 | 16.54 | 0.63 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 8.02 ( 0.28 ) | 4.9 ( 0.07 ) | 0.66 ( 0.01 ) | 0.08 |  8.12 | 0.85 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 5.85 ( 0.58 ) | 4.37 ( 0.07 ) | 0.69 ( 0.01 ) | 0.18 |  6.48 | 0.61 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 18.7 ( 0.93 ) | 1.88 ( 0.1 ) | 0.8 ( 0 ) | 0.16 | 21.82 | 0.77 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 0.83 ( 0 ) | 0.21 | 24.66 | 0.75 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 15.93 ( 0.73 ) 1.42 ( 0.1 )  0.81 ( 0 )    0.19 19.51     \n",
+       "76 1000 500  0.9 11.96 ( 0.46 ) 0.42 ( 0.06 ) 0.83 ( 0 )    0.31 16.54     \n",
+       "77 50   1000 0.9 8.02 ( 0.28 )  4.9 ( 0.07 )  0.66 ( 0.01 ) 0.08  8.12     \n",
+       "78 100  1000 0.9 5.85 ( 0.58 )  4.37 ( 0.07 ) 0.69 ( 0.01 ) 0.18  6.48     \n",
+       "79 500  1000 0.9 18.7 ( 0.93 )  1.88 ( 0.1 )  0.8 ( 0 )     0.16 21.82     \n",
+       "80 1000 1000 0.9 20.07 ( 0.72 ) 0.41 ( 0.05 ) 0.83 ( 0 )    0.21 24.66     \n",
+       "   FDR \n",
+       "75 0.73\n",
+       "76 0.63\n",
+       "77 0.85\n",
+       "78 0.61\n",
+       "79 0.77\n",
+       "80 0.75"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>3.65 ( 0.23 )</td><td>2.83 ( 0.11 )</td><td> 5.82</td><td>0.39</td><td>0.78</td><td>3.65</td><td>2.83</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.34</td><td>0.85 ( 0.01 )</td><td>7.35 ( 0.34 )</td><td>0.68 ( 0.09 )</td><td>11.67</td><td>0.5 </td><td>0.85</td><td>7.35</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.55</td><td>0.92 ( 0 )   </td><td>4.99 ( 0.22 )</td><td>0 ( 0 )      </td><td> 9.99</td><td>0.37</td><td>0.92</td><td>4.99</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.78</td><td>0.93 ( 0 )   </td><td>2.42 ( 0.12 )</td><td>0 ( 0 )      </td><td> 7.42</td><td>0.17</td><td>0.93</td><td>2.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>4.65 ( 0.29 )</td><td>2.93 ( 0.12 )</td><td> 6.72</td><td>0.48</td><td>0.74</td><td>4.65</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.11 ( 0.41 )</td><td>0.86 ( 0.09 )</td><td>12.25</td><td>0.52</td><td>0.85</td><td>8.11</td><td>0.86</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.32 & 0.78 ( 0.01 ) & 3.65 ( 0.23 ) & 2.83 ( 0.11 ) &  5.82 & 0.39 & 0.78 & 3.65 & 2.83\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.34 & 0.85 ( 0.01 ) & 7.35 ( 0.34 ) & 0.68 ( 0.09 ) & 11.67 & 0.5  & 0.85 & 7.35 & 0.68\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.55 & 0.92 ( 0 )    & 4.99 ( 0.22 ) & 0 ( 0 )       &  9.99 & 0.37 & 0.92 & 4.99 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.78 & 0.93 ( 0 )    & 2.42 ( 0.12 ) & 0 ( 0 )       &  7.42 & 0.17 & 0.93 & 2.42 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.26 & 0.74 ( 0.01 ) & 4.65 ( 0.29 ) & 2.93 ( 0.12 ) &  6.72 & 0.48 & 0.74 & 4.65 & 2.93\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.33 & 0.85 ( 0.01 ) & 8.11 ( 0.41 ) & 0.86 ( 0.09 ) & 12.25 & 0.52 & 0.85 & 8.11 & 0.86\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.32 | 0.78 ( 0.01 ) | 3.65 ( 0.23 ) | 2.83 ( 0.11 ) |  5.82 | 0.39 | 0.78 | 3.65 | 2.83 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.34 | 0.85 ( 0.01 ) | 7.35 ( 0.34 ) | 0.68 ( 0.09 ) | 11.67 | 0.5  | 0.85 | 7.35 | 0.68 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.55 | 0.92 ( 0 )    | 4.99 ( 0.22 ) | 0 ( 0 )       |  9.99 | 0.37 | 0.92 | 4.99 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.78 | 0.93 ( 0 )    | 2.42 ( 0.12 ) | 0 ( 0 )       |  7.42 | 0.17 | 0.93 | 2.42 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.26 | 0.74 ( 0.01 ) | 4.65 ( 0.29 ) | 2.93 ( 0.12 ) |  6.72 | 0.48 | 0.74 | 4.65 | 2.93 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.33 | 0.85 ( 0.01 ) | 8.11 ( 0.41 ) | 0.86 ( 0.09 ) | 12.25 | 0.52 | 0.85 | 8.11 | 0.86 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.32 0.78 ( 0.01 ) 3.65 ( 0.23 ) 2.83 ( 0.11 )  5.82     \n",
+       "2  100  50 0.1  0.50  0.34 0.85 ( 0.01 ) 7.35 ( 0.34 ) 0.68 ( 0.09 ) 11.67     \n",
+       "3  500  50 0.1  0.10  0.55 0.92 ( 0 )    4.99 ( 0.22 ) 0 ( 0 )        9.99     \n",
+       "4 1000  50 0.1  0.05  0.78 0.93 ( 0 )    2.42 ( 0.12 ) 0 ( 0 )        7.42     \n",
+       "5   50 100 0.1  2.00  0.26 0.74 ( 0.01 ) 4.65 ( 0.29 ) 2.93 ( 0.12 )  6.72     \n",
+       "6  100 100 0.1  1.00  0.33 0.85 ( 0.01 ) 8.11 ( 0.41 ) 0.86 ( 0.09 ) 12.25     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.39 0.78     3.65    2.83   \n",
+       "2 0.5  0.85     7.35    0.68   \n",
+       "3 0.37 0.92     4.99    0.00   \n",
+       "4 0.17 0.93     2.42    0.00   \n",
+       "5 0.48 0.74     4.65    2.93   \n",
+       "6 0.52 0.85     8.11    0.86   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.19</td><td>0.81 ( 0 )   </td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 ) </td><td>19.51</td><td>0.73</td><td>0.81</td><td>15.93</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.31</td><td>0.83 ( 0 )   </td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>16.54</td><td>0.63</td><td>0.83</td><td>11.96</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.08</td><td>0.66 ( 0.01 )</td><td>8.02 ( 0.28 ) </td><td>4.9 ( 0.07 ) </td><td> 8.12</td><td>0.85</td><td>0.66</td><td> 8.02</td><td>4.90</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.18</td><td>0.69 ( 0.01 )</td><td>5.85 ( 0.58 ) </td><td>4.37 ( 0.07 )</td><td> 6.48</td><td>0.61</td><td>0.69</td><td> 5.85</td><td>4.37</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.16</td><td>0.8 ( 0 )    </td><td>18.7 ( 0.93 ) </td><td>1.88 ( 0.1 ) </td><td>21.82</td><td>0.77</td><td>0.80</td><td>18.70</td><td>1.88</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.21</td><td>0.83 ( 0 )   </td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>24.66</td><td>0.75</td><td>0.83</td><td>20.07</td><td>0.41</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.19 & 0.81 ( 0 )    & 15.93 ( 0.73 ) & 1.42 ( 0.1 )  & 19.51 & 0.73 & 0.81 & 15.93 & 1.42\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.31 & 0.83 ( 0 )    & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 16.54 & 0.63 & 0.83 & 11.96 & 0.42\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.08 & 0.66 ( 0.01 ) & 8.02 ( 0.28 )  & 4.9 ( 0.07 )  &  8.12 & 0.85 & 0.66 &  8.02 & 4.90\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.18 & 0.69 ( 0.01 ) & 5.85 ( 0.58 )  & 4.37 ( 0.07 ) &  6.48 & 0.61 & 0.69 &  5.85 & 4.37\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.16 & 0.8 ( 0 )     & 18.7 ( 0.93 )  & 1.88 ( 0.1 )  & 21.82 & 0.77 & 0.80 & 18.70 & 1.88\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.21 & 0.83 ( 0 )    & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 24.66 & 0.75 & 0.83 & 20.07 & 0.41\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.19 | 0.81 ( 0 )    | 15.93 ( 0.73 ) | 1.42 ( 0.1 )  | 19.51 | 0.73 | 0.81 | 15.93 | 1.42 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.31 | 0.83 ( 0 )    | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 16.54 | 0.63 | 0.83 | 11.96 | 0.42 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.08 | 0.66 ( 0.01 ) | 8.02 ( 0.28 )  | 4.9 ( 0.07 )  |  8.12 | 0.85 | 0.66 |  8.02 | 4.90 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.18 | 0.69 ( 0.01 ) | 5.85 ( 0.58 )  | 4.37 ( 0.07 ) |  6.48 | 0.61 | 0.69 |  5.85 | 4.37 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.16 | 0.8 ( 0 )     | 18.7 ( 0.93 )  | 1.88 ( 0.1 )  | 21.82 | 0.77 | 0.80 | 18.70 | 1.88 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.21 | 0.83 ( 0 )    | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 24.66 | 0.75 | 0.83 | 20.07 | 0.41 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.19 0.81 ( 0 )    15.93 ( 0.73 ) 1.42 ( 0.1 ) \n",
+       "76 1000  500 0.9   0.5  0.31 0.83 ( 0 )    11.96 ( 0.46 ) 0.42 ( 0.06 )\n",
+       "77   50 1000 0.9  20.0  0.08 0.66 ( 0.01 ) 8.02 ( 0.28 )  4.9 ( 0.07 ) \n",
+       "78  100 1000 0.9  10.0  0.18 0.69 ( 0.01 ) 5.85 ( 0.58 )  4.37 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.16 0.8 ( 0 )     18.7 ( 0.93 )  1.88 ( 0.1 ) \n",
+       "80 1000 1000 0.9   1.0  0.21 0.83 ( 0 )    20.07 ( 0.72 ) 0.41 ( 0.05 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 19.51      0.73 0.81     15.93   1.42   \n",
+       "76 16.54      0.63 0.83     11.96   0.42   \n",
+       "77  8.12      0.85 0.66      8.02   4.90   \n",
+       "78  6.48      0.61 0.69      5.85   4.37   \n",
+       "79 21.82      0.77 0.80     18.70   1.88   \n",
+       "80 24.66      0.75 0.83     20.07   0.41   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>3.65 ( 0.23 ) </td><td>2.83 ( 0.11 )</td><td> 5.82</td><td>0.39</td><td>0.78</td><td> 3.65</td><td>2.83</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.34</td><td>0.85 ( 0.01 )</td><td>7.35 ( 0.34 ) </td><td>0.68 ( 0.09 )</td><td>11.67</td><td>0.5 </td><td>0.85</td><td> 7.35</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.55</td><td>0.92 ( 0 )   </td><td>4.99 ( 0.22 ) </td><td>0 ( 0 )      </td><td> 9.99</td><td>0.37</td><td>0.92</td><td> 4.99</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.78</td><td>0.93 ( 0 )   </td><td>2.42 ( 0.12 ) </td><td>0 ( 0 )      </td><td> 7.42</td><td>0.17</td><td>0.93</td><td> 2.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>4.65 ( 0.29 ) </td><td>2.93 ( 0.12 )</td><td> 6.72</td><td>0.48</td><td>0.74</td><td> 4.65</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.11 ( 0.41 ) </td><td>0.86 ( 0.09 )</td><td>12.25</td><td>0.52</td><td>0.85</td><td> 8.11</td><td>0.86</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.40</td><td>0.92 ( 0 )   </td><td>8.63 ( 0.29 ) </td><td>0 ( 0 )      </td><td>13.63</td><td>0.54</td><td>0.92</td><td> 8.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.64</td><td>0.92 ( 0 )   </td><td>4.02 ( 0.18 ) </td><td>0 ( 0 )      </td><td> 9.02</td><td>0.31</td><td>0.92</td><td> 4.02</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.12</td><td>0.71 ( 0.01 )</td><td>8.29 ( 0.25 ) </td><td>3.89 ( 0.1 ) </td><td> 9.40</td><td>0.77</td><td>0.71</td><td> 8.29</td><td>3.89</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.24</td><td>0.8 ( 0.01 ) </td><td>8.84 ( 0.61 ) </td><td>2.19 ( 0.12 )</td><td>11.65</td><td>0.57</td><td>0.80</td><td> 8.84</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.21</td><td>0.92 ( 0 )   </td><td>22.86 ( 0.75 )</td><td>0 ( 0 )      </td><td>27.86</td><td>0.77</td><td>0.92</td><td>22.86</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.33</td><td>0.93 ( 0 )   </td><td>12.73 ( 0.35 )</td><td>0 ( 0 )      </td><td>17.73</td><td>0.65</td><td>0.93</td><td>12.73</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.09</td><td>0.68 ( 0.01 )</td><td>10.35 ( 0.33 )</td><td>4.14 ( 0.09 )</td><td>11.21</td><td>0.82</td><td>0.68</td><td>10.35</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.19</td><td>0.77 ( 0.01 )</td><td>10.84 ( 0.75 )</td><td>2.46 ( 0.1 ) </td><td>13.38</td><td>0.63</td><td>0.77</td><td>10.84</td><td>2.46</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.16</td><td>0.92 ( 0 )   </td><td>30.83 ( 1.05 )</td><td>0 ( 0 )      </td><td>35.83</td><td>0.81</td><td>0.92</td><td>30.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.23</td><td>0.93 ( 0 )   </td><td>20.39 ( 0.59 )</td><td>0 ( 0 )      </td><td>25.39</td><td>0.75</td><td>0.93</td><td>20.39</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.28</td><td>0.75 ( 0.01 )</td><td>3.69 ( 0.22 ) </td><td>3.04 ( 0.1 ) </td><td> 5.65</td><td>0.4 </td><td>0.75</td><td> 3.69</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.32</td><td>0.84 ( 0.01 )</td><td>7.11 ( 0.33 ) </td><td>0.93 ( 0.1 ) </td><td>11.18</td><td>0.5 </td><td>0.84</td><td> 7.11</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.51</td><td>0.92 ( 0 )   </td><td>5.67 ( 0.22 ) </td><td>0 ( 0 )      </td><td>10.67</td><td>0.41</td><td>0.92</td><td> 5.67</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.78</td><td>0.92 ( 0 )   </td><td>2.48 ( 0.13 ) </td><td>0 ( 0 )      </td><td> 7.48</td><td>0.18</td><td>0.92</td><td> 2.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.26</td><td>0.75 ( 0.01 )</td><td>4.41 ( 0.27 ) </td><td>3.47 ( 0.09 )</td><td> 5.94</td><td>0.49</td><td>0.75</td><td> 4.41</td><td>3.47</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.28</td><td>0.82 ( 0.01 )</td><td>8.16 ( 0.53 ) </td><td>1.46 ( 0.11 )</td><td>11.70</td><td>0.52</td><td>0.82</td><td> 8.16</td><td>1.46</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.41</td><td>0.92 ( 0 )   </td><td>8.48 ( 0.34 ) </td><td>0 ( 0 )      </td><td>13.48</td><td>0.53</td><td>0.92</td><td> 8.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.67</td><td>0.92 ( 0 )   </td><td>3.71 ( 0.15 ) </td><td>0 ( 0 )      </td><td> 8.71</td><td>0.29</td><td>0.92</td><td> 3.71</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.13</td><td>0.69 ( 0.01 )</td><td>7.94 ( 0.3 )  </td><td>4.11 ( 0.08 )</td><td> 8.83</td><td>0.77</td><td>0.69</td><td> 7.94</td><td>4.11</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>10.16 ( 0.68 )</td><td>2.49 ( 0.11 )</td><td>12.67</td><td>0.63</td><td>0.78</td><td>10.16</td><td>2.49</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.20</td><td>0.91 ( 0 )   </td><td>23.65 ( 0.7 ) </td><td>0 ( 0 )      </td><td>28.65</td><td>0.78</td><td>0.91</td><td>23.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.34</td><td>0.92 ( 0 )   </td><td>12.28 ( 0.34 )</td><td>0 ( 0 )      </td><td>17.28</td><td>0.64</td><td>0.92</td><td>12.28</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.10</td><td>0.68 ( 0.01 )</td><td>9.77 ( 0.31 ) </td><td>4.3 ( 0.06 ) </td><td>10.47</td><td>0.83</td><td>0.68</td><td> 9.77</td><td>4.30</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.17</td><td>0.77 ( 0.01 )</td><td>10.68 ( 0.8 ) </td><td>3.02 ( 0.11 )</td><td>12.66</td><td>0.67</td><td>0.77</td><td>10.68</td><td>3.02</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.57</td><td>0.89 ( 0 )   </td><td>4.61 ( 0.17 ) </td><td>0.04 ( 0.02 )</td><td> 9.57</td><td>0.36</td><td>0.89</td><td> 4.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.74</td><td>0.89 ( 0 )   </td><td>2.8 ( 0.15 )  </td><td>0 ( 0 )      </td><td> 7.80</td><td>0.21</td><td>0.89</td><td> 2.80</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.24</td><td>0.71 ( 0.01 )</td><td>4.31 ( 0.28 ) </td><td>4.05 ( 0.09 )</td><td> 5.26</td><td>0.55</td><td>0.71</td><td> 4.31</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.27</td><td>0.79 ( 0.01 )</td><td>6.34 ( 0.4 )  </td><td>2.67 ( 0.12 )</td><td> 8.67</td><td>0.53</td><td>0.79</td><td> 6.34</td><td>2.67</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.43</td><td>0.89 ( 0 )   </td><td>7.92 ( 0.24 ) </td><td>0.04 ( 0.02 )</td><td>12.88</td><td>0.52</td><td>0.89</td><td> 7.92</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.66</td><td>0.9 ( 0 )    </td><td>3.85 ( 0.16 ) </td><td>0 ( 0 )      </td><td> 8.85</td><td>0.3 </td><td>0.90</td><td> 3.85</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.14</td><td>0.7 ( 0.01 ) </td><td>6.5 ( 0.24 )  </td><td>4.63 ( 0.06 )</td><td> 6.87</td><td>0.77</td><td>0.70</td><td> 6.50</td><td>4.63</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.21</td><td>0.76 ( 0.01 )</td><td>6.98 ( 0.58 ) </td><td>3.65 ( 0.08 )</td><td> 8.33</td><td>0.56</td><td>0.76</td><td> 6.98</td><td>3.65</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.20</td><td>0.88 ( 0 )   </td><td>22.04 ( 0.74 )</td><td>0.14 ( 0.04 )</td><td>26.90</td><td>0.76</td><td>0.88</td><td>22.04</td><td>0.14</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.33</td><td>0.89 ( 0 )   </td><td>13.15 ( 0.38 )</td><td>0 ( 0 )      </td><td>18.15</td><td>0.65</td><td>0.89</td><td>13.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.10</td><td>0.66 ( 0.01 )</td><td>8.82 ( 0.28 ) </td><td>4.72 ( 0.05 )</td><td> 9.10</td><td>0.85</td><td>0.66</td><td> 8.82</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.18</td><td>0.76 ( 0.01 )</td><td>6.93 ( 0.59 ) </td><td>4 ( 0.08 )   </td><td> 7.93</td><td>0.62</td><td>0.76</td><td> 6.93</td><td>4.00</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.16</td><td>0.87 ( 0 )   </td><td>29.07 ( 1 )   </td><td>0.16 ( 0.04 )</td><td>33.91</td><td>0.81</td><td>0.87</td><td>29.07</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.22</td><td>0.89 ( 0 )   </td><td>21.8 ( 0.63 ) </td><td>0 ( 0 )      </td><td>26.80</td><td>0.76</td><td>0.89</td><td>21.80</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.30</td><td>0.69 ( 0.01 )</td><td>2.9 ( 0.2 )   </td><td>4.22 ( 0.09 )</td><td> 3.68</td><td>0.41</td><td>0.69</td><td> 2.90</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.37</td><td>0.76 ( 0.01 )</td><td>3.93 ( 0.21 ) </td><td>2.74 ( 0.11 )</td><td> 6.19</td><td>0.41</td><td>0.76</td><td> 3.93</td><td>2.74</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.57</td><td>0.83 ( 0 )   </td><td>3.65 ( 0.16 ) </td><td>0.56 ( 0.08 )</td><td> 8.09</td><td>0.31</td><td>0.83</td><td> 3.65</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.76</td><td>0.83 ( 0 )   </td><td>2.41 ( 0.12 ) </td><td>0.23 ( 0.04 )</td><td> 7.18</td><td>0.18</td><td>0.83</td><td> 2.41</td><td>0.23</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.26</td><td>0.7 ( 0.01 ) </td><td>3.29 ( 0.23 ) </td><td>4.29 ( 0.09 )</td><td> 4.00</td><td>0.45</td><td>0.70</td><td> 3.29</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.32</td><td>0.76 ( 0.01 )</td><td>4.77 ( 0.33 ) </td><td>3.26 ( 0.09 )</td><td> 6.51</td><td>0.46</td><td>0.76</td><td> 4.77</td><td>3.26</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.45</td><td>0.82 ( 0 )   </td><td>5.65 ( 0.21 ) </td><td>0.92 ( 0.09 )</td><td> 9.73</td><td>0.45</td><td>0.82</td><td> 5.65</td><td>0.92</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.63</td><td>0.83 ( 0 )   </td><td>3.87 ( 0.18 ) </td><td>0.24 ( 0.05 )</td><td> 8.63</td><td>0.3 </td><td>0.83</td><td> 3.87</td><td>0.24</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.15</td><td>0.66 ( 0.01 )</td><td>5.8 ( 0.24 )  </td><td>4.65 ( 0.07 )</td><td> 6.15</td><td>0.75</td><td>0.66</td><td> 5.80</td><td>4.65</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.21</td><td>0.7 ( 0.01 ) </td><td>5.55 ( 0.55 ) </td><td>4.14 ( 0.09 )</td><td> 6.41</td><td>0.51</td><td>0.70</td><td> 5.55</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.19</td><td>0.81 ( 0 )   </td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 ) </td><td>19.51</td><td>0.73</td><td>0.81</td><td>15.93</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.31</td><td>0.83 ( 0 )   </td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>16.54</td><td>0.63</td><td>0.83</td><td>11.96</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.08</td><td>0.66 ( 0.01 )</td><td>8.02 ( 0.28 ) </td><td>4.9 ( 0.07 ) </td><td> 8.12</td><td>0.85</td><td>0.66</td><td> 8.02</td><td>4.90</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.18</td><td>0.69 ( 0.01 )</td><td>5.85 ( 0.58 ) </td><td>4.37 ( 0.07 )</td><td> 6.48</td><td>0.61</td><td>0.69</td><td> 5.85</td><td>4.37</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.16</td><td>0.8 ( 0 )    </td><td>18.7 ( 0.93 ) </td><td>1.88 ( 0.1 ) </td><td>21.82</td><td>0.77</td><td>0.80</td><td>18.70</td><td>1.88</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.21</td><td>0.83 ( 0 )   </td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>24.66</td><td>0.75</td><td>0.83</td><td>20.07</td><td>0.41</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.32 & 0.78 ( 0.01 ) & 3.65 ( 0.23 )  & 2.83 ( 0.11 ) &  5.82 & 0.39 & 0.78 &  3.65 & 2.83\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.34 & 0.85 ( 0.01 ) & 7.35 ( 0.34 )  & 0.68 ( 0.09 ) & 11.67 & 0.5  & 0.85 &  7.35 & 0.68\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.55 & 0.92 ( 0 )    & 4.99 ( 0.22 )  & 0 ( 0 )       &  9.99 & 0.37 & 0.92 &  4.99 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.78 & 0.93 ( 0 )    & 2.42 ( 0.12 )  & 0 ( 0 )       &  7.42 & 0.17 & 0.93 &  2.42 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.26 & 0.74 ( 0.01 ) & 4.65 ( 0.29 )  & 2.93 ( 0.12 ) &  6.72 & 0.48 & 0.74 &  4.65 & 2.93\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.33 & 0.85 ( 0.01 ) & 8.11 ( 0.41 )  & 0.86 ( 0.09 ) & 12.25 & 0.52 & 0.85 &  8.11 & 0.86\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.40 & 0.92 ( 0 )    & 8.63 ( 0.29 )  & 0 ( 0 )       & 13.63 & 0.54 & 0.92 &  8.63 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.64 & 0.92 ( 0 )    & 4.02 ( 0.18 )  & 0 ( 0 )       &  9.02 & 0.31 & 0.92 &  4.02 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.12 & 0.71 ( 0.01 ) & 8.29 ( 0.25 )  & 3.89 ( 0.1 )  &  9.40 & 0.77 & 0.71 &  8.29 & 3.89\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.24 & 0.8 ( 0.01 )  & 8.84 ( 0.61 )  & 2.19 ( 0.12 ) & 11.65 & 0.57 & 0.80 &  8.84 & 2.19\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.21 & 0.92 ( 0 )    & 22.86 ( 0.75 ) & 0 ( 0 )       & 27.86 & 0.77 & 0.92 & 22.86 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.33 & 0.93 ( 0 )    & 12.73 ( 0.35 ) & 0 ( 0 )       & 17.73 & 0.65 & 0.93 & 12.73 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.09 & 0.68 ( 0.01 ) & 10.35 ( 0.33 ) & 4.14 ( 0.09 ) & 11.21 & 0.82 & 0.68 & 10.35 & 4.14\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.19 & 0.77 ( 0.01 ) & 10.84 ( 0.75 ) & 2.46 ( 0.1 )  & 13.38 & 0.63 & 0.77 & 10.84 & 2.46\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.16 & 0.92 ( 0 )    & 30.83 ( 1.05 ) & 0 ( 0 )       & 35.83 & 0.81 & 0.92 & 30.83 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.23 & 0.93 ( 0 )    & 20.39 ( 0.59 ) & 0 ( 0 )       & 25.39 & 0.75 & 0.93 & 20.39 & 0.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.28 & 0.75 ( 0.01 ) & 3.69 ( 0.22 )  & 3.04 ( 0.1 )  &  5.65 & 0.4  & 0.75 &  3.69 & 3.04\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.32 & 0.84 ( 0.01 ) & 7.11 ( 0.33 )  & 0.93 ( 0.1 )  & 11.18 & 0.5  & 0.84 &  7.11 & 0.93\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.51 & 0.92 ( 0 )    & 5.67 ( 0.22 )  & 0 ( 0 )       & 10.67 & 0.41 & 0.92 &  5.67 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.78 & 0.92 ( 0 )    & 2.48 ( 0.13 )  & 0 ( 0 )       &  7.48 & 0.18 & 0.92 &  2.48 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.26 & 0.75 ( 0.01 ) & 4.41 ( 0.27 )  & 3.47 ( 0.09 ) &  5.94 & 0.49 & 0.75 &  4.41 & 3.47\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.28 & 0.82 ( 0.01 ) & 8.16 ( 0.53 )  & 1.46 ( 0.11 ) & 11.70 & 0.52 & 0.82 &  8.16 & 1.46\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.41 & 0.92 ( 0 )    & 8.48 ( 0.34 )  & 0 ( 0 )       & 13.48 & 0.53 & 0.92 &  8.48 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.67 & 0.92 ( 0 )    & 3.71 ( 0.15 )  & 0 ( 0 )       &  8.71 & 0.29 & 0.92 &  3.71 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.13 & 0.69 ( 0.01 ) & 7.94 ( 0.3 )   & 4.11 ( 0.08 ) &  8.83 & 0.77 & 0.69 &  7.94 & 4.11\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.20 & 0.78 ( 0.01 ) & 10.16 ( 0.68 ) & 2.49 ( 0.11 ) & 12.67 & 0.63 & 0.78 & 10.16 & 2.49\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.20 & 0.91 ( 0 )    & 23.65 ( 0.7 )  & 0 ( 0 )       & 28.65 & 0.78 & 0.91 & 23.65 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.34 & 0.92 ( 0 )    & 12.28 ( 0.34 ) & 0 ( 0 )       & 17.28 & 0.64 & 0.92 & 12.28 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.10 & 0.68 ( 0.01 ) & 9.77 ( 0.31 )  & 4.3 ( 0.06 )  & 10.47 & 0.83 & 0.68 &  9.77 & 4.30\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.17 & 0.77 ( 0.01 ) & 10.68 ( 0.8 )  & 3.02 ( 0.11 ) & 12.66 & 0.67 & 0.77 & 10.68 & 3.02\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.57 & 0.89 ( 0 )    & 4.61 ( 0.17 )  & 0.04 ( 0.02 ) &  9.57 & 0.36 & 0.89 &  4.61 & 0.04\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.74 & 0.89 ( 0 )    & 2.8 ( 0.15 )   & 0 ( 0 )       &  7.80 & 0.21 & 0.89 &  2.80 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.24 & 0.71 ( 0.01 ) & 4.31 ( 0.28 )  & 4.05 ( 0.09 ) &  5.26 & 0.55 & 0.71 &  4.31 & 4.05\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.27 & 0.79 ( 0.01 ) & 6.34 ( 0.4 )   & 2.67 ( 0.12 ) &  8.67 & 0.53 & 0.79 &  6.34 & 2.67\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.43 & 0.89 ( 0 )    & 7.92 ( 0.24 )  & 0.04 ( 0.02 ) & 12.88 & 0.52 & 0.89 &  7.92 & 0.04\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.66 & 0.9 ( 0 )     & 3.85 ( 0.16 )  & 0 ( 0 )       &  8.85 & 0.3  & 0.90 &  3.85 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.14 & 0.7 ( 0.01 )  & 6.5 ( 0.24 )   & 4.63 ( 0.06 ) &  6.87 & 0.77 & 0.70 &  6.50 & 4.63\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.21 & 0.76 ( 0.01 ) & 6.98 ( 0.58 )  & 3.65 ( 0.08 ) &  8.33 & 0.56 & 0.76 &  6.98 & 3.65\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.20 & 0.88 ( 0 )    & 22.04 ( 0.74 ) & 0.14 ( 0.04 ) & 26.90 & 0.76 & 0.88 & 22.04 & 0.14\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.33 & 0.89 ( 0 )    & 13.15 ( 0.38 ) & 0 ( 0 )       & 18.15 & 0.65 & 0.89 & 13.15 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.10 & 0.66 ( 0.01 ) & 8.82 ( 0.28 )  & 4.72 ( 0.05 ) &  9.10 & 0.85 & 0.66 &  8.82 & 4.72\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.18 & 0.76 ( 0.01 ) & 6.93 ( 0.59 )  & 4 ( 0.08 )    &  7.93 & 0.62 & 0.76 &  6.93 & 4.00\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.16 & 0.87 ( 0 )    & 29.07 ( 1 )    & 0.16 ( 0.04 ) & 33.91 & 0.81 & 0.87 & 29.07 & 0.16\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.22 & 0.89 ( 0 )    & 21.8 ( 0.63 )  & 0 ( 0 )       & 26.80 & 0.76 & 0.89 & 21.80 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.30 & 0.69 ( 0.01 ) & 2.9 ( 0.2 )    & 4.22 ( 0.09 ) &  3.68 & 0.41 & 0.69 &  2.90 & 4.22\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.37 & 0.76 ( 0.01 ) & 3.93 ( 0.21 )  & 2.74 ( 0.11 ) &  6.19 & 0.41 & 0.76 &  3.93 & 2.74\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.57 & 0.83 ( 0 )    & 3.65 ( 0.16 )  & 0.56 ( 0.08 ) &  8.09 & 0.31 & 0.83 &  3.65 & 0.56\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.76 & 0.83 ( 0 )    & 2.41 ( 0.12 )  & 0.23 ( 0.04 ) &  7.18 & 0.18 & 0.83 &  2.41 & 0.23\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.26 & 0.7 ( 0.01 )  & 3.29 ( 0.23 )  & 4.29 ( 0.09 ) &  4.00 & 0.45 & 0.70 &  3.29 & 4.29\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.32 & 0.76 ( 0.01 ) & 4.77 ( 0.33 )  & 3.26 ( 0.09 ) &  6.51 & 0.46 & 0.76 &  4.77 & 3.26\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.45 & 0.82 ( 0 )    & 5.65 ( 0.21 )  & 0.92 ( 0.09 ) &  9.73 & 0.45 & 0.82 &  5.65 & 0.92\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.63 & 0.83 ( 0 )    & 3.87 ( 0.18 )  & 0.24 ( 0.05 ) &  8.63 & 0.3  & 0.83 &  3.87 & 0.24\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.15 & 0.66 ( 0.01 ) & 5.8 ( 0.24 )   & 4.65 ( 0.07 ) &  6.15 & 0.75 & 0.66 &  5.80 & 4.65\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.21 & 0.7 ( 0.01 )  & 5.55 ( 0.55 )  & 4.14 ( 0.09 ) &  6.41 & 0.51 & 0.70 &  5.55 & 4.14\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.19 & 0.81 ( 0 )    & 15.93 ( 0.73 ) & 1.42 ( 0.1 )  & 19.51 & 0.73 & 0.81 & 15.93 & 1.42\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.31 & 0.83 ( 0 )    & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 16.54 & 0.63 & 0.83 & 11.96 & 0.42\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.08 & 0.66 ( 0.01 ) & 8.02 ( 0.28 )  & 4.9 ( 0.07 )  &  8.12 & 0.85 & 0.66 &  8.02 & 4.90\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.18 & 0.69 ( 0.01 ) & 5.85 ( 0.58 )  & 4.37 ( 0.07 ) &  6.48 & 0.61 & 0.69 &  5.85 & 4.37\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.16 & 0.8 ( 0 )     & 18.7 ( 0.93 )  & 1.88 ( 0.1 )  & 21.82 & 0.77 & 0.80 & 18.70 & 1.88\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.21 & 0.83 ( 0 )    & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 24.66 & 0.75 & 0.83 & 20.07 & 0.41\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.32 | 0.78 ( 0.01 ) | 3.65 ( 0.23 )  | 2.83 ( 0.11 ) |  5.82 | 0.39 | 0.78 |  3.65 | 2.83 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.34 | 0.85 ( 0.01 ) | 7.35 ( 0.34 )  | 0.68 ( 0.09 ) | 11.67 | 0.5  | 0.85 |  7.35 | 0.68 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.55 | 0.92 ( 0 )    | 4.99 ( 0.22 )  | 0 ( 0 )       |  9.99 | 0.37 | 0.92 |  4.99 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.78 | 0.93 ( 0 )    | 2.42 ( 0.12 )  | 0 ( 0 )       |  7.42 | 0.17 | 0.93 |  2.42 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.26 | 0.74 ( 0.01 ) | 4.65 ( 0.29 )  | 2.93 ( 0.12 ) |  6.72 | 0.48 | 0.74 |  4.65 | 2.93 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.33 | 0.85 ( 0.01 ) | 8.11 ( 0.41 )  | 0.86 ( 0.09 ) | 12.25 | 0.52 | 0.85 |  8.11 | 0.86 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.40 | 0.92 ( 0 )    | 8.63 ( 0.29 )  | 0 ( 0 )       | 13.63 | 0.54 | 0.92 |  8.63 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.64 | 0.92 ( 0 )    | 4.02 ( 0.18 )  | 0 ( 0 )       |  9.02 | 0.31 | 0.92 |  4.02 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.12 | 0.71 ( 0.01 ) | 8.29 ( 0.25 )  | 3.89 ( 0.1 )  |  9.40 | 0.77 | 0.71 |  8.29 | 3.89 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.24 | 0.8 ( 0.01 )  | 8.84 ( 0.61 )  | 2.19 ( 0.12 ) | 11.65 | 0.57 | 0.80 |  8.84 | 2.19 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.21 | 0.92 ( 0 )    | 22.86 ( 0.75 ) | 0 ( 0 )       | 27.86 | 0.77 | 0.92 | 22.86 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.33 | 0.93 ( 0 )    | 12.73 ( 0.35 ) | 0 ( 0 )       | 17.73 | 0.65 | 0.93 | 12.73 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.09 | 0.68 ( 0.01 ) | 10.35 ( 0.33 ) | 4.14 ( 0.09 ) | 11.21 | 0.82 | 0.68 | 10.35 | 4.14 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.19 | 0.77 ( 0.01 ) | 10.84 ( 0.75 ) | 2.46 ( 0.1 )  | 13.38 | 0.63 | 0.77 | 10.84 | 2.46 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.16 | 0.92 ( 0 )    | 30.83 ( 1.05 ) | 0 ( 0 )       | 35.83 | 0.81 | 0.92 | 30.83 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.23 | 0.93 ( 0 )    | 20.39 ( 0.59 ) | 0 ( 0 )       | 25.39 | 0.75 | 0.93 | 20.39 | 0.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.28 | 0.75 ( 0.01 ) | 3.69 ( 0.22 )  | 3.04 ( 0.1 )  |  5.65 | 0.4  | 0.75 |  3.69 | 3.04 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.32 | 0.84 ( 0.01 ) | 7.11 ( 0.33 )  | 0.93 ( 0.1 )  | 11.18 | 0.5  | 0.84 |  7.11 | 0.93 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.51 | 0.92 ( 0 )    | 5.67 ( 0.22 )  | 0 ( 0 )       | 10.67 | 0.41 | 0.92 |  5.67 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.78 | 0.92 ( 0 )    | 2.48 ( 0.13 )  | 0 ( 0 )       |  7.48 | 0.18 | 0.92 |  2.48 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.26 | 0.75 ( 0.01 ) | 4.41 ( 0.27 )  | 3.47 ( 0.09 ) |  5.94 | 0.49 | 0.75 |  4.41 | 3.47 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.28 | 0.82 ( 0.01 ) | 8.16 ( 0.53 )  | 1.46 ( 0.11 ) | 11.70 | 0.52 | 0.82 |  8.16 | 1.46 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.41 | 0.92 ( 0 )    | 8.48 ( 0.34 )  | 0 ( 0 )       | 13.48 | 0.53 | 0.92 |  8.48 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.67 | 0.92 ( 0 )    | 3.71 ( 0.15 )  | 0 ( 0 )       |  8.71 | 0.29 | 0.92 |  3.71 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.13 | 0.69 ( 0.01 ) | 7.94 ( 0.3 )   | 4.11 ( 0.08 ) |  8.83 | 0.77 | 0.69 |  7.94 | 4.11 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.20 | 0.78 ( 0.01 ) | 10.16 ( 0.68 ) | 2.49 ( 0.11 ) | 12.67 | 0.63 | 0.78 | 10.16 | 2.49 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.20 | 0.91 ( 0 )    | 23.65 ( 0.7 )  | 0 ( 0 )       | 28.65 | 0.78 | 0.91 | 23.65 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.34 | 0.92 ( 0 )    | 12.28 ( 0.34 ) | 0 ( 0 )       | 17.28 | 0.64 | 0.92 | 12.28 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.10 | 0.68 ( 0.01 ) | 9.77 ( 0.31 )  | 4.3 ( 0.06 )  | 10.47 | 0.83 | 0.68 |  9.77 | 4.30 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.17 | 0.77 ( 0.01 ) | 10.68 ( 0.8 )  | 3.02 ( 0.11 ) | 12.66 | 0.67 | 0.77 | 10.68 | 3.02 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.57 | 0.89 ( 0 )    | 4.61 ( 0.17 )  | 0.04 ( 0.02 ) |  9.57 | 0.36 | 0.89 |  4.61 | 0.04 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.74 | 0.89 ( 0 )    | 2.8 ( 0.15 )   | 0 ( 0 )       |  7.80 | 0.21 | 0.89 |  2.80 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.24 | 0.71 ( 0.01 ) | 4.31 ( 0.28 )  | 4.05 ( 0.09 ) |  5.26 | 0.55 | 0.71 |  4.31 | 4.05 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.27 | 0.79 ( 0.01 ) | 6.34 ( 0.4 )   | 2.67 ( 0.12 ) |  8.67 | 0.53 | 0.79 |  6.34 | 2.67 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.43 | 0.89 ( 0 )    | 7.92 ( 0.24 )  | 0.04 ( 0.02 ) | 12.88 | 0.52 | 0.89 |  7.92 | 0.04 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.66 | 0.9 ( 0 )     | 3.85 ( 0.16 )  | 0 ( 0 )       |  8.85 | 0.3  | 0.90 |  3.85 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.14 | 0.7 ( 0.01 )  | 6.5 ( 0.24 )   | 4.63 ( 0.06 ) |  6.87 | 0.77 | 0.70 |  6.50 | 4.63 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.21 | 0.76 ( 0.01 ) | 6.98 ( 0.58 )  | 3.65 ( 0.08 ) |  8.33 | 0.56 | 0.76 |  6.98 | 3.65 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.20 | 0.88 ( 0 )    | 22.04 ( 0.74 ) | 0.14 ( 0.04 ) | 26.90 | 0.76 | 0.88 | 22.04 | 0.14 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.33 | 0.89 ( 0 )    | 13.15 ( 0.38 ) | 0 ( 0 )       | 18.15 | 0.65 | 0.89 | 13.15 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.10 | 0.66 ( 0.01 ) | 8.82 ( 0.28 )  | 4.72 ( 0.05 ) |  9.10 | 0.85 | 0.66 |  8.82 | 4.72 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.18 | 0.76 ( 0.01 ) | 6.93 ( 0.59 )  | 4 ( 0.08 )    |  7.93 | 0.62 | 0.76 |  6.93 | 4.00 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.16 | 0.87 ( 0 )    | 29.07 ( 1 )    | 0.16 ( 0.04 ) | 33.91 | 0.81 | 0.87 | 29.07 | 0.16 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.22 | 0.89 ( 0 )    | 21.8 ( 0.63 )  | 0 ( 0 )       | 26.80 | 0.76 | 0.89 | 21.80 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.30 | 0.69 ( 0.01 ) | 2.9 ( 0.2 )    | 4.22 ( 0.09 ) |  3.68 | 0.41 | 0.69 |  2.90 | 4.22 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.37 | 0.76 ( 0.01 ) | 3.93 ( 0.21 )  | 2.74 ( 0.11 ) |  6.19 | 0.41 | 0.76 |  3.93 | 2.74 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.57 | 0.83 ( 0 )    | 3.65 ( 0.16 )  | 0.56 ( 0.08 ) |  8.09 | 0.31 | 0.83 |  3.65 | 0.56 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.76 | 0.83 ( 0 )    | 2.41 ( 0.12 )  | 0.23 ( 0.04 ) |  7.18 | 0.18 | 0.83 |  2.41 | 0.23 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.26 | 0.7 ( 0.01 )  | 3.29 ( 0.23 )  | 4.29 ( 0.09 ) |  4.00 | 0.45 | 0.70 |  3.29 | 4.29 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.32 | 0.76 ( 0.01 ) | 4.77 ( 0.33 )  | 3.26 ( 0.09 ) |  6.51 | 0.46 | 0.76 |  4.77 | 3.26 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.45 | 0.82 ( 0 )    | 5.65 ( 0.21 )  | 0.92 ( 0.09 ) |  9.73 | 0.45 | 0.82 |  5.65 | 0.92 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.63 | 0.83 ( 0 )    | 3.87 ( 0.18 )  | 0.24 ( 0.05 ) |  8.63 | 0.3  | 0.83 |  3.87 | 0.24 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.15 | 0.66 ( 0.01 ) | 5.8 ( 0.24 )   | 4.65 ( 0.07 ) |  6.15 | 0.75 | 0.66 |  5.80 | 4.65 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.21 | 0.7 ( 0.01 )  | 5.55 ( 0.55 )  | 4.14 ( 0.09 ) |  6.41 | 0.51 | 0.70 |  5.55 | 4.14 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.19 | 0.81 ( 0 )    | 15.93 ( 0.73 ) | 1.42 ( 0.1 )  | 19.51 | 0.73 | 0.81 | 15.93 | 1.42 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.31 | 0.83 ( 0 )    | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 16.54 | 0.63 | 0.83 | 11.96 | 0.42 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.08 | 0.66 ( 0.01 ) | 8.02 ( 0.28 )  | 4.9 ( 0.07 )  |  8.12 | 0.85 | 0.66 |  8.02 | 4.90 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.18 | 0.69 ( 0.01 ) | 5.85 ( 0.58 )  | 4.37 ( 0.07 ) |  6.48 | 0.61 | 0.69 |  5.85 | 4.37 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.16 | 0.8 ( 0 )     | 18.7 ( 0.93 )  | 1.88 ( 0.1 )  | 21.82 | 0.77 | 0.80 | 18.70 | 1.88 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.21 | 0.83 ( 0 )    | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 24.66 | 0.75 | 0.83 | 20.07 | 0.41 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.32 0.78 ( 0.01 ) 3.65 ( 0.23 )  2.83 ( 0.11 )\n",
+       "2   100   50 0.1   0.50 0.34 0.85 ( 0.01 ) 7.35 ( 0.34 )  0.68 ( 0.09 )\n",
+       "3   500   50 0.1   0.10 0.55 0.92 ( 0 )    4.99 ( 0.22 )  0 ( 0 )      \n",
+       "4  1000   50 0.1   0.05 0.78 0.93 ( 0 )    2.42 ( 0.12 )  0 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.26 0.74 ( 0.01 ) 4.65 ( 0.29 )  2.93 ( 0.12 )\n",
+       "6   100  100 0.1   1.00 0.33 0.85 ( 0.01 ) 8.11 ( 0.41 )  0.86 ( 0.09 )\n",
+       "7   500  100 0.1   0.20 0.40 0.92 ( 0 )    8.63 ( 0.29 )  0 ( 0 )      \n",
+       "8  1000  100 0.1   0.10 0.64 0.92 ( 0 )    4.02 ( 0.18 )  0 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.12 0.71 ( 0.01 ) 8.29 ( 0.25 )  3.89 ( 0.1 ) \n",
+       "10  100  500 0.1   5.00 0.24 0.8 ( 0.01 )  8.84 ( 0.61 )  2.19 ( 0.12 )\n",
+       "11  500  500 0.1   1.00 0.21 0.92 ( 0 )    22.86 ( 0.75 ) 0 ( 0 )      \n",
+       "12 1000  500 0.1   0.50 0.33 0.93 ( 0 )    12.73 ( 0.35 ) 0 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.09 0.68 ( 0.01 ) 10.35 ( 0.33 ) 4.14 ( 0.09 )\n",
+       "14  100 1000 0.1  10.00 0.19 0.77 ( 0.01 ) 10.84 ( 0.75 ) 2.46 ( 0.1 ) \n",
+       "15  500 1000 0.1   2.00 0.16 0.92 ( 0 )    30.83 ( 1.05 ) 0 ( 0 )      \n",
+       "16 1000 1000 0.1   1.00 0.23 0.93 ( 0 )    20.39 ( 0.59 ) 0 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.28 0.75 ( 0.01 ) 3.69 ( 0.22 )  3.04 ( 0.1 ) \n",
+       "18  100   50 0.3   0.50 0.32 0.84 ( 0.01 ) 7.11 ( 0.33 )  0.93 ( 0.1 ) \n",
+       "19  500   50 0.3   0.10 0.51 0.92 ( 0 )    5.67 ( 0.22 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.78 0.92 ( 0 )    2.48 ( 0.13 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.26 0.75 ( 0.01 ) 4.41 ( 0.27 )  3.47 ( 0.09 )\n",
+       "22  100  100 0.3   1.00 0.28 0.82 ( 0.01 ) 8.16 ( 0.53 )  1.46 ( 0.11 )\n",
+       "23  500  100 0.3   0.20 0.41 0.92 ( 0 )    8.48 ( 0.34 )  0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.67 0.92 ( 0 )    3.71 ( 0.15 )  0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.13 0.69 ( 0.01 ) 7.94 ( 0.3 )   4.11 ( 0.08 )\n",
+       "26  100  500 0.3   5.00 0.20 0.78 ( 0.01 ) 10.16 ( 0.68 ) 2.49 ( 0.11 )\n",
+       "27  500  500 0.3   1.00 0.20 0.91 ( 0 )    23.65 ( 0.7 )  0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.34 0.92 ( 0 )    12.28 ( 0.34 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.10 0.68 ( 0.01 ) 9.77 ( 0.31 )  4.3 ( 0.06 ) \n",
+       "30  100 1000 0.3  10.00 0.17 0.77 ( 0.01 ) 10.68 ( 0.8 )  3.02 ( 0.11 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.57 0.89 ( 0 )    4.61 ( 0.17 )  0.04 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.74 0.89 ( 0 )    2.8 ( 0.15 )   0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.24 0.71 ( 0.01 ) 4.31 ( 0.28 )  4.05 ( 0.09 )\n",
+       "54  100  100 0.7   1.00 0.27 0.79 ( 0.01 ) 6.34 ( 0.4 )   2.67 ( 0.12 )\n",
+       "55  500  100 0.7   0.20 0.43 0.89 ( 0 )    7.92 ( 0.24 )  0.04 ( 0.02 )\n",
+       "56 1000  100 0.7   0.10 0.66 0.9 ( 0 )     3.85 ( 0.16 )  0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.14 0.7 ( 0.01 )  6.5 ( 0.24 )   4.63 ( 0.06 )\n",
+       "58  100  500 0.7   5.00 0.21 0.76 ( 0.01 ) 6.98 ( 0.58 )  3.65 ( 0.08 )\n",
+       "59  500  500 0.7   1.00 0.20 0.88 ( 0 )    22.04 ( 0.74 ) 0.14 ( 0.04 )\n",
+       "60 1000  500 0.7   0.50 0.33 0.89 ( 0 )    13.15 ( 0.38 ) 0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.10 0.66 ( 0.01 ) 8.82 ( 0.28 )  4.72 ( 0.05 )\n",
+       "62  100 1000 0.7  10.00 0.18 0.76 ( 0.01 ) 6.93 ( 0.59 )  4 ( 0.08 )   \n",
+       "63  500 1000 0.7   2.00 0.16 0.87 ( 0 )    29.07 ( 1 )    0.16 ( 0.04 )\n",
+       "64 1000 1000 0.7   1.00 0.22 0.89 ( 0 )    21.8 ( 0.63 )  0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.30 0.69 ( 0.01 ) 2.9 ( 0.2 )    4.22 ( 0.09 )\n",
+       "66  100   50 0.9   0.50 0.37 0.76 ( 0.01 ) 3.93 ( 0.21 )  2.74 ( 0.11 )\n",
+       "67  500   50 0.9   0.10 0.57 0.83 ( 0 )    3.65 ( 0.16 )  0.56 ( 0.08 )\n",
+       "68 1000   50 0.9   0.05 0.76 0.83 ( 0 )    2.41 ( 0.12 )  0.23 ( 0.04 )\n",
+       "69   50  100 0.9   2.00 0.26 0.7 ( 0.01 )  3.29 ( 0.23 )  4.29 ( 0.09 )\n",
+       "70  100  100 0.9   1.00 0.32 0.76 ( 0.01 ) 4.77 ( 0.33 )  3.26 ( 0.09 )\n",
+       "71  500  100 0.9   0.20 0.45 0.82 ( 0 )    5.65 ( 0.21 )  0.92 ( 0.09 )\n",
+       "72 1000  100 0.9   0.10 0.63 0.83 ( 0 )    3.87 ( 0.18 )  0.24 ( 0.05 )\n",
+       "73   50  500 0.9  10.00 0.15 0.66 ( 0.01 ) 5.8 ( 0.24 )   4.65 ( 0.07 )\n",
+       "74  100  500 0.9   5.00 0.21 0.7 ( 0.01 )  5.55 ( 0.55 )  4.14 ( 0.09 )\n",
+       "75  500  500 0.9   1.00 0.19 0.81 ( 0 )    15.93 ( 0.73 ) 1.42 ( 0.1 ) \n",
+       "76 1000  500 0.9   0.50 0.31 0.83 ( 0 )    11.96 ( 0.46 ) 0.42 ( 0.06 )\n",
+       "77   50 1000 0.9  20.00 0.08 0.66 ( 0.01 ) 8.02 ( 0.28 )  4.9 ( 0.07 ) \n",
+       "78  100 1000 0.9  10.00 0.18 0.69 ( 0.01 ) 5.85 ( 0.58 )  4.37 ( 0.07 )\n",
+       "79  500 1000 0.9   2.00 0.16 0.8 ( 0 )     18.7 ( 0.93 )  1.88 ( 0.1 ) \n",
+       "80 1000 1000 0.9   1.00 0.21 0.83 ( 0 )    20.07 ( 0.72 ) 0.41 ( 0.05 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   5.82      0.39 0.78      3.65   2.83   \n",
+       "2  11.67      0.5  0.85      7.35   0.68   \n",
+       "3   9.99      0.37 0.92      4.99   0.00   \n",
+       "4   7.42      0.17 0.93      2.42   0.00   \n",
+       "5   6.72      0.48 0.74      4.65   2.93   \n",
+       "6  12.25      0.52 0.85      8.11   0.86   \n",
+       "7  13.63      0.54 0.92      8.63   0.00   \n",
+       "8   9.02      0.31 0.92      4.02   0.00   \n",
+       "9   9.40      0.77 0.71      8.29   3.89   \n",
+       "10 11.65      0.57 0.80      8.84   2.19   \n",
+       "11 27.86      0.77 0.92     22.86   0.00   \n",
+       "12 17.73      0.65 0.93     12.73   0.00   \n",
+       "13 11.21      0.82 0.68     10.35   4.14   \n",
+       "14 13.38      0.63 0.77     10.84   2.46   \n",
+       "15 35.83      0.81 0.92     30.83   0.00   \n",
+       "16 25.39      0.75 0.93     20.39   0.00   \n",
+       "17  5.65      0.4  0.75      3.69   3.04   \n",
+       "18 11.18      0.5  0.84      7.11   0.93   \n",
+       "19 10.67      0.41 0.92      5.67   0.00   \n",
+       "20  7.48      0.18 0.92      2.48   0.00   \n",
+       "21  5.94      0.49 0.75      4.41   3.47   \n",
+       "22 11.70      0.52 0.82      8.16   1.46   \n",
+       "23 13.48      0.53 0.92      8.48   0.00   \n",
+       "24  8.71      0.29 0.92      3.71   0.00   \n",
+       "25  8.83      0.77 0.69      7.94   4.11   \n",
+       "26 12.67      0.63 0.78     10.16   2.49   \n",
+       "27 28.65      0.78 0.91     23.65   0.00   \n",
+       "28 17.28      0.64 0.92     12.28   0.00   \n",
+       "29 10.47      0.83 0.68      9.77   4.30   \n",
+       "30 12.66      0.67 0.77     10.68   3.02   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51  9.57      0.36 0.89      4.61   0.04   \n",
+       "52  7.80      0.21 0.89      2.80   0.00   \n",
+       "53  5.26      0.55 0.71      4.31   4.05   \n",
+       "54  8.67      0.53 0.79      6.34   2.67   \n",
+       "55 12.88      0.52 0.89      7.92   0.04   \n",
+       "56  8.85      0.3  0.90      3.85   0.00   \n",
+       "57  6.87      0.77 0.70      6.50   4.63   \n",
+       "58  8.33      0.56 0.76      6.98   3.65   \n",
+       "59 26.90      0.76 0.88     22.04   0.14   \n",
+       "60 18.15      0.65 0.89     13.15   0.00   \n",
+       "61  9.10      0.85 0.66      8.82   4.72   \n",
+       "62  7.93      0.62 0.76      6.93   4.00   \n",
+       "63 33.91      0.81 0.87     29.07   0.16   \n",
+       "64 26.80      0.76 0.89     21.80   0.00   \n",
+       "65  3.68      0.41 0.69      2.90   4.22   \n",
+       "66  6.19      0.41 0.76      3.93   2.74   \n",
+       "67  8.09      0.31 0.83      3.65   0.56   \n",
+       "68  7.18      0.18 0.83      2.41   0.23   \n",
+       "69  4.00      0.45 0.70      3.29   4.29   \n",
+       "70  6.51      0.46 0.76      4.77   3.26   \n",
+       "71  9.73      0.45 0.82      5.65   0.92   \n",
+       "72  8.63      0.3  0.83      3.87   0.24   \n",
+       "73  6.15      0.75 0.66      5.80   4.65   \n",
+       "74  6.41      0.51 0.70      5.55   4.14   \n",
+       "75 19.51      0.73 0.81     15.93   1.42   \n",
+       "76 16.54      0.63 0.83     11.96   0.42   \n",
+       "77  8.12      0.85 0.66      8.02   4.90   \n",
+       "78  6.48      0.61 0.69      5.85   4.37   \n",
+       "79 21.82      0.77 0.80     18.70   1.88   \n",
+       "80 24.66      0.75 0.83     20.07   0.41   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_lasso_binary.txt', \n",
+    "            sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.2_sim_toe_elnet_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.2_sim_toe_elnet_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..b011e9c
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.2_sim_toe_elnet_binary_update-checkpoint.ipynb
@@ -0,0 +1,973 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
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+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>0.77 ( 0.01 )</td><td>0.13</td><td>16.51</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>0.85 ( 0.01 )</td><td>0.24</td><td>15.15</td><td>0.57</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>7.43 ( 0.6 )</td><td>0.02 ( 0.01 )</td><td>0.92 ( 0 )</td><td>0.41</td><td>12.41</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>7.82 ( 0.76 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.39</td><td>12.82</td><td>0.40</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>0.73 ( 0.01 )</td><td>0.09</td><td>24.91</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 )</td><td>0.83 ( 0.01 )</td><td>0.18</td><td>19.75</td><td>0.62</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 0.77 ( 0.01 ) & 0.13 & 16.51 & 0.63\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 0.85 ( 0.01 ) & 0.24 & 15.15 & 0.57\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 7.43 ( 0.6 ) & 0.02 ( 0.01 ) & 0.92 ( 0 ) & 0.41 & 12.41 & 0.42\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 7.82 ( 0.76 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.39 & 12.82 & 0.40\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 0.73 ( 0.01 ) & 0.09 & 24.91 & 0.73\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 15.59 ( 1.32 ) & 0.84 ( 0.1 ) & 0.83 ( 0.01 ) & 0.18 & 19.75 & 0.62\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 0.77 ( 0.01 ) | 0.13 | 16.51 | 0.63 |\n",
+       "| 2 | 100 | 50 | 0.1 | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 0.85 ( 0.01 ) | 0.24 | 15.15 | 0.57 |\n",
+       "| 3 | 500 | 50 | 0.1 | 7.43 ( 0.6 ) | 0.02 ( 0.01 ) | 0.92 ( 0 ) | 0.41 | 12.41 | 0.42 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 7.82 ( 0.76 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.39 | 12.82 | 0.40 |\n",
+       "| 5 | 50 | 100 | 0.1 | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 0.73 ( 0.01 ) | 0.09 | 24.91 | 0.73 |\n",
+       "| 6 | 100 | 100 | 0.1 | 15.59 ( 1.32 ) | 0.84 ( 0.1 ) | 0.83 ( 0.01 ) | 0.18 | 19.75 | 0.62 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 13.02 ( 1.02 ) 1.51 ( 0.11 ) 0.77 ( 0.01 ) 0.13 16.51      0.63\n",
+       "2 100  50  0.1 10.71 ( 0.62 ) 0.56 ( 0.07 ) 0.85 ( 0.01 ) 0.24 15.15      0.57\n",
+       "3 500  50  0.1 7.43 ( 0.6 )   0.02 ( 0.01 ) 0.92 ( 0 )    0.41 12.41      0.42\n",
+       "4 1000 50  0.1 7.82 ( 0.76 )  0 ( 0 )       0.92 ( 0 )    0.39 12.82      0.40\n",
+       "5 50   100 0.1 21.55 ( 1.94 ) 1.64 ( 0.11 ) 0.73 ( 0.01 ) 0.09 24.91      0.73\n",
+       "6 100  100 0.1 15.59 ( 1.32 ) 0.84 ( 0.1 )  0.83 ( 0.01 ) 0.18 19.75      0.62"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>0.81 ( 0 )</td><td>0.14</td><td>29.61</td><td>0.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>0.83 ( 0 )</td><td>0.21</td><td>28.34</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>0.65 ( 0.01 )</td><td>0.02</td><td>49.79</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>0.66 ( 0.01 )</td><td>0.05</td><td>34.18</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>0.8 ( 0 )</td><td>0.11</td><td>34.39</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>0.83 ( 0 )</td><td>0.14</td><td>40.43</td><td>0.82</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 0.81 ( 0 ) & 0.14 & 29.61 & 0.76\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 0.83 ( 0 ) & 0.21 & 28.34 & 0.75\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 0.65 ( 0.01 ) & 0.02 & 49.79 & 0.93\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 0.66 ( 0.01 ) & 0.05 & 34.18 & 0.80\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 0.8 ( 0 ) & 0.11 & 34.39 & 0.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 0.83 ( 0 ) & 0.14 & 40.43 & 0.82\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 0.81 ( 0 ) | 0.14 | 29.61 | 0.76 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 0.83 ( 0 ) | 0.21 | 28.34 | 0.75 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 0.65 ( 0.01 ) | 0.02 | 49.79 | 0.93 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 0.66 ( 0.01 ) | 0.05 | 34.18 | 0.80 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 0.8 ( 0 ) | 0.11 | 34.39 | 0.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 0.83 ( 0 ) | 0.14 | 40.43 | 0.82 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 25.68 ( 1.61 ) 1.07 ( 0.11 ) 0.81 ( 0 )    0.14 29.61     \n",
+       "76 1000 500  0.9 23.49 ( 1.26 ) 0.15 ( 0.05 ) 0.83 ( 0 )    0.21 28.34     \n",
+       "77 50   1000 0.9 49.08 ( 6.86 ) 4.29 ( 0.09 ) 0.65 ( 0.01 ) 0.02 49.79     \n",
+       "78 100  1000 0.9 32.99 ( 5.22 ) 3.81 ( 0.09 ) 0.66 ( 0.01 ) 0.05 34.18     \n",
+       "79 500  1000 0.9 30.91 ( 2.22 ) 1.52 ( 0.12 ) 0.8 ( 0 )     0.11 34.39     \n",
+       "80 1000 1000 0.9 35.66 ( 1.98 ) 0.23 ( 0.06 ) 0.83 ( 0 )    0.14 40.43     \n",
+       "   FDR \n",
+       "75 0.76\n",
+       "76 0.75\n",
+       "77 0.93\n",
+       "78 0.80\n",
+       "79 0.78\n",
+       "80 0.82"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.13</td><td>0.77 ( 0.01 )</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>16.51</td><td>0.63</td><td>0.77</td><td>13.02</td><td>1.51</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.24</td><td>0.85 ( 0.01 )</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>15.15</td><td>0.57</td><td>0.85</td><td>10.71</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.43 ( 0.6 )  </td><td>0.02 ( 0.01 )</td><td>12.41</td><td>0.42</td><td>0.92</td><td> 7.43</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.82 ( 0.76 ) </td><td>0 ( 0 )      </td><td>12.82</td><td>0.4 </td><td>0.92</td><td> 7.82</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.09</td><td>0.73 ( 0.01 )</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>24.91</td><td>0.73</td><td>0.73</td><td>21.55</td><td>1.64</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 ) </td><td>19.75</td><td>0.62</td><td>0.83</td><td>15.59</td><td>0.84</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.13 & 0.77 ( 0.01 ) & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 16.51 & 0.63 & 0.77 & 13.02 & 1.51\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.24 & 0.85 ( 0.01 ) & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 15.15 & 0.57 & 0.85 & 10.71 & 0.56\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.41 & 0.92 ( 0 )    & 7.43 ( 0.6 )   & 0.02 ( 0.01 ) & 12.41 & 0.42 & 0.92 &  7.43 & 0.02\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.39 & 0.92 ( 0 )    & 7.82 ( 0.76 )  & 0 ( 0 )       & 12.82 & 0.4  & 0.92 &  7.82 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.09 & 0.73 ( 0.01 ) & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 24.91 & 0.73 & 0.73 & 21.55 & 1.64\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.18 & 0.83 ( 0.01 ) & 15.59 ( 1.32 ) & 0.84 ( 0.1 )  & 19.75 & 0.62 & 0.83 & 15.59 & 0.84\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.13 | 0.77 ( 0.01 ) | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 16.51 | 0.63 | 0.77 | 13.02 | 1.51 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.24 | 0.85 ( 0.01 ) | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 15.15 | 0.57 | 0.85 | 10.71 | 0.56 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.41 | 0.92 ( 0 )    | 7.43 ( 0.6 )   | 0.02 ( 0.01 ) | 12.41 | 0.42 | 0.92 |  7.43 | 0.02 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.39 | 0.92 ( 0 )    | 7.82 ( 0.76 )  | 0 ( 0 )       | 12.82 | 0.4  | 0.92 |  7.82 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.09 | 0.73 ( 0.01 ) | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 24.91 | 0.73 | 0.73 | 21.55 | 1.64 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.18 | 0.83 ( 0.01 ) | 15.59 ( 1.32 ) | 0.84 ( 0.1 )  | 19.75 | 0.62 | 0.83 | 15.59 | 0.84 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.13 0.77 ( 0.01 ) 13.02 ( 1.02 ) 1.51 ( 0.11 )\n",
+       "2  100  50 0.1  0.50  0.24 0.85 ( 0.01 ) 10.71 ( 0.62 ) 0.56 ( 0.07 )\n",
+       "3  500  50 0.1  0.10  0.41 0.92 ( 0 )    7.43 ( 0.6 )   0.02 ( 0.01 )\n",
+       "4 1000  50 0.1  0.05  0.39 0.92 ( 0 )    7.82 ( 0.76 )  0 ( 0 )      \n",
+       "5   50 100 0.1  2.00  0.09 0.73 ( 0.01 ) 21.55 ( 1.94 ) 1.64 ( 0.11 )\n",
+       "6  100 100 0.1  1.00  0.18 0.83 ( 0.01 ) 15.59 ( 1.32 ) 0.84 ( 0.1 ) \n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 16.51      0.63 0.77     13.02   1.51   \n",
+       "2 15.15      0.57 0.85     10.71   0.56   \n",
+       "3 12.41      0.42 0.92      7.43   0.02   \n",
+       "4 12.82      0.4  0.92      7.82   0.00   \n",
+       "5 24.91      0.73 0.73     21.55   1.64   \n",
+       "6 19.75      0.62 0.83     15.59   0.84   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.14</td><td>0.81 ( 0 )   </td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>29.61</td><td>0.76</td><td>0.81</td><td>25.68</td><td>1.07</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.21</td><td>0.83 ( 0 )   </td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>28.34</td><td>0.75</td><td>0.83</td><td>23.49</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>49.79</td><td>0.93</td><td>0.65</td><td>49.08</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.05</td><td>0.66 ( 0.01 )</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>34.18</td><td>0.8 </td><td>0.66</td><td>32.99</td><td>3.81</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.11</td><td>0.8 ( 0 )    </td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>34.39</td><td>0.78</td><td>0.80</td><td>30.91</td><td>1.52</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.14</td><td>0.83 ( 0 )   </td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>40.43</td><td>0.82</td><td>0.83</td><td>35.66</td><td>0.23</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.14 & 0.81 ( 0 )    & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 29.61 & 0.76 & 0.81 & 25.68 & 1.07\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.21 & 0.83 ( 0 )    & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 28.34 & 0.75 & 0.83 & 23.49 & 0.15\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.02 & 0.65 ( 0.01 ) & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 49.79 & 0.93 & 0.65 & 49.08 & 4.29\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.05 & 0.66 ( 0.01 ) & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 34.18 & 0.8  & 0.66 & 32.99 & 3.81\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.11 & 0.8 ( 0 )     & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 34.39 & 0.78 & 0.80 & 30.91 & 1.52\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.14 & 0.83 ( 0 )    & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 40.43 & 0.82 & 0.83 & 35.66 & 0.23\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.14 | 0.81 ( 0 )    | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 29.61 | 0.76 | 0.81 | 25.68 | 1.07 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.21 | 0.83 ( 0 )    | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 28.34 | 0.75 | 0.83 | 23.49 | 0.15 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.02 | 0.65 ( 0.01 ) | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 49.79 | 0.93 | 0.65 | 49.08 | 4.29 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.05 | 0.66 ( 0.01 ) | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 34.18 | 0.8  | 0.66 | 32.99 | 3.81 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.11 | 0.8 ( 0 )     | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 34.39 | 0.78 | 0.80 | 30.91 | 1.52 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.14 | 0.83 ( 0 )    | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 40.43 | 0.82 | 0.83 | 35.66 | 0.23 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.14 0.81 ( 0 )    25.68 ( 1.61 ) 1.07 ( 0.11 )\n",
+       "76 1000  500 0.9   0.5  0.21 0.83 ( 0 )    23.49 ( 1.26 ) 0.15 ( 0.05 )\n",
+       "77   50 1000 0.9  20.0  0.02 0.65 ( 0.01 ) 49.08 ( 6.86 ) 4.29 ( 0.09 )\n",
+       "78  100 1000 0.9  10.0  0.05 0.66 ( 0.01 ) 32.99 ( 5.22 ) 3.81 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.11 0.8 ( 0 )     30.91 ( 2.22 ) 1.52 ( 0.12 )\n",
+       "80 1000 1000 0.9   1.0  0.14 0.83 ( 0 )    35.66 ( 1.98 ) 0.23 ( 0.06 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 29.61      0.76 0.81     25.68   1.07   \n",
+       "76 28.34      0.75 0.83     23.49   0.15   \n",
+       "77 49.79      0.93 0.65     49.08   4.29   \n",
+       "78 34.18      0.8  0.66     32.99   3.81   \n",
+       "79 34.39      0.78 0.80     30.91   1.52   \n",
+       "80 40.43      0.82 0.83     35.66   0.23   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.13</td><td>0.77 ( 0.01 )</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>16.51</td><td>0.63</td><td>0.77</td><td>13.02</td><td>1.51</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.24</td><td>0.85 ( 0.01 )</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>15.15</td><td>0.57</td><td>0.85</td><td>10.71</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.43 ( 0.6 )  </td><td>0.02 ( 0.01 )</td><td>12.41</td><td>0.42</td><td>0.92</td><td> 7.43</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.82 ( 0.76 ) </td><td>0 ( 0 )      </td><td>12.82</td><td>0.4 </td><td>0.92</td><td> 7.82</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.09</td><td>0.73 ( 0.01 )</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>24.91</td><td>0.73</td><td>0.73</td><td>21.55</td><td>1.64</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 ) </td><td>19.75</td><td>0.62</td><td>0.83</td><td>15.59</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.32</td><td>0.92 ( 0 )   </td><td>11.5 ( 0.9 )  </td><td>0 ( 0 )      </td><td>16.50</td><td>0.54</td><td>0.92</td><td>11.50</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.34</td><td>0.92 ( 0 )   </td><td>11.02 ( 1.05 )</td><td>0 ( 0 )      </td><td>16.02</td><td>0.49</td><td>0.92</td><td>11.02</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.03</td><td>0.66 ( 0.01 )</td><td>58.7 ( 5.86 ) </td><td>2.64 ( 0.12 )</td><td>61.06</td><td>0.89</td><td>0.66</td><td>58.70</td><td>2.64</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.14</td><td>0.78 ( 0.01 )</td><td>19.29 ( 1.92 )</td><td>1.76 ( 0.1 ) </td><td>22.53</td><td>0.68</td><td>0.78</td><td>19.29</td><td>1.76</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.26</td><td>0.91 ( 0 )   </td><td>17.05 ( 1.43 )</td><td>0 ( 0 )      </td><td>22.05</td><td>0.62</td><td>0.91</td><td>17.05</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.29</td><td>0.93 ( 0 )   </td><td>15.14 ( 1.39 )</td><td>0 ( 0 )      </td><td>20.14</td><td>0.54</td><td>0.93</td><td>15.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>85.97 ( 9 )   </td><td>3.03 ( 0.13 )</td><td>87.94</td><td>0.92</td><td>0.65</td><td>85.97</td><td>3.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.12</td><td>0.76 ( 0.01 )</td><td>22.02 ( 1.97 )</td><td>2.07 ( 0.1 ) </td><td>24.95</td><td>0.75</td><td>0.76</td><td>22.02</td><td>2.07</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.23</td><td>0.91 ( 0 )   </td><td>21.07 ( 1.75 )</td><td>0 ( 0 )      </td><td>26.07</td><td>0.66</td><td>0.91</td><td>21.07</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.24</td><td>0.92 ( 0 )   </td><td>19.56 ( 2.29 )</td><td>0 ( 0 )      </td><td>24.56</td><td>0.57</td><td>0.92</td><td>19.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.13</td><td>0.76 ( 0.01 )</td><td>11.32 ( 0.99 )</td><td>1.87 ( 0.14 )</td><td>14.45</td><td>0.59</td><td>0.76</td><td>11.32</td><td>1.87</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>12.96 ( 0.86 )</td><td>0.75 ( 0.09 )</td><td>17.21</td><td>0.61</td><td>0.83</td><td>12.96</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.33</td><td>0.91 ( 0 )   </td><td>9.89 ( 0.75 ) </td><td>0 ( 0 )      </td><td>14.89</td><td>0.51</td><td>0.91</td><td> 9.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.38</td><td>0.92 ( 0 )   </td><td>8.56 ( 0.69 ) </td><td>0 ( 0 )      </td><td>13.56</td><td>0.44</td><td>0.92</td><td> 8.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.08</td><td>0.74 ( 0.01 )</td><td>17.99 ( 1.78 )</td><td>2.32 ( 0.14 )</td><td>20.67</td><td>0.72</td><td>0.74</td><td>17.99</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.17</td><td>0.82 ( 0.01 )</td><td>14.36 ( 1.25 )</td><td>1.36 ( 0.12 )</td><td>18.00</td><td>0.6 </td><td>0.82</td><td>14.36</td><td>1.36</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.29</td><td>0.91 ( 0 )   </td><td>13.08 ( 1 )   </td><td>0 ( 0 )      </td><td>18.08</td><td>0.56</td><td>0.91</td><td>13.08</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.38</td><td>0.92 ( 0 )   </td><td>9.67 ( 0.76 ) </td><td>0 ( 0 )      </td><td>14.67</td><td>0.49</td><td>0.92</td><td> 9.67</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.03</td><td>0.66 ( 0.01 )</td><td>49.2 ( 5.65 ) </td><td>3.15 ( 0.13 )</td><td>51.05</td><td>0.89</td><td>0.66</td><td>49.20</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.11</td><td>0.77 ( 0.01 )</td><td>23.84 ( 2.37 )</td><td>2.05 ( 0.11 )</td><td>26.79</td><td>0.73</td><td>0.77</td><td>23.84</td><td>2.05</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.91 ( 0 )   </td><td>25.04 ( 1.69 )</td><td>0 ( 0 )      </td><td>30.04</td><td>0.72</td><td>0.91</td><td>25.04</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.92 ( 0 )   </td><td>18.51 ( 1.25 )</td><td>0 ( 0 )      </td><td>23.51</td><td>0.66</td><td>0.92</td><td>18.51</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>54.74 ( 6.8 ) </td><td>3.54 ( 0.12 )</td><td>56.20</td><td>0.91</td><td>0.67</td><td>54.74</td><td>3.54</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.08</td><td>0.75 ( 0.01 )</td><td>28.55 ( 5.11 )</td><td>2.58 ( 0.11 )</td><td>30.97</td><td>0.75</td><td>0.75</td><td>28.55</td><td>2.58</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.31</td><td>0.89 ( 0 )   </td><td>11.14 ( 0.74 )</td><td>0.02 ( 0.02 )</td><td>16.12</td><td>0.56</td><td>0.89</td><td>11.14</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.37</td><td>0.89 ( 0 )   </td><td>9.65 ( 0.77 ) </td><td>0 ( 0 )      </td><td>14.65</td><td>0.49</td><td>0.89</td><td> 9.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.08</td><td>0.71 ( 0.01 )</td><td>17.76 ( 1.65 )</td><td>2.81 ( 0.14 )</td><td>19.95</td><td>0.74</td><td>0.71</td><td>17.76</td><td>2.81</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.13</td><td>0.79 ( 0.01 )</td><td>16.43 ( 1.31 )</td><td>1.79 ( 0.13 )</td><td>19.64</td><td>0.68</td><td>0.79</td><td>16.43</td><td>1.79</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.25</td><td>0.89 ( 0 )   </td><td>16.01 ( 0.82 )</td><td>0.03 ( 0.03 )</td><td>20.98</td><td>0.67</td><td>0.89</td><td>16.01</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.33</td><td>0.89 ( 0 )   </td><td>12.77 ( 0.94 )</td><td>0 ( 0 )      </td><td>17.77</td><td>0.58</td><td>0.89</td><td>12.77</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.03</td><td>0.67 ( 0.01 )</td><td>43.83 ( 5.17 )</td><td>3.75 ( 0.12 )</td><td>45.08</td><td>0.9 </td><td>0.67</td><td>43.83</td><td>3.75</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.10</td><td>0.74 ( 0.01 )</td><td>19.1 ( 1.88 ) </td><td>3.08 ( 0.1 ) </td><td>21.02</td><td>0.73</td><td>0.74</td><td>19.10</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.15</td><td>0.87 ( 0 )   </td><td>31.23 ( 1.88 )</td><td>0.17 ( 0.04 )</td><td>36.06</td><td>0.78</td><td>0.87</td><td>31.23</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.22</td><td>0.89 ( 0 )   </td><td>22.46 ( 1.45 )</td><td>0 ( 0 )      </td><td>27.46</td><td>0.71</td><td>0.89</td><td>22.46</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.02</td><td>0.64 ( 0.01 )</td><td>47.17 ( 5.85 )</td><td>4.12 ( 0.09 )</td><td>48.05</td><td>0.92</td><td>0.64</td><td>47.17</td><td>4.12</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.08</td><td>0.75 ( 0.01 )</td><td>20.86 ( 2.56 )</td><td>3.59 ( 0.09 )</td><td>22.27</td><td>0.77</td><td>0.75</td><td>20.86</td><td>3.59</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.13</td><td>0.87 ( 0 )   </td><td>36.32 ( 2.18 )</td><td>0.23 ( 0.05 )</td><td>41.09</td><td>0.81</td><td>0.87</td><td>36.32</td><td>0.23</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.16</td><td>0.89 ( 0 )   </td><td>32.22 ( 2.11 )</td><td>0.01 ( 0.01 )</td><td>37.21</td><td>0.8 </td><td>0.89</td><td>32.22</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.09</td><td>0.71 ( 0.01 )</td><td>14.15 ( 1 )   </td><td>2.3 ( 0.14 ) </td><td>16.85</td><td>0.68</td><td>0.71</td><td>14.15</td><td>2.30</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>11.74 ( 0.88 )</td><td>1.74 ( 0.13 )</td><td>15.00</td><td>0.64</td><td>0.77</td><td>11.74</td><td>1.74</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.27</td><td>0.83 ( 0 )   </td><td>12.28 ( 0.9 ) </td><td>0.18 ( 0.06 )</td><td>17.10</td><td>0.57</td><td>0.83</td><td>12.28</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.39</td><td>0.84 ( 0 )   </td><td>9.58 ( 0.68 ) </td><td>0.01 ( 0.01 )</td><td>14.57</td><td>0.51</td><td>0.84</td><td> 9.58</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.08</td><td>0.7 ( 0.01 ) </td><td>15.1 ( 1.65 ) </td><td>3.16 ( 0.13 )</td><td>16.94</td><td>0.7 </td><td>0.70</td><td>15.10</td><td>3.16</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.15</td><td>0.75 ( 0.01 )</td><td>14.19 ( 1.38 )</td><td>2.23 ( 0.13 )</td><td>16.96</td><td>0.64</td><td>0.75</td><td>14.19</td><td>2.23</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.25</td><td>0.83 ( 0 )   </td><td>16.33 ( 1.01 )</td><td>0.2 ( 0.06 ) </td><td>21.13</td><td>0.67</td><td>0.83</td><td>16.33</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.32</td><td>0.84 ( 0 )   </td><td>13.82 ( 1.17 )</td><td>0.03 ( 0.02 )</td><td>18.79</td><td>0.6 </td><td>0.84</td><td>13.82</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.05</td><td>0.66 ( 0.02 )</td><td>28.64 ( 3.14 )</td><td>4.13 ( 0.07 )</td><td>29.51</td><td>0.88</td><td>0.66</td><td>28.64</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.07</td><td>0.7 ( 0.01 ) </td><td>25.71 ( 4.03 )</td><td>3.42 ( 0.1 ) </td><td>27.29</td><td>0.78</td><td>0.70</td><td>25.71</td><td>3.42</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.81 ( 0 )   </td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>29.61</td><td>0.76</td><td>0.81</td><td>25.68</td><td>1.07</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.21</td><td>0.83 ( 0 )   </td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>28.34</td><td>0.75</td><td>0.83</td><td>23.49</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>49.79</td><td>0.93</td><td>0.65</td><td>49.08</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.05</td><td>0.66 ( 0.01 )</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>34.18</td><td>0.8 </td><td>0.66</td><td>32.99</td><td>3.81</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.11</td><td>0.8 ( 0 )    </td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>34.39</td><td>0.78</td><td>0.80</td><td>30.91</td><td>1.52</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.83 ( 0 )   </td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>40.43</td><td>0.82</td><td>0.83</td><td>35.66</td><td>0.23</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.13 & 0.77 ( 0.01 ) & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 16.51 & 0.63 & 0.77 & 13.02 & 1.51\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.24 & 0.85 ( 0.01 ) & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 15.15 & 0.57 & 0.85 & 10.71 & 0.56\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.41 & 0.92 ( 0 )    & 7.43 ( 0.6 )   & 0.02 ( 0.01 ) & 12.41 & 0.42 & 0.92 &  7.43 & 0.02\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.39 & 0.92 ( 0 )    & 7.82 ( 0.76 )  & 0 ( 0 )       & 12.82 & 0.4  & 0.92 &  7.82 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.09 & 0.73 ( 0.01 ) & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 24.91 & 0.73 & 0.73 & 21.55 & 1.64\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.18 & 0.83 ( 0.01 ) & 15.59 ( 1.32 ) & 0.84 ( 0.1 )  & 19.75 & 0.62 & 0.83 & 15.59 & 0.84\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.32 & 0.92 ( 0 )    & 11.5 ( 0.9 )   & 0 ( 0 )       & 16.50 & 0.54 & 0.92 & 11.50 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.34 & 0.92 ( 0 )    & 11.02 ( 1.05 ) & 0 ( 0 )       & 16.02 & 0.49 & 0.92 & 11.02 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.03 & 0.66 ( 0.01 ) & 58.7 ( 5.86 )  & 2.64 ( 0.12 ) & 61.06 & 0.89 & 0.66 & 58.70 & 2.64\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.14 & 0.78 ( 0.01 ) & 19.29 ( 1.92 ) & 1.76 ( 0.1 )  & 22.53 & 0.68 & 0.78 & 19.29 & 1.76\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.26 & 0.91 ( 0 )    & 17.05 ( 1.43 ) & 0 ( 0 )       & 22.05 & 0.62 & 0.91 & 17.05 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.29 & 0.93 ( 0 )    & 15.14 ( 1.39 ) & 0 ( 0 )       & 20.14 & 0.54 & 0.93 & 15.14 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.02 & 0.65 ( 0.01 ) & 85.97 ( 9 )    & 3.03 ( 0.13 ) & 87.94 & 0.92 & 0.65 & 85.97 & 3.03\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.12 & 0.76 ( 0.01 ) & 22.02 ( 1.97 ) & 2.07 ( 0.1 )  & 24.95 & 0.75 & 0.76 & 22.02 & 2.07\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.23 & 0.91 ( 0 )    & 21.07 ( 1.75 ) & 0 ( 0 )       & 26.07 & 0.66 & 0.91 & 21.07 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.24 & 0.92 ( 0 )    & 19.56 ( 2.29 ) & 0 ( 0 )       & 24.56 & 0.57 & 0.92 & 19.56 & 0.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.13 & 0.76 ( 0.01 ) & 11.32 ( 0.99 ) & 1.87 ( 0.14 ) & 14.45 & 0.59 & 0.76 & 11.32 & 1.87\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.18 & 0.83 ( 0.01 ) & 12.96 ( 0.86 ) & 0.75 ( 0.09 ) & 17.21 & 0.61 & 0.83 & 12.96 & 0.75\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.33 & 0.91 ( 0 )    & 9.89 ( 0.75 )  & 0 ( 0 )       & 14.89 & 0.51 & 0.91 &  9.89 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.38 & 0.92 ( 0 )    & 8.56 ( 0.69 )  & 0 ( 0 )       & 13.56 & 0.44 & 0.92 &  8.56 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.08 & 0.74 ( 0.01 ) & 17.99 ( 1.78 ) & 2.32 ( 0.14 ) & 20.67 & 0.72 & 0.74 & 17.99 & 2.32\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.17 & 0.82 ( 0.01 ) & 14.36 ( 1.25 ) & 1.36 ( 0.12 ) & 18.00 & 0.6  & 0.82 & 14.36 & 1.36\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.29 & 0.91 ( 0 )    & 13.08 ( 1 )    & 0 ( 0 )       & 18.08 & 0.56 & 0.91 & 13.08 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.38 & 0.92 ( 0 )    & 9.67 ( 0.76 )  & 0 ( 0 )       & 14.67 & 0.49 & 0.92 &  9.67 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.03 & 0.66 ( 0.01 ) & 49.2 ( 5.65 )  & 3.15 ( 0.13 ) & 51.05 & 0.89 & 0.66 & 49.20 & 3.15\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.11 & 0.77 ( 0.01 ) & 23.84 ( 2.37 ) & 2.05 ( 0.11 ) & 26.79 & 0.73 & 0.77 & 23.84 & 2.05\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.19 & 0.91 ( 0 )    & 25.04 ( 1.69 ) & 0 ( 0 )       & 30.04 & 0.72 & 0.91 & 25.04 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.25 & 0.92 ( 0 )    & 18.51 ( 1.25 ) & 0 ( 0 )       & 23.51 & 0.66 & 0.92 & 18.51 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 0.67 ( 0.01 ) & 54.74 ( 6.8 )  & 3.54 ( 0.12 ) & 56.20 & 0.91 & 0.67 & 54.74 & 3.54\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.08 & 0.75 ( 0.01 ) & 28.55 ( 5.11 ) & 2.58 ( 0.11 ) & 30.97 & 0.75 & 0.75 & 28.55 & 2.58\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.31 & 0.89 ( 0 )    & 11.14 ( 0.74 ) & 0.02 ( 0.02 ) & 16.12 & 0.56 & 0.89 & 11.14 & 0.02\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.37 & 0.89 ( 0 )    & 9.65 ( 0.77 )  & 0 ( 0 )       & 14.65 & 0.49 & 0.89 &  9.65 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.08 & 0.71 ( 0.01 ) & 17.76 ( 1.65 ) & 2.81 ( 0.14 ) & 19.95 & 0.74 & 0.71 & 17.76 & 2.81\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.13 & 0.79 ( 0.01 ) & 16.43 ( 1.31 ) & 1.79 ( 0.13 ) & 19.64 & 0.68 & 0.79 & 16.43 & 1.79\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.25 & 0.89 ( 0 )    & 16.01 ( 0.82 ) & 0.03 ( 0.03 ) & 20.98 & 0.67 & 0.89 & 16.01 & 0.03\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.33 & 0.89 ( 0 )    & 12.77 ( 0.94 ) & 0 ( 0 )       & 17.77 & 0.58 & 0.89 & 12.77 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.03 & 0.67 ( 0.01 ) & 43.83 ( 5.17 ) & 3.75 ( 0.12 ) & 45.08 & 0.9  & 0.67 & 43.83 & 3.75\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.10 & 0.74 ( 0.01 ) & 19.1 ( 1.88 )  & 3.08 ( 0.1 )  & 21.02 & 0.73 & 0.74 & 19.10 & 3.08\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.15 & 0.87 ( 0 )    & 31.23 ( 1.88 ) & 0.17 ( 0.04 ) & 36.06 & 0.78 & 0.87 & 31.23 & 0.17\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.22 & 0.89 ( 0 )    & 22.46 ( 1.45 ) & 0 ( 0 )       & 27.46 & 0.71 & 0.89 & 22.46 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.02 & 0.64 ( 0.01 ) & 47.17 ( 5.85 ) & 4.12 ( 0.09 ) & 48.05 & 0.92 & 0.64 & 47.17 & 4.12\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.08 & 0.75 ( 0.01 ) & 20.86 ( 2.56 ) & 3.59 ( 0.09 ) & 22.27 & 0.77 & 0.75 & 20.86 & 3.59\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.13 & 0.87 ( 0 )    & 36.32 ( 2.18 ) & 0.23 ( 0.05 ) & 41.09 & 0.81 & 0.87 & 36.32 & 0.23\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.16 & 0.89 ( 0 )    & 32.22 ( 2.11 ) & 0.01 ( 0.01 ) & 37.21 & 0.8  & 0.89 & 32.22 & 0.01\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.09 & 0.71 ( 0.01 ) & 14.15 ( 1 )    & 2.3 ( 0.14 )  & 16.85 & 0.68 & 0.71 & 14.15 & 2.30\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.15 & 0.77 ( 0.01 ) & 11.74 ( 0.88 ) & 1.74 ( 0.13 ) & 15.00 & 0.64 & 0.77 & 11.74 & 1.74\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.27 & 0.83 ( 0 )    & 12.28 ( 0.9 )  & 0.18 ( 0.06 ) & 17.10 & 0.57 & 0.83 & 12.28 & 0.18\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.39 & 0.84 ( 0 )    & 9.58 ( 0.68 )  & 0.01 ( 0.01 ) & 14.57 & 0.51 & 0.84 &  9.58 & 0.01\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.08 & 0.7 ( 0.01 )  & 15.1 ( 1.65 )  & 3.16 ( 0.13 ) & 16.94 & 0.7  & 0.70 & 15.10 & 3.16\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.15 & 0.75 ( 0.01 ) & 14.19 ( 1.38 ) & 2.23 ( 0.13 ) & 16.96 & 0.64 & 0.75 & 14.19 & 2.23\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.25 & 0.83 ( 0 )    & 16.33 ( 1.01 ) & 0.2 ( 0.06 )  & 21.13 & 0.67 & 0.83 & 16.33 & 0.20\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.32 & 0.84 ( 0 )    & 13.82 ( 1.17 ) & 0.03 ( 0.02 ) & 18.79 & 0.6  & 0.84 & 13.82 & 0.03\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.05 & 0.66 ( 0.02 ) & 28.64 ( 3.14 ) & 4.13 ( 0.07 ) & 29.51 & 0.88 & 0.66 & 28.64 & 4.13\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.07 & 0.7 ( 0.01 )  & 25.71 ( 4.03 ) & 3.42 ( 0.1 )  & 27.29 & 0.78 & 0.70 & 25.71 & 3.42\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.14 & 0.81 ( 0 )    & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 29.61 & 0.76 & 0.81 & 25.68 & 1.07\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.21 & 0.83 ( 0 )    & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 28.34 & 0.75 & 0.83 & 23.49 & 0.15\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.02 & 0.65 ( 0.01 ) & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 49.79 & 0.93 & 0.65 & 49.08 & 4.29\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.05 & 0.66 ( 0.01 ) & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 34.18 & 0.8  & 0.66 & 32.99 & 3.81\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.11 & 0.8 ( 0 )     & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 34.39 & 0.78 & 0.80 & 30.91 & 1.52\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.14 & 0.83 ( 0 )    & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 40.43 & 0.82 & 0.83 & 35.66 & 0.23\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.13 | 0.77 ( 0.01 ) | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 16.51 | 0.63 | 0.77 | 13.02 | 1.51 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.24 | 0.85 ( 0.01 ) | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 15.15 | 0.57 | 0.85 | 10.71 | 0.56 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.41 | 0.92 ( 0 )    | 7.43 ( 0.6 )   | 0.02 ( 0.01 ) | 12.41 | 0.42 | 0.92 |  7.43 | 0.02 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.39 | 0.92 ( 0 )    | 7.82 ( 0.76 )  | 0 ( 0 )       | 12.82 | 0.4  | 0.92 |  7.82 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.09 | 0.73 ( 0.01 ) | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 24.91 | 0.73 | 0.73 | 21.55 | 1.64 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.18 | 0.83 ( 0.01 ) | 15.59 ( 1.32 ) | 0.84 ( 0.1 )  | 19.75 | 0.62 | 0.83 | 15.59 | 0.84 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.32 | 0.92 ( 0 )    | 11.5 ( 0.9 )   | 0 ( 0 )       | 16.50 | 0.54 | 0.92 | 11.50 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.34 | 0.92 ( 0 )    | 11.02 ( 1.05 ) | 0 ( 0 )       | 16.02 | 0.49 | 0.92 | 11.02 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.03 | 0.66 ( 0.01 ) | 58.7 ( 5.86 )  | 2.64 ( 0.12 ) | 61.06 | 0.89 | 0.66 | 58.70 | 2.64 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.14 | 0.78 ( 0.01 ) | 19.29 ( 1.92 ) | 1.76 ( 0.1 )  | 22.53 | 0.68 | 0.78 | 19.29 | 1.76 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.26 | 0.91 ( 0 )    | 17.05 ( 1.43 ) | 0 ( 0 )       | 22.05 | 0.62 | 0.91 | 17.05 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.29 | 0.93 ( 0 )    | 15.14 ( 1.39 ) | 0 ( 0 )       | 20.14 | 0.54 | 0.93 | 15.14 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.02 | 0.65 ( 0.01 ) | 85.97 ( 9 )    | 3.03 ( 0.13 ) | 87.94 | 0.92 | 0.65 | 85.97 | 3.03 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.12 | 0.76 ( 0.01 ) | 22.02 ( 1.97 ) | 2.07 ( 0.1 )  | 24.95 | 0.75 | 0.76 | 22.02 | 2.07 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.23 | 0.91 ( 0 )    | 21.07 ( 1.75 ) | 0 ( 0 )       | 26.07 | 0.66 | 0.91 | 21.07 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.24 | 0.92 ( 0 )    | 19.56 ( 2.29 ) | 0 ( 0 )       | 24.56 | 0.57 | 0.92 | 19.56 | 0.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.13 | 0.76 ( 0.01 ) | 11.32 ( 0.99 ) | 1.87 ( 0.14 ) | 14.45 | 0.59 | 0.76 | 11.32 | 1.87 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.18 | 0.83 ( 0.01 ) | 12.96 ( 0.86 ) | 0.75 ( 0.09 ) | 17.21 | 0.61 | 0.83 | 12.96 | 0.75 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.33 | 0.91 ( 0 )    | 9.89 ( 0.75 )  | 0 ( 0 )       | 14.89 | 0.51 | 0.91 |  9.89 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.38 | 0.92 ( 0 )    | 8.56 ( 0.69 )  | 0 ( 0 )       | 13.56 | 0.44 | 0.92 |  8.56 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.08 | 0.74 ( 0.01 ) | 17.99 ( 1.78 ) | 2.32 ( 0.14 ) | 20.67 | 0.72 | 0.74 | 17.99 | 2.32 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.17 | 0.82 ( 0.01 ) | 14.36 ( 1.25 ) | 1.36 ( 0.12 ) | 18.00 | 0.6  | 0.82 | 14.36 | 1.36 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.29 | 0.91 ( 0 )    | 13.08 ( 1 )    | 0 ( 0 )       | 18.08 | 0.56 | 0.91 | 13.08 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.38 | 0.92 ( 0 )    | 9.67 ( 0.76 )  | 0 ( 0 )       | 14.67 | 0.49 | 0.92 |  9.67 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.03 | 0.66 ( 0.01 ) | 49.2 ( 5.65 )  | 3.15 ( 0.13 ) | 51.05 | 0.89 | 0.66 | 49.20 | 3.15 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.11 | 0.77 ( 0.01 ) | 23.84 ( 2.37 ) | 2.05 ( 0.11 ) | 26.79 | 0.73 | 0.77 | 23.84 | 2.05 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.19 | 0.91 ( 0 )    | 25.04 ( 1.69 ) | 0 ( 0 )       | 30.04 | 0.72 | 0.91 | 25.04 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.25 | 0.92 ( 0 )    | 18.51 ( 1.25 ) | 0 ( 0 )       | 23.51 | 0.66 | 0.92 | 18.51 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 0.67 ( 0.01 ) | 54.74 ( 6.8 )  | 3.54 ( 0.12 ) | 56.20 | 0.91 | 0.67 | 54.74 | 3.54 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.08 | 0.75 ( 0.01 ) | 28.55 ( 5.11 ) | 2.58 ( 0.11 ) | 30.97 | 0.75 | 0.75 | 28.55 | 2.58 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.31 | 0.89 ( 0 )    | 11.14 ( 0.74 ) | 0.02 ( 0.02 ) | 16.12 | 0.56 | 0.89 | 11.14 | 0.02 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.37 | 0.89 ( 0 )    | 9.65 ( 0.77 )  | 0 ( 0 )       | 14.65 | 0.49 | 0.89 |  9.65 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.08 | 0.71 ( 0.01 ) | 17.76 ( 1.65 ) | 2.81 ( 0.14 ) | 19.95 | 0.74 | 0.71 | 17.76 | 2.81 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.13 | 0.79 ( 0.01 ) | 16.43 ( 1.31 ) | 1.79 ( 0.13 ) | 19.64 | 0.68 | 0.79 | 16.43 | 1.79 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.25 | 0.89 ( 0 )    | 16.01 ( 0.82 ) | 0.03 ( 0.03 ) | 20.98 | 0.67 | 0.89 | 16.01 | 0.03 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.33 | 0.89 ( 0 )    | 12.77 ( 0.94 ) | 0 ( 0 )       | 17.77 | 0.58 | 0.89 | 12.77 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.03 | 0.67 ( 0.01 ) | 43.83 ( 5.17 ) | 3.75 ( 0.12 ) | 45.08 | 0.9  | 0.67 | 43.83 | 3.75 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.10 | 0.74 ( 0.01 ) | 19.1 ( 1.88 )  | 3.08 ( 0.1 )  | 21.02 | 0.73 | 0.74 | 19.10 | 3.08 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.15 | 0.87 ( 0 )    | 31.23 ( 1.88 ) | 0.17 ( 0.04 ) | 36.06 | 0.78 | 0.87 | 31.23 | 0.17 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.22 | 0.89 ( 0 )    | 22.46 ( 1.45 ) | 0 ( 0 )       | 27.46 | 0.71 | 0.89 | 22.46 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.02 | 0.64 ( 0.01 ) | 47.17 ( 5.85 ) | 4.12 ( 0.09 ) | 48.05 | 0.92 | 0.64 | 47.17 | 4.12 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.08 | 0.75 ( 0.01 ) | 20.86 ( 2.56 ) | 3.59 ( 0.09 ) | 22.27 | 0.77 | 0.75 | 20.86 | 3.59 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.13 | 0.87 ( 0 )    | 36.32 ( 2.18 ) | 0.23 ( 0.05 ) | 41.09 | 0.81 | 0.87 | 36.32 | 0.23 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.16 | 0.89 ( 0 )    | 32.22 ( 2.11 ) | 0.01 ( 0.01 ) | 37.21 | 0.8  | 0.89 | 32.22 | 0.01 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.09 | 0.71 ( 0.01 ) | 14.15 ( 1 )    | 2.3 ( 0.14 )  | 16.85 | 0.68 | 0.71 | 14.15 | 2.30 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.15 | 0.77 ( 0.01 ) | 11.74 ( 0.88 ) | 1.74 ( 0.13 ) | 15.00 | 0.64 | 0.77 | 11.74 | 1.74 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.27 | 0.83 ( 0 )    | 12.28 ( 0.9 )  | 0.18 ( 0.06 ) | 17.10 | 0.57 | 0.83 | 12.28 | 0.18 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.39 | 0.84 ( 0 )    | 9.58 ( 0.68 )  | 0.01 ( 0.01 ) | 14.57 | 0.51 | 0.84 |  9.58 | 0.01 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.08 | 0.7 ( 0.01 )  | 15.1 ( 1.65 )  | 3.16 ( 0.13 ) | 16.94 | 0.7  | 0.70 | 15.10 | 3.16 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.15 | 0.75 ( 0.01 ) | 14.19 ( 1.38 ) | 2.23 ( 0.13 ) | 16.96 | 0.64 | 0.75 | 14.19 | 2.23 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.25 | 0.83 ( 0 )    | 16.33 ( 1.01 ) | 0.2 ( 0.06 )  | 21.13 | 0.67 | 0.83 | 16.33 | 0.20 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.32 | 0.84 ( 0 )    | 13.82 ( 1.17 ) | 0.03 ( 0.02 ) | 18.79 | 0.6  | 0.84 | 13.82 | 0.03 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.05 | 0.66 ( 0.02 ) | 28.64 ( 3.14 ) | 4.13 ( 0.07 ) | 29.51 | 0.88 | 0.66 | 28.64 | 4.13 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.07 | 0.7 ( 0.01 )  | 25.71 ( 4.03 ) | 3.42 ( 0.1 )  | 27.29 | 0.78 | 0.70 | 25.71 | 3.42 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.14 | 0.81 ( 0 )    | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 29.61 | 0.76 | 0.81 | 25.68 | 1.07 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.21 | 0.83 ( 0 )    | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 28.34 | 0.75 | 0.83 | 23.49 | 0.15 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.02 | 0.65 ( 0.01 ) | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 49.79 | 0.93 | 0.65 | 49.08 | 4.29 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.05 | 0.66 ( 0.01 ) | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 34.18 | 0.8  | 0.66 | 32.99 | 3.81 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.11 | 0.8 ( 0 )     | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 34.39 | 0.78 | 0.80 | 30.91 | 1.52 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.14 | 0.83 ( 0 )    | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 40.43 | 0.82 | 0.83 | 35.66 | 0.23 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.13 0.77 ( 0.01 ) 13.02 ( 1.02 ) 1.51 ( 0.11 )\n",
+       "2   100   50 0.1   0.50 0.24 0.85 ( 0.01 ) 10.71 ( 0.62 ) 0.56 ( 0.07 )\n",
+       "3   500   50 0.1   0.10 0.41 0.92 ( 0 )    7.43 ( 0.6 )   0.02 ( 0.01 )\n",
+       "4  1000   50 0.1   0.05 0.39 0.92 ( 0 )    7.82 ( 0.76 )  0 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.09 0.73 ( 0.01 ) 21.55 ( 1.94 ) 1.64 ( 0.11 )\n",
+       "6   100  100 0.1   1.00 0.18 0.83 ( 0.01 ) 15.59 ( 1.32 ) 0.84 ( 0.1 ) \n",
+       "7   500  100 0.1   0.20 0.32 0.92 ( 0 )    11.5 ( 0.9 )   0 ( 0 )      \n",
+       "8  1000  100 0.1   0.10 0.34 0.92 ( 0 )    11.02 ( 1.05 ) 0 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.03 0.66 ( 0.01 ) 58.7 ( 5.86 )  2.64 ( 0.12 )\n",
+       "10  100  500 0.1   5.00 0.14 0.78 ( 0.01 ) 19.29 ( 1.92 ) 1.76 ( 0.1 ) \n",
+       "11  500  500 0.1   1.00 0.26 0.91 ( 0 )    17.05 ( 1.43 ) 0 ( 0 )      \n",
+       "12 1000  500 0.1   0.50 0.29 0.93 ( 0 )    15.14 ( 1.39 ) 0 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.02 0.65 ( 0.01 ) 85.97 ( 9 )    3.03 ( 0.13 )\n",
+       "14  100 1000 0.1  10.00 0.12 0.76 ( 0.01 ) 22.02 ( 1.97 ) 2.07 ( 0.1 ) \n",
+       "15  500 1000 0.1   2.00 0.23 0.91 ( 0 )    21.07 ( 1.75 ) 0 ( 0 )      \n",
+       "16 1000 1000 0.1   1.00 0.24 0.92 ( 0 )    19.56 ( 2.29 ) 0 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.13 0.76 ( 0.01 ) 11.32 ( 0.99 ) 1.87 ( 0.14 )\n",
+       "18  100   50 0.3   0.50 0.18 0.83 ( 0.01 ) 12.96 ( 0.86 ) 0.75 ( 0.09 )\n",
+       "19  500   50 0.3   0.10 0.33 0.91 ( 0 )    9.89 ( 0.75 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.38 0.92 ( 0 )    8.56 ( 0.69 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.08 0.74 ( 0.01 ) 17.99 ( 1.78 ) 2.32 ( 0.14 )\n",
+       "22  100  100 0.3   1.00 0.17 0.82 ( 0.01 ) 14.36 ( 1.25 ) 1.36 ( 0.12 )\n",
+       "23  500  100 0.3   0.20 0.29 0.91 ( 0 )    13.08 ( 1 )    0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.38 0.92 ( 0 )    9.67 ( 0.76 )  0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.03 0.66 ( 0.01 ) 49.2 ( 5.65 )  3.15 ( 0.13 )\n",
+       "26  100  500 0.3   5.00 0.11 0.77 ( 0.01 ) 23.84 ( 2.37 ) 2.05 ( 0.11 )\n",
+       "27  500  500 0.3   1.00 0.19 0.91 ( 0 )    25.04 ( 1.69 ) 0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.25 0.92 ( 0 )    18.51 ( 1.25 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.02 0.67 ( 0.01 ) 54.74 ( 6.8 )  3.54 ( 0.12 )\n",
+       "30  100 1000 0.3  10.00 0.08 0.75 ( 0.01 ) 28.55 ( 5.11 ) 2.58 ( 0.11 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.31 0.89 ( 0 )    11.14 ( 0.74 ) 0.02 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.37 0.89 ( 0 )    9.65 ( 0.77 )  0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.08 0.71 ( 0.01 ) 17.76 ( 1.65 ) 2.81 ( 0.14 )\n",
+       "54  100  100 0.7   1.00 0.13 0.79 ( 0.01 ) 16.43 ( 1.31 ) 1.79 ( 0.13 )\n",
+       "55  500  100 0.7   0.20 0.25 0.89 ( 0 )    16.01 ( 0.82 ) 0.03 ( 0.03 )\n",
+       "56 1000  100 0.7   0.10 0.33 0.89 ( 0 )    12.77 ( 0.94 ) 0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.03 0.67 ( 0.01 ) 43.83 ( 5.17 ) 3.75 ( 0.12 )\n",
+       "58  100  500 0.7   5.00 0.10 0.74 ( 0.01 ) 19.1 ( 1.88 )  3.08 ( 0.1 ) \n",
+       "59  500  500 0.7   1.00 0.15 0.87 ( 0 )    31.23 ( 1.88 ) 0.17 ( 0.04 )\n",
+       "60 1000  500 0.7   0.50 0.22 0.89 ( 0 )    22.46 ( 1.45 ) 0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.02 0.64 ( 0.01 ) 47.17 ( 5.85 ) 4.12 ( 0.09 )\n",
+       "62  100 1000 0.7  10.00 0.08 0.75 ( 0.01 ) 20.86 ( 2.56 ) 3.59 ( 0.09 )\n",
+       "63  500 1000 0.7   2.00 0.13 0.87 ( 0 )    36.32 ( 2.18 ) 0.23 ( 0.05 )\n",
+       "64 1000 1000 0.7   1.00 0.16 0.89 ( 0 )    32.22 ( 2.11 ) 0.01 ( 0.01 )\n",
+       "65   50   50 0.9   1.00 0.09 0.71 ( 0.01 ) 14.15 ( 1 )    2.3 ( 0.14 ) \n",
+       "66  100   50 0.9   0.50 0.15 0.77 ( 0.01 ) 11.74 ( 0.88 ) 1.74 ( 0.13 )\n",
+       "67  500   50 0.9   0.10 0.27 0.83 ( 0 )    12.28 ( 0.9 )  0.18 ( 0.06 )\n",
+       "68 1000   50 0.9   0.05 0.39 0.84 ( 0 )    9.58 ( 0.68 )  0.01 ( 0.01 )\n",
+       "69   50  100 0.9   2.00 0.08 0.7 ( 0.01 )  15.1 ( 1.65 )  3.16 ( 0.13 )\n",
+       "70  100  100 0.9   1.00 0.15 0.75 ( 0.01 ) 14.19 ( 1.38 ) 2.23 ( 0.13 )\n",
+       "71  500  100 0.9   0.20 0.25 0.83 ( 0 )    16.33 ( 1.01 ) 0.2 ( 0.06 ) \n",
+       "72 1000  100 0.9   0.10 0.32 0.84 ( 0 )    13.82 ( 1.17 ) 0.03 ( 0.02 )\n",
+       "73   50  500 0.9  10.00 0.05 0.66 ( 0.02 ) 28.64 ( 3.14 ) 4.13 ( 0.07 )\n",
+       "74  100  500 0.9   5.00 0.07 0.7 ( 0.01 )  25.71 ( 4.03 ) 3.42 ( 0.1 ) \n",
+       "75  500  500 0.9   1.00 0.14 0.81 ( 0 )    25.68 ( 1.61 ) 1.07 ( 0.11 )\n",
+       "76 1000  500 0.9   0.50 0.21 0.83 ( 0 )    23.49 ( 1.26 ) 0.15 ( 0.05 )\n",
+       "77   50 1000 0.9  20.00 0.02 0.65 ( 0.01 ) 49.08 ( 6.86 ) 4.29 ( 0.09 )\n",
+       "78  100 1000 0.9  10.00 0.05 0.66 ( 0.01 ) 32.99 ( 5.22 ) 3.81 ( 0.09 )\n",
+       "79  500 1000 0.9   2.00 0.11 0.8 ( 0 )     30.91 ( 2.22 ) 1.52 ( 0.12 )\n",
+       "80 1000 1000 0.9   1.00 0.14 0.83 ( 0 )    35.66 ( 1.98 ) 0.23 ( 0.06 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1  16.51      0.63 0.77     13.02   1.51   \n",
+       "2  15.15      0.57 0.85     10.71   0.56   \n",
+       "3  12.41      0.42 0.92      7.43   0.02   \n",
+       "4  12.82      0.4  0.92      7.82   0.00   \n",
+       "5  24.91      0.73 0.73     21.55   1.64   \n",
+       "6  19.75      0.62 0.83     15.59   0.84   \n",
+       "7  16.50      0.54 0.92     11.50   0.00   \n",
+       "8  16.02      0.49 0.92     11.02   0.00   \n",
+       "9  61.06      0.89 0.66     58.70   2.64   \n",
+       "10 22.53      0.68 0.78     19.29   1.76   \n",
+       "11 22.05      0.62 0.91     17.05   0.00   \n",
+       "12 20.14      0.54 0.93     15.14   0.00   \n",
+       "13 87.94      0.92 0.65     85.97   3.03   \n",
+       "14 24.95      0.75 0.76     22.02   2.07   \n",
+       "15 26.07      0.66 0.91     21.07   0.00   \n",
+       "16 24.56      0.57 0.92     19.56   0.00   \n",
+       "17 14.45      0.59 0.76     11.32   1.87   \n",
+       "18 17.21      0.61 0.83     12.96   0.75   \n",
+       "19 14.89      0.51 0.91      9.89   0.00   \n",
+       "20 13.56      0.44 0.92      8.56   0.00   \n",
+       "21 20.67      0.72 0.74     17.99   2.32   \n",
+       "22 18.00      0.6  0.82     14.36   1.36   \n",
+       "23 18.08      0.56 0.91     13.08   0.00   \n",
+       "24 14.67      0.49 0.92      9.67   0.00   \n",
+       "25 51.05      0.89 0.66     49.20   3.15   \n",
+       "26 26.79      0.73 0.77     23.84   2.05   \n",
+       "27 30.04      0.72 0.91     25.04   0.00   \n",
+       "28 23.51      0.66 0.92     18.51   0.00   \n",
+       "29 56.20      0.91 0.67     54.74   3.54   \n",
+       "30 30.97      0.75 0.75     28.55   2.58   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 16.12      0.56 0.89     11.14   0.02   \n",
+       "52 14.65      0.49 0.89      9.65   0.00   \n",
+       "53 19.95      0.74 0.71     17.76   2.81   \n",
+       "54 19.64      0.68 0.79     16.43   1.79   \n",
+       "55 20.98      0.67 0.89     16.01   0.03   \n",
+       "56 17.77      0.58 0.89     12.77   0.00   \n",
+       "57 45.08      0.9  0.67     43.83   3.75   \n",
+       "58 21.02      0.73 0.74     19.10   3.08   \n",
+       "59 36.06      0.78 0.87     31.23   0.17   \n",
+       "60 27.46      0.71 0.89     22.46   0.00   \n",
+       "61 48.05      0.92 0.64     47.17   4.12   \n",
+       "62 22.27      0.77 0.75     20.86   3.59   \n",
+       "63 41.09      0.81 0.87     36.32   0.23   \n",
+       "64 37.21      0.8  0.89     32.22   0.01   \n",
+       "65 16.85      0.68 0.71     14.15   2.30   \n",
+       "66 15.00      0.64 0.77     11.74   1.74   \n",
+       "67 17.10      0.57 0.83     12.28   0.18   \n",
+       "68 14.57      0.51 0.84      9.58   0.01   \n",
+       "69 16.94      0.7  0.70     15.10   3.16   \n",
+       "70 16.96      0.64 0.75     14.19   2.23   \n",
+       "71 21.13      0.67 0.83     16.33   0.20   \n",
+       "72 18.79      0.6  0.84     13.82   0.03   \n",
+       "73 29.51      0.88 0.66     28.64   4.13   \n",
+       "74 27.29      0.78 0.70     25.71   3.42   \n",
+       "75 29.61      0.76 0.81     25.68   1.07   \n",
+       "76 28.34      0.75 0.83     23.49   0.15   \n",
+       "77 49.79      0.93 0.65     49.08   4.29   \n",
+       "78 34.18      0.8  0.66     32.99   3.81   \n",
+       "79 34.39      0.78 0.80     30.91   1.52   \n",
+       "80 40.43      0.82 0.83     35.66   0.23   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.3_sim_toe_rf_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.3_sim_toe_rf_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..a03a60e
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.3_sim_toe_rf_binary_update-checkpoint.ipynb
@@ -0,0 +1,1067 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
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+      "[1] \"indx: 50\"\n",
+      "[1] \"indx: 51\"\n",
+      "[1] \"indx: 52\"\n",
+      "[1] \"indx: 53\"\n",
+      "[1] \"indx: 54\"\n",
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+      "[1] \"indx: 56\"\n",
+      "[1] \"indx: 57\"\n",
+      "[1] \"indx: 58\"\n",
+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.05 ( 0.14 )</td><td>4.71 ( 0.08 )</td><td>1 ( 0 )</td><td>0.1</td><td>3.31</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>1.4 ( 0.12 )</td><td>2.4 ( 0.08 )</td><td>1 ( 0 )</td><td>0.5</td><td>5.00</td><td>0.25</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>0.98 ( 0.09 )</td><td>1.2 ( 0.08 )</td><td>1 ( 0 )</td><td>0.69</td><td>5.78</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>3.95 ( 0.21 )</td><td>4.66 ( 0.08 )</td><td>1 ( 0 )</td><td>0.06</td><td>5.29</td><td>0.72</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.05 ( 0.14 ) & 4.71 ( 0.08 ) & 1 ( 0 ) & 0.1 & 3.31 & 0.56\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 1.4 ( 0.12 ) & 2.4 ( 0.08 ) & 1 ( 0 ) & 0.5 & 5.00 & 0.25\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 0.98 ( 0.09 ) & 1.2 ( 0.08 ) & 1 ( 0 ) & 0.69 & 5.78 & 0.15\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 3.95 ( 0.21 ) & 4.66 ( 0.08 ) & 1 ( 0 ) & 0.06 & 5.29 & 0.72\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.05 ( 0.14 ) | 4.71 ( 0.08 ) | 1 ( 0 ) | 0.1 | 3.31 | 0.56 |\n",
+       "| 3 | 500 | 50 | 0.1 | 1.4 ( 0.12 ) | 2.4 ( 0.08 ) | 1 ( 0 ) | 0.5 | 5.00 | 0.25 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 0.98 ( 0.09 ) | 1.2 ( 0.08 ) | 1 ( 0 ) | 0.69 | 5.78 | 0.15 |\n",
+       "| 5 | 50 | 100 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0.1 | 3.95 ( 0.21 ) | 4.66 ( 0.08 ) | 1 ( 0 ) | 0.06 | 5.29 | 0.72 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0.1 2.05 ( 0.14 ) 4.71 ( 0.08 ) 1 ( 0 ) 0.1  3.31       0.56\n",
+       "3 500  50  0.1 1.4 ( 0.12 )  2.4 ( 0.08 )  1 ( 0 ) 0.5  5.00       0.25\n",
+       "4 1000 50  0.1 0.98 ( 0.09 ) 1.2 ( 0.08 )  1 ( 0 ) 0.69 5.78       0.15\n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0.1 3.95 ( 0.21 ) 4.66 ( 0.08 ) 1 ( 0 ) 0.06 5.29       0.72"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>1 ( 0 )</td><td>0.08</td><td>27.38</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>1 ( 0 )</td><td>0.13</td><td>27.69</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 )</td><td>1 ( 0 )</td><td>0</td><td>47.99</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>1 ( 0 )</td><td>0.04</td><td>50.33</td><td>0.95</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>1 ( 0 )</td><td>0.07</td><td>52.50</td><td>0.94</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 1 ( 0 ) & 0.08 & 27.38 & 0.91\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 1 ( 0 ) & 0.13 & 27.69 & 0.88\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 47.19 ( 0.96 ) & 5.2 ( 0.08 ) & 1 ( 0 ) & 0 & 47.99 & 0.98\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 1 ( 0 ) & 0.04 & 50.33 & 0.95\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 1 ( 0 ) & 0.07 & 52.50 & 0.94\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 1 ( 0 ) | 0.08 | 27.38 | 0.91 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 1 ( 0 ) | 0.13 | 27.69 | 0.88 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 78 | 100 | 1000 | 0.9 | 47.19 ( 0.96 ) | 5.2 ( 0.08 ) | 1 ( 0 ) | 0 | 47.99 | 0.98 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 1 ( 0 ) | 0.04 | 50.33 | 0.95 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 1 ( 0 ) | 0.07 | 52.50 | 0.94 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "75 500  500  0.9 24.91 ( 0.57 ) 3.53 ( 0.09 ) 1 ( 0 ) 0.08 27.38      0.91\n",
+       "76 1000 500  0.9 24.53 ( 0.53 ) 2.84 ( 0.08 ) 1 ( 0 ) 0.13 27.69      0.88\n",
+       "77 50   1000 0.9 1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "78 100  1000 0.9 47.19 ( 0.96 ) 5.2 ( 0.08 )  1 ( 0 ) 0    47.99      0.98\n",
+       "79 500  1000 0.9 48.11 ( 0.92 ) 3.78 ( 0.08 ) 1 ( 0 ) 0.04 50.33      0.95\n",
+       "80 1000 1000 0.9 49.47 ( 0.78 ) 2.97 ( 0.07 ) 1 ( 0 ) 0.07 52.50      0.94"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0.1 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0.1 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0.1 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0.1 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t17 & 50 &   50 & 0.3 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t21 & 50 &  100 & 0.3 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t25 & 50 &  500 & 0.3 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t29 & 50 & 1000 & 0.3 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t33 & 50 &   50 & 0.5 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t37 & 50 &  100 & 0.5 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t41 & 50 &  500 & 0.5 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t45 & 50 & 1000 & 0.5 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t49 & 50 &   50 & 0.7 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t53 & 50 &  100 & 0.7 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t57 & 50 &  500 & 0.7 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t61 & 50 & 1000 & 0.7 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t65 & 50 &   50 & 0.9 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t69 & 50 &  100 & 0.9 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t73 & 50 &  500 & 0.9 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0.1 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0.1 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0.1 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0.1 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 17 | 50 |   50 | 0.3 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 21 | 50 |  100 | 0.3 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 25 | 50 |  500 | 0.3 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 29 | 50 | 1000 | 0.3 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 33 | 50 |   50 | 0.5 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 37 | 50 |  100 | 0.5 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 41 | 50 |  500 | 0.5 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 45 | 50 | 1000 | 0.5 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 49 | 50 |   50 | 0.7 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 53 | 50 |  100 | 0.7 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 57 | 50 |  500 | 0.7 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 61 | 50 | 1000 | 0.7 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 65 | 50 |   50 | 0.9 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 69 | 50 |  100 | 0.9 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 73 | 50 |  500 | 0.9 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0.1   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0.1   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0.1  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0.1  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "17 50   50 0.3   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "21 50  100 0.3   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "25 50  500 0.3  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "29 50 1000 0.3  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "33 50   50 0.5   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0.5   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0.5  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0.5  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "49 50   50 0.7   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "53 50  100 0.7   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "57 50  500 0.7  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "61 50 1000 0.7  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "65 50   50 0.9   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "69 50  100 0.9   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "73 50  500 0.9  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "77 50 1000 0.9  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      \n",
+       "17 1       6      \n",
+       "21 1       6      \n",
+       "25 1       6      \n",
+       "29 1       6      \n",
+       "33 1       6      \n",
+       "37 1       6      \n",
+       "41 1       6      \n",
+       "45 1       6      \n",
+       "49 1       6      \n",
+       "53 1       6      \n",
+       "57 1       6      \n",
+       "61 1       6      \n",
+       "65 1       6      \n",
+       "69 1       6      \n",
+       "73 1       6      \n",
+       "77 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.10</td><td>1 ( 0 )</td><td>2.05 ( 0.14 )</td><td>4.71 ( 0.08 )</td><td>3.31</td><td>0.56</td><td>1</td><td>2.05</td><td>4.71</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.50</td><td>1 ( 0 )</td><td>1.4 ( 0.12 ) </td><td>2.4 ( 0.08 ) </td><td>5.00</td><td>0.25</td><td>1</td><td>1.40</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.69</td><td>1 ( 0 )</td><td>0.98 ( 0.09 )</td><td>1.2 ( 0.08 ) </td><td>5.78</td><td>0.15</td><td>1</td><td>0.98</td><td>1.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.06</td><td>1 ( 0 )</td><td>3.95 ( 0.21 )</td><td>4.66 ( 0.08 )</td><td>5.29</td><td>0.72</td><td>1</td><td>3.95</td><td>4.66</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.10 & 1 ( 0 ) & 2.05 ( 0.14 ) & 4.71 ( 0.08 ) & 3.31 & 0.56 & 1 & 2.05 & 4.71\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.50 & 1 ( 0 ) & 1.4 ( 0.12 )  & 2.4 ( 0.08 )  & 5.00 & 0.25 & 1 & 1.40 & 2.40\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.69 & 1 ( 0 ) & 0.98 ( 0.09 ) & 1.2 ( 0.08 )  & 5.78 & 0.15 & 1 & 0.98 & 1.20\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.06 & 1 ( 0 ) & 3.95 ( 0.21 ) & 4.66 ( 0.08 ) & 5.29 & 0.72 & 1 & 3.95 & 4.66\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.10 | 1 ( 0 ) | 2.05 ( 0.14 ) | 4.71 ( 0.08 ) | 3.31 | 0.56 | 1 | 2.05 | 4.71 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.50 | 1 ( 0 ) | 1.4 ( 0.12 )  | 2.4 ( 0.08 )  | 5.00 | 0.25 | 1 | 1.40 | 2.40 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.69 | 1 ( 0 ) | 0.98 ( 0.09 ) | 1.2 ( 0.08 )  | 5.78 | 0.15 | 1 | 0.98 | 1.20 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.06 | 1 ( 0 ) | 3.95 ( 0.21 ) | 4.66 ( 0.08 ) | 5.29 | 0.72 | 1 | 3.95 | 4.66 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0.1  1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0.1  0.50  0.10 1 ( 0 ) 2.05 ( 0.14 ) 4.71 ( 0.08 ) 3.31       0.56\n",
+       "3  500  50 0.1  0.10  0.50 1 ( 0 ) 1.4 ( 0.12 )  2.4 ( 0.08 )  5.00       0.25\n",
+       "4 1000  50 0.1  0.05  0.69 1 ( 0 ) 0.98 ( 0.09 ) 1.2 ( 0.08 )  5.78       0.15\n",
+       "5   50 100 0.1  2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0.1  1.00  0.06 1 ( 0 ) 3.95 ( 0.21 ) 4.66 ( 0.08 ) 5.29       0.72\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        2.05    4.71   \n",
+       "3 1        1.40    2.40   \n",
+       "4 1        0.98    1.20   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        3.95    4.66   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.08</td><td>1 ( 0 )</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>27.38</td><td>0.91</td><td>1</td><td>24.91</td><td>3.53</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.13</td><td>1 ( 0 )</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>27.69</td><td>0.88</td><td>1</td><td>24.53</td><td>2.84</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.00</td><td>1 ( 0 )</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 ) </td><td>47.99</td><td>0.98</td><td>1</td><td>47.19</td><td>5.20</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>50.33</td><td>0.95</td><td>1</td><td>48.11</td><td>3.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>52.50</td><td>0.94</td><td>1</td><td>49.47</td><td>2.97</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.08 & 1 ( 0 ) & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 27.38 & 0.91 & 1 & 24.91 & 3.53\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.13 & 1 ( 0 ) & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 27.69 & 0.88 & 1 & 24.53 & 2.84\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.00 & 1 ( 0 ) & 47.19 ( 0.96 ) & 5.2 ( 0.08 )  & 47.99 & 0.98 & 1 & 47.19 & 5.20\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.04 & 1 ( 0 ) & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 50.33 & 0.95 & 1 & 48.11 & 3.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 52.50 & 0.94 & 1 & 49.47 & 2.97\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.08 | 1 ( 0 ) | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 27.38 | 0.91 | 1 | 24.91 | 3.53 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.13 | 1 ( 0 ) | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 27.69 | 0.88 | 1 | 24.53 | 2.84 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.00 | 1 ( 0 ) | 47.19 ( 0.96 ) | 5.2 ( 0.08 )  | 47.99 | 0.98 | 1 | 47.19 | 5.20 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.04 | 1 ( 0 ) | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 50.33 | 0.95 | 1 | 48.11 | 3.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 52.50 | 0.94 | 1 | 49.47 | 2.97 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "75  500  500 0.9   1.0  0.08 1 ( 0 ) 24.91 ( 0.57 ) 3.53 ( 0.09 ) 27.38     \n",
+       "76 1000  500 0.9   0.5  0.13 1 ( 0 ) 24.53 ( 0.53 ) 2.84 ( 0.08 ) 27.69     \n",
+       "77   50 1000 0.9  20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.0  0.00 1 ( 0 ) 47.19 ( 0.96 ) 5.2 ( 0.08 )  47.99     \n",
+       "79  500 1000 0.9   2.0  0.04 1 ( 0 ) 48.11 ( 0.92 ) 3.78 ( 0.08 ) 50.33     \n",
+       "80 1000 1000 0.9   1.0  0.07 1 ( 0 ) 49.47 ( 0.78 ) 2.97 ( 0.07 ) 52.50     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "75 0.91 1        24.91   3.53   \n",
+       "76 0.88 1        24.53   2.84   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.19   5.20   \n",
+       "79 0.95 1        48.11   3.78   \n",
+       "80 0.94 1        49.47   2.97   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>2.05 ( 0.14 ) </td><td>4.71 ( 0.08 )</td><td> 3.31</td><td>0.56</td><td>1</td><td> 2.05</td><td>4.71</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.50</td><td>1 ( 0 )</td><td>1.4 ( 0.12 )  </td><td>2.4 ( 0.08 ) </td><td> 5.00</td><td>0.25</td><td>1</td><td> 1.40</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.69</td><td>1 ( 0 )</td><td>0.98 ( 0.09 ) </td><td>1.2 ( 0.08 ) </td><td> 5.78</td><td>0.15</td><td>1</td><td> 0.98</td><td>1.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>3.95 ( 0.21 ) </td><td>4.66 ( 0.08 )</td><td> 5.29</td><td>0.72</td><td>1</td><td> 3.95</td><td>4.66</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.35</td><td>1 ( 0 )</td><td>3.91 ( 0.21 ) </td><td>2.17 ( 0.08 )</td><td> 7.74</td><td>0.48</td><td>1</td><td> 3.91</td><td>2.17</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.51</td><td>1 ( 0 )</td><td>2.97 ( 0.15 ) </td><td>1.29 ( 0.07 )</td><td> 7.68</td><td>0.36</td><td>1</td><td> 2.97</td><td>1.29</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.57 ( 0.55 )</td><td>4.72 ( 0.08 )</td><td>25.85</td><td>0.95</td><td>1</td><td>24.57</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.09</td><td>1 ( 0 )</td><td>23.57 ( 0.51 )</td><td>2.43 ( 0.1 ) </td><td>27.14</td><td>0.86</td><td>1</td><td>23.57</td><td>2.43</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>22.61 ( 0.5 ) </td><td>1.31 ( 0.08 )</td><td>27.30</td><td>0.82</td><td>1</td><td>22.61</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>48.35 ( 0.66 )</td><td>4.88 ( 0.08 )</td><td>49.47</td><td>0.98</td><td>1</td><td>48.35</td><td>4.88</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.59 ( 0.73 )</td><td>2.71 ( 0.1 ) </td><td>51.88</td><td>0.94</td><td>1</td><td>48.59</td><td>2.71</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>46.74 ( 0.64 )</td><td>1.75 ( 0.09 )</td><td>50.99</td><td>0.92</td><td>1</td><td>46.74</td><td>1.75</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.68 ( 0.12 ) </td><td>4.86 ( 0.08 )</td><td> 2.81</td><td>0.54</td><td>1</td><td> 1.68</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>1 ( 0 )</td><td>1.57 ( 0.12 ) </td><td>2.74 ( 0.08 )</td><td> 4.83</td><td>0.3 </td><td>1</td><td> 1.57</td><td>2.74</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.62</td><td>1 ( 0 )</td><td>1.33 ( 0.1 )  </td><td>1.44 ( 0.08 )</td><td> 5.89</td><td>0.21</td><td>1</td><td> 1.33</td><td>1.44</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.45 ( 0.2 )  </td><td>4.85 ( 0.08 )</td><td> 5.60</td><td>0.78</td><td>1</td><td> 4.45</td><td>4.85</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.33</td><td>1 ( 0 )</td><td>3.36 ( 0.19 ) </td><td>2.57 ( 0.09 )</td><td> 6.79</td><td>0.46</td><td>1</td><td> 3.36</td><td>2.57</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.47</td><td>1 ( 0 )</td><td>3.43 ( 0.17 ) </td><td>1.61 ( 0.09 )</td><td> 7.82</td><td>0.41</td><td>1</td><td> 3.43</td><td>1.61</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.69 ( 0.53 )</td><td>4.87 ( 0.08 )</td><td>24.82</td><td>0.95</td><td>1</td><td>23.69</td><td>4.87</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>22.84 ( 0.44 )</td><td>3.11 ( 0.09 )</td><td>25.73</td><td>0.89</td><td>1</td><td>22.84</td><td>3.11</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>22.24 ( 0.43 )</td><td>1.77 ( 0.09 )</td><td>26.47</td><td>0.84</td><td>1</td><td>22.24</td><td>1.77</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>48.06 ( 0.76 )</td><td>5.02 ( 0.08 )</td><td>49.04</td><td>0.98</td><td>1</td><td>48.06</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.45</td><td>1 ( 0 )</td><td>2.14 ( 0.13 ) </td><td>3.36 ( 0.08 )</td><td> 4.78</td><td>0.43</td><td>1</td><td> 2.14</td><td>3.36</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.60</td><td>1 ( 0 )</td><td>2.66 ( 0.14 ) </td><td>2.27 ( 0.08 )</td><td> 6.39</td><td>0.39</td><td>1</td><td> 2.66</td><td>2.27</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>4.36 ( 0.22 ) </td><td>4.8 ( 0.09 ) </td><td> 5.56</td><td>0.77</td><td>1</td><td> 4.36</td><td>4.80</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.38</td><td>1 ( 0 )</td><td>4.03 ( 0.18 ) </td><td>3.1 ( 0.09 ) </td><td> 6.93</td><td>0.56</td><td>1</td><td> 4.03</td><td>3.10</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.50</td><td>1 ( 0 )</td><td>4.65 ( 0.22 ) </td><td>2 ( 0.07 )   </td><td> 8.65</td><td>0.51</td><td>1</td><td> 4.65</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.02</td><td>1 ( 0 )</td><td>23.58 ( 0.53 )</td><td>4.72 ( 0.09 )</td><td>24.86</td><td>0.95</td><td>1</td><td>23.58</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.10</td><td>1 ( 0 )</td><td>23.09 ( 0.56 )</td><td>3.38 ( 0.09 )</td><td>25.71</td><td>0.89</td><td>1</td><td>23.09</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>23.33 ( 0.53 )</td><td>2.58 ( 0.07 )</td><td>26.75</td><td>0.87</td><td>1</td><td>23.33</td><td>2.58</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>46.8 ( 0.69 ) </td><td>5.13 ( 0.07 )</td><td>47.67</td><td>0.98</td><td>1</td><td>46.80</td><td>5.13</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.05</td><td>1 ( 0 )</td><td>48.68 ( 0.77 )</td><td>3.54 ( 0.08 )</td><td>51.14</td><td>0.95</td><td>1</td><td>48.68</td><td>3.54</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.7 ) </td><td>2.73 ( 0.07 )</td><td>50.41</td><td>0.93</td><td>1</td><td>47.14</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.12</td><td>1 ( 0 )</td><td>2.09 ( 0.16 ) </td><td>4.98 ( 0.09 )</td><td> 3.06</td><td>0.62</td><td>1</td><td> 2.09</td><td>4.98</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.45</td><td>1 ( 0 )</td><td>2.6 ( 0.13 )  </td><td>3.33 ( 0.08 )</td><td> 5.27</td><td>0.47</td><td>1</td><td> 2.60</td><td>3.33</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.51</td><td>1 ( 0 )</td><td>3.48 ( 0.17 ) </td><td>2.35 ( 0.08 )</td><td> 7.13</td><td>0.47</td><td>1</td><td> 3.48</td><td>2.35</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>4.4 ( 0.23 )  </td><td>4.96 ( 0.09 )</td><td> 5.43</td><td>0.81</td><td>1</td><td> 4.40</td><td>4.96</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.35</td><td>1 ( 0 )</td><td>4.57 ( 0.24 ) </td><td>3.5 ( 0.08 ) </td><td> 7.07</td><td>0.62</td><td>1</td><td> 4.57</td><td>3.50</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.37</td><td>1 ( 0 )</td><td>6.43 ( 0.28 ) </td><td>2.57 ( 0.08 )</td><td> 9.86</td><td>0.63</td><td>1</td><td> 6.43</td><td>2.57</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.58 ( 0.66 )</td><td>5.1 ( 0.09 ) </td><td>25.48</td><td>0.96</td><td>1</td><td>24.58</td><td>5.10</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>27.38</td><td>0.91</td><td>1</td><td>24.91</td><td>3.53</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>27.69</td><td>0.88</td><td>1</td><td>24.53</td><td>2.84</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 ) </td><td>47.99</td><td>0.98</td><td>1</td><td>47.19</td><td>5.20</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>50.33</td><td>0.95</td><td>1</td><td>48.11</td><td>3.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>52.50</td><td>0.94</td><td>1</td><td>49.47</td><td>2.97</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.10 & 1 ( 0 ) & 2.05 ( 0.14 )  & 4.71 ( 0.08 ) &  3.31 & 0.56 & 1 &  2.05 & 4.71\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.50 & 1 ( 0 ) & 1.4 ( 0.12 )   & 2.4 ( 0.08 )  &  5.00 & 0.25 & 1 &  1.40 & 2.40\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.69 & 1 ( 0 ) & 0.98 ( 0.09 )  & 1.2 ( 0.08 )  &  5.78 & 0.15 & 1 &  0.98 & 1.20\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.06 & 1 ( 0 ) & 3.95 ( 0.21 )  & 4.66 ( 0.08 ) &  5.29 & 0.72 & 1 &  3.95 & 4.66\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.35 & 1 ( 0 ) & 3.91 ( 0.21 )  & 2.17 ( 0.08 ) &  7.74 & 0.48 & 1 &  3.91 & 2.17\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.51 & 1 ( 0 ) & 2.97 ( 0.15 )  & 1.29 ( 0.07 ) &  7.68 & 0.36 & 1 &  2.97 & 1.29\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.01 & 1 ( 0 ) & 24.57 ( 0.55 ) & 4.72 ( 0.08 ) & 25.85 & 0.95 & 1 & 24.57 & 4.72\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.09 & 1 ( 0 ) & 23.57 ( 0.51 ) & 2.43 ( 0.1 )  & 27.14 & 0.86 & 1 & 23.57 & 2.43\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.14 & 1 ( 0 ) & 22.61 ( 0.5 )  & 1.31 ( 0.08 ) & 27.30 & 0.82 & 1 & 22.61 & 1.31\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.01 & 1 ( 0 ) & 48.35 ( 0.66 ) & 4.88 ( 0.08 ) & 49.47 & 0.98 & 1 & 48.35 & 4.88\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.04 & 1 ( 0 ) & 48.59 ( 0.73 ) & 2.71 ( 0.1 )  & 51.88 & 0.94 & 1 & 48.59 & 2.71\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.06 & 1 ( 0 ) & 46.74 ( 0.64 ) & 1.75 ( 0.09 ) & 50.99 & 0.92 & 1 & 46.74 & 1.75\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.10 & 1 ( 0 ) & 1.68 ( 0.12 )  & 4.86 ( 0.08 ) &  2.81 & 0.54 & 1 &  1.68 & 4.86\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 1 ( 0 ) & 1.57 ( 0.12 )  & 2.74 ( 0.08 ) &  4.83 & 0.3  & 1 &  1.57 & 2.74\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.62 & 1 ( 0 ) & 1.33 ( 0.1 )   & 1.44 ( 0.08 ) &  5.89 & 0.21 & 1 &  1.33 & 1.44\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.05 & 1 ( 0 ) & 4.45 ( 0.2 )   & 4.85 ( 0.08 ) &  5.60 & 0.78 & 1 &  4.45 & 4.85\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.33 & 1 ( 0 ) & 3.36 ( 0.19 )  & 2.57 ( 0.09 ) &  6.79 & 0.46 & 1 &  3.36 & 2.57\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.47 & 1 ( 0 ) & 3.43 ( 0.17 )  & 1.61 ( 0.09 ) &  7.82 & 0.41 & 1 &  3.43 & 1.61\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.01 & 1 ( 0 ) & 23.69 ( 0.53 ) & 4.87 ( 0.08 ) & 24.82 & 0.95 & 1 & 23.69 & 4.87\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.07 & 1 ( 0 ) & 22.84 ( 0.44 ) & 3.11 ( 0.09 ) & 25.73 & 0.89 & 1 & 22.84 & 3.11\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.13 & 1 ( 0 ) & 22.24 ( 0.43 ) & 1.77 ( 0.09 ) & 26.47 & 0.84 & 1 & 22.24 & 1.77\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.01 & 1 ( 0 ) & 48.06 ( 0.76 ) & 5.02 ( 0.08 ) & 49.04 & 0.98 & 1 & 48.06 & 5.02\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.45 & 1 ( 0 ) & 2.14 ( 0.13 )  & 3.36 ( 0.08 ) &  4.78 & 0.43 & 1 &  2.14 & 3.36\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.60 & 1 ( 0 ) & 2.66 ( 0.14 )  & 2.27 ( 0.08 ) &  6.39 & 0.39 & 1 &  2.66 & 2.27\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.08 & 1 ( 0 ) & 4.36 ( 0.22 )  & 4.8 ( 0.09 )  &  5.56 & 0.77 & 1 &  4.36 & 4.80\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.38 & 1 ( 0 ) & 4.03 ( 0.18 )  & 3.1 ( 0.09 )  &  6.93 & 0.56 & 1 &  4.03 & 3.10\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.50 & 1 ( 0 ) & 4.65 ( 0.22 )  & 2 ( 0.07 )    &  8.65 & 0.51 & 1 &  4.65 & 2.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.02 & 1 ( 0 ) & 23.58 ( 0.53 ) & 4.72 ( 0.09 ) & 24.86 & 0.95 & 1 & 23.58 & 4.72\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.10 & 1 ( 0 ) & 23.09 ( 0.56 ) & 3.38 ( 0.09 ) & 25.71 & 0.89 & 1 & 23.09 & 3.38\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.14 & 1 ( 0 ) & 23.33 ( 0.53 ) & 2.58 ( 0.07 ) & 26.75 & 0.87 & 1 & 23.33 & 2.58\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.01 & 1 ( 0 ) & 46.8 ( 0.69 )  & 5.13 ( 0.07 ) & 47.67 & 0.98 & 1 & 46.80 & 5.13\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.05 & 1 ( 0 ) & 48.68 ( 0.77 ) & 3.54 ( 0.08 ) & 51.14 & 0.95 & 1 & 48.68 & 3.54\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.07 & 1 ( 0 ) & 47.14 ( 0.7 )  & 2.73 ( 0.07 ) & 50.41 & 0.93 & 1 & 47.14 & 2.73\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.12 & 1 ( 0 ) & 2.09 ( 0.16 )  & 4.98 ( 0.09 ) &  3.06 & 0.62 & 1 &  2.09 & 4.98\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.45 & 1 ( 0 ) & 2.6 ( 0.13 )   & 3.33 ( 0.08 ) &  5.27 & 0.47 & 1 &  2.60 & 3.33\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.51 & 1 ( 0 ) & 3.48 ( 0.17 )  & 2.35 ( 0.08 ) &  7.13 & 0.47 & 1 &  3.48 & 2.35\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 4.4 ( 0.23 )   & 4.96 ( 0.09 ) &  5.43 & 0.81 & 1 &  4.40 & 4.96\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.35 & 1 ( 0 ) & 4.57 ( 0.24 )  & 3.5 ( 0.08 )  &  7.07 & 0.62 & 1 &  4.57 & 3.50\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.37 & 1 ( 0 ) & 6.43 ( 0.28 )  & 2.57 ( 0.08 ) &  9.86 & 0.63 & 1 &  6.43 & 2.57\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.01 & 1 ( 0 ) & 24.58 ( 0.66 ) & 5.1 ( 0.09 )  & 25.48 & 0.96 & 1 & 24.58 & 5.10\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.08 & 1 ( 0 ) & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 27.38 & 0.91 & 1 & 24.91 & 3.53\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.13 & 1 ( 0 ) & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 27.69 & 0.88 & 1 & 24.53 & 2.84\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.00 & 1 ( 0 ) & 47.19 ( 0.96 ) & 5.2 ( 0.08 )  & 47.99 & 0.98 & 1 & 47.19 & 5.20\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.04 & 1 ( 0 ) & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 50.33 & 0.95 & 1 & 48.11 & 3.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 52.50 & 0.94 & 1 & 49.47 & 2.97\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.10 | 1 ( 0 ) | 2.05 ( 0.14 )  | 4.71 ( 0.08 ) |  3.31 | 0.56 | 1 |  2.05 | 4.71 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.50 | 1 ( 0 ) | 1.4 ( 0.12 )   | 2.4 ( 0.08 )  |  5.00 | 0.25 | 1 |  1.40 | 2.40 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.69 | 1 ( 0 ) | 0.98 ( 0.09 )  | 1.2 ( 0.08 )  |  5.78 | 0.15 | 1 |  0.98 | 1.20 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.06 | 1 ( 0 ) | 3.95 ( 0.21 )  | 4.66 ( 0.08 ) |  5.29 | 0.72 | 1 |  3.95 | 4.66 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.35 | 1 ( 0 ) | 3.91 ( 0.21 )  | 2.17 ( 0.08 ) |  7.74 | 0.48 | 1 |  3.91 | 2.17 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.51 | 1 ( 0 ) | 2.97 ( 0.15 )  | 1.29 ( 0.07 ) |  7.68 | 0.36 | 1 |  2.97 | 1.29 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.01 | 1 ( 0 ) | 24.57 ( 0.55 ) | 4.72 ( 0.08 ) | 25.85 | 0.95 | 1 | 24.57 | 4.72 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.09 | 1 ( 0 ) | 23.57 ( 0.51 ) | 2.43 ( 0.1 )  | 27.14 | 0.86 | 1 | 23.57 | 2.43 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.14 | 1 ( 0 ) | 22.61 ( 0.5 )  | 1.31 ( 0.08 ) | 27.30 | 0.82 | 1 | 22.61 | 1.31 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.01 | 1 ( 0 ) | 48.35 ( 0.66 ) | 4.88 ( 0.08 ) | 49.47 | 0.98 | 1 | 48.35 | 4.88 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.04 | 1 ( 0 ) | 48.59 ( 0.73 ) | 2.71 ( 0.1 )  | 51.88 | 0.94 | 1 | 48.59 | 2.71 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.06 | 1 ( 0 ) | 46.74 ( 0.64 ) | 1.75 ( 0.09 ) | 50.99 | 0.92 | 1 | 46.74 | 1.75 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.10 | 1 ( 0 ) | 1.68 ( 0.12 )  | 4.86 ( 0.08 ) |  2.81 | 0.54 | 1 |  1.68 | 4.86 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 1 ( 0 ) | 1.57 ( 0.12 )  | 2.74 ( 0.08 ) |  4.83 | 0.3  | 1 |  1.57 | 2.74 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.62 | 1 ( 0 ) | 1.33 ( 0.1 )   | 1.44 ( 0.08 ) |  5.89 | 0.21 | 1 |  1.33 | 1.44 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.05 | 1 ( 0 ) | 4.45 ( 0.2 )   | 4.85 ( 0.08 ) |  5.60 | 0.78 | 1 |  4.45 | 4.85 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.33 | 1 ( 0 ) | 3.36 ( 0.19 )  | 2.57 ( 0.09 ) |  6.79 | 0.46 | 1 |  3.36 | 2.57 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.47 | 1 ( 0 ) | 3.43 ( 0.17 )  | 1.61 ( 0.09 ) |  7.82 | 0.41 | 1 |  3.43 | 1.61 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.01 | 1 ( 0 ) | 23.69 ( 0.53 ) | 4.87 ( 0.08 ) | 24.82 | 0.95 | 1 | 23.69 | 4.87 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.07 | 1 ( 0 ) | 22.84 ( 0.44 ) | 3.11 ( 0.09 ) | 25.73 | 0.89 | 1 | 22.84 | 3.11 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.13 | 1 ( 0 ) | 22.24 ( 0.43 ) | 1.77 ( 0.09 ) | 26.47 | 0.84 | 1 | 22.24 | 1.77 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.01 | 1 ( 0 ) | 48.06 ( 0.76 ) | 5.02 ( 0.08 ) | 49.04 | 0.98 | 1 | 48.06 | 5.02 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.45 | 1 ( 0 ) | 2.14 ( 0.13 )  | 3.36 ( 0.08 ) |  4.78 | 0.43 | 1 |  2.14 | 3.36 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.60 | 1 ( 0 ) | 2.66 ( 0.14 )  | 2.27 ( 0.08 ) |  6.39 | 0.39 | 1 |  2.66 | 2.27 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.08 | 1 ( 0 ) | 4.36 ( 0.22 )  | 4.8 ( 0.09 )  |  5.56 | 0.77 | 1 |  4.36 | 4.80 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.38 | 1 ( 0 ) | 4.03 ( 0.18 )  | 3.1 ( 0.09 )  |  6.93 | 0.56 | 1 |  4.03 | 3.10 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.50 | 1 ( 0 ) | 4.65 ( 0.22 )  | 2 ( 0.07 )    |  8.65 | 0.51 | 1 |  4.65 | 2.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.02 | 1 ( 0 ) | 23.58 ( 0.53 ) | 4.72 ( 0.09 ) | 24.86 | 0.95 | 1 | 23.58 | 4.72 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.10 | 1 ( 0 ) | 23.09 ( 0.56 ) | 3.38 ( 0.09 ) | 25.71 | 0.89 | 1 | 23.09 | 3.38 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.14 | 1 ( 0 ) | 23.33 ( 0.53 ) | 2.58 ( 0.07 ) | 26.75 | 0.87 | 1 | 23.33 | 2.58 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.01 | 1 ( 0 ) | 46.8 ( 0.69 )  | 5.13 ( 0.07 ) | 47.67 | 0.98 | 1 | 46.80 | 5.13 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.05 | 1 ( 0 ) | 48.68 ( 0.77 ) | 3.54 ( 0.08 ) | 51.14 | 0.95 | 1 | 48.68 | 3.54 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.07 | 1 ( 0 ) | 47.14 ( 0.7 )  | 2.73 ( 0.07 ) | 50.41 | 0.93 | 1 | 47.14 | 2.73 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.12 | 1 ( 0 ) | 2.09 ( 0.16 )  | 4.98 ( 0.09 ) |  3.06 | 0.62 | 1 |  2.09 | 4.98 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.45 | 1 ( 0 ) | 2.6 ( 0.13 )   | 3.33 ( 0.08 ) |  5.27 | 0.47 | 1 |  2.60 | 3.33 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.51 | 1 ( 0 ) | 3.48 ( 0.17 )  | 2.35 ( 0.08 ) |  7.13 | 0.47 | 1 |  3.48 | 2.35 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 4.4 ( 0.23 )   | 4.96 ( 0.09 ) |  5.43 | 0.81 | 1 |  4.40 | 4.96 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.35 | 1 ( 0 ) | 4.57 ( 0.24 )  | 3.5 ( 0.08 )  |  7.07 | 0.62 | 1 |  4.57 | 3.50 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.37 | 1 ( 0 ) | 6.43 ( 0.28 )  | 2.57 ( 0.08 ) |  9.86 | 0.63 | 1 |  6.43 | 2.57 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.01 | 1 ( 0 ) | 24.58 ( 0.66 ) | 5.1 ( 0.09 )  | 25.48 | 0.96 | 1 | 24.58 | 5.10 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.08 | 1 ( 0 ) | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 27.38 | 0.91 | 1 | 24.91 | 3.53 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.13 | 1 ( 0 ) | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 27.69 | 0.88 | 1 | 24.53 | 2.84 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.00 | 1 ( 0 ) | 47.19 ( 0.96 ) | 5.2 ( 0.08 )  | 47.99 | 0.98 | 1 | 47.19 | 5.20 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.04 | 1 ( 0 ) | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 50.33 | 0.95 | 1 | 48.11 | 3.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 52.50 | 0.94 | 1 | 49.47 | 2.97 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0.1   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0.1   0.50 0.10 1 ( 0 ) 2.05 ( 0.14 )  4.71 ( 0.08 )  3.31     \n",
+       "3   500   50 0.1   0.10 0.50 1 ( 0 ) 1.4 ( 0.12 )   2.4 ( 0.08 )   5.00     \n",
+       "4  1000   50 0.1   0.05 0.69 1 ( 0 ) 0.98 ( 0.09 )  1.2 ( 0.08 )   5.78     \n",
+       "5    50  100 0.1   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0.1   1.00 0.06 1 ( 0 ) 3.95 ( 0.21 )  4.66 ( 0.08 )  5.29     \n",
+       "7   500  100 0.1   0.20 0.35 1 ( 0 ) 3.91 ( 0.21 )  2.17 ( 0.08 )  7.74     \n",
+       "8  1000  100 0.1   0.10 0.51 1 ( 0 ) 2.97 ( 0.15 )  1.29 ( 0.07 )  7.68     \n",
+       "9    50  500 0.1  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0.1   5.00 0.01 1 ( 0 ) 24.57 ( 0.55 ) 4.72 ( 0.08 ) 25.85     \n",
+       "11  500  500 0.1   1.00 0.09 1 ( 0 ) 23.57 ( 0.51 ) 2.43 ( 0.1 )  27.14     \n",
+       "12 1000  500 0.1   0.50 0.14 1 ( 0 ) 22.61 ( 0.5 )  1.31 ( 0.08 ) 27.30     \n",
+       "13   50 1000 0.1  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0.1  10.00 0.01 1 ( 0 ) 48.35 ( 0.66 ) 4.88 ( 0.08 ) 49.47     \n",
+       "15  500 1000 0.1   2.00 0.04 1 ( 0 ) 48.59 ( 0.73 ) 2.71 ( 0.1 )  51.88     \n",
+       "16 1000 1000 0.1   1.00 0.06 1 ( 0 ) 46.74 ( 0.64 ) 1.75 ( 0.09 ) 50.99     \n",
+       "17   50   50 0.3   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "18  100   50 0.3   0.50 0.10 1 ( 0 ) 1.68 ( 0.12 )  4.86 ( 0.08 )  2.81     \n",
+       "19  500   50 0.3   0.10 0.44 1 ( 0 ) 1.57 ( 0.12 )  2.74 ( 0.08 )  4.83     \n",
+       "20 1000   50 0.3   0.05 0.62 1 ( 0 ) 1.33 ( 0.1 )   1.44 ( 0.08 )  5.89     \n",
+       "21   50  100 0.3   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "22  100  100 0.3   1.00 0.05 1 ( 0 ) 4.45 ( 0.2 )   4.85 ( 0.08 )  5.60     \n",
+       "23  500  100 0.3   0.20 0.33 1 ( 0 ) 3.36 ( 0.19 )  2.57 ( 0.09 )  6.79     \n",
+       "24 1000  100 0.3   0.10 0.47 1 ( 0 ) 3.43 ( 0.17 )  1.61 ( 0.09 )  7.82     \n",
+       "25   50  500 0.3  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "26  100  500 0.3   5.00 0.01 1 ( 0 ) 23.69 ( 0.53 ) 4.87 ( 0.08 ) 24.82     \n",
+       "27  500  500 0.3   1.00 0.07 1 ( 0 ) 22.84 ( 0.44 ) 3.11 ( 0.09 ) 25.73     \n",
+       "28 1000  500 0.3   0.50 0.13 1 ( 0 ) 22.24 ( 0.43 ) 1.77 ( 0.09 ) 26.47     \n",
+       "29   50 1000 0.3  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "30  100 1000 0.3  10.00 0.01 1 ( 0 ) 48.06 ( 0.76 ) 5.02 ( 0.08 ) 49.04     \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮       ⋮              ⋮             ⋮         \n",
+       "51  500   50 0.7   0.10 0.45 1 ( 0 ) 2.14 ( 0.13 )  3.36 ( 0.08 )  4.78     \n",
+       "52 1000   50 0.7   0.05 0.60 1 ( 0 ) 2.66 ( 0.14 )  2.27 ( 0.08 )  6.39     \n",
+       "53   50  100 0.7   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "54  100  100 0.7   1.00 0.08 1 ( 0 ) 4.36 ( 0.22 )  4.8 ( 0.09 )   5.56     \n",
+       "55  500  100 0.7   0.20 0.38 1 ( 0 ) 4.03 ( 0.18 )  3.1 ( 0.09 )   6.93     \n",
+       "56 1000  100 0.7   0.10 0.50 1 ( 0 ) 4.65 ( 0.22 )  2 ( 0.07 )     8.65     \n",
+       "57   50  500 0.7  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "58  100  500 0.7   5.00 0.02 1 ( 0 ) 23.58 ( 0.53 ) 4.72 ( 0.09 ) 24.86     \n",
+       "59  500  500 0.7   1.00 0.10 1 ( 0 ) 23.09 ( 0.56 ) 3.38 ( 0.09 ) 25.71     \n",
+       "60 1000  500 0.7   0.50 0.14 1 ( 0 ) 23.33 ( 0.53 ) 2.58 ( 0.07 ) 26.75     \n",
+       "61   50 1000 0.7  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "62  100 1000 0.7  10.00 0.01 1 ( 0 ) 46.8 ( 0.69 )  5.13 ( 0.07 ) 47.67     \n",
+       "63  500 1000 0.7   2.00 0.05 1 ( 0 ) 48.68 ( 0.77 ) 3.54 ( 0.08 ) 51.14     \n",
+       "64 1000 1000 0.7   1.00 0.07 1 ( 0 ) 47.14 ( 0.7 )  2.73 ( 0.07 ) 50.41     \n",
+       "65   50   50 0.9   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "66  100   50 0.9   0.50 0.12 1 ( 0 ) 2.09 ( 0.16 )  4.98 ( 0.09 )  3.06     \n",
+       "67  500   50 0.9   0.10 0.45 1 ( 0 ) 2.6 ( 0.13 )   3.33 ( 0.08 )  5.27     \n",
+       "68 1000   50 0.9   0.05 0.51 1 ( 0 ) 3.48 ( 0.17 )  2.35 ( 0.08 )  7.13     \n",
+       "69   50  100 0.9   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "70  100  100 0.9   1.00 0.07 1 ( 0 ) 4.4 ( 0.23 )   4.96 ( 0.09 )  5.43     \n",
+       "71  500  100 0.9   0.20 0.35 1 ( 0 ) 4.57 ( 0.24 )  3.5 ( 0.08 )   7.07     \n",
+       "72 1000  100 0.9   0.10 0.37 1 ( 0 ) 6.43 ( 0.28 )  2.57 ( 0.08 )  9.86     \n",
+       "73   50  500 0.9  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "74  100  500 0.9   5.00 0.01 1 ( 0 ) 24.58 ( 0.66 ) 5.1 ( 0.09 )  25.48     \n",
+       "75  500  500 0.9   1.00 0.08 1 ( 0 ) 24.91 ( 0.57 ) 3.53 ( 0.09 ) 27.38     \n",
+       "76 1000  500 0.9   0.50 0.13 1 ( 0 ) 24.53 ( 0.53 ) 2.84 ( 0.08 ) 27.69     \n",
+       "77   50 1000 0.9  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.00 0.00 1 ( 0 ) 47.19 ( 0.96 ) 5.2 ( 0.08 )  47.99     \n",
+       "79  500 1000 0.9   2.00 0.04 1 ( 0 ) 48.11 ( 0.92 ) 3.78 ( 0.08 ) 50.33     \n",
+       "80 1000 1000 0.9   1.00 0.07 1 ( 0 ) 49.47 ( 0.78 ) 2.97 ( 0.07 ) 52.50     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.56 1         2.05   4.71   \n",
+       "3  0.25 1         1.40   2.40   \n",
+       "4  0.15 1         0.98   1.20   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.72 1         3.95   4.66   \n",
+       "7  0.48 1         3.91   2.17   \n",
+       "8  0.36 1         2.97   1.29   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.95 1        24.57   4.72   \n",
+       "11 0.86 1        23.57   2.43   \n",
+       "12 0.82 1        22.61   1.31   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        48.35   4.88   \n",
+       "15 0.94 1        48.59   2.71   \n",
+       "16 0.92 1        46.74   1.75   \n",
+       "17 NaN  1         1.00   6.00   \n",
+       "18 0.54 1         1.68   4.86   \n",
+       "19 0.3  1         1.57   2.74   \n",
+       "20 0.21 1         1.33   1.44   \n",
+       "21 NaN  1         1.00   6.00   \n",
+       "22 0.78 1         4.45   4.85   \n",
+       "23 0.46 1         3.36   2.57   \n",
+       "24 0.41 1         3.43   1.61   \n",
+       "25 NaN  1         1.00   6.00   \n",
+       "26 0.95 1        23.69   4.87   \n",
+       "27 0.89 1        22.84   3.11   \n",
+       "28 0.84 1        22.24   1.77   \n",
+       "29 NaN  1         1.00   6.00   \n",
+       "30 0.98 1        48.06   5.02   \n",
+       "⋮  ⋮    ⋮        ⋮       ⋮      \n",
+       "51 0.43 1         2.14   3.36   \n",
+       "52 0.39 1         2.66   2.27   \n",
+       "53 NaN  1         1.00   6.00   \n",
+       "54 0.77 1         4.36   4.80   \n",
+       "55 0.56 1         4.03   3.10   \n",
+       "56 0.51 1         4.65   2.00   \n",
+       "57 NaN  1         1.00   6.00   \n",
+       "58 0.95 1        23.58   4.72   \n",
+       "59 0.89 1        23.09   3.38   \n",
+       "60 0.87 1        23.33   2.58   \n",
+       "61 NaN  1         1.00   6.00   \n",
+       "62 0.98 1        46.80   5.13   \n",
+       "63 0.95 1        48.68   3.54   \n",
+       "64 0.93 1        47.14   2.73   \n",
+       "65 NaN  1         1.00   6.00   \n",
+       "66 0.62 1         2.09   4.98   \n",
+       "67 0.47 1         2.60   3.33   \n",
+       "68 0.47 1         3.48   2.35   \n",
+       "69 NaN  1         1.00   6.00   \n",
+       "70 0.81 1         4.40   4.96   \n",
+       "71 0.62 1         4.57   3.50   \n",
+       "72 0.63 1         6.43   2.57   \n",
+       "73 NaN  1         1.00   6.00   \n",
+       "74 0.96 1        24.58   5.10   \n",
+       "75 0.91 1        24.91   3.53   \n",
+       "76 0.88 1        24.53   2.84   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.19   5.20   \n",
+       "79 0.95 1        48.11   3.78   \n",
+       "80 0.94 1        49.47   2.97   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.4_sim_toe_compLasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.4_sim_toe_compLasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..1e829f4
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/1.4_sim_toe_compLasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
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+      "[1] \"indx: 38\"\n",
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+      "[1] \"indx: 40\"\n",
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+      "[1] \"indx: 42\"\n",
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+      "[1] \"indx: 49\"\n",
+      "[1] \"indx: 50\"\n",
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+      "[1] \"indx: 53\"\n",
+      "[1] \"indx: 54\"\n",
+      "[1] \"indx: 55\"\n",
+      "[1] \"indx: 56\"\n",
+      "[1] \"indx: 57\"\n",
+      "[1] \"indx: 58\"\n",
+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>14.84 ( 0.9 )</td><td>0.93 ( 0.09 )</td><td>0.97 ( 0 )</td><td>0.13</td><td>19.91</td><td>0.70</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>7.19 ( 0.43 )</td><td>0.49 ( 0.07 )</td><td>0.93 ( 0 )</td><td>0.33</td><td>12.70</td><td>0.53</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>1.93 ( 0.34 )</td><td>0.16 ( 0.04 )</td><td>0.9 ( 0 )</td><td>0.7</td><td> 7.77</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>1.37 ( 0.42 )</td><td>0.01 ( 0.01 )</td><td>0.9 ( 0 )</td><td>0.79</td><td> 7.36</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>36.64 ( 2.16 )</td><td>0.65 ( 0.07 )</td><td>0.99 ( 0 )</td><td>0.07</td><td>41.99</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>16.16 ( 1.22 )</td><td>0.47 ( 0.06 )</td><td>0.94 ( 0 )</td><td>0.2</td><td>21.69</td><td>0.70</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 14.84 ( 0.9 ) & 0.93 ( 0.09 ) & 0.97 ( 0 ) & 0.13 & 19.91 & 0.70\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 7.19 ( 0.43 ) & 0.49 ( 0.07 ) & 0.93 ( 0 ) & 0.33 & 12.70 & 0.53\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 1.93 ( 0.34 ) & 0.16 ( 0.04 ) & 0.9 ( 0 ) & 0.7 &  7.77 & 0.16\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 1.37 ( 0.42 ) & 0.01 ( 0.01 ) & 0.9 ( 0 ) & 0.79 &  7.36 & 0.10\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 36.64 ( 2.16 ) & 0.65 ( 0.07 ) & 0.99 ( 0 ) & 0.07 & 41.99 & 0.84\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 16.16 ( 1.22 ) & 0.47 ( 0.06 ) & 0.94 ( 0 ) & 0.2 & 21.69 & 0.70\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 14.84 ( 0.9 ) | 0.93 ( 0.09 ) | 0.97 ( 0 ) | 0.13 | 19.91 | 0.70 |\n",
+       "| 2 | 100 | 50 | 0.1 | 7.19 ( 0.43 ) | 0.49 ( 0.07 ) | 0.93 ( 0 ) | 0.33 | 12.70 | 0.53 |\n",
+       "| 3 | 500 | 50 | 0.1 | 1.93 ( 0.34 ) | 0.16 ( 0.04 ) | 0.9 ( 0 ) | 0.7 |  7.77 | 0.16 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 1.37 ( 0.42 ) | 0.01 ( 0.01 ) | 0.9 ( 0 ) | 0.79 |  7.36 | 0.10 |\n",
+       "| 5 | 50 | 100 | 0.1 | 36.64 ( 2.16 ) | 0.65 ( 0.07 ) | 0.99 ( 0 ) | 0.07 | 41.99 | 0.84 |\n",
+       "| 6 | 100 | 100 | 0.1 | 16.16 ( 1.22 ) | 0.47 ( 0.06 ) | 0.94 ( 0 ) | 0.2 | 21.69 | 0.70 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC        Stab num_select FDR \n",
+       "1 50   50  0.1 14.84 ( 0.9 )  0.93 ( 0.09 ) 0.97 ( 0 ) 0.13 19.91      0.70\n",
+       "2 100  50  0.1 7.19 ( 0.43 )  0.49 ( 0.07 ) 0.93 ( 0 ) 0.33 12.70      0.53\n",
+       "3 500  50  0.1 1.93 ( 0.34 )  0.16 ( 0.04 ) 0.9 ( 0 )  0.7   7.77      0.16\n",
+       "4 1000 50  0.1 1.37 ( 0.42 )  0.01 ( 0.01 ) 0.9 ( 0 )  0.79  7.36      0.10\n",
+       "5 50   100 0.1 36.64 ( 2.16 ) 0.65 ( 0.07 ) 0.99 ( 0 ) 0.07 41.99      0.84\n",
+       "6 100  100 0.1 16.16 ( 1.22 ) 0.47 ( 0.06 ) 0.94 ( 0 ) 0.2  21.69      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>6.75 ( 0.73 )</td><td>2.76 ( 0.07 )</td><td>0.79 ( 0 )</td><td>0.4</td><td>  9.99</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>2.48 ( 0.28 )</td><td>2.79 ( 0.05 )</td><td>0.8 ( 0 )</td><td>0.73</td><td>  5.69</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>1 ( 0 )</td><td>0.01</td><td>205.64</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>70.81 ( 4.4 )</td><td>3.05 ( 0.12 )</td><td>0.95 ( 0 )</td><td>0.03</td><td> 73.76</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>7.71 ( 1.09 )</td><td>2.85 ( 0.06 )</td><td>0.79 ( 0 )</td><td>0.38</td><td> 10.86</td><td>0.59</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>2.41 ( 0.17 )</td><td>2.77 ( 0.07 )</td><td>0.8 ( 0 )</td><td>0.76</td><td>  5.64</td><td>0.39</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 6.75 ( 0.73 ) & 2.76 ( 0.07 ) & 0.79 ( 0 ) & 0.4 &   9.99 & 0.56\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 2.48 ( 0.28 ) & 2.79 ( 0.05 ) & 0.8 ( 0 ) & 0.73 &   5.69 & 0.37\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 1 ( 0 ) & 0.01 & 205.64 & 0.98\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 70.81 ( 4.4 ) & 3.05 ( 0.12 ) & 0.95 ( 0 ) & 0.03 &  73.76 & 0.96\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 7.71 ( 1.09 ) & 2.85 ( 0.06 ) & 0.79 ( 0 ) & 0.38 &  10.86 & 0.59\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 2.41 ( 0.17 ) & 2.77 ( 0.07 ) & 0.8 ( 0 ) & 0.76 &   5.64 & 0.39\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 6.75 ( 0.73 ) | 2.76 ( 0.07 ) | 0.79 ( 0 ) | 0.4 |   9.99 | 0.56 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 2.48 ( 0.28 ) | 2.79 ( 0.05 ) | 0.8 ( 0 ) | 0.73 |   5.69 | 0.37 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 1 ( 0 ) | 0.01 | 205.64 | 0.98 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 70.81 ( 4.4 ) | 3.05 ( 0.12 ) | 0.95 ( 0 ) | 0.03 |  73.76 | 0.96 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 7.71 ( 1.09 ) | 2.85 ( 0.06 ) | 0.79 ( 0 ) | 0.38 |  10.86 | 0.59 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 2.41 ( 0.17 ) | 2.77 ( 0.07 ) | 0.8 ( 0 ) | 0.76 |   5.64 | 0.39 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP               FN            ROC        Stab num_select FDR \n",
+       "75 500  500  0.9 6.75 ( 0.73 )    2.76 ( 0.07 ) 0.79 ( 0 ) 0.4    9.99     0.56\n",
+       "76 1000 500  0.9 2.48 ( 0.28 )    2.79 ( 0.05 ) 0.8 ( 0 )  0.73   5.69     0.37\n",
+       "77 50   1000 0.9 201.98 ( 10.15 ) 2.34 ( 0.16 ) 1 ( 0 )    0.01 205.64     0.98\n",
+       "78 100  1000 0.9 70.81 ( 4.4 )    3.05 ( 0.12 ) 0.95 ( 0 ) 0.03  73.76     0.96\n",
+       "79 500  1000 0.9 7.71 ( 1.09 )    2.85 ( 0.06 ) 0.79 ( 0 ) 0.38  10.86     0.59\n",
+       "80 1000 1000 0.9 2.41 ( 0.17 )    2.77 ( 0.07 ) 0.8 ( 0 )  0.76   5.64     0.39"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>14.84 ( 0.9 ) </td><td>0.93 ( 0.09 )</td><td>19.91</td><td>0.7 </td><td>0.97</td><td>14.84</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.33</td><td>0.93 ( 0 )</td><td>7.19 ( 0.43 ) </td><td>0.49 ( 0.07 )</td><td>12.70</td><td>0.53</td><td>0.93</td><td> 7.19</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.70</td><td>0.9 ( 0 ) </td><td>1.93 ( 0.34 ) </td><td>0.16 ( 0.04 )</td><td> 7.77</td><td>0.16</td><td>0.90</td><td> 1.93</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.79</td><td>0.9 ( 0 ) </td><td>1.37 ( 0.42 ) </td><td>0.01 ( 0.01 )</td><td> 7.36</td><td>0.1 </td><td>0.90</td><td> 1.37</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.07</td><td>0.99 ( 0 )</td><td>36.64 ( 2.16 )</td><td>0.65 ( 0.07 )</td><td>41.99</td><td>0.84</td><td>0.99</td><td>36.64</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.16 ( 1.22 )</td><td>0.47 ( 0.06 )</td><td>21.69</td><td>0.7 </td><td>0.94</td><td>16.16</td><td>0.47</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.13 & 0.97 ( 0 ) & 14.84 ( 0.9 )  & 0.93 ( 0.09 ) & 19.91 & 0.7  & 0.97 & 14.84 & 0.93\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.33 & 0.93 ( 0 ) & 7.19 ( 0.43 )  & 0.49 ( 0.07 ) & 12.70 & 0.53 & 0.93 &  7.19 & 0.49\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.70 & 0.9 ( 0 )  & 1.93 ( 0.34 )  & 0.16 ( 0.04 ) &  7.77 & 0.16 & 0.90 &  1.93 & 0.16\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.79 & 0.9 ( 0 )  & 1.37 ( 0.42 )  & 0.01 ( 0.01 ) &  7.36 & 0.1  & 0.90 &  1.37 & 0.01\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.07 & 0.99 ( 0 ) & 36.64 ( 2.16 ) & 0.65 ( 0.07 ) & 41.99 & 0.84 & 0.99 & 36.64 & 0.65\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.20 & 0.94 ( 0 ) & 16.16 ( 1.22 ) & 0.47 ( 0.06 ) & 21.69 & 0.7  & 0.94 & 16.16 & 0.47\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.13 | 0.97 ( 0 ) | 14.84 ( 0.9 )  | 0.93 ( 0.09 ) | 19.91 | 0.7  | 0.97 | 14.84 | 0.93 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.33 | 0.93 ( 0 ) | 7.19 ( 0.43 )  | 0.49 ( 0.07 ) | 12.70 | 0.53 | 0.93 |  7.19 | 0.49 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.70 | 0.9 ( 0 )  | 1.93 ( 0.34 )  | 0.16 ( 0.04 ) |  7.77 | 0.16 | 0.90 |  1.93 | 0.16 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.79 | 0.9 ( 0 )  | 1.37 ( 0.42 )  | 0.01 ( 0.01 ) |  7.36 | 0.1  | 0.90 |  1.37 | 0.01 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.07 | 0.99 ( 0 ) | 36.64 ( 2.16 ) | 0.65 ( 0.07 ) | 41.99 | 0.84 | 0.99 | 36.64 | 0.65 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.20 | 0.94 ( 0 ) | 16.16 ( 1.22 ) | 0.47 ( 0.06 ) | 21.69 | 0.7  | 0.94 | 16.16 | 0.47 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC        FP             FN            num_select\n",
+       "1   50  50 0.1  1.00  0.13 0.97 ( 0 ) 14.84 ( 0.9 )  0.93 ( 0.09 ) 19.91     \n",
+       "2  100  50 0.1  0.50  0.33 0.93 ( 0 ) 7.19 ( 0.43 )  0.49 ( 0.07 ) 12.70     \n",
+       "3  500  50 0.1  0.10  0.70 0.9 ( 0 )  1.93 ( 0.34 )  0.16 ( 0.04 )  7.77     \n",
+       "4 1000  50 0.1  0.05  0.79 0.9 ( 0 )  1.37 ( 0.42 )  0.01 ( 0.01 )  7.36     \n",
+       "5   50 100 0.1  2.00  0.07 0.99 ( 0 ) 36.64 ( 2.16 ) 0.65 ( 0.07 ) 41.99     \n",
+       "6  100 100 0.1  1.00  0.20 0.94 ( 0 ) 16.16 ( 1.22 ) 0.47 ( 0.06 ) 21.69     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.7  0.97     14.84   0.93   \n",
+       "2 0.53 0.93      7.19   0.49   \n",
+       "3 0.16 0.90      1.93   0.16   \n",
+       "4 0.1  0.90      1.37   0.01   \n",
+       "5 0.84 0.99     36.64   0.65   \n",
+       "6 0.7  0.94     16.16   0.47   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.40</td><td>0.79 ( 0 )</td><td>6.75 ( 0.73 )   </td><td>2.76 ( 0.07 )</td><td>  9.99</td><td>0.56</td><td>0.79</td><td>  6.75</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.73</td><td>0.8 ( 0 ) </td><td>2.48 ( 0.28 )   </td><td>2.79 ( 0.05 )</td><td>  5.69</td><td>0.37</td><td>0.80</td><td>  2.48</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>205.64</td><td>0.98</td><td>1.00</td><td>201.98</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.03</td><td>0.95 ( 0 )</td><td>70.81 ( 4.4 )   </td><td>3.05 ( 0.12 )</td><td> 73.76</td><td>0.96</td><td>0.95</td><td> 70.81</td><td>3.05</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.38</td><td>0.79 ( 0 )</td><td>7.71 ( 1.09 )   </td><td>2.85 ( 0.06 )</td><td> 10.86</td><td>0.59</td><td>0.79</td><td>  7.71</td><td>2.85</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.76</td><td>0.8 ( 0 ) </td><td>2.41 ( 0.17 )   </td><td>2.77 ( 0.07 )</td><td>  5.64</td><td>0.39</td><td>0.80</td><td>  2.41</td><td>2.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.40 & 0.79 ( 0 ) & 6.75 ( 0.73 )    & 2.76 ( 0.07 ) &   9.99 & 0.56 & 0.79 &   6.75 & 2.76\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.73 & 0.8 ( 0 )  & 2.48 ( 0.28 )    & 2.79 ( 0.05 ) &   5.69 & 0.37 & 0.80 &   2.48 & 2.79\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.01 & 1 ( 0 )    & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 205.64 & 0.98 & 1.00 & 201.98 & 2.34\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.03 & 0.95 ( 0 ) & 70.81 ( 4.4 )    & 3.05 ( 0.12 ) &  73.76 & 0.96 & 0.95 &  70.81 & 3.05\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.38 & 0.79 ( 0 ) & 7.71 ( 1.09 )    & 2.85 ( 0.06 ) &  10.86 & 0.59 & 0.79 &   7.71 & 2.85\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.76 & 0.8 ( 0 )  & 2.41 ( 0.17 )    & 2.77 ( 0.07 ) &   5.64 & 0.39 & 0.80 &   2.41 & 2.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.40 | 0.79 ( 0 ) | 6.75 ( 0.73 )    | 2.76 ( 0.07 ) |   9.99 | 0.56 | 0.79 |   6.75 | 2.76 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.73 | 0.8 ( 0 )  | 2.48 ( 0.28 )    | 2.79 ( 0.05 ) |   5.69 | 0.37 | 0.80 |   2.48 | 2.79 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.01 | 1 ( 0 )    | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 205.64 | 0.98 | 1.00 | 201.98 | 2.34 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.03 | 0.95 ( 0 ) | 70.81 ( 4.4 )    | 3.05 ( 0.12 ) |  73.76 | 0.96 | 0.95 |  70.81 | 3.05 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.38 | 0.79 ( 0 ) | 7.71 ( 1.09 )    | 2.85 ( 0.06 ) |  10.86 | 0.59 | 0.79 |   7.71 | 2.85 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.76 | 0.8 ( 0 )  | 2.41 ( 0.17 )    | 2.77 ( 0.07 ) |   5.64 | 0.39 | 0.80 |   2.41 | 2.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP               FN           \n",
+       "75  500  500 0.9   1.0  0.40 0.79 ( 0 ) 6.75 ( 0.73 )    2.76 ( 0.07 )\n",
+       "76 1000  500 0.9   0.5  0.73 0.8 ( 0 )  2.48 ( 0.28 )    2.79 ( 0.05 )\n",
+       "77   50 1000 0.9  20.0  0.01 1 ( 0 )    201.98 ( 10.15 ) 2.34 ( 0.16 )\n",
+       "78  100 1000 0.9  10.0  0.03 0.95 ( 0 ) 70.81 ( 4.4 )    3.05 ( 0.12 )\n",
+       "79  500 1000 0.9   2.0  0.38 0.79 ( 0 ) 7.71 ( 1.09 )    2.85 ( 0.06 )\n",
+       "80 1000 1000 0.9   1.0  0.76 0.8 ( 0 )  2.41 ( 0.17 )    2.77 ( 0.07 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75   9.99     0.56 0.79       6.75  2.76   \n",
+       "76   5.69     0.37 0.80       2.48  2.79   \n",
+       "77 205.64     0.98 1.00     201.98  2.34   \n",
+       "78  73.76     0.96 0.95      70.81  3.05   \n",
+       "79  10.86     0.59 0.79       7.71  2.85   \n",
+       "80   5.64     0.39 0.80       2.41  2.77   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>14.84 ( 0.9 )  </td><td>0.93 ( 0.09 )</td><td> 19.91</td><td>0.7 </td><td>0.97</td><td> 14.84</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.33</td><td>0.93 ( 0 )</td><td>7.19 ( 0.43 )  </td><td>0.49 ( 0.07 )</td><td> 12.70</td><td>0.53</td><td>0.93</td><td>  7.19</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.70</td><td>0.9 ( 0 ) </td><td>1.93 ( 0.34 )  </td><td>0.16 ( 0.04 )</td><td>  7.77</td><td>0.16</td><td>0.90</td><td>  1.93</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.79</td><td>0.9 ( 0 ) </td><td>1.37 ( 0.42 )  </td><td>0.01 ( 0.01 )</td><td>  7.36</td><td>0.1 </td><td>0.90</td><td>  1.37</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.07</td><td>0.99 ( 0 )</td><td>36.64 ( 2.16 ) </td><td>0.65 ( 0.07 )</td><td> 41.99</td><td>0.84</td><td>0.99</td><td> 36.64</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.16 ( 1.22 ) </td><td>0.47 ( 0.06 )</td><td> 21.69</td><td>0.7 </td><td>0.94</td><td> 16.16</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.36</td><td>0.9 ( 0 ) </td><td>8.64 ( 1.35 )  </td><td>0.08 ( 0.03 )</td><td> 14.56</td><td>0.37</td><td>0.90</td><td>  8.64</td><td>0.08</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.47</td><td>0.9 ( 0 ) </td><td>5.83 ( 1.4 )   </td><td>0.02 ( 0.01 )</td><td> 11.81</td><td>0.22</td><td>0.90</td><td>  5.83</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>248.06 ( 7.56 )</td><td>0.56 ( 0.07 )</td><td>253.50</td><td>0.98</td><td>1.00</td><td>248.06</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.03</td><td>1 ( 0 )   </td><td>132.04 ( 7.47 )</td><td>0.33 ( 0.05 )</td><td>137.71</td><td>0.95</td><td>1.00</td><td>132.04</td><td>0.33</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.34</td><td>0.91 ( 0 )</td><td>12.55 ( 1.81 ) </td><td>0.04 ( 0.02 )</td><td> 18.51</td><td>0.52</td><td>0.91</td><td> 12.55</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.69</td><td>0.9 ( 0 ) </td><td>3.02 ( 0.6 )   </td><td>0.01 ( 0.01 )</td><td>  9.01</td><td>0.22</td><td>0.90</td><td>  3.02</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>398.98 ( 6.9 ) </td><td>0.8 ( 0.08 ) </td><td>404.18</td><td>0.99</td><td>1.00</td><td>398.98</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>296.19 ( 8.29 )</td><td>0.35 ( 0.05 )</td><td>301.84</td><td>0.98</td><td>1.00</td><td>296.19</td><td>0.35</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.39</td><td>0.9 ( 0 ) </td><td>10.2 ( 0.98 )  </td><td>0.1 ( 0.03 ) </td><td> 16.10</td><td>0.52</td><td>0.90</td><td> 10.20</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.83</td><td>0.9 ( 0 ) </td><td>1.76 ( 0.26 )  </td><td>0.02 ( 0.01 )</td><td>  7.74</td><td>0.18</td><td>0.90</td><td>  1.76</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.96 ( 0 )</td><td>13.96 ( 0.83 ) </td><td>1.01 ( 0.09 )</td><td> 18.95</td><td>0.7 </td><td>0.96</td><td> 13.96</td><td>1.01</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.92 ( 0 )</td><td>9.38 ( 0.67 )  </td><td>0.68 ( 0.07 )</td><td> 14.70</td><td>0.58</td><td>0.92</td><td>  9.38</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>0.89 ( 0 )</td><td>5.43 ( 0.9 )   </td><td>0.14 ( 0.03 )</td><td> 11.29</td><td>0.3 </td><td>0.89</td><td>  5.43</td><td>0.14</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.48</td><td>0.88 ( 0 )</td><td>5.03 ( 1 )     </td><td>0.09 ( 0.03 )</td><td> 10.94</td><td>0.23</td><td>0.88</td><td>  5.03</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>36.33 ( 2.24 ) </td><td>0.9 ( 0.09 ) </td><td> 41.43</td><td>0.85</td><td>0.99</td><td> 36.33</td><td>0.90</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.93 ( 0 )</td><td>16.08 ( 0.95 ) </td><td>0.75 ( 0.07 )</td><td> 21.33</td><td>0.72</td><td>0.93</td><td> 16.08</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.37</td><td>0.89 ( 0 )</td><td>8.66 ( 1.15 )  </td><td>0.11 ( 0.03 )</td><td> 14.55</td><td>0.42</td><td>0.89</td><td>  8.66</td><td>0.11</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.39</td><td>0.88 ( 0 )</td><td>8.45 ( 1.17 )  </td><td>0.12 ( 0.03 )</td><td> 14.33</td><td>0.39</td><td>0.88</td><td>  8.45</td><td>0.12</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>228.37 ( 7.07 )</td><td>0.6 ( 0.08 ) </td><td>233.77</td><td>0.97</td><td>1.00</td><td>228.37</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.04</td><td>0.99 ( 0 )</td><td>114.53 ( 5.78 )</td><td>0.43 ( 0.06 )</td><td>120.10</td><td>0.94</td><td>0.99</td><td>114.53</td><td>0.43</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.31</td><td>0.89 ( 0 )</td><td>13.64 ( 2.08 ) </td><td>0.2 ( 0.04 ) </td><td> 19.44</td><td>0.57</td><td>0.89</td><td> 13.64</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.54</td><td>0.88 ( 0 )</td><td>6.3 ( 1.15 )   </td><td>0.09 ( 0.04 )</td><td> 12.21</td><td>0.38</td><td>0.88</td><td>  6.30</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>412.73 ( 7.91 )</td><td>0.88 ( 0.1 ) </td><td>417.85</td><td>0.99</td><td>1.00</td><td>412.73</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>279.68 ( 7.94 )</td><td>0.45 ( 0.07 )</td><td>285.23</td><td>0.98</td><td>1.00</td><td>279.68</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.67</td><td>0.86 ( 0 )   </td><td>1.32 ( 0.25 )   </td><td>2.42 ( 0.06 )</td><td>  4.90</td><td>0.19</td><td>0.86</td><td>  1.32</td><td>2.42</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.74</td><td>0.85 ( 0 )   </td><td>0.84 ( 0.14 )   </td><td>2.48 ( 0.06 )</td><td>  4.36</td><td>0.13</td><td>0.85</td><td>  0.84</td><td>2.48</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.07</td><td>0.96 ( 0 )   </td><td>24.54 ( 1.62 )  </td><td>2.08 ( 0.11 )</td><td> 28.46</td><td>0.84</td><td>0.96</td><td> 24.54</td><td>2.08</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.16</td><td>0.89 ( 0 )   </td><td>14.69 ( 1.28 )  </td><td>1.85 ( 0.08 )</td><td> 18.84</td><td>0.71</td><td>0.89</td><td> 14.69</td><td>1.85</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.55</td><td>0.86 ( 0 )   </td><td>2.19 ( 0.64 )   </td><td>2.33 ( 0.07 )</td><td>  5.86</td><td>0.18</td><td>0.86</td><td>  2.19</td><td>2.33</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.81</td><td>0.85 ( 0 )   </td><td>0.51 ( 0.1 )    </td><td>2.56 ( 0.05 )</td><td>  3.95</td><td>0.09</td><td>0.85</td><td>  0.51</td><td>2.56</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>1 ( 0 )      </td><td>161.23 ( 7.83 ) </td><td>1.29 ( 0.1 ) </td><td>165.94</td><td>0.96</td><td>1.00</td><td>161.23</td><td>1.29</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.06</td><td>0.96 ( 0 )   </td><td>60.05 ( 4.29 )  </td><td>1.63 ( 0.12 )</td><td> 64.42</td><td>0.92</td><td>0.96</td><td> 60.05</td><td>1.63</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.50</td><td>0.85 ( 0 )   </td><td>3.12 ( 1.31 )   </td><td>2.68 ( 0.07 )</td><td>  6.44</td><td>0.19</td><td>0.85</td><td>  3.12</td><td>2.68</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.89</td><td>0.85 ( 0 )   </td><td>0.39 ( 0.06 )   </td><td>2.88 ( 0.04 )</td><td>  3.51</td><td>0.09</td><td>0.85</td><td>  0.39</td><td>2.88</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>1 ( 0 )      </td><td>363.91 ( 11.45 )</td><td>1.39 ( 0.1 ) </td><td>368.52</td><td>0.99</td><td>1.00</td><td>363.91</td><td>1.39</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.03</td><td>0.99 ( 0 )   </td><td>145.9 ( 7.62 )  </td><td>1.24 ( 0.11 )</td><td>150.66</td><td>0.96</td><td>0.99</td><td>145.90</td><td>1.24</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.61</td><td>0.85 ( 0 )   </td><td>2.38 ( 0.4 )    </td><td>2.7 ( 0.06 ) </td><td>  5.68</td><td>0.27</td><td>0.85</td><td>  2.38</td><td>2.70</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.87</td><td>0.85 ( 0 )   </td><td>0.65 ( 0.08 )   </td><td>2.87 ( 0.04 )</td><td>  3.78</td><td>0.14</td><td>0.85</td><td>  0.65</td><td>2.87</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.84 ( 0.01 )</td><td>9.98 ( 0.78 )   </td><td>2.68 ( 0.11 )</td><td> 13.30</td><td>0.7 </td><td>0.84</td><td>  9.98</td><td>2.68</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.25</td><td>0.8 ( 0 )    </td><td>6.57 ( 0.7 )    </td><td>2.73 ( 0.09 )</td><td>  9.84</td><td>0.58</td><td>0.80</td><td>  6.57</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.52</td><td>0.8 ( 0 )    </td><td>2.9 ( 0.5 )     </td><td>2.75 ( 0.07 )</td><td>  6.15</td><td>0.33</td><td>0.80</td><td>  2.90</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.70</td><td>0.8 ( 0 )    </td><td>1.88 ( 0.19 )   </td><td>2.79 ( 0.05 )</td><td>  5.09</td><td>0.3 </td><td>0.80</td><td>  1.88</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.10</td><td>0.87 ( 0.01 )</td><td>15.93 ( 1 )     </td><td>2.88 ( 0.1 ) </td><td> 19.05</td><td>0.81</td><td>0.87</td><td> 15.93</td><td>2.88</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.81 ( 0 )   </td><td>14.34 ( 1.4 )   </td><td>2.75 ( 0.1 ) </td><td> 17.59</td><td>0.73</td><td>0.81</td><td> 14.34</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.56</td><td>0.8 ( 0 )    </td><td>3.51 ( 0.44 )   </td><td>2.86 ( 0.04 )</td><td>  6.65</td><td>0.4 </td><td>0.80</td><td>  3.51</td><td>2.86</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.57</td><td>0.8 ( 0 )    </td><td>3.18 ( 0.59 )   </td><td>2.86 ( 0.04 )</td><td>  6.32</td><td>0.34</td><td>0.80</td><td>  3.18</td><td>2.86</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.02</td><td>0.99 ( 0 )   </td><td>95.52 ( 6.42 )  </td><td>2.37 ( 0.15 )</td><td> 99.15</td><td>0.95</td><td>0.99</td><td> 95.52</td><td>2.37</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.04</td><td>0.88 ( 0 )   </td><td>53.08 ( 3.93 )  </td><td>2.8 ( 0.12 ) </td><td> 56.28</td><td>0.93</td><td>0.88</td><td> 53.08</td><td>2.80</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.40</td><td>0.79 ( 0 )   </td><td>6.75 ( 0.73 )   </td><td>2.76 ( 0.07 )</td><td>  9.99</td><td>0.56</td><td>0.79</td><td>  6.75</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.73</td><td>0.8 ( 0 )    </td><td>2.48 ( 0.28 )   </td><td>2.79 ( 0.05 )</td><td>  5.69</td><td>0.37</td><td>0.80</td><td>  2.48</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.01</td><td>1 ( 0 )      </td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>205.64</td><td>0.98</td><td>1.00</td><td>201.98</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.03</td><td>0.95 ( 0 )   </td><td>70.81 ( 4.4 )   </td><td>3.05 ( 0.12 )</td><td> 73.76</td><td>0.96</td><td>0.95</td><td> 70.81</td><td>3.05</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.38</td><td>0.79 ( 0 )   </td><td>7.71 ( 1.09 )   </td><td>2.85 ( 0.06 )</td><td> 10.86</td><td>0.59</td><td>0.79</td><td>  7.71</td><td>2.85</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.76</td><td>0.8 ( 0 )    </td><td>2.41 ( 0.17 )   </td><td>2.77 ( 0.07 )</td><td>  5.64</td><td>0.39</td><td>0.80</td><td>  2.41</td><td>2.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.13 & 0.97 ( 0 ) & 14.84 ( 0.9 )   & 0.93 ( 0.09 ) &  19.91 & 0.7  & 0.97 &  14.84 & 0.93\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.33 & 0.93 ( 0 ) & 7.19 ( 0.43 )   & 0.49 ( 0.07 ) &  12.70 & 0.53 & 0.93 &   7.19 & 0.49\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.70 & 0.9 ( 0 )  & 1.93 ( 0.34 )   & 0.16 ( 0.04 ) &   7.77 & 0.16 & 0.90 &   1.93 & 0.16\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.79 & 0.9 ( 0 )  & 1.37 ( 0.42 )   & 0.01 ( 0.01 ) &   7.36 & 0.1  & 0.90 &   1.37 & 0.01\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.07 & 0.99 ( 0 ) & 36.64 ( 2.16 )  & 0.65 ( 0.07 ) &  41.99 & 0.84 & 0.99 &  36.64 & 0.65\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.20 & 0.94 ( 0 ) & 16.16 ( 1.22 )  & 0.47 ( 0.06 ) &  21.69 & 0.7  & 0.94 &  16.16 & 0.47\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.36 & 0.9 ( 0 )  & 8.64 ( 1.35 )   & 0.08 ( 0.03 ) &  14.56 & 0.37 & 0.90 &   8.64 & 0.08\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.47 & 0.9 ( 0 )  & 5.83 ( 1.4 )    & 0.02 ( 0.01 ) &  11.81 & 0.22 & 0.90 &   5.83 & 0.02\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.01 & 1 ( 0 )    & 248.06 ( 7.56 ) & 0.56 ( 0.07 ) & 253.50 & 0.98 & 1.00 & 248.06 & 0.56\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.03 & 1 ( 0 )    & 132.04 ( 7.47 ) & 0.33 ( 0.05 ) & 137.71 & 0.95 & 1.00 & 132.04 & 0.33\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.34 & 0.91 ( 0 ) & 12.55 ( 1.81 )  & 0.04 ( 0.02 ) &  18.51 & 0.52 & 0.91 &  12.55 & 0.04\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.69 & 0.9 ( 0 )  & 3.02 ( 0.6 )    & 0.01 ( 0.01 ) &   9.01 & 0.22 & 0.90 &   3.02 & 0.01\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.01 & 1 ( 0 )    & 398.98 ( 6.9 )  & 0.8 ( 0.08 )  & 404.18 & 0.99 & 1.00 & 398.98 & 0.80\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.02 & 1 ( 0 )    & 296.19 ( 8.29 ) & 0.35 ( 0.05 ) & 301.84 & 0.98 & 1.00 & 296.19 & 0.35\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.39 & 0.9 ( 0 )  & 10.2 ( 0.98 )   & 0.1 ( 0.03 )  &  16.10 & 0.52 & 0.90 &  10.20 & 0.10\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.83 & 0.9 ( 0 )  & 1.76 ( 0.26 )   & 0.02 ( 0.01 ) &   7.74 & 0.18 & 0.90 &   1.76 & 0.02\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.14 & 0.96 ( 0 ) & 13.96 ( 0.83 )  & 1.01 ( 0.09 ) &  18.95 & 0.7  & 0.96 &  13.96 & 1.01\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.25 & 0.92 ( 0 ) & 9.38 ( 0.67 )   & 0.68 ( 0.07 ) &  14.70 & 0.58 & 0.92 &   9.38 & 0.68\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 0.89 ( 0 ) & 5.43 ( 0.9 )    & 0.14 ( 0.03 ) &  11.29 & 0.3  & 0.89 &   5.43 & 0.14\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.48 & 0.88 ( 0 ) & 5.03 ( 1 )      & 0.09 ( 0.03 ) &  10.94 & 0.23 & 0.88 &   5.03 & 0.09\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.06 & 0.99 ( 0 ) & 36.33 ( 2.24 )  & 0.9 ( 0.09 )  &  41.43 & 0.85 & 0.99 &  36.33 & 0.90\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.19 & 0.93 ( 0 ) & 16.08 ( 0.95 )  & 0.75 ( 0.07 ) &  21.33 & 0.72 & 0.93 &  16.08 & 0.75\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.37 & 0.89 ( 0 ) & 8.66 ( 1.15 )   & 0.11 ( 0.03 ) &  14.55 & 0.42 & 0.89 &   8.66 & 0.11\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.39 & 0.88 ( 0 ) & 8.45 ( 1.17 )   & 0.12 ( 0.03 ) &  14.33 & 0.39 & 0.88 &   8.45 & 0.12\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.01 & 1 ( 0 )    & 228.37 ( 7.07 ) & 0.6 ( 0.08 )  & 233.77 & 0.97 & 1.00 & 228.37 & 0.60\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.04 & 0.99 ( 0 ) & 114.53 ( 5.78 ) & 0.43 ( 0.06 ) & 120.10 & 0.94 & 0.99 & 114.53 & 0.43\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.31 & 0.89 ( 0 ) & 13.64 ( 2.08 )  & 0.2 ( 0.04 )  &  19.44 & 0.57 & 0.89 &  13.64 & 0.20\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.54 & 0.88 ( 0 ) & 6.3 ( 1.15 )    & 0.09 ( 0.04 ) &  12.21 & 0.38 & 0.88 &   6.30 & 0.09\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.01 & 1 ( 0 )    & 412.73 ( 7.91 ) & 0.88 ( 0.1 )  & 417.85 & 0.99 & 1.00 & 412.73 & 0.88\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.02 & 1 ( 0 )    & 279.68 ( 7.94 ) & 0.45 ( 0.07 ) & 285.23 & 0.98 & 1.00 & 279.68 & 0.45\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.67 & 0.86 ( 0 )    & 1.32 ( 0.25 )    & 2.42 ( 0.06 ) &   4.90 & 0.19 & 0.86 &   1.32 & 2.42\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.74 & 0.85 ( 0 )    & 0.84 ( 0.14 )    & 2.48 ( 0.06 ) &   4.36 & 0.13 & 0.85 &   0.84 & 2.48\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.07 & 0.96 ( 0 )    & 24.54 ( 1.62 )   & 2.08 ( 0.11 ) &  28.46 & 0.84 & 0.96 &  24.54 & 2.08\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.16 & 0.89 ( 0 )    & 14.69 ( 1.28 )   & 1.85 ( 0.08 ) &  18.84 & 0.71 & 0.89 &  14.69 & 1.85\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.55 & 0.86 ( 0 )    & 2.19 ( 0.64 )    & 2.33 ( 0.07 ) &   5.86 & 0.18 & 0.86 &   2.19 & 2.33\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.81 & 0.85 ( 0 )    & 0.51 ( 0.1 )     & 2.56 ( 0.05 ) &   3.95 & 0.09 & 0.85 &   0.51 & 2.56\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 1 ( 0 )       & 161.23 ( 7.83 )  & 1.29 ( 0.1 )  & 165.94 & 0.96 & 1.00 & 161.23 & 1.29\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.06 & 0.96 ( 0 )    & 60.05 ( 4.29 )   & 1.63 ( 0.12 ) &  64.42 & 0.92 & 0.96 &  60.05 & 1.63\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.50 & 0.85 ( 0 )    & 3.12 ( 1.31 )    & 2.68 ( 0.07 ) &   6.44 & 0.19 & 0.85 &   3.12 & 2.68\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.89 & 0.85 ( 0 )    & 0.39 ( 0.06 )    & 2.88 ( 0.04 ) &   3.51 & 0.09 & 0.85 &   0.39 & 2.88\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 1 ( 0 )       & 363.91 ( 11.45 ) & 1.39 ( 0.1 )  & 368.52 & 0.99 & 1.00 & 363.91 & 1.39\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.03 & 0.99 ( 0 )    & 145.9 ( 7.62 )   & 1.24 ( 0.11 ) & 150.66 & 0.96 & 0.99 & 145.90 & 1.24\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.61 & 0.85 ( 0 )    & 2.38 ( 0.4 )     & 2.7 ( 0.06 )  &   5.68 & 0.27 & 0.85 &   2.38 & 2.70\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.87 & 0.85 ( 0 )    & 0.65 ( 0.08 )    & 2.87 ( 0.04 ) &   3.78 & 0.14 & 0.85 &   0.65 & 2.87\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.14 & 0.84 ( 0.01 ) & 9.98 ( 0.78 )    & 2.68 ( 0.11 ) &  13.30 & 0.7  & 0.84 &   9.98 & 2.68\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.25 & 0.8 ( 0 )     & 6.57 ( 0.7 )     & 2.73 ( 0.09 ) &   9.84 & 0.58 & 0.80 &   6.57 & 2.73\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.52 & 0.8 ( 0 )     & 2.9 ( 0.5 )      & 2.75 ( 0.07 ) &   6.15 & 0.33 & 0.80 &   2.90 & 2.75\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.70 & 0.8 ( 0 )     & 1.88 ( 0.19 )    & 2.79 ( 0.05 ) &   5.09 & 0.3  & 0.80 &   1.88 & 2.79\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.10 & 0.87 ( 0.01 ) & 15.93 ( 1 )      & 2.88 ( 0.1 )  &  19.05 & 0.81 & 0.87 &  15.93 & 2.88\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.14 & 0.81 ( 0 )    & 14.34 ( 1.4 )    & 2.75 ( 0.1 )  &  17.59 & 0.73 & 0.81 &  14.34 & 2.75\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.56 & 0.8 ( 0 )     & 3.51 ( 0.44 )    & 2.86 ( 0.04 ) &   6.65 & 0.4  & 0.80 &   3.51 & 2.86\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.57 & 0.8 ( 0 )     & 3.18 ( 0.59 )    & 2.86 ( 0.04 ) &   6.32 & 0.34 & 0.80 &   3.18 & 2.86\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.02 & 0.99 ( 0 )    & 95.52 ( 6.42 )   & 2.37 ( 0.15 ) &  99.15 & 0.95 & 0.99 &  95.52 & 2.37\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.04 & 0.88 ( 0 )    & 53.08 ( 3.93 )   & 2.8 ( 0.12 )  &  56.28 & 0.93 & 0.88 &  53.08 & 2.80\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.40 & 0.79 ( 0 )    & 6.75 ( 0.73 )    & 2.76 ( 0.07 ) &   9.99 & 0.56 & 0.79 &   6.75 & 2.76\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.73 & 0.8 ( 0 )     & 2.48 ( 0.28 )    & 2.79 ( 0.05 ) &   5.69 & 0.37 & 0.80 &   2.48 & 2.79\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.01 & 1 ( 0 )       & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 205.64 & 0.98 & 1.00 & 201.98 & 2.34\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.03 & 0.95 ( 0 )    & 70.81 ( 4.4 )    & 3.05 ( 0.12 ) &  73.76 & 0.96 & 0.95 &  70.81 & 3.05\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.38 & 0.79 ( 0 )    & 7.71 ( 1.09 )    & 2.85 ( 0.06 ) &  10.86 & 0.59 & 0.79 &   7.71 & 2.85\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.76 & 0.8 ( 0 )     & 2.41 ( 0.17 )    & 2.77 ( 0.07 ) &   5.64 & 0.39 & 0.80 &   2.41 & 2.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.13 | 0.97 ( 0 ) | 14.84 ( 0.9 )   | 0.93 ( 0.09 ) |  19.91 | 0.7  | 0.97 |  14.84 | 0.93 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.33 | 0.93 ( 0 ) | 7.19 ( 0.43 )   | 0.49 ( 0.07 ) |  12.70 | 0.53 | 0.93 |   7.19 | 0.49 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.70 | 0.9 ( 0 )  | 1.93 ( 0.34 )   | 0.16 ( 0.04 ) |   7.77 | 0.16 | 0.90 |   1.93 | 0.16 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.79 | 0.9 ( 0 )  | 1.37 ( 0.42 )   | 0.01 ( 0.01 ) |   7.36 | 0.1  | 0.90 |   1.37 | 0.01 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.07 | 0.99 ( 0 ) | 36.64 ( 2.16 )  | 0.65 ( 0.07 ) |  41.99 | 0.84 | 0.99 |  36.64 | 0.65 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.20 | 0.94 ( 0 ) | 16.16 ( 1.22 )  | 0.47 ( 0.06 ) |  21.69 | 0.7  | 0.94 |  16.16 | 0.47 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.36 | 0.9 ( 0 )  | 8.64 ( 1.35 )   | 0.08 ( 0.03 ) |  14.56 | 0.37 | 0.90 |   8.64 | 0.08 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.47 | 0.9 ( 0 )  | 5.83 ( 1.4 )    | 0.02 ( 0.01 ) |  11.81 | 0.22 | 0.90 |   5.83 | 0.02 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.01 | 1 ( 0 )    | 248.06 ( 7.56 ) | 0.56 ( 0.07 ) | 253.50 | 0.98 | 1.00 | 248.06 | 0.56 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.03 | 1 ( 0 )    | 132.04 ( 7.47 ) | 0.33 ( 0.05 ) | 137.71 | 0.95 | 1.00 | 132.04 | 0.33 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.34 | 0.91 ( 0 ) | 12.55 ( 1.81 )  | 0.04 ( 0.02 ) |  18.51 | 0.52 | 0.91 |  12.55 | 0.04 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.69 | 0.9 ( 0 )  | 3.02 ( 0.6 )    | 0.01 ( 0.01 ) |   9.01 | 0.22 | 0.90 |   3.02 | 0.01 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.01 | 1 ( 0 )    | 398.98 ( 6.9 )  | 0.8 ( 0.08 )  | 404.18 | 0.99 | 1.00 | 398.98 | 0.80 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.02 | 1 ( 0 )    | 296.19 ( 8.29 ) | 0.35 ( 0.05 ) | 301.84 | 0.98 | 1.00 | 296.19 | 0.35 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.39 | 0.9 ( 0 )  | 10.2 ( 0.98 )   | 0.1 ( 0.03 )  |  16.10 | 0.52 | 0.90 |  10.20 | 0.10 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.83 | 0.9 ( 0 )  | 1.76 ( 0.26 )   | 0.02 ( 0.01 ) |   7.74 | 0.18 | 0.90 |   1.76 | 0.02 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.14 | 0.96 ( 0 ) | 13.96 ( 0.83 )  | 1.01 ( 0.09 ) |  18.95 | 0.7  | 0.96 |  13.96 | 1.01 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.25 | 0.92 ( 0 ) | 9.38 ( 0.67 )   | 0.68 ( 0.07 ) |  14.70 | 0.58 | 0.92 |   9.38 | 0.68 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 0.89 ( 0 ) | 5.43 ( 0.9 )    | 0.14 ( 0.03 ) |  11.29 | 0.3  | 0.89 |   5.43 | 0.14 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.48 | 0.88 ( 0 ) | 5.03 ( 1 )      | 0.09 ( 0.03 ) |  10.94 | 0.23 | 0.88 |   5.03 | 0.09 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.06 | 0.99 ( 0 ) | 36.33 ( 2.24 )  | 0.9 ( 0.09 )  |  41.43 | 0.85 | 0.99 |  36.33 | 0.90 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.19 | 0.93 ( 0 ) | 16.08 ( 0.95 )  | 0.75 ( 0.07 ) |  21.33 | 0.72 | 0.93 |  16.08 | 0.75 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.37 | 0.89 ( 0 ) | 8.66 ( 1.15 )   | 0.11 ( 0.03 ) |  14.55 | 0.42 | 0.89 |   8.66 | 0.11 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.39 | 0.88 ( 0 ) | 8.45 ( 1.17 )   | 0.12 ( 0.03 ) |  14.33 | 0.39 | 0.88 |   8.45 | 0.12 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.01 | 1 ( 0 )    | 228.37 ( 7.07 ) | 0.6 ( 0.08 )  | 233.77 | 0.97 | 1.00 | 228.37 | 0.60 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.04 | 0.99 ( 0 ) | 114.53 ( 5.78 ) | 0.43 ( 0.06 ) | 120.10 | 0.94 | 0.99 | 114.53 | 0.43 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.31 | 0.89 ( 0 ) | 13.64 ( 2.08 )  | 0.2 ( 0.04 )  |  19.44 | 0.57 | 0.89 |  13.64 | 0.20 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.54 | 0.88 ( 0 ) | 6.3 ( 1.15 )    | 0.09 ( 0.04 ) |  12.21 | 0.38 | 0.88 |   6.30 | 0.09 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.01 | 1 ( 0 )    | 412.73 ( 7.91 ) | 0.88 ( 0.1 )  | 417.85 | 0.99 | 1.00 | 412.73 | 0.88 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.02 | 1 ( 0 )    | 279.68 ( 7.94 ) | 0.45 ( 0.07 ) | 285.23 | 0.98 | 1.00 | 279.68 | 0.45 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.67 | 0.86 ( 0 )    | 1.32 ( 0.25 )    | 2.42 ( 0.06 ) |   4.90 | 0.19 | 0.86 |   1.32 | 2.42 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.74 | 0.85 ( 0 )    | 0.84 ( 0.14 )    | 2.48 ( 0.06 ) |   4.36 | 0.13 | 0.85 |   0.84 | 2.48 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.07 | 0.96 ( 0 )    | 24.54 ( 1.62 )   | 2.08 ( 0.11 ) |  28.46 | 0.84 | 0.96 |  24.54 | 2.08 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.16 | 0.89 ( 0 )    | 14.69 ( 1.28 )   | 1.85 ( 0.08 ) |  18.84 | 0.71 | 0.89 |  14.69 | 1.85 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.55 | 0.86 ( 0 )    | 2.19 ( 0.64 )    | 2.33 ( 0.07 ) |   5.86 | 0.18 | 0.86 |   2.19 | 2.33 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.81 | 0.85 ( 0 )    | 0.51 ( 0.1 )     | 2.56 ( 0.05 ) |   3.95 | 0.09 | 0.85 |   0.51 | 2.56 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 1 ( 0 )       | 161.23 ( 7.83 )  | 1.29 ( 0.1 )  | 165.94 | 0.96 | 1.00 | 161.23 | 1.29 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.06 | 0.96 ( 0 )    | 60.05 ( 4.29 )   | 1.63 ( 0.12 ) |  64.42 | 0.92 | 0.96 |  60.05 | 1.63 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.50 | 0.85 ( 0 )    | 3.12 ( 1.31 )    | 2.68 ( 0.07 ) |   6.44 | 0.19 | 0.85 |   3.12 | 2.68 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.89 | 0.85 ( 0 )    | 0.39 ( 0.06 )    | 2.88 ( 0.04 ) |   3.51 | 0.09 | 0.85 |   0.39 | 2.88 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 1 ( 0 )       | 363.91 ( 11.45 ) | 1.39 ( 0.1 )  | 368.52 | 0.99 | 1.00 | 363.91 | 1.39 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.03 | 0.99 ( 0 )    | 145.9 ( 7.62 )   | 1.24 ( 0.11 ) | 150.66 | 0.96 | 0.99 | 145.90 | 1.24 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.61 | 0.85 ( 0 )    | 2.38 ( 0.4 )     | 2.7 ( 0.06 )  |   5.68 | 0.27 | 0.85 |   2.38 | 2.70 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.87 | 0.85 ( 0 )    | 0.65 ( 0.08 )    | 2.87 ( 0.04 ) |   3.78 | 0.14 | 0.85 |   0.65 | 2.87 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.14 | 0.84 ( 0.01 ) | 9.98 ( 0.78 )    | 2.68 ( 0.11 ) |  13.30 | 0.7  | 0.84 |   9.98 | 2.68 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.25 | 0.8 ( 0 )     | 6.57 ( 0.7 )     | 2.73 ( 0.09 ) |   9.84 | 0.58 | 0.80 |   6.57 | 2.73 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.52 | 0.8 ( 0 )     | 2.9 ( 0.5 )      | 2.75 ( 0.07 ) |   6.15 | 0.33 | 0.80 |   2.90 | 2.75 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.70 | 0.8 ( 0 )     | 1.88 ( 0.19 )    | 2.79 ( 0.05 ) |   5.09 | 0.3  | 0.80 |   1.88 | 2.79 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.10 | 0.87 ( 0.01 ) | 15.93 ( 1 )      | 2.88 ( 0.1 )  |  19.05 | 0.81 | 0.87 |  15.93 | 2.88 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.14 | 0.81 ( 0 )    | 14.34 ( 1.4 )    | 2.75 ( 0.1 )  |  17.59 | 0.73 | 0.81 |  14.34 | 2.75 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.56 | 0.8 ( 0 )     | 3.51 ( 0.44 )    | 2.86 ( 0.04 ) |   6.65 | 0.4  | 0.80 |   3.51 | 2.86 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.57 | 0.8 ( 0 )     | 3.18 ( 0.59 )    | 2.86 ( 0.04 ) |   6.32 | 0.34 | 0.80 |   3.18 | 2.86 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.02 | 0.99 ( 0 )    | 95.52 ( 6.42 )   | 2.37 ( 0.15 ) |  99.15 | 0.95 | 0.99 |  95.52 | 2.37 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.04 | 0.88 ( 0 )    | 53.08 ( 3.93 )   | 2.8 ( 0.12 )  |  56.28 | 0.93 | 0.88 |  53.08 | 2.80 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.40 | 0.79 ( 0 )    | 6.75 ( 0.73 )    | 2.76 ( 0.07 ) |   9.99 | 0.56 | 0.79 |   6.75 | 2.76 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.73 | 0.8 ( 0 )     | 2.48 ( 0.28 )    | 2.79 ( 0.05 ) |   5.69 | 0.37 | 0.80 |   2.48 | 2.79 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.01 | 1 ( 0 )       | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 205.64 | 0.98 | 1.00 | 201.98 | 2.34 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.03 | 0.95 ( 0 )    | 70.81 ( 4.4 )    | 3.05 ( 0.12 ) |  73.76 | 0.96 | 0.95 |  70.81 | 3.05 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.38 | 0.79 ( 0 )    | 7.71 ( 1.09 )    | 2.85 ( 0.06 ) |  10.86 | 0.59 | 0.79 |   7.71 | 2.85 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.76 | 0.8 ( 0 )     | 2.41 ( 0.17 )    | 2.77 ( 0.07 ) |   5.64 | 0.39 | 0.80 |   2.41 | 2.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "1    50   50 0.1   1.00 0.13 0.97 ( 0 )    14.84 ( 0.9 )    0.93 ( 0.09 )\n",
+       "2   100   50 0.1   0.50 0.33 0.93 ( 0 )    7.19 ( 0.43 )    0.49 ( 0.07 )\n",
+       "3   500   50 0.1   0.10 0.70 0.9 ( 0 )     1.93 ( 0.34 )    0.16 ( 0.04 )\n",
+       "4  1000   50 0.1   0.05 0.79 0.9 ( 0 )     1.37 ( 0.42 )    0.01 ( 0.01 )\n",
+       "5    50  100 0.1   2.00 0.07 0.99 ( 0 )    36.64 ( 2.16 )   0.65 ( 0.07 )\n",
+       "6   100  100 0.1   1.00 0.20 0.94 ( 0 )    16.16 ( 1.22 )   0.47 ( 0.06 )\n",
+       "7   500  100 0.1   0.20 0.36 0.9 ( 0 )     8.64 ( 1.35 )    0.08 ( 0.03 )\n",
+       "8  1000  100 0.1   0.10 0.47 0.9 ( 0 )     5.83 ( 1.4 )     0.02 ( 0.01 )\n",
+       "9    50  500 0.1  10.00 0.01 1 ( 0 )       248.06 ( 7.56 )  0.56 ( 0.07 )\n",
+       "10  100  500 0.1   5.00 0.03 1 ( 0 )       132.04 ( 7.47 )  0.33 ( 0.05 )\n",
+       "11  500  500 0.1   1.00 0.34 0.91 ( 0 )    12.55 ( 1.81 )   0.04 ( 0.02 )\n",
+       "12 1000  500 0.1   0.50 0.69 0.9 ( 0 )     3.02 ( 0.6 )     0.01 ( 0.01 )\n",
+       "13   50 1000 0.1  20.00 0.01 1 ( 0 )       398.98 ( 6.9 )   0.8 ( 0.08 ) \n",
+       "14  100 1000 0.1  10.00 0.02 1 ( 0 )       296.19 ( 8.29 )  0.35 ( 0.05 )\n",
+       "15  500 1000 0.1   2.00 0.39 0.9 ( 0 )     10.2 ( 0.98 )    0.1 ( 0.03 ) \n",
+       "16 1000 1000 0.1   1.00 0.83 0.9 ( 0 )     1.76 ( 0.26 )    0.02 ( 0.01 )\n",
+       "17   50   50 0.3   1.00 0.14 0.96 ( 0 )    13.96 ( 0.83 )   1.01 ( 0.09 )\n",
+       "18  100   50 0.3   0.50 0.25 0.92 ( 0 )    9.38 ( 0.67 )    0.68 ( 0.07 )\n",
+       "19  500   50 0.3   0.10 0.44 0.89 ( 0 )    5.43 ( 0.9 )     0.14 ( 0.03 )\n",
+       "20 1000   50 0.3   0.05 0.48 0.88 ( 0 )    5.03 ( 1 )       0.09 ( 0.03 )\n",
+       "21   50  100 0.3   2.00 0.06 0.99 ( 0 )    36.33 ( 2.24 )   0.9 ( 0.09 ) \n",
+       "22  100  100 0.3   1.00 0.19 0.93 ( 0 )    16.08 ( 0.95 )   0.75 ( 0.07 )\n",
+       "23  500  100 0.3   0.20 0.37 0.89 ( 0 )    8.66 ( 1.15 )    0.11 ( 0.03 )\n",
+       "24 1000  100 0.3   0.10 0.39 0.88 ( 0 )    8.45 ( 1.17 )    0.12 ( 0.03 )\n",
+       "25   50  500 0.3  10.00 0.01 1 ( 0 )       228.37 ( 7.07 )  0.6 ( 0.08 ) \n",
+       "26  100  500 0.3   5.00 0.04 0.99 ( 0 )    114.53 ( 5.78 )  0.43 ( 0.06 )\n",
+       "27  500  500 0.3   1.00 0.31 0.89 ( 0 )    13.64 ( 2.08 )   0.2 ( 0.04 ) \n",
+       "28 1000  500 0.3   0.50 0.54 0.88 ( 0 )    6.3 ( 1.15 )     0.09 ( 0.04 )\n",
+       "29   50 1000 0.3  20.00 0.01 1 ( 0 )       412.73 ( 7.91 )  0.88 ( 0.1 ) \n",
+       "30  100 1000 0.3  10.00 0.02 1 ( 0 )       279.68 ( 7.94 )  0.45 ( 0.07 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮                ⋮            \n",
+       "51  500   50 0.7   0.10 0.67 0.86 ( 0 )    1.32 ( 0.25 )    2.42 ( 0.06 )\n",
+       "52 1000   50 0.7   0.05 0.74 0.85 ( 0 )    0.84 ( 0.14 )    2.48 ( 0.06 )\n",
+       "53   50  100 0.7   2.00 0.07 0.96 ( 0 )    24.54 ( 1.62 )   2.08 ( 0.11 )\n",
+       "54  100  100 0.7   1.00 0.16 0.89 ( 0 )    14.69 ( 1.28 )   1.85 ( 0.08 )\n",
+       "55  500  100 0.7   0.20 0.55 0.86 ( 0 )    2.19 ( 0.64 )    2.33 ( 0.07 )\n",
+       "56 1000  100 0.7   0.10 0.81 0.85 ( 0 )    0.51 ( 0.1 )     2.56 ( 0.05 )\n",
+       "57   50  500 0.7  10.00 0.02 1 ( 0 )       161.23 ( 7.83 )  1.29 ( 0.1 ) \n",
+       "58  100  500 0.7   5.00 0.06 0.96 ( 0 )    60.05 ( 4.29 )   1.63 ( 0.12 )\n",
+       "59  500  500 0.7   1.00 0.50 0.85 ( 0 )    3.12 ( 1.31 )    2.68 ( 0.07 )\n",
+       "60 1000  500 0.7   0.50 0.89 0.85 ( 0 )    0.39 ( 0.06 )    2.88 ( 0.04 )\n",
+       "61   50 1000 0.7  20.00 0.01 1 ( 0 )       363.91 ( 11.45 ) 1.39 ( 0.1 ) \n",
+       "62  100 1000 0.7  10.00 0.03 0.99 ( 0 )    145.9 ( 7.62 )   1.24 ( 0.11 )\n",
+       "63  500 1000 0.7   2.00 0.61 0.85 ( 0 )    2.38 ( 0.4 )     2.7 ( 0.06 ) \n",
+       "64 1000 1000 0.7   1.00 0.87 0.85 ( 0 )    0.65 ( 0.08 )    2.87 ( 0.04 )\n",
+       "65   50   50 0.9   1.00 0.14 0.84 ( 0.01 ) 9.98 ( 0.78 )    2.68 ( 0.11 )\n",
+       "66  100   50 0.9   0.50 0.25 0.8 ( 0 )     6.57 ( 0.7 )     2.73 ( 0.09 )\n",
+       "67  500   50 0.9   0.10 0.52 0.8 ( 0 )     2.9 ( 0.5 )      2.75 ( 0.07 )\n",
+       "68 1000   50 0.9   0.05 0.70 0.8 ( 0 )     1.88 ( 0.19 )    2.79 ( 0.05 )\n",
+       "69   50  100 0.9   2.00 0.10 0.87 ( 0.01 ) 15.93 ( 1 )      2.88 ( 0.1 ) \n",
+       "70  100  100 0.9   1.00 0.14 0.81 ( 0 )    14.34 ( 1.4 )    2.75 ( 0.1 ) \n",
+       "71  500  100 0.9   0.20 0.56 0.8 ( 0 )     3.51 ( 0.44 )    2.86 ( 0.04 )\n",
+       "72 1000  100 0.9   0.10 0.57 0.8 ( 0 )     3.18 ( 0.59 )    2.86 ( 0.04 )\n",
+       "73   50  500 0.9  10.00 0.02 0.99 ( 0 )    95.52 ( 6.42 )   2.37 ( 0.15 )\n",
+       "74  100  500 0.9   5.00 0.04 0.88 ( 0 )    53.08 ( 3.93 )   2.8 ( 0.12 ) \n",
+       "75  500  500 0.9   1.00 0.40 0.79 ( 0 )    6.75 ( 0.73 )    2.76 ( 0.07 )\n",
+       "76 1000  500 0.9   0.50 0.73 0.8 ( 0 )     2.48 ( 0.28 )    2.79 ( 0.05 )\n",
+       "77   50 1000 0.9  20.00 0.01 1 ( 0 )       201.98 ( 10.15 ) 2.34 ( 0.16 )\n",
+       "78  100 1000 0.9  10.00 0.03 0.95 ( 0 )    70.81 ( 4.4 )    3.05 ( 0.12 )\n",
+       "79  500 1000 0.9   2.00 0.38 0.79 ( 0 )    7.71 ( 1.09 )    2.85 ( 0.06 )\n",
+       "80 1000 1000 0.9   1.00 0.76 0.8 ( 0 )     2.41 ( 0.17 )    2.77 ( 0.07 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   19.91     0.7  0.97      14.84  0.93   \n",
+       "2   12.70     0.53 0.93       7.19  0.49   \n",
+       "3    7.77     0.16 0.90       1.93  0.16   \n",
+       "4    7.36     0.1  0.90       1.37  0.01   \n",
+       "5   41.99     0.84 0.99      36.64  0.65   \n",
+       "6   21.69     0.7  0.94      16.16  0.47   \n",
+       "7   14.56     0.37 0.90       8.64  0.08   \n",
+       "8   11.81     0.22 0.90       5.83  0.02   \n",
+       "9  253.50     0.98 1.00     248.06  0.56   \n",
+       "10 137.71     0.95 1.00     132.04  0.33   \n",
+       "11  18.51     0.52 0.91      12.55  0.04   \n",
+       "12   9.01     0.22 0.90       3.02  0.01   \n",
+       "13 404.18     0.99 1.00     398.98  0.80   \n",
+       "14 301.84     0.98 1.00     296.19  0.35   \n",
+       "15  16.10     0.52 0.90      10.20  0.10   \n",
+       "16   7.74     0.18 0.90       1.76  0.02   \n",
+       "17  18.95     0.7  0.96      13.96  1.01   \n",
+       "18  14.70     0.58 0.92       9.38  0.68   \n",
+       "19  11.29     0.3  0.89       5.43  0.14   \n",
+       "20  10.94     0.23 0.88       5.03  0.09   \n",
+       "21  41.43     0.85 0.99      36.33  0.90   \n",
+       "22  21.33     0.72 0.93      16.08  0.75   \n",
+       "23  14.55     0.42 0.89       8.66  0.11   \n",
+       "24  14.33     0.39 0.88       8.45  0.12   \n",
+       "25 233.77     0.97 1.00     228.37  0.60   \n",
+       "26 120.10     0.94 0.99     114.53  0.43   \n",
+       "27  19.44     0.57 0.89      13.64  0.20   \n",
+       "28  12.21     0.38 0.88       6.30  0.09   \n",
+       "29 417.85     0.99 1.00     412.73  0.88   \n",
+       "30 285.23     0.98 1.00     279.68  0.45   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51   4.90     0.19 0.86       1.32  2.42   \n",
+       "52   4.36     0.13 0.85       0.84  2.48   \n",
+       "53  28.46     0.84 0.96      24.54  2.08   \n",
+       "54  18.84     0.71 0.89      14.69  1.85   \n",
+       "55   5.86     0.18 0.86       2.19  2.33   \n",
+       "56   3.95     0.09 0.85       0.51  2.56   \n",
+       "57 165.94     0.96 1.00     161.23  1.29   \n",
+       "58  64.42     0.92 0.96      60.05  1.63   \n",
+       "59   6.44     0.19 0.85       3.12  2.68   \n",
+       "60   3.51     0.09 0.85       0.39  2.88   \n",
+       "61 368.52     0.99 1.00     363.91  1.39   \n",
+       "62 150.66     0.96 0.99     145.90  1.24   \n",
+       "63   5.68     0.27 0.85       2.38  2.70   \n",
+       "64   3.78     0.14 0.85       0.65  2.87   \n",
+       "65  13.30     0.7  0.84       9.98  2.68   \n",
+       "66   9.84     0.58 0.80       6.57  2.73   \n",
+       "67   6.15     0.33 0.80       2.90  2.75   \n",
+       "68   5.09     0.3  0.80       1.88  2.79   \n",
+       "69  19.05     0.81 0.87      15.93  2.88   \n",
+       "70  17.59     0.73 0.81      14.34  2.75   \n",
+       "71   6.65     0.4  0.80       3.51  2.86   \n",
+       "72   6.32     0.34 0.80       3.18  2.86   \n",
+       "73  99.15     0.95 0.99      95.52  2.37   \n",
+       "74  56.28     0.93 0.88      53.08  2.80   \n",
+       "75   9.99     0.56 0.79       6.75  2.76   \n",
+       "76   5.69     0.37 0.80       2.48  2.79   \n",
+       "77 205.64     0.98 1.00     201.98  2.34   \n",
+       "78  73.76     0.96 0.95      70.81  3.05   \n",
+       "79  10.86     0.59 0.79       7.71  2.85   \n",
+       "80   5.64     0.39 0.80       2.41  2.77   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_GencompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.1_sim_block_lasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.1_sim_block_lasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..484e80b
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.1_sim_block_lasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,971 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
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+      "[1] \"indx: 19\"\n",
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+      "[1] \"indx: 21\"\n",
+      "[1] \"indx: 22\"\n",
+      "[1] \"indx: 23\"\n",
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+      "[1] \"indx: 25\"\n",
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+      "[1] \"indx: 27\"\n",
+      "[1] \"indx: 28\"\n",
+      "[1] \"indx: 29\"\n",
+      "[1] \"indx: 30\"\n",
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+      "[1] \"indx: 32\"\n",
+      "[1] \"indx: 33\"\n",
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+      "[1] \"indx: 35\"\n",
+      "[1] \"indx: 36\"\n",
+      "[1] \"indx: 37\"\n",
+      "[1] \"indx: 38\"\n",
+      "[1] \"indx: 39\"\n",
+      "[1] \"indx: 40\"\n",
+      "[1] \"indx: 41\"\n",
+      "[1] \"indx: 42\"\n",
+      "[1] \"indx: 43\"\n",
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+      "[1] \"indx: 46\"\n",
+      "[1] \"indx: 47\"\n",
+      "[1] \"indx: 48\"\n",
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+      "[1] \"indx: 50\"\n",
+      "[1] \"indx: 51\"\n",
+      "[1] \"indx: 52\"\n",
+      "[1] \"indx: 53\"\n",
+      "[1] \"indx: 54\"\n",
+      "[1] \"indx: 55\"\n",
+      "[1] \"indx: 56\"\n",
+      "[1] \"indx: 57\"\n",
+      "[1] \"indx: 58\"\n",
+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 )</td><td>0.59 ( 0.01 )</td><td>0.03</td><td>2.39</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>0.56 ( 0.01 )</td><td>0.11</td><td>2.00</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )</td><td>0.63 ( 0 )</td><td>0.28</td><td>6.52</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 )</td><td>0.64 ( 0 )</td><td>0.39</td><td>7.64</td><td>0.44</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>0.62 ( 0.01 )</td><td>0.02</td><td>3.48</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>0.56 ( 0.01 )</td><td>0.07</td><td>2.02</td><td>0.57</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 2.69 ( 0.14 ) & 5.3 ( 0.07 ) & 0.59 ( 0.01 ) & 0.03 & 2.39 & 0.67\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 0.56 ( 0.01 ) & 0.11 & 2.00 & 0.39\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 4.52 ( 0.29 ) & 3 ( 0.11 ) & 0.63 ( 0 ) & 0.28 & 6.52 & 0.45\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 4.74 ( 0.22 ) & 2.1 ( 0.09 ) & 0.64 ( 0 ) & 0.39 & 7.64 & 0.44\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 0.62 ( 0.01 ) & 0.02 & 3.48 & 0.78\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 0.56 ( 0.01 ) & 0.07 & 2.02 & 0.57\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 2.69 ( 0.14 ) | 5.3 ( 0.07 ) | 0.59 ( 0.01 ) | 0.03 | 2.39 | 0.67 |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 0.56 ( 0.01 ) | 0.11 | 2.00 | 0.39 |\n",
+       "| 3 | 500 | 50 | 0.1 | 4.52 ( 0.29 ) | 3 ( 0.11 ) | 0.63 ( 0 ) | 0.28 | 6.52 | 0.45 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 4.74 ( 0.22 ) | 2.1 ( 0.09 ) | 0.64 ( 0 ) | 0.39 | 7.64 | 0.44 |\n",
+       "| 5 | 50 | 100 | 0.1 | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 0.62 ( 0.01 ) | 0.02 | 3.48 | 0.78 |\n",
+       "| 6 | 100 | 100 | 0.1 | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 0.56 ( 0.01 ) | 0.07 | 2.02 | 0.57 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 2.69 ( 0.14 ) 5.3 ( 0.07 )  0.59 ( 0.01 ) 0.03 2.39       0.67\n",
+       "2 100  50  0.1 2.02 ( 0.15 ) 5.02 ( 0.08 ) 0.56 ( 0.01 ) 0.11 2.00       0.39\n",
+       "3 500  50  0.1 4.52 ( 0.29 ) 3 ( 0.11 )    0.63 ( 0 )    0.28 6.52       0.45\n",
+       "4 1000 50  0.1 4.74 ( 0.22 ) 2.1 ( 0.09 )  0.64 ( 0 )    0.39 7.64       0.44\n",
+       "5 50   100 0.1 3.82 ( 0.16 ) 5.34 ( 0.07 ) 0.62 ( 0.01 ) 0.02 3.48       0.78\n",
+       "6 100  100 0.1 2.29 ( 0.19 ) 5.27 ( 0.08 ) 0.56 ( 0.01 ) 0.07 2.02       0.57"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.91 ( 0.78 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.19</td><td>29.91</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>14.12 ( 0.43 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.31</td><td>19.12</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>0.64 ( 0.01 )</td><td>0.07</td><td>11.59</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>0.74 ( 0.01 )</td><td>0.19</td><td>13.71</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>34.98 ( 1.04 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.14</td><td>39.98</td><td>0.83</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>22.21 ( 0.73 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.22</td><td>27.21</td><td>0.76</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.91 ( 0.78 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.19 & 29.91 & 0.78\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 14.12 ( 0.43 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.31 & 19.12 & 0.67\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 0.64 ( 0.01 ) & 0.07 & 11.59 & 0.84\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 0.74 ( 0.01 ) & 0.19 & 13.71 & 0.65\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 34.98 ( 1.04 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.14 & 39.98 & 0.83\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 22.21 ( 0.73 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.22 & 27.21 & 0.76\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.91 ( 0.78 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.19 | 29.91 | 0.78 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 14.12 ( 0.43 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.31 | 19.12 | 0.67 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 0.64 ( 0.01 ) | 0.07 | 11.59 | 0.84 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 0.74 ( 0.01 ) | 0.19 | 13.71 | 0.65 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 34.98 ( 1.04 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.14 | 39.98 | 0.83 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 22.21 ( 0.73 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.22 | 27.21 | 0.76 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 24.91 ( 0.78 ) 0 ( 0 )       0.91 ( 0 )    0.19 29.91     \n",
+       "76 1000 500  0.9 14.12 ( 0.43 ) 0 ( 0 )       0.92 ( 0 )    0.31 19.12     \n",
+       "77 50   1000 0.9 10.81 ( 0.29 ) 4.22 ( 0.08 ) 0.64 ( 0.01 ) 0.07 11.59     \n",
+       "78 100  1000 0.9 11.15 ( 0.75 ) 2.44 ( 0.11 ) 0.74 ( 0.01 ) 0.19 13.71     \n",
+       "79 500  1000 0.9 34.98 ( 1.04 ) 0 ( 0 )       0.91 ( 0 )    0.14 39.98     \n",
+       "80 1000 1000 0.9 22.21 ( 0.73 ) 0 ( 0 )       0.92 ( 0 )    0.22 27.21     \n",
+       "   FDR \n",
+       "75 0.78\n",
+       "76 0.67\n",
+       "77 0.84\n",
+       "78 0.65\n",
+       "79 0.83\n",
+       "80 0.76"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 ) </td><td>2.39</td><td>0.67</td><td>0.59</td><td>2.69</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>2.00</td><td>0.39</td><td>0.56</td><td>2.02</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )   </td><td>6.52</td><td>0.45</td><td>0.63</td><td>4.52</td><td>3.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 ) </td><td>7.64</td><td>0.44</td><td>0.64</td><td>4.74</td><td>2.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>3.48</td><td>0.78</td><td>0.62</td><td>3.82</td><td>5.34</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>2.02</td><td>0.57</td><td>0.56</td><td>2.29</td><td>5.27</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 ) & 5.3 ( 0.07 )  & 2.39 & 0.67 & 0.59 & 2.69 & 5.30\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 2.00 & 0.39 & 0.56 & 2.02 & 5.02\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 ) & 3 ( 0.11 )    & 6.52 & 0.45 & 0.63 & 4.52 & 3.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 ) & 2.1 ( 0.09 )  & 7.64 & 0.44 & 0.64 & 4.74 & 2.10\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 3.48 & 0.78 & 0.62 & 3.82 & 5.34\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 2.02 & 0.57 & 0.56 & 2.29 & 5.27\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 ) | 5.3 ( 0.07 )  | 2.39 | 0.67 | 0.59 | 2.69 | 5.30 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 2.00 | 0.39 | 0.56 | 2.02 | 5.02 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 ) | 3 ( 0.11 )    | 6.52 | 0.45 | 0.63 | 4.52 | 3.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 ) | 2.1 ( 0.09 )  | 7.64 | 0.44 | 0.64 | 4.74 | 2.10 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 3.48 | 0.78 | 0.62 | 3.82 | 5.34 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 2.02 | 0.57 | 0.56 | 2.29 | 5.27 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.03 0.59 ( 0.01 ) 2.69 ( 0.14 ) 5.3 ( 0.07 )  2.39      \n",
+       "2  100  50 0.1  0.50  0.11 0.56 ( 0.01 ) 2.02 ( 0.15 ) 5.02 ( 0.08 ) 2.00      \n",
+       "3  500  50 0.1  0.10  0.28 0.63 ( 0 )    4.52 ( 0.29 ) 3 ( 0.11 )    6.52      \n",
+       "4 1000  50 0.1  0.05  0.39 0.64 ( 0 )    4.74 ( 0.22 ) 2.1 ( 0.09 )  7.64      \n",
+       "5   50 100 0.1  2.00  0.02 0.62 ( 0.01 ) 3.82 ( 0.16 ) 5.34 ( 0.07 ) 3.48      \n",
+       "6  100 100 0.1  1.00  0.07 0.56 ( 0.01 ) 2.29 ( 0.19 ) 5.27 ( 0.08 ) 2.02      \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.67 0.59     2.69    5.30   \n",
+       "2 0.39 0.56     2.02    5.02   \n",
+       "3 0.45 0.63     4.52    3.00   \n",
+       "4 0.44 0.64     4.74    2.10   \n",
+       "5 0.78 0.62     3.82    5.34   \n",
+       "6 0.57 0.56     2.29    5.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.19</td><td>0.91 ( 0 )   </td><td>24.91 ( 0.78 )</td><td>0 ( 0 )      </td><td>29.91</td><td>0.78</td><td>0.91</td><td>24.91</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.31</td><td>0.92 ( 0 )   </td><td>14.12 ( 0.43 )</td><td>0 ( 0 )      </td><td>19.12</td><td>0.67</td><td>0.92</td><td>14.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.07</td><td>0.64 ( 0.01 )</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>11.59</td><td>0.84</td><td>0.64</td><td>10.81</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.19</td><td>0.74 ( 0.01 )</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>13.71</td><td>0.65</td><td>0.74</td><td>11.15</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.14</td><td>0.91 ( 0 )   </td><td>34.98 ( 1.04 )</td><td>0 ( 0 )      </td><td>39.98</td><td>0.83</td><td>0.91</td><td>34.98</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.22</td><td>0.92 ( 0 )   </td><td>22.21 ( 0.73 )</td><td>0 ( 0 )      </td><td>27.21</td><td>0.76</td><td>0.92</td><td>22.21</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.19 & 0.91 ( 0 )    & 24.91 ( 0.78 ) & 0 ( 0 )       & 29.91 & 0.78 & 0.91 & 24.91 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.31 & 0.92 ( 0 )    & 14.12 ( 0.43 ) & 0 ( 0 )       & 19.12 & 0.67 & 0.92 & 14.12 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.07 & 0.64 ( 0.01 ) & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 11.59 & 0.84 & 0.64 & 10.81 & 4.22\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.19 & 0.74 ( 0.01 ) & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 13.71 & 0.65 & 0.74 & 11.15 & 2.44\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.14 & 0.91 ( 0 )    & 34.98 ( 1.04 ) & 0 ( 0 )       & 39.98 & 0.83 & 0.91 & 34.98 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.22 & 0.92 ( 0 )    & 22.21 ( 0.73 ) & 0 ( 0 )       & 27.21 & 0.76 & 0.92 & 22.21 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.19 | 0.91 ( 0 )    | 24.91 ( 0.78 ) | 0 ( 0 )       | 29.91 | 0.78 | 0.91 | 24.91 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.31 | 0.92 ( 0 )    | 14.12 ( 0.43 ) | 0 ( 0 )       | 19.12 | 0.67 | 0.92 | 14.12 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.07 | 0.64 ( 0.01 ) | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 11.59 | 0.84 | 0.64 | 10.81 | 4.22 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.19 | 0.74 ( 0.01 ) | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 13.71 | 0.65 | 0.74 | 11.15 | 2.44 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.14 | 0.91 ( 0 )    | 34.98 ( 1.04 ) | 0 ( 0 )       | 39.98 | 0.83 | 0.91 | 34.98 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.22 | 0.92 ( 0 )    | 22.21 ( 0.73 ) | 0 ( 0 )       | 27.21 | 0.76 | 0.92 | 22.21 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.19 0.91 ( 0 )    24.91 ( 0.78 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.31 0.92 ( 0 )    14.12 ( 0.43 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.07 0.64 ( 0.01 ) 10.81 ( 0.29 ) 4.22 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.19 0.74 ( 0.01 ) 11.15 ( 0.75 ) 2.44 ( 0.11 )\n",
+       "79  500 1000 0.9   2.0  0.14 0.91 ( 0 )    34.98 ( 1.04 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.22 0.92 ( 0 )    22.21 ( 0.73 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 29.91      0.78 0.91     24.91   0.00   \n",
+       "76 19.12      0.67 0.92     14.12   0.00   \n",
+       "77 11.59      0.84 0.64     10.81   4.22   \n",
+       "78 13.71      0.65 0.74     11.15   2.44   \n",
+       "79 39.98      0.83 0.91     34.98   0.00   \n",
+       "80 27.21      0.76 0.92     22.21   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 ) </td><td>5.3 ( 0.07 ) </td><td> 2.39</td><td>0.67</td><td>0.59</td><td> 2.69</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 ) </td><td>5.02 ( 0.08 )</td><td> 2.00</td><td>0.39</td><td>0.56</td><td> 2.02</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 ) </td><td>3 ( 0.11 )   </td><td> 6.52</td><td>0.45</td><td>0.63</td><td> 4.52</td><td>3.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 ) </td><td>2.1 ( 0.09 ) </td><td> 7.64</td><td>0.44</td><td>0.64</td><td> 4.74</td><td>2.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 ) </td><td>5.34 ( 0.07 )</td><td> 3.48</td><td>0.78</td><td>0.62</td><td> 3.82</td><td>5.34</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 ) </td><td>5.27 ( 0.08 )</td><td> 2.02</td><td>0.57</td><td>0.56</td><td> 2.29</td><td>5.27</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.23</td><td>0.61 ( 0 )   </td><td>5.54 ( 0.52 ) </td><td>3.27 ( 0.12 )</td><td> 7.27</td><td>0.47</td><td>0.61</td><td> 5.54</td><td>3.27</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.27</td><td>0.64 ( 0 )   </td><td>7.69 ( 0.42 ) </td><td>2.25 ( 0.1 ) </td><td>10.44</td><td>0.56</td><td>0.64</td><td> 7.69</td><td>2.25</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.01</td><td>0.6 ( 0.01 ) </td><td>8.69 ( 0.24 ) </td><td>5.55 ( 0.06 )</td><td> 8.14</td><td>0.94</td><td>0.60</td><td> 8.69</td><td>5.55</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.03</td><td>0.56 ( 0.01 )</td><td>4.39 ( 0.34 ) </td><td>5.4 ( 0.07 ) </td><td> 3.99</td><td>0.82</td><td>0.56</td><td> 4.39</td><td>5.40</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.27</td><td>0.61 ( 0.01 )</td><td>3.84 ( 0.28 ) </td><td>3.93 ( 0.08 )</td><td> 4.91</td><td>0.47</td><td>0.61</td><td> 3.84</td><td>3.93</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.20</td><td>0.63 ( 0 )   </td><td>9.87 ( 1.4 )  </td><td>3.18 ( 0.09 )</td><td>11.69</td><td>0.49</td><td>0.63</td><td> 9.87</td><td>3.18</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.00</td><td>0.62 ( 0.01 )</td><td>10.42 ( 0.22 )</td><td>5.56 ( 0.06 )</td><td> 9.86</td><td>0.95</td><td>0.62</td><td>10.42</td><td>5.56</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.01</td><td>0.56 ( 0.01 )</td><td>5.93 ( 0.28 ) </td><td>5.53 ( 0.06 )</td><td> 5.40</td><td>0.9 </td><td>0.56</td><td> 5.93</td><td>5.53</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.24</td><td>0.62 ( 0.01 )</td><td>4.22 ( 0.48 ) </td><td>4.05 ( 0.09 )</td><td> 5.17</td><td>0.46</td><td>0.62</td><td> 4.22</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.33</td><td>0.63 ( 0 )   </td><td>5.03 ( 0.78 ) </td><td>3.35 ( 0.06 )</td><td> 6.68</td><td>0.42</td><td>0.63</td><td> 5.03</td><td>3.35</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.17</td><td>0.65 ( 0.01 )</td><td>3.87 ( 0.23 ) </td><td>3.68 ( 0.12 )</td><td> 5.19</td><td>0.5 </td><td>0.65</td><td> 3.87</td><td>3.68</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>6.48 ( 0.32 ) </td><td>1.13 ( 0.11 )</td><td>10.35</td><td>0.49</td><td>0.78</td><td> 6.48</td><td>1.13</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>0.85 ( 0 )   </td><td>6.84 ( 0.26 ) </td><td>0 ( 0 )      </td><td>11.84</td><td>0.47</td><td>0.85</td><td> 6.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.65</td><td>0.86 ( 0 )   </td><td>3.69 ( 0.19 ) </td><td>0 ( 0 )      </td><td> 8.69</td><td>0.28</td><td>0.86</td><td> 3.69</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.14</td><td>0.63 ( 0.01 )</td><td>5.12 ( 0.25 ) </td><td>3.91 ( 0.1 ) </td><td> 6.21</td><td>0.63</td><td>0.63</td><td> 5.12</td><td>3.91</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>7.62 ( 0.48 ) </td><td>1.8 ( 0.12 ) </td><td>10.82</td><td>0.53</td><td>0.74</td><td> 7.62</td><td>1.80</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.36</td><td>0.86 ( 0 )   </td><td>10.19 ( 0.4 ) </td><td>0 ( 0 )      </td><td>15.19</td><td>0.57</td><td>0.86</td><td>10.19</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.52</td><td>0.86 ( 0 )   </td><td>6 ( 0.27 )    </td><td>0 ( 0 )      </td><td>11.00</td><td>0.42</td><td>0.86</td><td> 6.00</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.03</td><td>0.61 ( 0.01 )</td><td>10.46 ( 0.31 )</td><td>4.69 ( 0.08 )</td><td>10.77</td><td>0.87</td><td>0.61</td><td>10.46</td><td>4.69</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.17</td><td>0.66 ( 0.01 )</td><td>8.6 ( 0.65 )  </td><td>3.19 ( 0.12 )</td><td>10.41</td><td>0.64</td><td>0.66</td><td> 8.60</td><td>3.19</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.85 ( 0 )   </td><td>25.12 ( 1.08 )</td><td>0 ( 0 )      </td><td>30.12</td><td>0.77</td><td>0.85</td><td>25.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.86 ( 0 )   </td><td>18.48 ( 0.73 )</td><td>0 ( 0 )      </td><td>23.48</td><td>0.72</td><td>0.86</td><td>18.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>0.6 ( 0.01 ) </td><td>12.08 ( 0.26 )</td><td>4.97 ( 0.08 )</td><td>12.11</td><td>0.91</td><td>0.60</td><td>12.08</td><td>4.97</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.13</td><td>0.65 ( 0.01 )</td><td>9 ( 0.66 )    </td><td>3.58 ( 0.12 )</td><td>10.42</td><td>0.72</td><td>0.65</td><td> 9.00</td><td>3.58</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.49</td><td>0.91 ( 0 )   </td><td>5.84 ( 0.26 ) </td><td>0 ( 0 )      </td><td>10.84</td><td>0.42</td><td>0.91</td><td> 5.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.71</td><td>0.92 ( 0 )   </td><td>3.03 ( 0.15 ) </td><td>0 ( 0 )      </td><td> 8.03</td><td>0.23</td><td>0.92</td><td> 3.03</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.21</td><td>0.69 ( 0.01 )</td><td>5.07 ( 0.25 ) </td><td>3.13 ( 0.1 ) </td><td> 6.94</td><td>0.55</td><td>0.69</td><td> 5.07</td><td>3.13</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.28</td><td>0.82 ( 0.01 )</td><td>9.65 ( 0.48 ) </td><td>0.97 ( 0.1 ) </td><td>13.68</td><td>0.59</td><td>0.82</td><td> 9.65</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.36</td><td>0.91 ( 0 )   </td><td>10.19 ( 0.37 )</td><td>0 ( 0 )      </td><td>15.19</td><td>0.58</td><td>0.91</td><td>10.19</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.59</td><td>0.92 ( 0 )   </td><td>4.81 ( 0.19 ) </td><td>0 ( 0 )      </td><td> 9.81</td><td>0.37</td><td>0.92</td><td> 4.81</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.09</td><td>0.66 ( 0.01 )</td><td>9.13 ( 0.25 ) </td><td>4.14 ( 0.08 )</td><td> 9.99</td><td>0.81</td><td>0.66</td><td> 9.13</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.21</td><td>0.75 ( 0.01 )</td><td>9.59 ( 0.72 ) </td><td>2.44 ( 0.11 )</td><td>12.15</td><td>0.61</td><td>0.75</td><td> 9.59</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.18</td><td>0.9 ( 0 )    </td><td>26.2 ( 0.86 ) </td><td>0 ( 0 )      </td><td>31.20</td><td>0.79</td><td>0.90</td><td>26.20</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.6 ( 0.55 ) </td><td>0 ( 0 )      </td><td>20.60</td><td>0.68</td><td>0.91</td><td>15.60</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.05</td><td>0.64 ( 0.01 )</td><td>11.44 ( 0.28 )</td><td>4.51 ( 0.08 )</td><td>11.93</td><td>0.87</td><td>0.64</td><td>11.44</td><td>4.51</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.18</td><td>0.71 ( 0.01 )</td><td>10.03 ( 0.71 )</td><td>2.95 ( 0.1 ) </td><td>12.08</td><td>0.65</td><td>0.71</td><td>10.03</td><td>2.95</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.14</td><td>0.9 ( 0 )    </td><td>34.85 ( 1.14 )</td><td>0 ( 0 )      </td><td>39.85</td><td>0.83</td><td>0.90</td><td>34.85</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.20</td><td>0.91 ( 0 )   </td><td>24.83 ( 0.82 )</td><td>0 ( 0 )      </td><td>29.83</td><td>0.78</td><td>0.91</td><td>24.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.30</td><td>0.73 ( 0.01 )</td><td>4.4 ( 0.26 )  </td><td>2.55 ( 0.11 )</td><td> 6.85</td><td>0.44</td><td>0.73</td><td> 4.40</td><td>2.55</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.14 ( 0.35 ) </td><td>0.42 ( 0.06 )</td><td>12.72</td><td>0.53</td><td>0.85</td><td> 8.14</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.54</td><td>0.92 ( 0 )   </td><td>5.15 ( 0.22 ) </td><td>0 ( 0 )      </td><td>10.15</td><td>0.38</td><td>0.92</td><td> 5.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.76</td><td>0.92 ( 0 )   </td><td>2.61 ( 0.13 ) </td><td>0 ( 0 )      </td><td> 7.61</td><td>0.19</td><td>0.92</td><td> 2.61</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.23</td><td>0.7 ( 0.01 ) </td><td>4.84 ( 0.24 ) </td><td>3.3 ( 0.09 ) </td><td> 6.54</td><td>0.54</td><td>0.70</td><td> 4.84</td><td>3.30</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.28</td><td>0.81 ( 0.01 )</td><td>9.92 ( 0.51 ) </td><td>0.87 ( 0.08 )</td><td>14.05</td><td>0.57</td><td>0.81</td><td> 9.92</td><td>0.87</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.37</td><td>0.92 ( 0 )   </td><td>9.65 ( 0.35 ) </td><td>0 ( 0 )      </td><td>14.65</td><td>0.57</td><td>0.92</td><td> 9.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.62</td><td>0.93 ( 0 )   </td><td>4.29 ( 0.18 ) </td><td>0 ( 0 )      </td><td> 9.29</td><td>0.33</td><td>0.93</td><td> 4.29</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.08</td><td>0.62 ( 0.01 )</td><td>8.99 ( 0.26 ) </td><td>4.13 ( 0.09 )</td><td> 9.86</td><td>0.8 </td><td>0.62</td><td> 8.99</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.23</td><td>0.74 ( 0.01 )</td><td>8.85 ( 0.62 ) </td><td>2.4 ( 0.12 ) </td><td>11.45</td><td>0.6 </td><td>0.74</td><td> 8.85</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.19</td><td>0.91 ( 0 )   </td><td>24.91 ( 0.78 )</td><td>0 ( 0 )      </td><td>29.91</td><td>0.78</td><td>0.91</td><td>24.91</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.31</td><td>0.92 ( 0 )   </td><td>14.12 ( 0.43 )</td><td>0 ( 0 )      </td><td>19.12</td><td>0.67</td><td>0.92</td><td>14.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.07</td><td>0.64 ( 0.01 )</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>11.59</td><td>0.84</td><td>0.64</td><td>10.81</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.19</td><td>0.74 ( 0.01 )</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>13.71</td><td>0.65</td><td>0.74</td><td>11.15</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.14</td><td>0.91 ( 0 )   </td><td>34.98 ( 1.04 )</td><td>0 ( 0 )      </td><td>39.98</td><td>0.83</td><td>0.91</td><td>34.98</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.22</td><td>0.92 ( 0 )   </td><td>22.21 ( 0.73 )</td><td>0 ( 0 )      </td><td>27.21</td><td>0.76</td><td>0.92</td><td>22.21</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 )  & 5.3 ( 0.07 )  &  2.39 & 0.67 & 0.59 &  2.69 & 5.30\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 )  & 5.02 ( 0.08 ) &  2.00 & 0.39 & 0.56 &  2.02 & 5.02\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 )  & 3 ( 0.11 )    &  6.52 & 0.45 & 0.63 &  4.52 & 3.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 )  & 2.1 ( 0.09 )  &  7.64 & 0.44 & 0.64 &  4.74 & 2.10\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 )  & 5.34 ( 0.07 ) &  3.48 & 0.78 & 0.62 &  3.82 & 5.34\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 )  & 5.27 ( 0.08 ) &  2.02 & 0.57 & 0.56 &  2.29 & 5.27\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.23 & 0.61 ( 0 )    & 5.54 ( 0.52 )  & 3.27 ( 0.12 ) &  7.27 & 0.47 & 0.61 &  5.54 & 3.27\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.27 & 0.64 ( 0 )    & 7.69 ( 0.42 )  & 2.25 ( 0.1 )  & 10.44 & 0.56 & 0.64 &  7.69 & 2.25\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.01 & 0.6 ( 0.01 )  & 8.69 ( 0.24 )  & 5.55 ( 0.06 ) &  8.14 & 0.94 & 0.60 &  8.69 & 5.55\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.03 & 0.56 ( 0.01 ) & 4.39 ( 0.34 )  & 5.4 ( 0.07 )  &  3.99 & 0.82 & 0.56 &  4.39 & 5.40\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.27 & 0.61 ( 0.01 ) & 3.84 ( 0.28 )  & 3.93 ( 0.08 ) &  4.91 & 0.47 & 0.61 &  3.84 & 3.93\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.20 & 0.63 ( 0 )    & 9.87 ( 1.4 )   & 3.18 ( 0.09 ) & 11.69 & 0.49 & 0.63 &  9.87 & 3.18\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.00 & 0.62 ( 0.01 ) & 10.42 ( 0.22 ) & 5.56 ( 0.06 ) &  9.86 & 0.95 & 0.62 & 10.42 & 5.56\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.01 & 0.56 ( 0.01 ) & 5.93 ( 0.28 )  & 5.53 ( 0.06 ) &  5.40 & 0.9  & 0.56 &  5.93 & 5.53\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.24 & 0.62 ( 0.01 ) & 4.22 ( 0.48 )  & 4.05 ( 0.09 ) &  5.17 & 0.46 & 0.62 &  4.22 & 4.05\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.33 & 0.63 ( 0 )    & 5.03 ( 0.78 )  & 3.35 ( 0.06 ) &  6.68 & 0.42 & 0.63 &  5.03 & 3.35\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.17 & 0.65 ( 0.01 ) & 3.87 ( 0.23 )  & 3.68 ( 0.12 ) &  5.19 & 0.5  & 0.65 &  3.87 & 3.68\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.32 & 0.78 ( 0.01 ) & 6.48 ( 0.32 )  & 1.13 ( 0.11 ) & 10.35 & 0.49 & 0.78 &  6.48 & 1.13\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 0.85 ( 0 )    & 6.84 ( 0.26 )  & 0 ( 0 )       & 11.84 & 0.47 & 0.85 &  6.84 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.65 & 0.86 ( 0 )    & 3.69 ( 0.19 )  & 0 ( 0 )       &  8.69 & 0.28 & 0.86 &  3.69 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.14 & 0.63 ( 0.01 ) & 5.12 ( 0.25 )  & 3.91 ( 0.1 )  &  6.21 & 0.63 & 0.63 &  5.12 & 3.91\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.26 & 0.74 ( 0.01 ) & 7.62 ( 0.48 )  & 1.8 ( 0.12 )  & 10.82 & 0.53 & 0.74 &  7.62 & 1.80\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.36 & 0.86 ( 0 )    & 10.19 ( 0.4 )  & 0 ( 0 )       & 15.19 & 0.57 & 0.86 & 10.19 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.52 & 0.86 ( 0 )    & 6 ( 0.27 )     & 0 ( 0 )       & 11.00 & 0.42 & 0.86 &  6.00 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.03 & 0.61 ( 0.01 ) & 10.46 ( 0.31 ) & 4.69 ( 0.08 ) & 10.77 & 0.87 & 0.61 & 10.46 & 4.69\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.17 & 0.66 ( 0.01 ) & 8.6 ( 0.65 )   & 3.19 ( 0.12 ) & 10.41 & 0.64 & 0.66 &  8.60 & 3.19\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.19 & 0.85 ( 0 )    & 25.12 ( 1.08 ) & 0 ( 0 )       & 30.12 & 0.77 & 0.85 & 25.12 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.25 & 0.86 ( 0 )    & 18.48 ( 0.73 ) & 0 ( 0 )       & 23.48 & 0.72 & 0.86 & 18.48 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 0.6 ( 0.01 )  & 12.08 ( 0.26 ) & 4.97 ( 0.08 ) & 12.11 & 0.91 & 0.60 & 12.08 & 4.97\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.13 & 0.65 ( 0.01 ) & 9 ( 0.66 )     & 3.58 ( 0.12 ) & 10.42 & 0.72 & 0.65 &  9.00 & 3.58\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.49 & 0.91 ( 0 )    & 5.84 ( 0.26 )  & 0 ( 0 )       & 10.84 & 0.42 & 0.91 &  5.84 & 0.00\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.71 & 0.92 ( 0 )    & 3.03 ( 0.15 )  & 0 ( 0 )       &  8.03 & 0.23 & 0.92 &  3.03 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.21 & 0.69 ( 0.01 ) & 5.07 ( 0.25 )  & 3.13 ( 0.1 )  &  6.94 & 0.55 & 0.69 &  5.07 & 3.13\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.28 & 0.82 ( 0.01 ) & 9.65 ( 0.48 )  & 0.97 ( 0.1 )  & 13.68 & 0.59 & 0.82 &  9.65 & 0.97\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.36 & 0.91 ( 0 )    & 10.19 ( 0.37 ) & 0 ( 0 )       & 15.19 & 0.58 & 0.91 & 10.19 & 0.00\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.59 & 0.92 ( 0 )    & 4.81 ( 0.19 )  & 0 ( 0 )       &  9.81 & 0.37 & 0.92 &  4.81 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.09 & 0.66 ( 0.01 ) & 9.13 ( 0.25 )  & 4.14 ( 0.08 ) &  9.99 & 0.81 & 0.66 &  9.13 & 4.14\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.21 & 0.75 ( 0.01 ) & 9.59 ( 0.72 )  & 2.44 ( 0.11 ) & 12.15 & 0.61 & 0.75 &  9.59 & 2.44\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.18 & 0.9 ( 0 )     & 26.2 ( 0.86 )  & 0 ( 0 )       & 31.20 & 0.79 & 0.90 & 26.20 & 0.00\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.28 & 0.91 ( 0 )    & 15.6 ( 0.55 )  & 0 ( 0 )       & 20.60 & 0.68 & 0.91 & 15.60 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.05 & 0.64 ( 0.01 ) & 11.44 ( 0.28 ) & 4.51 ( 0.08 ) & 11.93 & 0.87 & 0.64 & 11.44 & 4.51\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.18 & 0.71 ( 0.01 ) & 10.03 ( 0.71 ) & 2.95 ( 0.1 )  & 12.08 & 0.65 & 0.71 & 10.03 & 2.95\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.14 & 0.9 ( 0 )     & 34.85 ( 1.14 ) & 0 ( 0 )       & 39.85 & 0.83 & 0.90 & 34.85 & 0.00\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.20 & 0.91 ( 0 )    & 24.83 ( 0.82 ) & 0 ( 0 )       & 29.83 & 0.78 & 0.91 & 24.83 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.30 & 0.73 ( 0.01 ) & 4.4 ( 0.26 )   & 2.55 ( 0.11 ) &  6.85 & 0.44 & 0.73 &  4.40 & 2.55\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.33 & 0.85 ( 0.01 ) & 8.14 ( 0.35 )  & 0.42 ( 0.06 ) & 12.72 & 0.53 & 0.85 &  8.14 & 0.42\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.54 & 0.92 ( 0 )    & 5.15 ( 0.22 )  & 0 ( 0 )       & 10.15 & 0.38 & 0.92 &  5.15 & 0.00\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.76 & 0.92 ( 0 )    & 2.61 ( 0.13 )  & 0 ( 0 )       &  7.61 & 0.19 & 0.92 &  2.61 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.23 & 0.7 ( 0.01 )  & 4.84 ( 0.24 )  & 3.3 ( 0.09 )  &  6.54 & 0.54 & 0.70 &  4.84 & 3.30\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.28 & 0.81 ( 0.01 ) & 9.92 ( 0.51 )  & 0.87 ( 0.08 ) & 14.05 & 0.57 & 0.81 &  9.92 & 0.87\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.37 & 0.92 ( 0 )    & 9.65 ( 0.35 )  & 0 ( 0 )       & 14.65 & 0.57 & 0.92 &  9.65 & 0.00\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.62 & 0.93 ( 0 )    & 4.29 ( 0.18 )  & 0 ( 0 )       &  9.29 & 0.33 & 0.93 &  4.29 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.08 & 0.62 ( 0.01 ) & 8.99 ( 0.26 )  & 4.13 ( 0.09 ) &  9.86 & 0.8  & 0.62 &  8.99 & 4.13\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.23 & 0.74 ( 0.01 ) & 8.85 ( 0.62 )  & 2.4 ( 0.12 )  & 11.45 & 0.6  & 0.74 &  8.85 & 2.40\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.19 & 0.91 ( 0 )    & 24.91 ( 0.78 ) & 0 ( 0 )       & 29.91 & 0.78 & 0.91 & 24.91 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.31 & 0.92 ( 0 )    & 14.12 ( 0.43 ) & 0 ( 0 )       & 19.12 & 0.67 & 0.92 & 14.12 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.07 & 0.64 ( 0.01 ) & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 11.59 & 0.84 & 0.64 & 10.81 & 4.22\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.19 & 0.74 ( 0.01 ) & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 13.71 & 0.65 & 0.74 & 11.15 & 2.44\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.14 & 0.91 ( 0 )    & 34.98 ( 1.04 ) & 0 ( 0 )       & 39.98 & 0.83 & 0.91 & 34.98 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.22 & 0.92 ( 0 )    & 22.21 ( 0.73 ) & 0 ( 0 )       & 27.21 & 0.76 & 0.92 & 22.21 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 )  | 5.3 ( 0.07 )  |  2.39 | 0.67 | 0.59 |  2.69 | 5.30 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 )  | 5.02 ( 0.08 ) |  2.00 | 0.39 | 0.56 |  2.02 | 5.02 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 )  | 3 ( 0.11 )    |  6.52 | 0.45 | 0.63 |  4.52 | 3.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 )  | 2.1 ( 0.09 )  |  7.64 | 0.44 | 0.64 |  4.74 | 2.10 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 )  | 5.34 ( 0.07 ) |  3.48 | 0.78 | 0.62 |  3.82 | 5.34 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 )  | 5.27 ( 0.08 ) |  2.02 | 0.57 | 0.56 |  2.29 | 5.27 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.23 | 0.61 ( 0 )    | 5.54 ( 0.52 )  | 3.27 ( 0.12 ) |  7.27 | 0.47 | 0.61 |  5.54 | 3.27 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.27 | 0.64 ( 0 )    | 7.69 ( 0.42 )  | 2.25 ( 0.1 )  | 10.44 | 0.56 | 0.64 |  7.69 | 2.25 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.01 | 0.6 ( 0.01 )  | 8.69 ( 0.24 )  | 5.55 ( 0.06 ) |  8.14 | 0.94 | 0.60 |  8.69 | 5.55 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.03 | 0.56 ( 0.01 ) | 4.39 ( 0.34 )  | 5.4 ( 0.07 )  |  3.99 | 0.82 | 0.56 |  4.39 | 5.40 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.27 | 0.61 ( 0.01 ) | 3.84 ( 0.28 )  | 3.93 ( 0.08 ) |  4.91 | 0.47 | 0.61 |  3.84 | 3.93 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.20 | 0.63 ( 0 )    | 9.87 ( 1.4 )   | 3.18 ( 0.09 ) | 11.69 | 0.49 | 0.63 |  9.87 | 3.18 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.00 | 0.62 ( 0.01 ) | 10.42 ( 0.22 ) | 5.56 ( 0.06 ) |  9.86 | 0.95 | 0.62 | 10.42 | 5.56 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.01 | 0.56 ( 0.01 ) | 5.93 ( 0.28 )  | 5.53 ( 0.06 ) |  5.40 | 0.9  | 0.56 |  5.93 | 5.53 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.24 | 0.62 ( 0.01 ) | 4.22 ( 0.48 )  | 4.05 ( 0.09 ) |  5.17 | 0.46 | 0.62 |  4.22 | 4.05 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.33 | 0.63 ( 0 )    | 5.03 ( 0.78 )  | 3.35 ( 0.06 ) |  6.68 | 0.42 | 0.63 |  5.03 | 3.35 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.17 | 0.65 ( 0.01 ) | 3.87 ( 0.23 )  | 3.68 ( 0.12 ) |  5.19 | 0.5  | 0.65 |  3.87 | 3.68 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.32 | 0.78 ( 0.01 ) | 6.48 ( 0.32 )  | 1.13 ( 0.11 ) | 10.35 | 0.49 | 0.78 |  6.48 | 1.13 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 0.85 ( 0 )    | 6.84 ( 0.26 )  | 0 ( 0 )       | 11.84 | 0.47 | 0.85 |  6.84 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.65 | 0.86 ( 0 )    | 3.69 ( 0.19 )  | 0 ( 0 )       |  8.69 | 0.28 | 0.86 |  3.69 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.14 | 0.63 ( 0.01 ) | 5.12 ( 0.25 )  | 3.91 ( 0.1 )  |  6.21 | 0.63 | 0.63 |  5.12 | 3.91 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.26 | 0.74 ( 0.01 ) | 7.62 ( 0.48 )  | 1.8 ( 0.12 )  | 10.82 | 0.53 | 0.74 |  7.62 | 1.80 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.36 | 0.86 ( 0 )    | 10.19 ( 0.4 )  | 0 ( 0 )       | 15.19 | 0.57 | 0.86 | 10.19 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.52 | 0.86 ( 0 )    | 6 ( 0.27 )     | 0 ( 0 )       | 11.00 | 0.42 | 0.86 |  6.00 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.03 | 0.61 ( 0.01 ) | 10.46 ( 0.31 ) | 4.69 ( 0.08 ) | 10.77 | 0.87 | 0.61 | 10.46 | 4.69 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.17 | 0.66 ( 0.01 ) | 8.6 ( 0.65 )   | 3.19 ( 0.12 ) | 10.41 | 0.64 | 0.66 |  8.60 | 3.19 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.19 | 0.85 ( 0 )    | 25.12 ( 1.08 ) | 0 ( 0 )       | 30.12 | 0.77 | 0.85 | 25.12 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.25 | 0.86 ( 0 )    | 18.48 ( 0.73 ) | 0 ( 0 )       | 23.48 | 0.72 | 0.86 | 18.48 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 0.6 ( 0.01 )  | 12.08 ( 0.26 ) | 4.97 ( 0.08 ) | 12.11 | 0.91 | 0.60 | 12.08 | 4.97 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.13 | 0.65 ( 0.01 ) | 9 ( 0.66 )     | 3.58 ( 0.12 ) | 10.42 | 0.72 | 0.65 |  9.00 | 3.58 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.49 | 0.91 ( 0 )    | 5.84 ( 0.26 )  | 0 ( 0 )       | 10.84 | 0.42 | 0.91 |  5.84 | 0.00 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.71 | 0.92 ( 0 )    | 3.03 ( 0.15 )  | 0 ( 0 )       |  8.03 | 0.23 | 0.92 |  3.03 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.21 | 0.69 ( 0.01 ) | 5.07 ( 0.25 )  | 3.13 ( 0.1 )  |  6.94 | 0.55 | 0.69 |  5.07 | 3.13 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.28 | 0.82 ( 0.01 ) | 9.65 ( 0.48 )  | 0.97 ( 0.1 )  | 13.68 | 0.59 | 0.82 |  9.65 | 0.97 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.36 | 0.91 ( 0 )    | 10.19 ( 0.37 ) | 0 ( 0 )       | 15.19 | 0.58 | 0.91 | 10.19 | 0.00 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.59 | 0.92 ( 0 )    | 4.81 ( 0.19 )  | 0 ( 0 )       |  9.81 | 0.37 | 0.92 |  4.81 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.09 | 0.66 ( 0.01 ) | 9.13 ( 0.25 )  | 4.14 ( 0.08 ) |  9.99 | 0.81 | 0.66 |  9.13 | 4.14 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.21 | 0.75 ( 0.01 ) | 9.59 ( 0.72 )  | 2.44 ( 0.11 ) | 12.15 | 0.61 | 0.75 |  9.59 | 2.44 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.18 | 0.9 ( 0 )     | 26.2 ( 0.86 )  | 0 ( 0 )       | 31.20 | 0.79 | 0.90 | 26.20 | 0.00 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.28 | 0.91 ( 0 )    | 15.6 ( 0.55 )  | 0 ( 0 )       | 20.60 | 0.68 | 0.91 | 15.60 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.05 | 0.64 ( 0.01 ) | 11.44 ( 0.28 ) | 4.51 ( 0.08 ) | 11.93 | 0.87 | 0.64 | 11.44 | 4.51 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.18 | 0.71 ( 0.01 ) | 10.03 ( 0.71 ) | 2.95 ( 0.1 )  | 12.08 | 0.65 | 0.71 | 10.03 | 2.95 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.14 | 0.9 ( 0 )     | 34.85 ( 1.14 ) | 0 ( 0 )       | 39.85 | 0.83 | 0.90 | 34.85 | 0.00 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.20 | 0.91 ( 0 )    | 24.83 ( 0.82 ) | 0 ( 0 )       | 29.83 | 0.78 | 0.91 | 24.83 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.30 | 0.73 ( 0.01 ) | 4.4 ( 0.26 )   | 2.55 ( 0.11 ) |  6.85 | 0.44 | 0.73 |  4.40 | 2.55 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.33 | 0.85 ( 0.01 ) | 8.14 ( 0.35 )  | 0.42 ( 0.06 ) | 12.72 | 0.53 | 0.85 |  8.14 | 0.42 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.54 | 0.92 ( 0 )    | 5.15 ( 0.22 )  | 0 ( 0 )       | 10.15 | 0.38 | 0.92 |  5.15 | 0.00 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.76 | 0.92 ( 0 )    | 2.61 ( 0.13 )  | 0 ( 0 )       |  7.61 | 0.19 | 0.92 |  2.61 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.23 | 0.7 ( 0.01 )  | 4.84 ( 0.24 )  | 3.3 ( 0.09 )  |  6.54 | 0.54 | 0.70 |  4.84 | 3.30 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.28 | 0.81 ( 0.01 ) | 9.92 ( 0.51 )  | 0.87 ( 0.08 ) | 14.05 | 0.57 | 0.81 |  9.92 | 0.87 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.37 | 0.92 ( 0 )    | 9.65 ( 0.35 )  | 0 ( 0 )       | 14.65 | 0.57 | 0.92 |  9.65 | 0.00 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.62 | 0.93 ( 0 )    | 4.29 ( 0.18 )  | 0 ( 0 )       |  9.29 | 0.33 | 0.93 |  4.29 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.08 | 0.62 ( 0.01 ) | 8.99 ( 0.26 )  | 4.13 ( 0.09 ) |  9.86 | 0.8  | 0.62 |  8.99 | 4.13 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.23 | 0.74 ( 0.01 ) | 8.85 ( 0.62 )  | 2.4 ( 0.12 )  | 11.45 | 0.6  | 0.74 |  8.85 | 2.40 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.19 | 0.91 ( 0 )    | 24.91 ( 0.78 ) | 0 ( 0 )       | 29.91 | 0.78 | 0.91 | 24.91 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.31 | 0.92 ( 0 )    | 14.12 ( 0.43 ) | 0 ( 0 )       | 19.12 | 0.67 | 0.92 | 14.12 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.07 | 0.64 ( 0.01 ) | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 11.59 | 0.84 | 0.64 | 10.81 | 4.22 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.19 | 0.74 ( 0.01 ) | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 13.71 | 0.65 | 0.74 | 11.15 | 2.44 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.14 | 0.91 ( 0 )    | 34.98 ( 1.04 ) | 0 ( 0 )       | 39.98 | 0.83 | 0.91 | 34.98 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.22 | 0.92 ( 0 )    | 22.21 ( 0.73 ) | 0 ( 0 )       | 27.21 | 0.76 | 0.92 | 22.21 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.03 0.59 ( 0.01 ) 2.69 ( 0.14 )  5.3 ( 0.07 ) \n",
+       "2   100   50 0.1   0.50 0.11 0.56 ( 0.01 ) 2.02 ( 0.15 )  5.02 ( 0.08 )\n",
+       "3   500   50 0.1   0.10 0.28 0.63 ( 0 )    4.52 ( 0.29 )  3 ( 0.11 )   \n",
+       "4  1000   50 0.1   0.05 0.39 0.64 ( 0 )    4.74 ( 0.22 )  2.1 ( 0.09 ) \n",
+       "5    50  100 0.1   2.00 0.02 0.62 ( 0.01 ) 3.82 ( 0.16 )  5.34 ( 0.07 )\n",
+       "6   100  100 0.1   1.00 0.07 0.56 ( 0.01 ) 2.29 ( 0.19 )  5.27 ( 0.08 )\n",
+       "7   500  100 0.1   0.20 0.23 0.61 ( 0 )    5.54 ( 0.52 )  3.27 ( 0.12 )\n",
+       "8  1000  100 0.1   0.10 0.27 0.64 ( 0 )    7.69 ( 0.42 )  2.25 ( 0.1 ) \n",
+       "9    50  500 0.1  10.00 0.01 0.6 ( 0.01 )  8.69 ( 0.24 )  5.55 ( 0.06 )\n",
+       "10  100  500 0.1   5.00 0.03 0.56 ( 0.01 ) 4.39 ( 0.34 )  5.4 ( 0.07 ) \n",
+       "11  500  500 0.1   1.00 0.27 0.61 ( 0.01 ) 3.84 ( 0.28 )  3.93 ( 0.08 )\n",
+       "12 1000  500 0.1   0.50 0.20 0.63 ( 0 )    9.87 ( 1.4 )   3.18 ( 0.09 )\n",
+       "13   50 1000 0.1  20.00 0.00 0.62 ( 0.01 ) 10.42 ( 0.22 ) 5.56 ( 0.06 )\n",
+       "14  100 1000 0.1  10.00 0.01 0.56 ( 0.01 ) 5.93 ( 0.28 )  5.53 ( 0.06 )\n",
+       "15  500 1000 0.1   2.00 0.24 0.62 ( 0.01 ) 4.22 ( 0.48 )  4.05 ( 0.09 )\n",
+       "16 1000 1000 0.1   1.00 0.33 0.63 ( 0 )    5.03 ( 0.78 )  3.35 ( 0.06 )\n",
+       "17   50   50 0.3   1.00 0.17 0.65 ( 0.01 ) 3.87 ( 0.23 )  3.68 ( 0.12 )\n",
+       "18  100   50 0.3   0.50 0.32 0.78 ( 0.01 ) 6.48 ( 0.32 )  1.13 ( 0.11 )\n",
+       "19  500   50 0.3   0.10 0.44 0.85 ( 0 )    6.84 ( 0.26 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.65 0.86 ( 0 )    3.69 ( 0.19 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.14 0.63 ( 0.01 ) 5.12 ( 0.25 )  3.91 ( 0.1 ) \n",
+       "22  100  100 0.3   1.00 0.26 0.74 ( 0.01 ) 7.62 ( 0.48 )  1.8 ( 0.12 ) \n",
+       "23  500  100 0.3   0.20 0.36 0.86 ( 0 )    10.19 ( 0.4 )  0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.52 0.86 ( 0 )    6 ( 0.27 )     0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.03 0.61 ( 0.01 ) 10.46 ( 0.31 ) 4.69 ( 0.08 )\n",
+       "26  100  500 0.3   5.00 0.17 0.66 ( 0.01 ) 8.6 ( 0.65 )   3.19 ( 0.12 )\n",
+       "27  500  500 0.3   1.00 0.19 0.85 ( 0 )    25.12 ( 1.08 ) 0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.25 0.86 ( 0 )    18.48 ( 0.73 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.02 0.6 ( 0.01 )  12.08 ( 0.26 ) 4.97 ( 0.08 )\n",
+       "30  100 1000 0.3  10.00 0.13 0.65 ( 0.01 ) 9 ( 0.66 )     3.58 ( 0.12 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.49 0.91 ( 0 )    5.84 ( 0.26 )  0 ( 0 )      \n",
+       "52 1000   50 0.7   0.05 0.71 0.92 ( 0 )    3.03 ( 0.15 )  0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.21 0.69 ( 0.01 ) 5.07 ( 0.25 )  3.13 ( 0.1 ) \n",
+       "54  100  100 0.7   1.00 0.28 0.82 ( 0.01 ) 9.65 ( 0.48 )  0.97 ( 0.1 ) \n",
+       "55  500  100 0.7   0.20 0.36 0.91 ( 0 )    10.19 ( 0.37 ) 0 ( 0 )      \n",
+       "56 1000  100 0.7   0.10 0.59 0.92 ( 0 )    4.81 ( 0.19 )  0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.09 0.66 ( 0.01 ) 9.13 ( 0.25 )  4.14 ( 0.08 )\n",
+       "58  100  500 0.7   5.00 0.21 0.75 ( 0.01 ) 9.59 ( 0.72 )  2.44 ( 0.11 )\n",
+       "59  500  500 0.7   1.00 0.18 0.9 ( 0 )     26.2 ( 0.86 )  0 ( 0 )      \n",
+       "60 1000  500 0.7   0.50 0.28 0.91 ( 0 )    15.6 ( 0.55 )  0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.05 0.64 ( 0.01 ) 11.44 ( 0.28 ) 4.51 ( 0.08 )\n",
+       "62  100 1000 0.7  10.00 0.18 0.71 ( 0.01 ) 10.03 ( 0.71 ) 2.95 ( 0.1 ) \n",
+       "63  500 1000 0.7   2.00 0.14 0.9 ( 0 )     34.85 ( 1.14 ) 0 ( 0 )      \n",
+       "64 1000 1000 0.7   1.00 0.20 0.91 ( 0 )    24.83 ( 0.82 ) 0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.30 0.73 ( 0.01 ) 4.4 ( 0.26 )   2.55 ( 0.11 )\n",
+       "66  100   50 0.9   0.50 0.33 0.85 ( 0.01 ) 8.14 ( 0.35 )  0.42 ( 0.06 )\n",
+       "67  500   50 0.9   0.10 0.54 0.92 ( 0 )    5.15 ( 0.22 )  0 ( 0 )      \n",
+       "68 1000   50 0.9   0.05 0.76 0.92 ( 0 )    2.61 ( 0.13 )  0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.23 0.7 ( 0.01 )  4.84 ( 0.24 )  3.3 ( 0.09 ) \n",
+       "70  100  100 0.9   1.00 0.28 0.81 ( 0.01 ) 9.92 ( 0.51 )  0.87 ( 0.08 )\n",
+       "71  500  100 0.9   0.20 0.37 0.92 ( 0 )    9.65 ( 0.35 )  0 ( 0 )      \n",
+       "72 1000  100 0.9   0.10 0.62 0.93 ( 0 )    4.29 ( 0.18 )  0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.08 0.62 ( 0.01 ) 8.99 ( 0.26 )  4.13 ( 0.09 )\n",
+       "74  100  500 0.9   5.00 0.23 0.74 ( 0.01 ) 8.85 ( 0.62 )  2.4 ( 0.12 ) \n",
+       "75  500  500 0.9   1.00 0.19 0.91 ( 0 )    24.91 ( 0.78 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.50 0.31 0.92 ( 0 )    14.12 ( 0.43 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.07 0.64 ( 0.01 ) 10.81 ( 0.29 ) 4.22 ( 0.08 )\n",
+       "78  100 1000 0.9  10.00 0.19 0.74 ( 0.01 ) 11.15 ( 0.75 ) 2.44 ( 0.11 )\n",
+       "79  500 1000 0.9   2.00 0.14 0.91 ( 0 )    34.98 ( 1.04 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.00 0.22 0.92 ( 0 )    22.21 ( 0.73 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   2.39      0.67 0.59      2.69   5.30   \n",
+       "2   2.00      0.39 0.56      2.02   5.02   \n",
+       "3   6.52      0.45 0.63      4.52   3.00   \n",
+       "4   7.64      0.44 0.64      4.74   2.10   \n",
+       "5   3.48      0.78 0.62      3.82   5.34   \n",
+       "6   2.02      0.57 0.56      2.29   5.27   \n",
+       "7   7.27      0.47 0.61      5.54   3.27   \n",
+       "8  10.44      0.56 0.64      7.69   2.25   \n",
+       "9   8.14      0.94 0.60      8.69   5.55   \n",
+       "10  3.99      0.82 0.56      4.39   5.40   \n",
+       "11  4.91      0.47 0.61      3.84   3.93   \n",
+       "12 11.69      0.49 0.63      9.87   3.18   \n",
+       "13  9.86      0.95 0.62     10.42   5.56   \n",
+       "14  5.40      0.9  0.56      5.93   5.53   \n",
+       "15  5.17      0.46 0.62      4.22   4.05   \n",
+       "16  6.68      0.42 0.63      5.03   3.35   \n",
+       "17  5.19      0.5  0.65      3.87   3.68   \n",
+       "18 10.35      0.49 0.78      6.48   1.13   \n",
+       "19 11.84      0.47 0.85      6.84   0.00   \n",
+       "20  8.69      0.28 0.86      3.69   0.00   \n",
+       "21  6.21      0.63 0.63      5.12   3.91   \n",
+       "22 10.82      0.53 0.74      7.62   1.80   \n",
+       "23 15.19      0.57 0.86     10.19   0.00   \n",
+       "24 11.00      0.42 0.86      6.00   0.00   \n",
+       "25 10.77      0.87 0.61     10.46   4.69   \n",
+       "26 10.41      0.64 0.66      8.60   3.19   \n",
+       "27 30.12      0.77 0.85     25.12   0.00   \n",
+       "28 23.48      0.72 0.86     18.48   0.00   \n",
+       "29 12.11      0.91 0.60     12.08   4.97   \n",
+       "30 10.42      0.72 0.65      9.00   3.58   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 10.84      0.42 0.91      5.84   0.00   \n",
+       "52  8.03      0.23 0.92      3.03   0.00   \n",
+       "53  6.94      0.55 0.69      5.07   3.13   \n",
+       "54 13.68      0.59 0.82      9.65   0.97   \n",
+       "55 15.19      0.58 0.91     10.19   0.00   \n",
+       "56  9.81      0.37 0.92      4.81   0.00   \n",
+       "57  9.99      0.81 0.66      9.13   4.14   \n",
+       "58 12.15      0.61 0.75      9.59   2.44   \n",
+       "59 31.20      0.79 0.90     26.20   0.00   \n",
+       "60 20.60      0.68 0.91     15.60   0.00   \n",
+       "61 11.93      0.87 0.64     11.44   4.51   \n",
+       "62 12.08      0.65 0.71     10.03   2.95   \n",
+       "63 39.85      0.83 0.90     34.85   0.00   \n",
+       "64 29.83      0.78 0.91     24.83   0.00   \n",
+       "65  6.85      0.44 0.73      4.40   2.55   \n",
+       "66 12.72      0.53 0.85      8.14   0.42   \n",
+       "67 10.15      0.38 0.92      5.15   0.00   \n",
+       "68  7.61      0.19 0.92      2.61   0.00   \n",
+       "69  6.54      0.54 0.70      4.84   3.30   \n",
+       "70 14.05      0.57 0.81      9.92   0.87   \n",
+       "71 14.65      0.57 0.92      9.65   0.00   \n",
+       "72  9.29      0.33 0.93      4.29   0.00   \n",
+       "73  9.86      0.8  0.62      8.99   4.13   \n",
+       "74 11.45      0.6  0.74      8.85   2.40   \n",
+       "75 29.91      0.78 0.91     24.91   0.00   \n",
+       "76 19.12      0.67 0.92     14.12   0.00   \n",
+       "77 11.59      0.84 0.64     10.81   4.22   \n",
+       "78 13.71      0.65 0.74     11.15   2.44   \n",
+       "79 39.98      0.83 0.91     34.98   0.00   \n",
+       "80 27.21      0.76 0.92     22.21   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_lasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.2_sim_block_elnet_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.2_sim_block_elnet_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..26fb822
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.2_sim_block_elnet_binary_update-checkpoint.ipynb
@@ -0,0 +1,972 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
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+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
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+      "[1] \"indx: 50\"\n",
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+      "[1] \"indx: 62\"\n",
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+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>17.05 ( 1.31 )</td><td>3.17 ( 0.19 )</td><td>0.61 ( 0.01 )</td><td>0.01</td><td>18.88</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>13.62 ( 1.31 )</td><td>2.9 ( 0.17 )</td><td>0.59 ( 0.01 )</td><td>0.04</td><td>15.72</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>16.89 ( 1.14 )</td><td>1.1 ( 0.13 )</td><td>0.64 ( 0 )</td><td>0.11</td><td>20.79</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>19.43 ( 1.02 )</td><td>0.42 ( 0.08 )</td><td>0.66 ( 0 )</td><td>0.12</td><td>24.01</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>27.39 ( 2.11 )</td><td>3.38 ( 0.17 )</td><td>0.63 ( 0.01 )</td><td>0.01</td><td>29.01</td><td>0.87</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>26.17 ( 2.4 )</td><td>3.34 ( 0.16 )</td><td>0.59 ( 0.01 )</td><td>0.02</td><td>27.83</td><td>0.84</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 17.05 ( 1.31 ) & 3.17 ( 0.19 ) & 0.61 ( 0.01 ) & 0.01 & 18.88 & 0.80\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 13.62 ( 1.31 ) & 2.9 ( 0.17 ) & 0.59 ( 0.01 ) & 0.04 & 15.72 & 0.64\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 16.89 ( 1.14 ) & 1.1 ( 0.13 ) & 0.64 ( 0 ) & 0.11 & 20.79 & 0.68\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 19.43 ( 1.02 ) & 0.42 ( 0.08 ) & 0.66 ( 0 ) & 0.12 & 24.01 & 0.71\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 27.39 ( 2.11 ) & 3.38 ( 0.17 ) & 0.63 ( 0.01 ) & 0.01 & 29.01 & 0.87\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 26.17 ( 2.4 ) & 3.34 ( 0.16 ) & 0.59 ( 0.01 ) & 0.02 & 27.83 & 0.84\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 17.05 ( 1.31 ) | 3.17 ( 0.19 ) | 0.61 ( 0.01 ) | 0.01 | 18.88 | 0.80 |\n",
+       "| 2 | 100 | 50 | 0.1 | 13.62 ( 1.31 ) | 2.9 ( 0.17 ) | 0.59 ( 0.01 ) | 0.04 | 15.72 | 0.64 |\n",
+       "| 3 | 500 | 50 | 0.1 | 16.89 ( 1.14 ) | 1.1 ( 0.13 ) | 0.64 ( 0 ) | 0.11 | 20.79 | 0.68 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 19.43 ( 1.02 ) | 0.42 ( 0.08 ) | 0.66 ( 0 ) | 0.12 | 24.01 | 0.71 |\n",
+       "| 5 | 50 | 100 | 0.1 | 27.39 ( 2.11 ) | 3.38 ( 0.17 ) | 0.63 ( 0.01 ) | 0.01 | 29.01 | 0.87 |\n",
+       "| 6 | 100 | 100 | 0.1 | 26.17 ( 2.4 ) | 3.34 ( 0.16 ) | 0.59 ( 0.01 ) | 0.02 | 27.83 | 0.84 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 17.05 ( 1.31 ) 3.17 ( 0.19 ) 0.61 ( 0.01 ) 0.01 18.88      0.80\n",
+       "2 100  50  0.1 13.62 ( 1.31 ) 2.9 ( 0.17 )  0.59 ( 0.01 ) 0.04 15.72      0.64\n",
+       "3 500  50  0.1 16.89 ( 1.14 ) 1.1 ( 0.13 )  0.64 ( 0 )    0.11 20.79      0.68\n",
+       "4 1000 50  0.1 19.43 ( 1.02 ) 0.42 ( 0.08 ) 0.66 ( 0 )    0.12 24.01      0.71\n",
+       "5 50   100 0.1 27.39 ( 2.11 ) 3.38 ( 0.17 ) 0.63 ( 0.01 ) 0.01 29.01      0.87\n",
+       "6 100  100 0.1 26.17 ( 2.4 )  3.34 ( 0.16 ) 0.59 ( 0.01 ) 0.02 27.83      0.84"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>15.52 ( 1.4 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.28</td><td>20.52</td><td>0.57</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>14.84 ( 1.41 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.29</td><td>19.84</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>0.63 ( 0.01 )</td><td>0.02</td><td>80.57</td><td>0.92</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>0.74 ( 0.01 )</td><td>0.06</td><td>46.93</td><td>0.79</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>18.95 ( 1.93 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.25</td><td>23.95</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>15.24 ( 1.68 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.29</td><td>20.24</td><td>0.52</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 15.52 ( 1.4 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.28 & 20.52 & 0.57\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 14.84 ( 1.41 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.29 & 19.84 & 0.56\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 0.63 ( 0.01 ) & 0.02 & 80.57 & 0.92\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 0.74 ( 0.01 ) & 0.06 & 46.93 & 0.79\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 18.95 ( 1.93 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.25 & 23.95 & 0.60\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 15.24 ( 1.68 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.29 & 20.24 & 0.52\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 15.52 ( 1.4 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.28 | 20.52 | 0.57 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 14.84 ( 1.41 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.29 | 19.84 | 0.56 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 0.63 ( 0.01 ) | 0.02 | 80.57 | 0.92 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 0.74 ( 0.01 ) | 0.06 | 46.93 | 0.79 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 18.95 ( 1.93 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.25 | 23.95 | 0.60 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 15.24 ( 1.68 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.29 | 20.24 | 0.52 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 15.52 ( 1.4 )  0 ( 0 )       0.91 ( 0 )    0.28 20.52     \n",
+       "76 1000 500  0.9 14.84 ( 1.41 ) 0 ( 0 )       0.92 ( 0 )    0.29 19.84     \n",
+       "77 50   1000 0.9 78.65 ( 8.46 ) 3.08 ( 0.12 ) 0.63 ( 0.01 ) 0.02 80.57     \n",
+       "78 100  1000 0.9 43.94 ( 6.95 ) 2.01 ( 0.11 ) 0.74 ( 0.01 ) 0.06 46.93     \n",
+       "79 500  1000 0.9 18.95 ( 1.93 ) 0 ( 0 )       0.91 ( 0 )    0.25 23.95     \n",
+       "80 1000 1000 0.9 15.24 ( 1.68 ) 0 ( 0 )       0.92 ( 0 )    0.29 20.24     \n",
+       "   FDR \n",
+       "75 0.57\n",
+       "76 0.56\n",
+       "77 0.92\n",
+       "78 0.79\n",
+       "79 0.60\n",
+       "80 0.52"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.01</td><td>0.61 ( 0.01 )</td><td>17.05 ( 1.31 )</td><td>3.17 ( 0.19 )</td><td>18.88</td><td>0.8 </td><td>0.61</td><td>17.05</td><td>3.17</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.04</td><td>0.59 ( 0.01 )</td><td>13.62 ( 1.31 )</td><td>2.9 ( 0.17 ) </td><td>15.72</td><td>0.64</td><td>0.59</td><td>13.62</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.11</td><td>0.64 ( 0 )   </td><td>16.89 ( 1.14 )</td><td>1.1 ( 0.13 ) </td><td>20.79</td><td>0.68</td><td>0.64</td><td>16.89</td><td>1.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.12</td><td>0.66 ( 0 )   </td><td>19.43 ( 1.02 )</td><td>0.42 ( 0.08 )</td><td>24.01</td><td>0.71</td><td>0.66</td><td>19.43</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>27.39 ( 2.11 )</td><td>3.38 ( 0.17 )</td><td>29.01</td><td>0.87</td><td>0.63</td><td>27.39</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.02</td><td>0.59 ( 0.01 )</td><td>26.17 ( 2.4 ) </td><td>3.34 ( 0.16 )</td><td>27.83</td><td>0.84</td><td>0.59</td><td>26.17</td><td>3.34</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.01 & 0.61 ( 0.01 ) & 17.05 ( 1.31 ) & 3.17 ( 0.19 ) & 18.88 & 0.8  & 0.61 & 17.05 & 3.17\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.04 & 0.59 ( 0.01 ) & 13.62 ( 1.31 ) & 2.9 ( 0.17 )  & 15.72 & 0.64 & 0.59 & 13.62 & 2.90\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.11 & 0.64 ( 0 )    & 16.89 ( 1.14 ) & 1.1 ( 0.13 )  & 20.79 & 0.68 & 0.64 & 16.89 & 1.10\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.12 & 0.66 ( 0 )    & 19.43 ( 1.02 ) & 0.42 ( 0.08 ) & 24.01 & 0.71 & 0.66 & 19.43 & 0.42\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.01 & 0.63 ( 0.01 ) & 27.39 ( 2.11 ) & 3.38 ( 0.17 ) & 29.01 & 0.87 & 0.63 & 27.39 & 3.38\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.02 & 0.59 ( 0.01 ) & 26.17 ( 2.4 )  & 3.34 ( 0.16 ) & 27.83 & 0.84 & 0.59 & 26.17 & 3.34\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.01 | 0.61 ( 0.01 ) | 17.05 ( 1.31 ) | 3.17 ( 0.19 ) | 18.88 | 0.8  | 0.61 | 17.05 | 3.17 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.04 | 0.59 ( 0.01 ) | 13.62 ( 1.31 ) | 2.9 ( 0.17 )  | 15.72 | 0.64 | 0.59 | 13.62 | 2.90 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.11 | 0.64 ( 0 )    | 16.89 ( 1.14 ) | 1.1 ( 0.13 )  | 20.79 | 0.68 | 0.64 | 16.89 | 1.10 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.12 | 0.66 ( 0 )    | 19.43 ( 1.02 ) | 0.42 ( 0.08 ) | 24.01 | 0.71 | 0.66 | 19.43 | 0.42 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.01 | 0.63 ( 0.01 ) | 27.39 ( 2.11 ) | 3.38 ( 0.17 ) | 29.01 | 0.87 | 0.63 | 27.39 | 3.38 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.02 | 0.59 ( 0.01 ) | 26.17 ( 2.4 )  | 3.34 ( 0.16 ) | 27.83 | 0.84 | 0.59 | 26.17 | 3.34 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.01 0.61 ( 0.01 ) 17.05 ( 1.31 ) 3.17 ( 0.19 )\n",
+       "2  100  50 0.1  0.50  0.04 0.59 ( 0.01 ) 13.62 ( 1.31 ) 2.9 ( 0.17 ) \n",
+       "3  500  50 0.1  0.10  0.11 0.64 ( 0 )    16.89 ( 1.14 ) 1.1 ( 0.13 ) \n",
+       "4 1000  50 0.1  0.05  0.12 0.66 ( 0 )    19.43 ( 1.02 ) 0.42 ( 0.08 )\n",
+       "5   50 100 0.1  2.00  0.01 0.63 ( 0.01 ) 27.39 ( 2.11 ) 3.38 ( 0.17 )\n",
+       "6  100 100 0.1  1.00  0.02 0.59 ( 0.01 ) 26.17 ( 2.4 )  3.34 ( 0.16 )\n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 18.88      0.8  0.61     17.05   3.17   \n",
+       "2 15.72      0.64 0.59     13.62   2.90   \n",
+       "3 20.79      0.68 0.64     16.89   1.10   \n",
+       "4 24.01      0.71 0.66     19.43   0.42   \n",
+       "5 29.01      0.87 0.63     27.39   3.38   \n",
+       "6 27.83      0.84 0.59     26.17   3.34   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.52 ( 1.4 ) </td><td>0 ( 0 )      </td><td>20.52</td><td>0.57</td><td>0.91</td><td>15.52</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.29</td><td>0.92 ( 0 )   </td><td>14.84 ( 1.41 )</td><td>0 ( 0 )      </td><td>19.84</td><td>0.56</td><td>0.92</td><td>14.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.02</td><td>0.63 ( 0.01 )</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>80.57</td><td>0.92</td><td>0.63</td><td>78.65</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.06</td><td>0.74 ( 0.01 )</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>46.93</td><td>0.79</td><td>0.74</td><td>43.94</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.25</td><td>0.91 ( 0 )   </td><td>18.95 ( 1.93 )</td><td>0 ( 0 )      </td><td>23.95</td><td>0.6 </td><td>0.91</td><td>18.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.29</td><td>0.92 ( 0 )   </td><td>15.24 ( 1.68 )</td><td>0 ( 0 )      </td><td>20.24</td><td>0.52</td><td>0.92</td><td>15.24</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.28 & 0.91 ( 0 )    & 15.52 ( 1.4 )  & 0 ( 0 )       & 20.52 & 0.57 & 0.91 & 15.52 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.29 & 0.92 ( 0 )    & 14.84 ( 1.41 ) & 0 ( 0 )       & 19.84 & 0.56 & 0.92 & 14.84 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.02 & 0.63 ( 0.01 ) & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 80.57 & 0.92 & 0.63 & 78.65 & 3.08\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.06 & 0.74 ( 0.01 ) & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 46.93 & 0.79 & 0.74 & 43.94 & 2.01\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.25 & 0.91 ( 0 )    & 18.95 ( 1.93 ) & 0 ( 0 )       & 23.95 & 0.6  & 0.91 & 18.95 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.29 & 0.92 ( 0 )    & 15.24 ( 1.68 ) & 0 ( 0 )       & 20.24 & 0.52 & 0.92 & 15.24 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.28 | 0.91 ( 0 )    | 15.52 ( 1.4 )  | 0 ( 0 )       | 20.52 | 0.57 | 0.91 | 15.52 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.29 | 0.92 ( 0 )    | 14.84 ( 1.41 ) | 0 ( 0 )       | 19.84 | 0.56 | 0.92 | 14.84 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.02 | 0.63 ( 0.01 ) | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 80.57 | 0.92 | 0.63 | 78.65 | 3.08 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.06 | 0.74 ( 0.01 ) | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 46.93 | 0.79 | 0.74 | 43.94 | 2.01 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.25 | 0.91 ( 0 )    | 18.95 ( 1.93 ) | 0 ( 0 )       | 23.95 | 0.6  | 0.91 | 18.95 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.29 | 0.92 ( 0 )    | 15.24 ( 1.68 ) | 0 ( 0 )       | 20.24 | 0.52 | 0.92 | 15.24 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.28 0.91 ( 0 )    15.52 ( 1.4 )  0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.29 0.92 ( 0 )    14.84 ( 1.41 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.02 0.63 ( 0.01 ) 78.65 ( 8.46 ) 3.08 ( 0.12 )\n",
+       "78  100 1000 0.9  10.0  0.06 0.74 ( 0.01 ) 43.94 ( 6.95 ) 2.01 ( 0.11 )\n",
+       "79  500 1000 0.9   2.0  0.25 0.91 ( 0 )    18.95 ( 1.93 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.29 0.92 ( 0 )    15.24 ( 1.68 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 20.52      0.57 0.91     15.52   0.00   \n",
+       "76 19.84      0.56 0.92     14.84   0.00   \n",
+       "77 80.57      0.92 0.63     78.65   3.08   \n",
+       "78 46.93      0.79 0.74     43.94   2.01   \n",
+       "79 23.95      0.6  0.91     18.95   0.00   \n",
+       "80 20.24      0.52 0.92     15.24   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.01</td><td>0.61 ( 0.01 )</td><td>17.05 ( 1.31 )  </td><td>3.17 ( 0.19 )</td><td> 18.88</td><td>0.8 </td><td>0.61</td><td> 17.05</td><td>3.17</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.04</td><td>0.59 ( 0.01 )</td><td>13.62 ( 1.31 )  </td><td>2.9 ( 0.17 ) </td><td> 15.72</td><td>0.64</td><td>0.59</td><td> 13.62</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.11</td><td>0.64 ( 0 )   </td><td>16.89 ( 1.14 )  </td><td>1.1 ( 0.13 ) </td><td> 20.79</td><td>0.68</td><td>0.64</td><td> 16.89</td><td>1.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.12</td><td>0.66 ( 0 )   </td><td>19.43 ( 1.02 )  </td><td>0.42 ( 0.08 )</td><td> 24.01</td><td>0.71</td><td>0.66</td><td> 19.43</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>27.39 ( 2.11 )  </td><td>3.38 ( 0.17 )</td><td> 29.01</td><td>0.87</td><td>0.63</td><td> 27.39</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.02</td><td>0.59 ( 0.01 )</td><td>26.17 ( 2.4 )   </td><td>3.34 ( 0.16 )</td><td> 27.83</td><td>0.84</td><td>0.59</td><td> 26.17</td><td>3.34</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.09</td><td>0.62 ( 0 )   </td><td>22.75 ( 1.87 )  </td><td>1.61 ( 0.13 )</td><td> 26.14</td><td>0.69</td><td>0.62</td><td> 22.75</td><td>1.61</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.10</td><td>0.64 ( 0 )   </td><td>28.32 ( 1.88 )  </td><td>0.66 ( 0.1 ) </td><td> 32.66</td><td>0.77</td><td>0.64</td><td> 28.32</td><td>0.66</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.61 ( 0.01 )</td><td>69.03 ( 7.45 )  </td><td>4.13 ( 0.19 )</td><td> 69.90</td><td>0.96</td><td>0.61</td><td> 69.03</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.00</td><td>0.57 ( 0.01 )</td><td>70.64 ( 9.13 )  </td><td>3.91 ( 0.17 )</td><td> 71.73</td><td>0.91</td><td>0.57</td><td> 70.64</td><td>3.91</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.10</td><td>0.61 ( 0.01 )</td><td>20.55 ( 4.33 )  </td><td>3.15 ( 0.1 ) </td><td> 22.40</td><td>0.62</td><td>0.61</td><td> 20.55</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.12</td><td>0.63 ( 0 )   </td><td>26.03 ( 3.45 )  </td><td>2.38 ( 0.11 )</td><td> 28.65</td><td>0.62</td><td>0.63</td><td> 26.03</td><td>2.38</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.00</td><td>0.59 ( 0.01 )</td><td>103.61 ( 10.54 )</td><td>4.34 ( 0.18 )</td><td>104.27</td><td>0.98</td><td>0.59</td><td>103.61</td><td>4.34</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.56 ( 0.01 )</td><td>103.21 ( 12.75 )</td><td>4.15 ( 0.17 )</td><td>104.06</td><td>0.95</td><td>0.56</td><td>103.21</td><td>4.15</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.07</td><td>0.61 ( 0 )   </td><td>29.41 ( 7.4 )   </td><td>3.25 ( 0.09 )</td><td> 31.16</td><td>0.62</td><td>0.61</td><td> 29.41</td><td>3.25</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.18</td><td>0.62 ( 0 )   </td><td>16.15 ( 2.77 )  </td><td>2.73 ( 0.09 )</td><td> 18.42</td><td>0.53</td><td>0.62</td><td> 16.15</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.08</td><td>0.68 ( 0.01 )</td><td>14.8 ( 1.01 )   </td><td>1.73 ( 0.15 )</td><td> 18.07</td><td>0.69</td><td>0.68</td><td> 14.80</td><td>1.73</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>15.15 ( 1 )     </td><td>0.64 ( 0.09 )</td><td> 19.51</td><td>0.65</td><td>0.77</td><td> 15.15</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.35</td><td>0.85 ( 0 )   </td><td>8.95 ( 0.69 )   </td><td>0 ( 0 )      </td><td> 13.95</td><td>0.46</td><td>0.85</td><td>  8.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.33</td><td>0.86 ( 0 )   </td><td>9.66 ( 0.93 )   </td><td>0 ( 0 )      </td><td> 14.66</td><td>0.44</td><td>0.86</td><td>  9.66</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.05</td><td>0.67 ( 0.01 )</td><td>23.92 ( 2 )     </td><td>2.13 ( 0.15 )</td><td> 26.79</td><td>0.77</td><td>0.67</td><td> 23.92</td><td>2.13</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.74 ( 0.01 )</td><td>17.75 ( 1.51 )  </td><td>1.13 ( 0.11 )</td><td> 21.62</td><td>0.67</td><td>0.74</td><td> 17.75</td><td>1.13</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.32</td><td>0.86 ( 0 )   </td><td>11.53 ( 1.02 )  </td><td>0.01 ( 0.01 )</td><td> 16.52</td><td>0.51</td><td>0.86</td><td> 11.53</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.33</td><td>0.86 ( 0 )   </td><td>11.15 ( 1.03 )  </td><td>0 ( 0 )      </td><td> 16.15</td><td>0.49</td><td>0.86</td><td> 11.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.01</td><td>0.6 ( 0.01 ) </td><td>81.07 ( 6.91 )  </td><td>2.92 ( 0.14 )</td><td> 83.15</td><td>0.93</td><td>0.60</td><td> 81.07</td><td>2.92</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.06</td><td>0.66 ( 0.01 )</td><td>38.14 ( 5.11 )  </td><td>2.16 ( 0.13 )</td><td> 40.98</td><td>0.78</td><td>0.66</td><td> 38.14</td><td>2.16</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.22</td><td>0.84 ( 0 )   </td><td>20.87 ( 1.93 )  </td><td>0.02 ( 0.01 )</td><td> 25.85</td><td>0.63</td><td>0.84</td><td> 20.87</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.29</td><td>0.86 ( 0 )   </td><td>15.35 ( 1.6 )   </td><td>0 ( 0 )      </td><td> 20.35</td><td>0.53</td><td>0.86</td><td> 15.35</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>104.78 ( 9.53 ) </td><td>3.37 ( 0.14 )</td><td>106.41</td><td>0.96</td><td>0.63</td><td>104.78</td><td>3.37</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.04</td><td>0.64 ( 0.01 )</td><td>55.95 ( 7.57 )  </td><td>2.5 ( 0.13 ) </td><td> 58.45</td><td>0.85</td><td>0.64</td><td> 55.95</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.34</td><td>0.91 ( 0 )   </td><td>9.34 ( 0.72 ) </td><td>0 ( 0 )      </td><td>14.34</td><td>0.48</td><td>0.91</td><td> 9.34</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.36</td><td>0.91 ( 0 )   </td><td>8.58 ( 0.78 ) </td><td>0 ( 0 )      </td><td>13.58</td><td>0.42</td><td>0.91</td><td> 8.58</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.07</td><td>0.69 ( 0.01 )</td><td>24.95 ( 1.99 )</td><td>1.47 ( 0.1 ) </td><td>28.48</td><td>0.76</td><td>0.69</td><td>24.95</td><td>1.47</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.17</td><td>0.79 ( 0.01 )</td><td>15.63 ( 1.26 )</td><td>0.96 ( 0.11 )</td><td>19.67</td><td>0.63</td><td>0.79</td><td>15.63</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.34</td><td>0.9 ( 0 )    </td><td>10.78 ( 0.81 )</td><td>0.01 ( 0.01 )</td><td>15.77</td><td>0.5 </td><td>0.90</td><td>10.78</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.38</td><td>0.91 ( 0 )   </td><td>9.42 ( 0.9 )  </td><td>0 ( 0 )      </td><td>14.42</td><td>0.44</td><td>0.91</td><td> 9.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>66.01 ( 6.05 )</td><td>2.75 ( 0.12 )</td><td>68.26</td><td>0.92</td><td>0.62</td><td>66.01</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.10</td><td>0.74 ( 0.01 )</td><td>25.15 ( 2.8 ) </td><td>1.84 ( 0.12 )</td><td>28.31</td><td>0.71</td><td>0.74</td><td>25.15</td><td>1.84</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.25</td><td>0.9 ( 0 )    </td><td>17.68 ( 1.71 )</td><td>0 ( 0 )      </td><td>22.68</td><td>0.59</td><td>0.90</td><td>17.68</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.31</td><td>0.91 ( 0 )   </td><td>13.6 ( 1.43 ) </td><td>0 ( 0 )      </td><td>18.60</td><td>0.51</td><td>0.91</td><td>13.60</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>0.62 ( 0.01 )</td><td>86.38 ( 8.91 )</td><td>3.09 ( 0.13 )</td><td>88.29</td><td>0.93</td><td>0.62</td><td>86.38</td><td>3.09</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.06</td><td>0.69 ( 0.01 )</td><td>44.56 ( 6.27 )</td><td>2.12 ( 0.11 )</td><td>47.44</td><td>0.82</td><td>0.69</td><td>44.56</td><td>2.12</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.23</td><td>0.9 ( 0 )    </td><td>20.2 ( 1.89 ) </td><td>0.03 ( 0.02 )</td><td>25.17</td><td>0.62</td><td>0.90</td><td>20.20</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.27</td><td>0.91 ( 0 )   </td><td>16.62 ( 1.81 )</td><td>0 ( 0 )      </td><td>21.62</td><td>0.54</td><td>0.91</td><td>16.62</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.74 ( 0.01 )</td><td>12.38 ( 0.9 ) </td><td>1.46 ( 0.1 ) </td><td>15.92</td><td>0.63</td><td>0.74</td><td>12.38</td><td>1.46</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.21</td><td>0.84 ( 0.01 )</td><td>12.36 ( 0.84 )</td><td>0.46 ( 0.08 )</td><td>16.90</td><td>0.58</td><td>0.84</td><td>12.36</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.89 ( 0.68 ) </td><td>0 ( 0 )      </td><td>12.89</td><td>0.42</td><td>0.92</td><td> 7.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.38 ( 0.78 ) </td><td>0 ( 0 )      </td><td>12.38</td><td>0.38</td><td>0.92</td><td> 7.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.09</td><td>0.7 ( 0.01 ) </td><td>19.37 ( 1.75 )</td><td>2.08 ( 0.13 )</td><td>22.29</td><td>0.75</td><td>0.70</td><td>19.37</td><td>2.08</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.20</td><td>0.81 ( 0.01 )</td><td>14.28 ( 1.07 )</td><td>0.93 ( 0.09 )</td><td>18.35</td><td>0.61</td><td>0.81</td><td>14.28</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.36</td><td>0.92 ( 0 )   </td><td>10.15 ( 0.82 )</td><td>0 ( 0 )      </td><td>15.15</td><td>0.48</td><td>0.92</td><td>10.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.38</td><td>0.93 ( 0 )   </td><td>9.4 ( 1.07 )  </td><td>0 ( 0 )      </td><td>14.40</td><td>0.42</td><td>0.93</td><td> 9.40</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>62.39 ( 5.92 )</td><td>2.69 ( 0.14 )</td><td>64.70</td><td>0.9 </td><td>0.62</td><td>62.39</td><td>2.69</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.12</td><td>0.74 ( 0.01 )</td><td>21.21 ( 2.91 )</td><td>2.02 ( 0.12 )</td><td>24.19</td><td>0.69</td><td>0.74</td><td>21.21</td><td>2.02</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.52 ( 1.4 ) </td><td>0 ( 0 )      </td><td>20.52</td><td>0.57</td><td>0.91</td><td>15.52</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.29</td><td>0.92 ( 0 )   </td><td>14.84 ( 1.41 )</td><td>0 ( 0 )      </td><td>19.84</td><td>0.56</td><td>0.92</td><td>14.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.02</td><td>0.63 ( 0.01 )</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>80.57</td><td>0.92</td><td>0.63</td><td>78.65</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.06</td><td>0.74 ( 0.01 )</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>46.93</td><td>0.79</td><td>0.74</td><td>43.94</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.25</td><td>0.91 ( 0 )   </td><td>18.95 ( 1.93 )</td><td>0 ( 0 )      </td><td>23.95</td><td>0.6 </td><td>0.91</td><td>18.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.29</td><td>0.92 ( 0 )   </td><td>15.24 ( 1.68 )</td><td>0 ( 0 )      </td><td>20.24</td><td>0.52</td><td>0.92</td><td>15.24</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.01 & 0.61 ( 0.01 ) & 17.05 ( 1.31 )   & 3.17 ( 0.19 ) &  18.88 & 0.8  & 0.61 &  17.05 & 3.17\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.04 & 0.59 ( 0.01 ) & 13.62 ( 1.31 )   & 2.9 ( 0.17 )  &  15.72 & 0.64 & 0.59 &  13.62 & 2.90\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.11 & 0.64 ( 0 )    & 16.89 ( 1.14 )   & 1.1 ( 0.13 )  &  20.79 & 0.68 & 0.64 &  16.89 & 1.10\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.12 & 0.66 ( 0 )    & 19.43 ( 1.02 )   & 0.42 ( 0.08 ) &  24.01 & 0.71 & 0.66 &  19.43 & 0.42\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.01 & 0.63 ( 0.01 ) & 27.39 ( 2.11 )   & 3.38 ( 0.17 ) &  29.01 & 0.87 & 0.63 &  27.39 & 3.38\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.02 & 0.59 ( 0.01 ) & 26.17 ( 2.4 )    & 3.34 ( 0.16 ) &  27.83 & 0.84 & 0.59 &  26.17 & 3.34\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.09 & 0.62 ( 0 )    & 22.75 ( 1.87 )   & 1.61 ( 0.13 ) &  26.14 & 0.69 & 0.62 &  22.75 & 1.61\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.10 & 0.64 ( 0 )    & 28.32 ( 1.88 )   & 0.66 ( 0.1 )  &  32.66 & 0.77 & 0.64 &  28.32 & 0.66\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.00 & 0.61 ( 0.01 ) & 69.03 ( 7.45 )   & 4.13 ( 0.19 ) &  69.90 & 0.96 & 0.61 &  69.03 & 4.13\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.00 & 0.57 ( 0.01 ) & 70.64 ( 9.13 )   & 3.91 ( 0.17 ) &  71.73 & 0.91 & 0.57 &  70.64 & 3.91\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.10 & 0.61 ( 0.01 ) & 20.55 ( 4.33 )   & 3.15 ( 0.1 )  &  22.40 & 0.62 & 0.61 &  20.55 & 3.15\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.12 & 0.63 ( 0 )    & 26.03 ( 3.45 )   & 2.38 ( 0.11 ) &  28.65 & 0.62 & 0.63 &  26.03 & 2.38\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.00 & 0.59 ( 0.01 ) & 103.61 ( 10.54 ) & 4.34 ( 0.18 ) & 104.27 & 0.98 & 0.59 & 103.61 & 4.34\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.00 & 0.56 ( 0.01 ) & 103.21 ( 12.75 ) & 4.15 ( 0.17 ) & 104.06 & 0.95 & 0.56 & 103.21 & 4.15\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.07 & 0.61 ( 0 )    & 29.41 ( 7.4 )    & 3.25 ( 0.09 ) &  31.16 & 0.62 & 0.61 &  29.41 & 3.25\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.18 & 0.62 ( 0 )    & 16.15 ( 2.77 )   & 2.73 ( 0.09 ) &  18.42 & 0.53 & 0.62 &  16.15 & 2.73\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.08 & 0.68 ( 0.01 ) & 14.8 ( 1.01 )    & 1.73 ( 0.15 ) &  18.07 & 0.69 & 0.68 &  14.80 & 1.73\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.15 & 0.77 ( 0.01 ) & 15.15 ( 1 )      & 0.64 ( 0.09 ) &  19.51 & 0.65 & 0.77 &  15.15 & 0.64\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.35 & 0.85 ( 0 )    & 8.95 ( 0.69 )    & 0 ( 0 )       &  13.95 & 0.46 & 0.85 &   8.95 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.33 & 0.86 ( 0 )    & 9.66 ( 0.93 )    & 0 ( 0 )       &  14.66 & 0.44 & 0.86 &   9.66 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.05 & 0.67 ( 0.01 ) & 23.92 ( 2 )      & 2.13 ( 0.15 ) &  26.79 & 0.77 & 0.67 &  23.92 & 2.13\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.14 & 0.74 ( 0.01 ) & 17.75 ( 1.51 )   & 1.13 ( 0.11 ) &  21.62 & 0.67 & 0.74 &  17.75 & 1.13\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.32 & 0.86 ( 0 )    & 11.53 ( 1.02 )   & 0.01 ( 0.01 ) &  16.52 & 0.51 & 0.86 &  11.53 & 0.01\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.33 & 0.86 ( 0 )    & 11.15 ( 1.03 )   & 0 ( 0 )       &  16.15 & 0.49 & 0.86 &  11.15 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.01 & 0.6 ( 0.01 )  & 81.07 ( 6.91 )   & 2.92 ( 0.14 ) &  83.15 & 0.93 & 0.60 &  81.07 & 2.92\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.06 & 0.66 ( 0.01 ) & 38.14 ( 5.11 )   & 2.16 ( 0.13 ) &  40.98 & 0.78 & 0.66 &  38.14 & 2.16\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.22 & 0.84 ( 0 )    & 20.87 ( 1.93 )   & 0.02 ( 0.01 ) &  25.85 & 0.63 & 0.84 &  20.87 & 0.02\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.29 & 0.86 ( 0 )    & 15.35 ( 1.6 )    & 0 ( 0 )       &  20.35 & 0.53 & 0.86 &  15.35 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.01 & 0.63 ( 0.01 ) & 104.78 ( 9.53 )  & 3.37 ( 0.14 ) & 106.41 & 0.96 & 0.63 & 104.78 & 3.37\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.04 & 0.64 ( 0.01 ) & 55.95 ( 7.57 )   & 2.5 ( 0.13 )  &  58.45 & 0.85 & 0.64 &  55.95 & 2.50\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.34 & 0.91 ( 0 )    & 9.34 ( 0.72 )  & 0 ( 0 )       & 14.34 & 0.48 & 0.91 &  9.34 & 0.00\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.36 & 0.91 ( 0 )    & 8.58 ( 0.78 )  & 0 ( 0 )       & 13.58 & 0.42 & 0.91 &  8.58 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.07 & 0.69 ( 0.01 ) & 24.95 ( 1.99 ) & 1.47 ( 0.1 )  & 28.48 & 0.76 & 0.69 & 24.95 & 1.47\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.17 & 0.79 ( 0.01 ) & 15.63 ( 1.26 ) & 0.96 ( 0.11 ) & 19.67 & 0.63 & 0.79 & 15.63 & 0.96\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.34 & 0.9 ( 0 )     & 10.78 ( 0.81 ) & 0.01 ( 0.01 ) & 15.77 & 0.5  & 0.90 & 10.78 & 0.01\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.38 & 0.91 ( 0 )    & 9.42 ( 0.9 )   & 0 ( 0 )       & 14.42 & 0.44 & 0.91 &  9.42 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 0.62 ( 0.01 ) & 66.01 ( 6.05 ) & 2.75 ( 0.12 ) & 68.26 & 0.92 & 0.62 & 66.01 & 2.75\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.10 & 0.74 ( 0.01 ) & 25.15 ( 2.8 )  & 1.84 ( 0.12 ) & 28.31 & 0.71 & 0.74 & 25.15 & 1.84\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.25 & 0.9 ( 0 )     & 17.68 ( 1.71 ) & 0 ( 0 )       & 22.68 & 0.59 & 0.90 & 17.68 & 0.00\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.31 & 0.91 ( 0 )    & 13.6 ( 1.43 )  & 0 ( 0 )       & 18.60 & 0.51 & 0.91 & 13.60 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 0.62 ( 0.01 ) & 86.38 ( 8.91 ) & 3.09 ( 0.13 ) & 88.29 & 0.93 & 0.62 & 86.38 & 3.09\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.06 & 0.69 ( 0.01 ) & 44.56 ( 6.27 ) & 2.12 ( 0.11 ) & 47.44 & 0.82 & 0.69 & 44.56 & 2.12\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.23 & 0.9 ( 0 )     & 20.2 ( 1.89 )  & 0.03 ( 0.02 ) & 25.17 & 0.62 & 0.90 & 20.20 & 0.03\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.27 & 0.91 ( 0 )    & 16.62 ( 1.81 ) & 0 ( 0 )       & 21.62 & 0.54 & 0.91 & 16.62 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.14 & 0.74 ( 0.01 ) & 12.38 ( 0.9 )  & 1.46 ( 0.1 )  & 15.92 & 0.63 & 0.74 & 12.38 & 1.46\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.21 & 0.84 ( 0.01 ) & 12.36 ( 0.84 ) & 0.46 ( 0.08 ) & 16.90 & 0.58 & 0.84 & 12.36 & 0.46\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.39 & 0.92 ( 0 )    & 7.89 ( 0.68 )  & 0 ( 0 )       & 12.89 & 0.42 & 0.92 &  7.89 & 0.00\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.41 & 0.92 ( 0 )    & 7.38 ( 0.78 )  & 0 ( 0 )       & 12.38 & 0.38 & 0.92 &  7.38 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.09 & 0.7 ( 0.01 )  & 19.37 ( 1.75 ) & 2.08 ( 0.13 ) & 22.29 & 0.75 & 0.70 & 19.37 & 2.08\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.20 & 0.81 ( 0.01 ) & 14.28 ( 1.07 ) & 0.93 ( 0.09 ) & 18.35 & 0.61 & 0.81 & 14.28 & 0.93\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.36 & 0.92 ( 0 )    & 10.15 ( 0.82 ) & 0 ( 0 )       & 15.15 & 0.48 & 0.92 & 10.15 & 0.00\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.38 & 0.93 ( 0 )    & 9.4 ( 1.07 )   & 0 ( 0 )       & 14.40 & 0.42 & 0.93 &  9.40 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.02 & 0.62 ( 0.01 ) & 62.39 ( 5.92 ) & 2.69 ( 0.14 ) & 64.70 & 0.9  & 0.62 & 62.39 & 2.69\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.12 & 0.74 ( 0.01 ) & 21.21 ( 2.91 ) & 2.02 ( 0.12 ) & 24.19 & 0.69 & 0.74 & 21.21 & 2.02\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.28 & 0.91 ( 0 )    & 15.52 ( 1.4 )  & 0 ( 0 )       & 20.52 & 0.57 & 0.91 & 15.52 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.29 & 0.92 ( 0 )    & 14.84 ( 1.41 ) & 0 ( 0 )       & 19.84 & 0.56 & 0.92 & 14.84 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.02 & 0.63 ( 0.01 ) & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 80.57 & 0.92 & 0.63 & 78.65 & 3.08\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.06 & 0.74 ( 0.01 ) & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 46.93 & 0.79 & 0.74 & 43.94 & 2.01\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.25 & 0.91 ( 0 )    & 18.95 ( 1.93 ) & 0 ( 0 )       & 23.95 & 0.6  & 0.91 & 18.95 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.29 & 0.92 ( 0 )    & 15.24 ( 1.68 ) & 0 ( 0 )       & 20.24 & 0.52 & 0.92 & 15.24 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.01 | 0.61 ( 0.01 ) | 17.05 ( 1.31 )   | 3.17 ( 0.19 ) |  18.88 | 0.8  | 0.61 |  17.05 | 3.17 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.04 | 0.59 ( 0.01 ) | 13.62 ( 1.31 )   | 2.9 ( 0.17 )  |  15.72 | 0.64 | 0.59 |  13.62 | 2.90 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.11 | 0.64 ( 0 )    | 16.89 ( 1.14 )   | 1.1 ( 0.13 )  |  20.79 | 0.68 | 0.64 |  16.89 | 1.10 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.12 | 0.66 ( 0 )    | 19.43 ( 1.02 )   | 0.42 ( 0.08 ) |  24.01 | 0.71 | 0.66 |  19.43 | 0.42 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.01 | 0.63 ( 0.01 ) | 27.39 ( 2.11 )   | 3.38 ( 0.17 ) |  29.01 | 0.87 | 0.63 |  27.39 | 3.38 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.02 | 0.59 ( 0.01 ) | 26.17 ( 2.4 )    | 3.34 ( 0.16 ) |  27.83 | 0.84 | 0.59 |  26.17 | 3.34 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.09 | 0.62 ( 0 )    | 22.75 ( 1.87 )   | 1.61 ( 0.13 ) |  26.14 | 0.69 | 0.62 |  22.75 | 1.61 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.10 | 0.64 ( 0 )    | 28.32 ( 1.88 )   | 0.66 ( 0.1 )  |  32.66 | 0.77 | 0.64 |  28.32 | 0.66 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.00 | 0.61 ( 0.01 ) | 69.03 ( 7.45 )   | 4.13 ( 0.19 ) |  69.90 | 0.96 | 0.61 |  69.03 | 4.13 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.00 | 0.57 ( 0.01 ) | 70.64 ( 9.13 )   | 3.91 ( 0.17 ) |  71.73 | 0.91 | 0.57 |  70.64 | 3.91 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.10 | 0.61 ( 0.01 ) | 20.55 ( 4.33 )   | 3.15 ( 0.1 )  |  22.40 | 0.62 | 0.61 |  20.55 | 3.15 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.12 | 0.63 ( 0 )    | 26.03 ( 3.45 )   | 2.38 ( 0.11 ) |  28.65 | 0.62 | 0.63 |  26.03 | 2.38 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.00 | 0.59 ( 0.01 ) | 103.61 ( 10.54 ) | 4.34 ( 0.18 ) | 104.27 | 0.98 | 0.59 | 103.61 | 4.34 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.00 | 0.56 ( 0.01 ) | 103.21 ( 12.75 ) | 4.15 ( 0.17 ) | 104.06 | 0.95 | 0.56 | 103.21 | 4.15 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.07 | 0.61 ( 0 )    | 29.41 ( 7.4 )    | 3.25 ( 0.09 ) |  31.16 | 0.62 | 0.61 |  29.41 | 3.25 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.18 | 0.62 ( 0 )    | 16.15 ( 2.77 )   | 2.73 ( 0.09 ) |  18.42 | 0.53 | 0.62 |  16.15 | 2.73 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.08 | 0.68 ( 0.01 ) | 14.8 ( 1.01 )    | 1.73 ( 0.15 ) |  18.07 | 0.69 | 0.68 |  14.80 | 1.73 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.15 | 0.77 ( 0.01 ) | 15.15 ( 1 )      | 0.64 ( 0.09 ) |  19.51 | 0.65 | 0.77 |  15.15 | 0.64 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.35 | 0.85 ( 0 )    | 8.95 ( 0.69 )    | 0 ( 0 )       |  13.95 | 0.46 | 0.85 |   8.95 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.33 | 0.86 ( 0 )    | 9.66 ( 0.93 )    | 0 ( 0 )       |  14.66 | 0.44 | 0.86 |   9.66 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.05 | 0.67 ( 0.01 ) | 23.92 ( 2 )      | 2.13 ( 0.15 ) |  26.79 | 0.77 | 0.67 |  23.92 | 2.13 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.14 | 0.74 ( 0.01 ) | 17.75 ( 1.51 )   | 1.13 ( 0.11 ) |  21.62 | 0.67 | 0.74 |  17.75 | 1.13 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.32 | 0.86 ( 0 )    | 11.53 ( 1.02 )   | 0.01 ( 0.01 ) |  16.52 | 0.51 | 0.86 |  11.53 | 0.01 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.33 | 0.86 ( 0 )    | 11.15 ( 1.03 )   | 0 ( 0 )       |  16.15 | 0.49 | 0.86 |  11.15 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.01 | 0.6 ( 0.01 )  | 81.07 ( 6.91 )   | 2.92 ( 0.14 ) |  83.15 | 0.93 | 0.60 |  81.07 | 2.92 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.06 | 0.66 ( 0.01 ) | 38.14 ( 5.11 )   | 2.16 ( 0.13 ) |  40.98 | 0.78 | 0.66 |  38.14 | 2.16 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.22 | 0.84 ( 0 )    | 20.87 ( 1.93 )   | 0.02 ( 0.01 ) |  25.85 | 0.63 | 0.84 |  20.87 | 0.02 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.29 | 0.86 ( 0 )    | 15.35 ( 1.6 )    | 0 ( 0 )       |  20.35 | 0.53 | 0.86 |  15.35 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.01 | 0.63 ( 0.01 ) | 104.78 ( 9.53 )  | 3.37 ( 0.14 ) | 106.41 | 0.96 | 0.63 | 104.78 | 3.37 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.04 | 0.64 ( 0.01 ) | 55.95 ( 7.57 )   | 2.5 ( 0.13 )  |  58.45 | 0.85 | 0.64 |  55.95 | 2.50 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.34 | 0.91 ( 0 )    | 9.34 ( 0.72 )  | 0 ( 0 )       | 14.34 | 0.48 | 0.91 |  9.34 | 0.00 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.36 | 0.91 ( 0 )    | 8.58 ( 0.78 )  | 0 ( 0 )       | 13.58 | 0.42 | 0.91 |  8.58 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.07 | 0.69 ( 0.01 ) | 24.95 ( 1.99 ) | 1.47 ( 0.1 )  | 28.48 | 0.76 | 0.69 | 24.95 | 1.47 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.17 | 0.79 ( 0.01 ) | 15.63 ( 1.26 ) | 0.96 ( 0.11 ) | 19.67 | 0.63 | 0.79 | 15.63 | 0.96 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.34 | 0.9 ( 0 )     | 10.78 ( 0.81 ) | 0.01 ( 0.01 ) | 15.77 | 0.5  | 0.90 | 10.78 | 0.01 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.38 | 0.91 ( 0 )    | 9.42 ( 0.9 )   | 0 ( 0 )       | 14.42 | 0.44 | 0.91 |  9.42 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 0.62 ( 0.01 ) | 66.01 ( 6.05 ) | 2.75 ( 0.12 ) | 68.26 | 0.92 | 0.62 | 66.01 | 2.75 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.10 | 0.74 ( 0.01 ) | 25.15 ( 2.8 )  | 1.84 ( 0.12 ) | 28.31 | 0.71 | 0.74 | 25.15 | 1.84 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.25 | 0.9 ( 0 )     | 17.68 ( 1.71 ) | 0 ( 0 )       | 22.68 | 0.59 | 0.90 | 17.68 | 0.00 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.31 | 0.91 ( 0 )    | 13.6 ( 1.43 )  | 0 ( 0 )       | 18.60 | 0.51 | 0.91 | 13.60 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 0.62 ( 0.01 ) | 86.38 ( 8.91 ) | 3.09 ( 0.13 ) | 88.29 | 0.93 | 0.62 | 86.38 | 3.09 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.06 | 0.69 ( 0.01 ) | 44.56 ( 6.27 ) | 2.12 ( 0.11 ) | 47.44 | 0.82 | 0.69 | 44.56 | 2.12 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.23 | 0.9 ( 0 )     | 20.2 ( 1.89 )  | 0.03 ( 0.02 ) | 25.17 | 0.62 | 0.90 | 20.20 | 0.03 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.27 | 0.91 ( 0 )    | 16.62 ( 1.81 ) | 0 ( 0 )       | 21.62 | 0.54 | 0.91 | 16.62 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.14 | 0.74 ( 0.01 ) | 12.38 ( 0.9 )  | 1.46 ( 0.1 )  | 15.92 | 0.63 | 0.74 | 12.38 | 1.46 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.21 | 0.84 ( 0.01 ) | 12.36 ( 0.84 ) | 0.46 ( 0.08 ) | 16.90 | 0.58 | 0.84 | 12.36 | 0.46 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.39 | 0.92 ( 0 )    | 7.89 ( 0.68 )  | 0 ( 0 )       | 12.89 | 0.42 | 0.92 |  7.89 | 0.00 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.41 | 0.92 ( 0 )    | 7.38 ( 0.78 )  | 0 ( 0 )       | 12.38 | 0.38 | 0.92 |  7.38 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.09 | 0.7 ( 0.01 )  | 19.37 ( 1.75 ) | 2.08 ( 0.13 ) | 22.29 | 0.75 | 0.70 | 19.37 | 2.08 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.20 | 0.81 ( 0.01 ) | 14.28 ( 1.07 ) | 0.93 ( 0.09 ) | 18.35 | 0.61 | 0.81 | 14.28 | 0.93 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.36 | 0.92 ( 0 )    | 10.15 ( 0.82 ) | 0 ( 0 )       | 15.15 | 0.48 | 0.92 | 10.15 | 0.00 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.38 | 0.93 ( 0 )    | 9.4 ( 1.07 )   | 0 ( 0 )       | 14.40 | 0.42 | 0.93 |  9.40 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.02 | 0.62 ( 0.01 ) | 62.39 ( 5.92 ) | 2.69 ( 0.14 ) | 64.70 | 0.9  | 0.62 | 62.39 | 2.69 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.12 | 0.74 ( 0.01 ) | 21.21 ( 2.91 ) | 2.02 ( 0.12 ) | 24.19 | 0.69 | 0.74 | 21.21 | 2.02 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.28 | 0.91 ( 0 )    | 15.52 ( 1.4 )  | 0 ( 0 )       | 20.52 | 0.57 | 0.91 | 15.52 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.29 | 0.92 ( 0 )    | 14.84 ( 1.41 ) | 0 ( 0 )       | 19.84 | 0.56 | 0.92 | 14.84 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.02 | 0.63 ( 0.01 ) | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 80.57 | 0.92 | 0.63 | 78.65 | 3.08 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.06 | 0.74 ( 0.01 ) | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 46.93 | 0.79 | 0.74 | 43.94 | 2.01 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.25 | 0.91 ( 0 )    | 18.95 ( 1.93 ) | 0 ( 0 )       | 23.95 | 0.6  | 0.91 | 18.95 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.29 | 0.92 ( 0 )    | 15.24 ( 1.68 ) | 0 ( 0 )       | 20.24 | 0.52 | 0.92 | 15.24 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "1    50   50 0.1   1.00 0.01 0.61 ( 0.01 ) 17.05 ( 1.31 )   3.17 ( 0.19 )\n",
+       "2   100   50 0.1   0.50 0.04 0.59 ( 0.01 ) 13.62 ( 1.31 )   2.9 ( 0.17 ) \n",
+       "3   500   50 0.1   0.10 0.11 0.64 ( 0 )    16.89 ( 1.14 )   1.1 ( 0.13 ) \n",
+       "4  1000   50 0.1   0.05 0.12 0.66 ( 0 )    19.43 ( 1.02 )   0.42 ( 0.08 )\n",
+       "5    50  100 0.1   2.00 0.01 0.63 ( 0.01 ) 27.39 ( 2.11 )   3.38 ( 0.17 )\n",
+       "6   100  100 0.1   1.00 0.02 0.59 ( 0.01 ) 26.17 ( 2.4 )    3.34 ( 0.16 )\n",
+       "7   500  100 0.1   0.20 0.09 0.62 ( 0 )    22.75 ( 1.87 )   1.61 ( 0.13 )\n",
+       "8  1000  100 0.1   0.10 0.10 0.64 ( 0 )    28.32 ( 1.88 )   0.66 ( 0.1 ) \n",
+       "9    50  500 0.1  10.00 0.00 0.61 ( 0.01 ) 69.03 ( 7.45 )   4.13 ( 0.19 )\n",
+       "10  100  500 0.1   5.00 0.00 0.57 ( 0.01 ) 70.64 ( 9.13 )   3.91 ( 0.17 )\n",
+       "11  500  500 0.1   1.00 0.10 0.61 ( 0.01 ) 20.55 ( 4.33 )   3.15 ( 0.1 ) \n",
+       "12 1000  500 0.1   0.50 0.12 0.63 ( 0 )    26.03 ( 3.45 )   2.38 ( 0.11 )\n",
+       "13   50 1000 0.1  20.00 0.00 0.59 ( 0.01 ) 103.61 ( 10.54 ) 4.34 ( 0.18 )\n",
+       "14  100 1000 0.1  10.00 0.00 0.56 ( 0.01 ) 103.21 ( 12.75 ) 4.15 ( 0.17 )\n",
+       "15  500 1000 0.1   2.00 0.07 0.61 ( 0 )    29.41 ( 7.4 )    3.25 ( 0.09 )\n",
+       "16 1000 1000 0.1   1.00 0.18 0.62 ( 0 )    16.15 ( 2.77 )   2.73 ( 0.09 )\n",
+       "17   50   50 0.3   1.00 0.08 0.68 ( 0.01 ) 14.8 ( 1.01 )    1.73 ( 0.15 )\n",
+       "18  100   50 0.3   0.50 0.15 0.77 ( 0.01 ) 15.15 ( 1 )      0.64 ( 0.09 )\n",
+       "19  500   50 0.3   0.10 0.35 0.85 ( 0 )    8.95 ( 0.69 )    0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.33 0.86 ( 0 )    9.66 ( 0.93 )    0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.05 0.67 ( 0.01 ) 23.92 ( 2 )      2.13 ( 0.15 )\n",
+       "22  100  100 0.3   1.00 0.14 0.74 ( 0.01 ) 17.75 ( 1.51 )   1.13 ( 0.11 )\n",
+       "23  500  100 0.3   0.20 0.32 0.86 ( 0 )    11.53 ( 1.02 )   0.01 ( 0.01 )\n",
+       "24 1000  100 0.3   0.10 0.33 0.86 ( 0 )    11.15 ( 1.03 )   0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.01 0.6 ( 0.01 )  81.07 ( 6.91 )   2.92 ( 0.14 )\n",
+       "26  100  500 0.3   5.00 0.06 0.66 ( 0.01 ) 38.14 ( 5.11 )   2.16 ( 0.13 )\n",
+       "27  500  500 0.3   1.00 0.22 0.84 ( 0 )    20.87 ( 1.93 )   0.02 ( 0.01 )\n",
+       "28 1000  500 0.3   0.50 0.29 0.86 ( 0 )    15.35 ( 1.6 )    0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.01 0.63 ( 0.01 ) 104.78 ( 9.53 )  3.37 ( 0.14 )\n",
+       "30  100 1000 0.3  10.00 0.04 0.64 ( 0.01 ) 55.95 ( 7.57 )   2.5 ( 0.13 ) \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮                ⋮            \n",
+       "51  500   50 0.7   0.10 0.34 0.91 ( 0 )    9.34 ( 0.72 )    0 ( 0 )      \n",
+       "52 1000   50 0.7   0.05 0.36 0.91 ( 0 )    8.58 ( 0.78 )    0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.07 0.69 ( 0.01 ) 24.95 ( 1.99 )   1.47 ( 0.1 ) \n",
+       "54  100  100 0.7   1.00 0.17 0.79 ( 0.01 ) 15.63 ( 1.26 )   0.96 ( 0.11 )\n",
+       "55  500  100 0.7   0.20 0.34 0.9 ( 0 )     10.78 ( 0.81 )   0.01 ( 0.01 )\n",
+       "56 1000  100 0.7   0.10 0.38 0.91 ( 0 )    9.42 ( 0.9 )     0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.02 0.62 ( 0.01 ) 66.01 ( 6.05 )   2.75 ( 0.12 )\n",
+       "58  100  500 0.7   5.00 0.10 0.74 ( 0.01 ) 25.15 ( 2.8 )    1.84 ( 0.12 )\n",
+       "59  500  500 0.7   1.00 0.25 0.9 ( 0 )     17.68 ( 1.71 )   0 ( 0 )      \n",
+       "60 1000  500 0.7   0.50 0.31 0.91 ( 0 )    13.6 ( 1.43 )    0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.01 0.62 ( 0.01 ) 86.38 ( 8.91 )   3.09 ( 0.13 )\n",
+       "62  100 1000 0.7  10.00 0.06 0.69 ( 0.01 ) 44.56 ( 6.27 )   2.12 ( 0.11 )\n",
+       "63  500 1000 0.7   2.00 0.23 0.9 ( 0 )     20.2 ( 1.89 )    0.03 ( 0.02 )\n",
+       "64 1000 1000 0.7   1.00 0.27 0.91 ( 0 )    16.62 ( 1.81 )   0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.14 0.74 ( 0.01 ) 12.38 ( 0.9 )    1.46 ( 0.1 ) \n",
+       "66  100   50 0.9   0.50 0.21 0.84 ( 0.01 ) 12.36 ( 0.84 )   0.46 ( 0.08 )\n",
+       "67  500   50 0.9   0.10 0.39 0.92 ( 0 )    7.89 ( 0.68 )    0 ( 0 )      \n",
+       "68 1000   50 0.9   0.05 0.41 0.92 ( 0 )    7.38 ( 0.78 )    0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.09 0.7 ( 0.01 )  19.37 ( 1.75 )   2.08 ( 0.13 )\n",
+       "70  100  100 0.9   1.00 0.20 0.81 ( 0.01 ) 14.28 ( 1.07 )   0.93 ( 0.09 )\n",
+       "71  500  100 0.9   0.20 0.36 0.92 ( 0 )    10.15 ( 0.82 )   0 ( 0 )      \n",
+       "72 1000  100 0.9   0.10 0.38 0.93 ( 0 )    9.4 ( 1.07 )     0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.02 0.62 ( 0.01 ) 62.39 ( 5.92 )   2.69 ( 0.14 )\n",
+       "74  100  500 0.9   5.00 0.12 0.74 ( 0.01 ) 21.21 ( 2.91 )   2.02 ( 0.12 )\n",
+       "75  500  500 0.9   1.00 0.28 0.91 ( 0 )    15.52 ( 1.4 )    0 ( 0 )      \n",
+       "76 1000  500 0.9   0.50 0.29 0.92 ( 0 )    14.84 ( 1.41 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.02 0.63 ( 0.01 ) 78.65 ( 8.46 )   3.08 ( 0.12 )\n",
+       "78  100 1000 0.9  10.00 0.06 0.74 ( 0.01 ) 43.94 ( 6.95 )   2.01 ( 0.11 )\n",
+       "79  500 1000 0.9   2.00 0.25 0.91 ( 0 )    18.95 ( 1.93 )   0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.00 0.29 0.92 ( 0 )    15.24 ( 1.68 )   0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   18.88     0.8  0.61      17.05  3.17   \n",
+       "2   15.72     0.64 0.59      13.62  2.90   \n",
+       "3   20.79     0.68 0.64      16.89  1.10   \n",
+       "4   24.01     0.71 0.66      19.43  0.42   \n",
+       "5   29.01     0.87 0.63      27.39  3.38   \n",
+       "6   27.83     0.84 0.59      26.17  3.34   \n",
+       "7   26.14     0.69 0.62      22.75  1.61   \n",
+       "8   32.66     0.77 0.64      28.32  0.66   \n",
+       "9   69.90     0.96 0.61      69.03  4.13   \n",
+       "10  71.73     0.91 0.57      70.64  3.91   \n",
+       "11  22.40     0.62 0.61      20.55  3.15   \n",
+       "12  28.65     0.62 0.63      26.03  2.38   \n",
+       "13 104.27     0.98 0.59     103.61  4.34   \n",
+       "14 104.06     0.95 0.56     103.21  4.15   \n",
+       "15  31.16     0.62 0.61      29.41  3.25   \n",
+       "16  18.42     0.53 0.62      16.15  2.73   \n",
+       "17  18.07     0.69 0.68      14.80  1.73   \n",
+       "18  19.51     0.65 0.77      15.15  0.64   \n",
+       "19  13.95     0.46 0.85       8.95  0.00   \n",
+       "20  14.66     0.44 0.86       9.66  0.00   \n",
+       "21  26.79     0.77 0.67      23.92  2.13   \n",
+       "22  21.62     0.67 0.74      17.75  1.13   \n",
+       "23  16.52     0.51 0.86      11.53  0.01   \n",
+       "24  16.15     0.49 0.86      11.15  0.00   \n",
+       "25  83.15     0.93 0.60      81.07  2.92   \n",
+       "26  40.98     0.78 0.66      38.14  2.16   \n",
+       "27  25.85     0.63 0.84      20.87  0.02   \n",
+       "28  20.35     0.53 0.86      15.35  0.00   \n",
+       "29 106.41     0.96 0.63     104.78  3.37   \n",
+       "30  58.45     0.85 0.64      55.95  2.50   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 14.34      0.48 0.91      9.34   0.00   \n",
+       "52 13.58      0.42 0.91      8.58   0.00   \n",
+       "53 28.48      0.76 0.69     24.95   1.47   \n",
+       "54 19.67      0.63 0.79     15.63   0.96   \n",
+       "55 15.77      0.5  0.90     10.78   0.01   \n",
+       "56 14.42      0.44 0.91      9.42   0.00   \n",
+       "57 68.26      0.92 0.62     66.01   2.75   \n",
+       "58 28.31      0.71 0.74     25.15   1.84   \n",
+       "59 22.68      0.59 0.90     17.68   0.00   \n",
+       "60 18.60      0.51 0.91     13.60   0.00   \n",
+       "61 88.29      0.93 0.62     86.38   3.09   \n",
+       "62 47.44      0.82 0.69     44.56   2.12   \n",
+       "63 25.17      0.62 0.90     20.20   0.03   \n",
+       "64 21.62      0.54 0.91     16.62   0.00   \n",
+       "65 15.92      0.63 0.74     12.38   1.46   \n",
+       "66 16.90      0.58 0.84     12.36   0.46   \n",
+       "67 12.89      0.42 0.92      7.89   0.00   \n",
+       "68 12.38      0.38 0.92      7.38   0.00   \n",
+       "69 22.29      0.75 0.70     19.37   2.08   \n",
+       "70 18.35      0.61 0.81     14.28   0.93   \n",
+       "71 15.15      0.48 0.92     10.15   0.00   \n",
+       "72 14.40      0.42 0.93      9.40   0.00   \n",
+       "73 64.70      0.9  0.62     62.39   2.69   \n",
+       "74 24.19      0.69 0.74     21.21   2.02   \n",
+       "75 20.52      0.57 0.91     15.52   0.00   \n",
+       "76 19.84      0.56 0.92     14.84   0.00   \n",
+       "77 80.57      0.92 0.63     78.65   3.08   \n",
+       "78 46.93      0.79 0.74     43.94   2.01   \n",
+       "79 23.95      0.6  0.91     18.95   0.00   \n",
+       "80 20.24      0.52 0.92     15.24   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.3_sim_block_rf_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.3_sim_block_rf_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..6322392
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.3_sim_block_rf_binary_update-checkpoint.ipynb
@@ -0,0 +1,1067 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    #if(i == 16) next \n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.4 ( 0.16 )</td><td>5.3 ( 0.11 )</td><td>1 ( 0 )</td><td>0.01</td><td>3.00</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>2.96 ( 0.21 )</td><td>4.29 ( 0.15 )</td><td>1 ( 0 )</td><td>0.12</td><td>4.63</td><td>0.62</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>3.28 ( 0.21 )</td><td>3.04 ( 0.12 )</td><td>1 ( 0 )</td><td>0.33</td><td>6.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>5.09 ( 0.21 )</td><td>5.6 ( 0.09 )</td><td>1 ( 0 )</td><td>0</td><td>5.48</td><td>0.93</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.4 ( 0.16 ) & 5.3 ( 0.11 ) & 1 ( 0 ) & 0.01 & 3.00 & 0.78\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 2.96 ( 0.21 ) & 4.29 ( 0.15 ) & 1 ( 0 ) & 0.12 & 4.63 & 0.62\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 3.28 ( 0.21 ) & 3.04 ( 0.12 ) & 1 ( 0 ) & 0.33 & 6.24 & 0.49\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 5.09 ( 0.21 ) & 5.6 ( 0.09 ) & 1 ( 0 ) & 0 & 5.48 & 0.93\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.4 ( 0.16 ) | 5.3 ( 0.11 ) | 1 ( 0 ) | 0.01 | 3.00 | 0.78 |\n",
+       "| 3 | 500 | 50 | 0.1 | 2.96 ( 0.21 ) | 4.29 ( 0.15 ) | 1 ( 0 ) | 0.12 | 4.63 | 0.62 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 3.28 ( 0.21 ) | 3.04 ( 0.12 ) | 1 ( 0 ) | 0.33 | 6.24 | 0.49 |\n",
+       "| 5 | 50 | 100 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0.1 | 5.09 ( 0.21 ) | 5.6 ( 0.09 ) | 1 ( 0 ) | 0 | 5.48 | 0.93 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0.1 2.4 ( 0.16 )  5.3 ( 0.11 )  1 ( 0 ) 0.01 3.00       0.78\n",
+       "3 500  50  0.1 2.96 ( 0.21 ) 4.29 ( 0.15 ) 1 ( 0 ) 0.12 4.63       0.62\n",
+       "4 1000 50  0.1 3.28 ( 0.21 ) 3.04 ( 0.12 ) 1 ( 0 ) 0.33 6.24       0.49\n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0.1 5.09 ( 0.21 ) 5.6 ( 0.09 )  1 ( 0 ) 0    5.48       0.93"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>1 ( 0 )</td><td>0.07</td><td>27.64</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>1 ( 0 )</td><td>0.13</td><td>25.93</td><td>0.82</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>1 ( 0 )</td><td>0</td><td>48.53</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>48.09 ( 0.7 )</td><td>2.94 ( 0.09 )</td><td>1 ( 0 )</td><td>0.04</td><td>51.15</td><td>0.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>45.6 ( 0.72 )</td><td>1.63 ( 0.08 )</td><td>1 ( 0 )</td><td>0.07</td><td>49.97</td><td>0.91</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 1 ( 0 ) & 0.07 & 27.64 & 0.88\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 1 ( 0 ) & 0.13 & 25.93 & 0.82\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 1 ( 0 ) & 0 & 48.53 & 0.98\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 48.09 ( 0.7 ) & 2.94 ( 0.09 ) & 1 ( 0 ) & 0.04 & 51.15 & 0.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 45.6 ( 0.72 ) & 1.63 ( 0.08 ) & 1 ( 0 ) & 0.07 & 49.97 & 0.91\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 1 ( 0 ) | 0.07 | 27.64 | 0.88 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 1 ( 0 ) | 0.13 | 25.93 | 0.82 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 78 | 100 | 1000 | 0.9 | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 1 ( 0 ) | 0 | 48.53 | 0.98 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 48.09 ( 0.7 ) | 2.94 ( 0.09 ) | 1 ( 0 ) | 0.04 | 51.15 | 0.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 45.6 ( 0.72 ) | 1.63 ( 0.08 ) | 1 ( 0 ) | 0.07 | 49.97 | 0.91 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "75 500  500  0.9 24.29 ( 0.42 ) 2.65 ( 0.09 ) 1 ( 0 ) 0.07 27.64      0.88\n",
+       "76 1000 500  0.9 21.42 ( 0.42 ) 1.49 ( 0.08 ) 1 ( 0 ) 0.13 25.93      0.82\n",
+       "77 50   1000 0.9 1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "78 100  1000 0.9 47.48 ( 0.72 ) 4.95 ( 0.08 ) 1 ( 0 ) 0    48.53      0.98\n",
+       "79 500  1000 0.9 48.09 ( 0.7 )  2.94 ( 0.09 ) 1 ( 0 ) 0.04 51.15      0.94\n",
+       "80 1000 1000 0.9 45.6 ( 0.72 )  1.63 ( 0.08 ) 1 ( 0 ) 0.07 49.97      0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0.1 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0.1 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0.1 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0.1 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t17 & 50 &   50 & 0.3 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t21 & 50 &  100 & 0.3 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t25 & 50 &  500 & 0.3 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t29 & 50 & 1000 & 0.3 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t33 & 50 &   50 & 0.5 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t37 & 50 &  100 & 0.5 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t41 & 50 &  500 & 0.5 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t45 & 50 & 1000 & 0.5 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t49 & 50 &   50 & 0.7 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t53 & 50 &  100 & 0.7 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t57 & 50 &  500 & 0.7 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t61 & 50 & 1000 & 0.7 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t65 & 50 &   50 & 0.9 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t69 & 50 &  100 & 0.9 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t73 & 50 &  500 & 0.9 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0.1 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0.1 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0.1 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0.1 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 17 | 50 |   50 | 0.3 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 21 | 50 |  100 | 0.3 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 25 | 50 |  500 | 0.3 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 29 | 50 | 1000 | 0.3 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 33 | 50 |   50 | 0.5 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 37 | 50 |  100 | 0.5 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 41 | 50 |  500 | 0.5 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 45 | 50 | 1000 | 0.5 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 49 | 50 |   50 | 0.7 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 53 | 50 |  100 | 0.7 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 57 | 50 |  500 | 0.7 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 61 | 50 | 1000 | 0.7 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 65 | 50 |   50 | 0.9 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 69 | 50 |  100 | 0.9 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 73 | 50 |  500 | 0.9 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0.1   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0.1   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0.1  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0.1  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "17 50   50 0.3   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "21 50  100 0.3   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "25 50  500 0.3  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "29 50 1000 0.3  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "33 50   50 0.5   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0.5   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0.5  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0.5  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "49 50   50 0.7   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "53 50  100 0.7   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "57 50  500 0.7  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "61 50 1000 0.7  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "65 50   50 0.9   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "69 50  100 0.9   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "73 50  500 0.9  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "77 50 1000 0.9  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      \n",
+       "17 1       6      \n",
+       "21 1       6      \n",
+       "25 1       6      \n",
+       "29 1       6      \n",
+       "33 1       6      \n",
+       "37 1       6      \n",
+       "41 1       6      \n",
+       "45 1       6      \n",
+       "49 1       6      \n",
+       "53 1       6      \n",
+       "57 1       6      \n",
+       "61 1       6      \n",
+       "65 1       6      \n",
+       "69 1       6      \n",
+       "73 1       6      \n",
+       "77 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.01</td><td>1 ( 0 )</td><td>2.4 ( 0.16 ) </td><td>5.3 ( 0.11 ) </td><td>3.00</td><td>0.78</td><td>1</td><td>2.40</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.12</td><td>1 ( 0 )</td><td>2.96 ( 0.21 )</td><td>4.29 ( 0.15 )</td><td>4.63</td><td>0.62</td><td>1</td><td>2.96</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.33</td><td>1 ( 0 )</td><td>3.28 ( 0.21 )</td><td>3.04 ( 0.12 )</td><td>6.24</td><td>0.49</td><td>1</td><td>3.28</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.00</td><td>1 ( 0 )</td><td>5.09 ( 0.21 )</td><td>5.6 ( 0.09 ) </td><td>5.48</td><td>0.93</td><td>1</td><td>5.09</td><td>5.60</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.01 & 1 ( 0 ) & 2.4 ( 0.16 )  & 5.3 ( 0.11 )  & 3.00 & 0.78 & 1 & 2.40 & 5.30\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.12 & 1 ( 0 ) & 2.96 ( 0.21 ) & 4.29 ( 0.15 ) & 4.63 & 0.62 & 1 & 2.96 & 4.29\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.33 & 1 ( 0 ) & 3.28 ( 0.21 ) & 3.04 ( 0.12 ) & 6.24 & 0.49 & 1 & 3.28 & 3.04\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.00 & 1 ( 0 ) & 5.09 ( 0.21 ) & 5.6 ( 0.09 )  & 5.48 & 0.93 & 1 & 5.09 & 5.60\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.01 | 1 ( 0 ) | 2.4 ( 0.16 )  | 5.3 ( 0.11 )  | 3.00 | 0.78 | 1 | 2.40 | 5.30 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.12 | 1 ( 0 ) | 2.96 ( 0.21 ) | 4.29 ( 0.15 ) | 4.63 | 0.62 | 1 | 2.96 | 4.29 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.33 | 1 ( 0 ) | 3.28 ( 0.21 ) | 3.04 ( 0.12 ) | 6.24 | 0.49 | 1 | 3.28 | 3.04 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.00 | 1 ( 0 ) | 5.09 ( 0.21 ) | 5.6 ( 0.09 )  | 5.48 | 0.93 | 1 | 5.09 | 5.60 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0.1  1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0.1  0.50  0.01 1 ( 0 ) 2.4 ( 0.16 )  5.3 ( 0.11 )  3.00       0.78\n",
+       "3  500  50 0.1  0.10  0.12 1 ( 0 ) 2.96 ( 0.21 ) 4.29 ( 0.15 ) 4.63       0.62\n",
+       "4 1000  50 0.1  0.05  0.33 1 ( 0 ) 3.28 ( 0.21 ) 3.04 ( 0.12 ) 6.24       0.49\n",
+       "5   50 100 0.1  2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0.1  1.00  0.00 1 ( 0 ) 5.09 ( 0.21 ) 5.6 ( 0.09 )  5.48       0.93\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        2.40    5.30   \n",
+       "3 1        2.96    4.29   \n",
+       "4 1        3.28    3.04   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        5.09    5.60   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>27.64</td><td>0.88</td><td>1</td><td>24.29</td><td>2.65</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.13</td><td>1 ( 0 )</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>25.93</td><td>0.82</td><td>1</td><td>21.42</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.00</td><td>1 ( 0 )</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>48.53</td><td>0.98</td><td>1</td><td>47.48</td><td>4.95</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>48.09 ( 0.7 ) </td><td>2.94 ( 0.09 )</td><td>51.15</td><td>0.94</td><td>1</td><td>48.09</td><td>2.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>45.6 ( 0.72 ) </td><td>1.63 ( 0.08 )</td><td>49.97</td><td>0.91</td><td>1</td><td>45.60</td><td>1.63</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 27.64 & 0.88 & 1 & 24.29 & 2.65\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.13 & 1 ( 0 ) & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 25.93 & 0.82 & 1 & 21.42 & 1.49\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.00 & 1 ( 0 ) & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 48.53 & 0.98 & 1 & 47.48 & 4.95\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.04 & 1 ( 0 ) & 48.09 ( 0.7 )  & 2.94 ( 0.09 ) & 51.15 & 0.94 & 1 & 48.09 & 2.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 45.6 ( 0.72 )  & 1.63 ( 0.08 ) & 49.97 & 0.91 & 1 & 45.60 & 1.63\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 27.64 | 0.88 | 1 | 24.29 | 2.65 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.13 | 1 ( 0 ) | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 25.93 | 0.82 | 1 | 21.42 | 1.49 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.00 | 1 ( 0 ) | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 48.53 | 0.98 | 1 | 47.48 | 4.95 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.04 | 1 ( 0 ) | 48.09 ( 0.7 )  | 2.94 ( 0.09 ) | 51.15 | 0.94 | 1 | 48.09 | 2.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 45.6 ( 0.72 )  | 1.63 ( 0.08 ) | 49.97 | 0.91 | 1 | 45.60 | 1.63 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "75  500  500 0.9   1.0  0.07 1 ( 0 ) 24.29 ( 0.42 ) 2.65 ( 0.09 ) 27.64     \n",
+       "76 1000  500 0.9   0.5  0.13 1 ( 0 ) 21.42 ( 0.42 ) 1.49 ( 0.08 ) 25.93     \n",
+       "77   50 1000 0.9  20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.0  0.00 1 ( 0 ) 47.48 ( 0.72 ) 4.95 ( 0.08 ) 48.53     \n",
+       "79  500 1000 0.9   2.0  0.04 1 ( 0 ) 48.09 ( 0.7 )  2.94 ( 0.09 ) 51.15     \n",
+       "80 1000 1000 0.9   1.0  0.07 1 ( 0 ) 45.6 ( 0.72 )  1.63 ( 0.08 ) 49.97     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "75 0.88 1        24.29   2.65   \n",
+       "76 0.82 1        21.42   1.49   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.48   4.95   \n",
+       "79 0.94 1        48.09   2.94   \n",
+       "80 0.91 1        45.60   1.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.01</td><td>1 ( 0 )</td><td>2.4 ( 0.16 )  </td><td>5.3 ( 0.11 ) </td><td> 3.00</td><td>0.78</td><td>1</td><td> 2.40</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.12</td><td>1 ( 0 )</td><td>2.96 ( 0.21 ) </td><td>4.29 ( 0.15 )</td><td> 4.63</td><td>0.62</td><td>1</td><td> 2.96</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.33</td><td>1 ( 0 )</td><td>3.28 ( 0.21 ) </td><td>3.04 ( 0.12 )</td><td> 6.24</td><td>0.49</td><td>1</td><td> 3.28</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.00</td><td>1 ( 0 )</td><td>5.09 ( 0.21 ) </td><td>5.6 ( 0.09 ) </td><td> 5.48</td><td>0.93</td><td>1</td><td> 5.09</td><td>5.60</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.10</td><td>1 ( 0 )</td><td>5.39 ( 0.29 ) </td><td>4.05 ( 0.16 )</td><td> 7.34</td><td>0.72</td><td>1</td><td> 5.39</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.25</td><td>1 ( 0 )</td><td>5.43 ( 0.26 ) </td><td>2.89 ( 0.14 )</td><td> 8.54</td><td>0.62</td><td>1</td><td> 5.43</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.00</td><td>1 ( 0 )</td><td>24.14 ( 0.56 )</td><td>5.35 ( 0.11 )</td><td>24.79</td><td>0.97</td><td>1</td><td>24.14</td><td>5.35</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>1 ( 0 )</td><td>24.48 ( 0.54 )</td><td>4.25 ( 0.16 )</td><td>26.23</td><td>0.93</td><td>1</td><td>24.48</td><td>4.25</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.07</td><td>1 ( 0 )</td><td>24.08 ( 0.54 )</td><td>3.1 ( 0.16 ) </td><td>26.98</td><td>0.89</td><td>1</td><td>24.08</td><td>3.10</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>48.22 ( 0.62 )</td><td>5.53 ( 0.09 )</td><td>48.69</td><td>0.99</td><td>1</td><td>48.22</td><td>5.53</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.01</td><td>1 ( 0 )</td><td>50.18 ( 0.78 )</td><td>4.63 ( 0.12 )</td><td>51.55</td><td>0.97</td><td>1</td><td>50.18</td><td>4.63</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.04</td><td>1 ( 0 )</td><td>49.43 ( 0.66 )</td><td>3.23 ( 0.14 )</td><td>52.20</td><td>0.95</td><td>1</td><td>49.43</td><td>3.23</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.02</td><td>1 ( 0 )</td><td>2.13 ( 0.12 ) </td><td>5.14 ( 0.08 )</td><td> 2.96</td><td>0.71</td><td>1</td><td> 2.13</td><td>5.14</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.46</td><td>1 ( 0 )</td><td>1.43 ( 0.11 ) </td><td>2.59 ( 0.11 )</td><td> 4.84</td><td>0.27</td><td>1</td><td> 1.43</td><td>2.59</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.60</td><td>1 ( 0 )</td><td>1.44 ( 0.13 ) </td><td>1.35 ( 0.08 )</td><td> 6.09</td><td>0.21</td><td>1</td><td> 1.44</td><td>1.35</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.01</td><td>1 ( 0 )</td><td>4.84 ( 0.18 ) </td><td>5.1 ( 0.08 ) </td><td> 5.73</td><td>0.84</td><td>1</td><td> 4.84</td><td>5.10</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.28</td><td>1 ( 0 )</td><td>3.77 ( 0.17 ) </td><td>2.76 ( 0.1 ) </td><td> 7.01</td><td>0.52</td><td>1</td><td> 3.77</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>1 ( 0 )</td><td>3.43 ( 0.18 ) </td><td>1.5 ( 0.09 ) </td><td> 7.93</td><td>0.41</td><td>1</td><td> 3.43</td><td>1.50</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.00</td><td>1 ( 0 )</td><td>24.49 ( 0.47 )</td><td>5.11 ( 0.08 )</td><td>25.38</td><td>0.96</td><td>1</td><td>24.49</td><td>5.11</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>23.05 ( 0.49 )</td><td>3.15 ( 0.09 )</td><td>25.90</td><td>0.89</td><td>1</td><td>23.05</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.11</td><td>1 ( 0 )</td><td>22.18 ( 0.49 )</td><td>1.91 ( 0.09 )</td><td>26.27</td><td>0.84</td><td>1</td><td>22.18</td><td>1.91</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>46.96 ( 0.66 )</td><td>5.18 ( 0.08 )</td><td>47.78</td><td>0.98</td><td>1</td><td>46.96</td><td>5.18</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>1.29 ( 0.11 ) </td><td>2.25 ( 0.08 )</td><td> 5.04</td><td>0.23</td><td>1</td><td> 1.29</td><td>2.25</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.63</td><td>1 ( 0 )</td><td>1.29 ( 0.11 ) </td><td>1.3 ( 0.08 ) </td><td> 5.99</td><td>0.19</td><td>1</td><td> 1.29</td><td>1.30</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.03</td><td>1 ( 0 )</td><td>4.35 ( 0.18 ) </td><td>4.98 ( 0.07 )</td><td> 5.37</td><td>0.8 </td><td>1</td><td> 4.35</td><td>4.98</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.34</td><td>1 ( 0 )</td><td>3.46 ( 0.18 ) </td><td>2.53 ( 0.09 )</td><td> 6.93</td><td>0.47</td><td>1</td><td> 3.46</td><td>2.53</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.48</td><td>1 ( 0 )</td><td>3.04 ( 0.15 ) </td><td>1.27 ( 0.08 )</td><td> 7.77</td><td>0.37</td><td>1</td><td> 3.04</td><td>1.27</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.78 ( 0.44 )</td><td>4.93 ( 0.08 )</td><td>24.85</td><td>0.96</td><td>1</td><td>23.78</td><td>4.93</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>23.14 ( 0.52 )</td><td>2.73 ( 0.09 )</td><td>26.41</td><td>0.87</td><td>1</td><td>23.14</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>21.58 ( 0.49 )</td><td>1.57 ( 0.08 )</td><td>26.01</td><td>0.82</td><td>1</td><td>21.58</td><td>1.57</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.84 ( 0.72 )</td><td>5 ( 0.09 )   </td><td>48.84</td><td>0.98</td><td>1</td><td>47.84</td><td>5.00</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>47.31 ( 0.68 )</td><td>2.9 ( 0.1 )  </td><td>50.41</td><td>0.94</td><td>1</td><td>47.31</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>46.57 ( 0.73 )</td><td>1.71 ( 0.08 )</td><td>50.86</td><td>0.91</td><td>1</td><td>46.57</td><td>1.71</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.05</td><td>1 ( 0 )</td><td>2.2 ( 0.13 )  </td><td>5 ( 0.08 )   </td><td> 3.19</td><td>0.66</td><td>1</td><td> 2.20</td><td>5.00</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>1.28 ( 0.1 )  </td><td>2.32 ( 0.08 )</td><td> 4.96</td><td>0.23</td><td>1</td><td> 1.28</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.67</td><td>1 ( 0 )</td><td>0.93 ( 0.09 ) </td><td>1.31 ( 0.09 )</td><td> 5.62</td><td>0.15</td><td>1</td><td> 0.93</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.04</td><td>1 ( 0 )</td><td>4.24 ( 0.16 ) </td><td>4.83 ( 0.09 )</td><td> 5.40</td><td>0.79</td><td>1</td><td> 4.24</td><td>4.83</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.36</td><td>1 ( 0 )</td><td>3.54 ( 0.19 ) </td><td>2.29 ( 0.09 )</td><td> 7.25</td><td>0.46</td><td>1</td><td> 3.54</td><td>2.29</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>2.53 ( 0.14 ) </td><td>1.31 ( 0.08 )</td><td> 7.22</td><td>0.33</td><td>1</td><td> 2.53</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.63 ( 0.45 )</td><td>5.04 ( 0.08 )</td><td>24.59</td><td>0.96</td><td>1</td><td>23.63</td><td>5.04</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>27.64</td><td>0.88</td><td>1</td><td>24.29</td><td>2.65</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>25.93</td><td>0.82</td><td>1</td><td>21.42</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>48.53</td><td>0.98</td><td>1</td><td>47.48</td><td>4.95</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.09 ( 0.7 ) </td><td>2.94 ( 0.09 )</td><td>51.15</td><td>0.94</td><td>1</td><td>48.09</td><td>2.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>45.6 ( 0.72 ) </td><td>1.63 ( 0.08 )</td><td>49.97</td><td>0.91</td><td>1</td><td>45.60</td><td>1.63</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.01 & 1 ( 0 ) & 2.4 ( 0.16 )   & 5.3 ( 0.11 )  &  3.00 & 0.78 & 1 &  2.40 & 5.30\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.12 & 1 ( 0 ) & 2.96 ( 0.21 )  & 4.29 ( 0.15 ) &  4.63 & 0.62 & 1 &  2.96 & 4.29\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.33 & 1 ( 0 ) & 3.28 ( 0.21 )  & 3.04 ( 0.12 ) &  6.24 & 0.49 & 1 &  3.28 & 3.04\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.00 & 1 ( 0 ) & 5.09 ( 0.21 )  & 5.6 ( 0.09 )  &  5.48 & 0.93 & 1 &  5.09 & 5.60\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.10 & 1 ( 0 ) & 5.39 ( 0.29 )  & 4.05 ( 0.16 ) &  7.34 & 0.72 & 1 &  5.39 & 4.05\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.25 & 1 ( 0 ) & 5.43 ( 0.26 )  & 2.89 ( 0.14 ) &  8.54 & 0.62 & 1 &  5.43 & 2.89\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.00 & 1 ( 0 ) & 24.14 ( 0.56 ) & 5.35 ( 0.11 ) & 24.79 & 0.97 & 1 & 24.14 & 5.35\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.03 & 1 ( 0 ) & 24.48 ( 0.54 ) & 4.25 ( 0.16 ) & 26.23 & 0.93 & 1 & 24.48 & 4.25\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.07 & 1 ( 0 ) & 24.08 ( 0.54 ) & 3.1 ( 0.16 )  & 26.98 & 0.89 & 1 & 24.08 & 3.10\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.00 & 1 ( 0 ) & 48.22 ( 0.62 ) & 5.53 ( 0.09 ) & 48.69 & 0.99 & 1 & 48.22 & 5.53\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.01 & 1 ( 0 ) & 50.18 ( 0.78 ) & 4.63 ( 0.12 ) & 51.55 & 0.97 & 1 & 50.18 & 4.63\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.04 & 1 ( 0 ) & 49.43 ( 0.66 ) & 3.23 ( 0.14 ) & 52.20 & 0.95 & 1 & 49.43 & 3.23\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.02 & 1 ( 0 ) & 2.13 ( 0.12 )  & 5.14 ( 0.08 ) &  2.96 & 0.71 & 1 &  2.13 & 5.14\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.46 & 1 ( 0 ) & 1.43 ( 0.11 )  & 2.59 ( 0.11 ) &  4.84 & 0.27 & 1 &  1.43 & 2.59\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.60 & 1 ( 0 ) & 1.44 ( 0.13 )  & 1.35 ( 0.08 ) &  6.09 & 0.21 & 1 &  1.44 & 1.35\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.01 & 1 ( 0 ) & 4.84 ( 0.18 )  & 5.1 ( 0.08 )  &  5.73 & 0.84 & 1 &  4.84 & 5.10\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.28 & 1 ( 0 ) & 3.77 ( 0.17 )  & 2.76 ( 0.1 )  &  7.01 & 0.52 & 1 &  3.77 & 2.76\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.44 & 1 ( 0 ) & 3.43 ( 0.18 )  & 1.5 ( 0.09 )  &  7.93 & 0.41 & 1 &  3.43 & 1.50\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.00 & 1 ( 0 ) & 24.49 ( 0.47 ) & 5.11 ( 0.08 ) & 25.38 & 0.96 & 1 & 24.49 & 5.11\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.06 & 1 ( 0 ) & 23.05 ( 0.49 ) & 3.15 ( 0.09 ) & 25.90 & 0.89 & 1 & 23.05 & 3.15\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.11 & 1 ( 0 ) & 22.18 ( 0.49 ) & 1.91 ( 0.09 ) & 26.27 & 0.84 & 1 & 22.18 & 1.91\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.00 & 1 ( 0 ) & 46.96 ( 0.66 ) & 5.18 ( 0.08 ) & 47.78 & 0.98 & 1 & 46.96 & 5.18\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.53 & 1 ( 0 ) & 1.29 ( 0.11 )  & 2.25 ( 0.08 ) &  5.04 & 0.23 & 1 &  1.29 & 2.25\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.63 & 1 ( 0 ) & 1.29 ( 0.11 )  & 1.3 ( 0.08 )  &  5.99 & 0.19 & 1 &  1.29 & 1.30\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.03 & 1 ( 0 ) & 4.35 ( 0.18 )  & 4.98 ( 0.07 ) &  5.37 & 0.8  & 1 &  4.35 & 4.98\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.34 & 1 ( 0 ) & 3.46 ( 0.18 )  & 2.53 ( 0.09 ) &  6.93 & 0.47 & 1 &  3.46 & 2.53\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.48 & 1 ( 0 ) & 3.04 ( 0.15 )  & 1.27 ( 0.08 ) &  7.77 & 0.37 & 1 &  3.04 & 1.27\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.01 & 1 ( 0 ) & 23.78 ( 0.44 ) & 4.93 ( 0.08 ) & 24.85 & 0.96 & 1 & 23.78 & 4.93\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.08 & 1 ( 0 ) & 23.14 ( 0.52 ) & 2.73 ( 0.09 ) & 26.41 & 0.87 & 1 & 23.14 & 2.73\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.13 & 1 ( 0 ) & 21.58 ( 0.49 ) & 1.57 ( 0.08 ) & 26.01 & 0.82 & 1 & 21.58 & 1.57\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.00 & 1 ( 0 ) & 47.84 ( 0.72 ) & 5 ( 0.09 )    & 48.84 & 0.98 & 1 & 47.84 & 5.00\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.04 & 1 ( 0 ) & 47.31 ( 0.68 ) & 2.9 ( 0.1 )   & 50.41 & 0.94 & 1 & 47.31 & 2.90\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.06 & 1 ( 0 ) & 46.57 ( 0.73 ) & 1.71 ( 0.08 ) & 50.86 & 0.91 & 1 & 46.57 & 1.71\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.05 & 1 ( 0 ) & 2.2 ( 0.13 )   & 5 ( 0.08 )    &  3.19 & 0.66 & 1 &  2.20 & 5.00\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.53 & 1 ( 0 ) & 1.28 ( 0.1 )   & 2.32 ( 0.08 ) &  4.96 & 0.23 & 1 &  1.28 & 2.32\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.67 & 1 ( 0 ) & 0.93 ( 0.09 )  & 1.31 ( 0.09 ) &  5.62 & 0.15 & 1 &  0.93 & 1.31\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.04 & 1 ( 0 ) & 4.24 ( 0.16 )  & 4.83 ( 0.09 ) &  5.40 & 0.79 & 1 &  4.24 & 4.83\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.36 & 1 ( 0 ) & 3.54 ( 0.19 )  & 2.29 ( 0.09 ) &  7.25 & 0.46 & 1 &  3.54 & 2.29\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.53 & 1 ( 0 ) & 2.53 ( 0.14 )  & 1.31 ( 0.08 ) &  7.22 & 0.33 & 1 &  2.53 & 1.31\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.01 & 1 ( 0 ) & 23.63 ( 0.45 ) & 5.04 ( 0.08 ) & 24.59 & 0.96 & 1 & 23.63 & 5.04\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 27.64 & 0.88 & 1 & 24.29 & 2.65\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.13 & 1 ( 0 ) & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 25.93 & 0.82 & 1 & 21.42 & 1.49\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.00 & 1 ( 0 ) & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 48.53 & 0.98 & 1 & 47.48 & 4.95\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.04 & 1 ( 0 ) & 48.09 ( 0.7 )  & 2.94 ( 0.09 ) & 51.15 & 0.94 & 1 & 48.09 & 2.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 45.6 ( 0.72 )  & 1.63 ( 0.08 ) & 49.97 & 0.91 & 1 & 45.60 & 1.63\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.01 | 1 ( 0 ) | 2.4 ( 0.16 )   | 5.3 ( 0.11 )  |  3.00 | 0.78 | 1 |  2.40 | 5.30 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.12 | 1 ( 0 ) | 2.96 ( 0.21 )  | 4.29 ( 0.15 ) |  4.63 | 0.62 | 1 |  2.96 | 4.29 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.33 | 1 ( 0 ) | 3.28 ( 0.21 )  | 3.04 ( 0.12 ) |  6.24 | 0.49 | 1 |  3.28 | 3.04 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.00 | 1 ( 0 ) | 5.09 ( 0.21 )  | 5.6 ( 0.09 )  |  5.48 | 0.93 | 1 |  5.09 | 5.60 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.10 | 1 ( 0 ) | 5.39 ( 0.29 )  | 4.05 ( 0.16 ) |  7.34 | 0.72 | 1 |  5.39 | 4.05 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.25 | 1 ( 0 ) | 5.43 ( 0.26 )  | 2.89 ( 0.14 ) |  8.54 | 0.62 | 1 |  5.43 | 2.89 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.00 | 1 ( 0 ) | 24.14 ( 0.56 ) | 5.35 ( 0.11 ) | 24.79 | 0.97 | 1 | 24.14 | 5.35 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.03 | 1 ( 0 ) | 24.48 ( 0.54 ) | 4.25 ( 0.16 ) | 26.23 | 0.93 | 1 | 24.48 | 4.25 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.07 | 1 ( 0 ) | 24.08 ( 0.54 ) | 3.1 ( 0.16 )  | 26.98 | 0.89 | 1 | 24.08 | 3.10 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.00 | 1 ( 0 ) | 48.22 ( 0.62 ) | 5.53 ( 0.09 ) | 48.69 | 0.99 | 1 | 48.22 | 5.53 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.01 | 1 ( 0 ) | 50.18 ( 0.78 ) | 4.63 ( 0.12 ) | 51.55 | 0.97 | 1 | 50.18 | 4.63 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.04 | 1 ( 0 ) | 49.43 ( 0.66 ) | 3.23 ( 0.14 ) | 52.20 | 0.95 | 1 | 49.43 | 3.23 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.02 | 1 ( 0 ) | 2.13 ( 0.12 )  | 5.14 ( 0.08 ) |  2.96 | 0.71 | 1 |  2.13 | 5.14 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.46 | 1 ( 0 ) | 1.43 ( 0.11 )  | 2.59 ( 0.11 ) |  4.84 | 0.27 | 1 |  1.43 | 2.59 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.60 | 1 ( 0 ) | 1.44 ( 0.13 )  | 1.35 ( 0.08 ) |  6.09 | 0.21 | 1 |  1.44 | 1.35 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.01 | 1 ( 0 ) | 4.84 ( 0.18 )  | 5.1 ( 0.08 )  |  5.73 | 0.84 | 1 |  4.84 | 5.10 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.28 | 1 ( 0 ) | 3.77 ( 0.17 )  | 2.76 ( 0.1 )  |  7.01 | 0.52 | 1 |  3.77 | 2.76 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.44 | 1 ( 0 ) | 3.43 ( 0.18 )  | 1.5 ( 0.09 )  |  7.93 | 0.41 | 1 |  3.43 | 1.50 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.00 | 1 ( 0 ) | 24.49 ( 0.47 ) | 5.11 ( 0.08 ) | 25.38 | 0.96 | 1 | 24.49 | 5.11 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.06 | 1 ( 0 ) | 23.05 ( 0.49 ) | 3.15 ( 0.09 ) | 25.90 | 0.89 | 1 | 23.05 | 3.15 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.11 | 1 ( 0 ) | 22.18 ( 0.49 ) | 1.91 ( 0.09 ) | 26.27 | 0.84 | 1 | 22.18 | 1.91 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.00 | 1 ( 0 ) | 46.96 ( 0.66 ) | 5.18 ( 0.08 ) | 47.78 | 0.98 | 1 | 46.96 | 5.18 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.53 | 1 ( 0 ) | 1.29 ( 0.11 )  | 2.25 ( 0.08 ) |  5.04 | 0.23 | 1 |  1.29 | 2.25 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.63 | 1 ( 0 ) | 1.29 ( 0.11 )  | 1.3 ( 0.08 )  |  5.99 | 0.19 | 1 |  1.29 | 1.30 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.03 | 1 ( 0 ) | 4.35 ( 0.18 )  | 4.98 ( 0.07 ) |  5.37 | 0.8  | 1 |  4.35 | 4.98 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.34 | 1 ( 0 ) | 3.46 ( 0.18 )  | 2.53 ( 0.09 ) |  6.93 | 0.47 | 1 |  3.46 | 2.53 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.48 | 1 ( 0 ) | 3.04 ( 0.15 )  | 1.27 ( 0.08 ) |  7.77 | 0.37 | 1 |  3.04 | 1.27 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.01 | 1 ( 0 ) | 23.78 ( 0.44 ) | 4.93 ( 0.08 ) | 24.85 | 0.96 | 1 | 23.78 | 4.93 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.08 | 1 ( 0 ) | 23.14 ( 0.52 ) | 2.73 ( 0.09 ) | 26.41 | 0.87 | 1 | 23.14 | 2.73 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.13 | 1 ( 0 ) | 21.58 ( 0.49 ) | 1.57 ( 0.08 ) | 26.01 | 0.82 | 1 | 21.58 | 1.57 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.00 | 1 ( 0 ) | 47.84 ( 0.72 ) | 5 ( 0.09 )    | 48.84 | 0.98 | 1 | 47.84 | 5.00 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.04 | 1 ( 0 ) | 47.31 ( 0.68 ) | 2.9 ( 0.1 )   | 50.41 | 0.94 | 1 | 47.31 | 2.90 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.06 | 1 ( 0 ) | 46.57 ( 0.73 ) | 1.71 ( 0.08 ) | 50.86 | 0.91 | 1 | 46.57 | 1.71 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.05 | 1 ( 0 ) | 2.2 ( 0.13 )   | 5 ( 0.08 )    |  3.19 | 0.66 | 1 |  2.20 | 5.00 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.53 | 1 ( 0 ) | 1.28 ( 0.1 )   | 2.32 ( 0.08 ) |  4.96 | 0.23 | 1 |  1.28 | 2.32 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.67 | 1 ( 0 ) | 0.93 ( 0.09 )  | 1.31 ( 0.09 ) |  5.62 | 0.15 | 1 |  0.93 | 1.31 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.04 | 1 ( 0 ) | 4.24 ( 0.16 )  | 4.83 ( 0.09 ) |  5.40 | 0.79 | 1 |  4.24 | 4.83 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.36 | 1 ( 0 ) | 3.54 ( 0.19 )  | 2.29 ( 0.09 ) |  7.25 | 0.46 | 1 |  3.54 | 2.29 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.53 | 1 ( 0 ) | 2.53 ( 0.14 )  | 1.31 ( 0.08 ) |  7.22 | 0.33 | 1 |  2.53 | 1.31 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.01 | 1 ( 0 ) | 23.63 ( 0.45 ) | 5.04 ( 0.08 ) | 24.59 | 0.96 | 1 | 23.63 | 5.04 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 27.64 | 0.88 | 1 | 24.29 | 2.65 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.13 | 1 ( 0 ) | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 25.93 | 0.82 | 1 | 21.42 | 1.49 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.00 | 1 ( 0 ) | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 48.53 | 0.98 | 1 | 47.48 | 4.95 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.04 | 1 ( 0 ) | 48.09 ( 0.7 )  | 2.94 ( 0.09 ) | 51.15 | 0.94 | 1 | 48.09 | 2.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 45.6 ( 0.72 )  | 1.63 ( 0.08 ) | 49.97 | 0.91 | 1 | 45.60 | 1.63 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0.1   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0.1   0.50 0.01 1 ( 0 ) 2.4 ( 0.16 )   5.3 ( 0.11 )   3.00     \n",
+       "3   500   50 0.1   0.10 0.12 1 ( 0 ) 2.96 ( 0.21 )  4.29 ( 0.15 )  4.63     \n",
+       "4  1000   50 0.1   0.05 0.33 1 ( 0 ) 3.28 ( 0.21 )  3.04 ( 0.12 )  6.24     \n",
+       "5    50  100 0.1   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0.1   1.00 0.00 1 ( 0 ) 5.09 ( 0.21 )  5.6 ( 0.09 )   5.48     \n",
+       "7   500  100 0.1   0.20 0.10 1 ( 0 ) 5.39 ( 0.29 )  4.05 ( 0.16 )  7.34     \n",
+       "8  1000  100 0.1   0.10 0.25 1 ( 0 ) 5.43 ( 0.26 )  2.89 ( 0.14 )  8.54     \n",
+       "9    50  500 0.1  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0.1   5.00 0.00 1 ( 0 ) 24.14 ( 0.56 ) 5.35 ( 0.11 ) 24.79     \n",
+       "11  500  500 0.1   1.00 0.03 1 ( 0 ) 24.48 ( 0.54 ) 4.25 ( 0.16 ) 26.23     \n",
+       "12 1000  500 0.1   0.50 0.07 1 ( 0 ) 24.08 ( 0.54 ) 3.1 ( 0.16 )  26.98     \n",
+       "13   50 1000 0.1  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0.1  10.00 0.00 1 ( 0 ) 48.22 ( 0.62 ) 5.53 ( 0.09 ) 48.69     \n",
+       "15  500 1000 0.1   2.00 0.01 1 ( 0 ) 50.18 ( 0.78 ) 4.63 ( 0.12 ) 51.55     \n",
+       "16 1000 1000 0.1   1.00 0.04 1 ( 0 ) 49.43 ( 0.66 ) 3.23 ( 0.14 ) 52.20     \n",
+       "17   50   50 0.3   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "18  100   50 0.3   0.50 0.02 1 ( 0 ) 2.13 ( 0.12 )  5.14 ( 0.08 )  2.96     \n",
+       "19  500   50 0.3   0.10 0.46 1 ( 0 ) 1.43 ( 0.11 )  2.59 ( 0.11 )  4.84     \n",
+       "20 1000   50 0.3   0.05 0.60 1 ( 0 ) 1.44 ( 0.13 )  1.35 ( 0.08 )  6.09     \n",
+       "21   50  100 0.3   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "22  100  100 0.3   1.00 0.01 1 ( 0 ) 4.84 ( 0.18 )  5.1 ( 0.08 )   5.73     \n",
+       "23  500  100 0.3   0.20 0.28 1 ( 0 ) 3.77 ( 0.17 )  2.76 ( 0.1 )   7.01     \n",
+       "24 1000  100 0.3   0.10 0.44 1 ( 0 ) 3.43 ( 0.18 )  1.5 ( 0.09 )   7.93     \n",
+       "25   50  500 0.3  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "26  100  500 0.3   5.00 0.00 1 ( 0 ) 24.49 ( 0.47 ) 5.11 ( 0.08 ) 25.38     \n",
+       "27  500  500 0.3   1.00 0.06 1 ( 0 ) 23.05 ( 0.49 ) 3.15 ( 0.09 ) 25.90     \n",
+       "28 1000  500 0.3   0.50 0.11 1 ( 0 ) 22.18 ( 0.49 ) 1.91 ( 0.09 ) 26.27     \n",
+       "29   50 1000 0.3  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "30  100 1000 0.3  10.00 0.00 1 ( 0 ) 46.96 ( 0.66 ) 5.18 ( 0.08 ) 47.78     \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮       ⋮              ⋮             ⋮         \n",
+       "51  500   50 0.7   0.10 0.53 1 ( 0 ) 1.29 ( 0.11 )  2.25 ( 0.08 )  5.04     \n",
+       "52 1000   50 0.7   0.05 0.63 1 ( 0 ) 1.29 ( 0.11 )  1.3 ( 0.08 )   5.99     \n",
+       "53   50  100 0.7   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "54  100  100 0.7   1.00 0.03 1 ( 0 ) 4.35 ( 0.18 )  4.98 ( 0.07 )  5.37     \n",
+       "55  500  100 0.7   0.20 0.34 1 ( 0 ) 3.46 ( 0.18 )  2.53 ( 0.09 )  6.93     \n",
+       "56 1000  100 0.7   0.10 0.48 1 ( 0 ) 3.04 ( 0.15 )  1.27 ( 0.08 )  7.77     \n",
+       "57   50  500 0.7  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "58  100  500 0.7   5.00 0.01 1 ( 0 ) 23.78 ( 0.44 ) 4.93 ( 0.08 ) 24.85     \n",
+       "59  500  500 0.7   1.00 0.08 1 ( 0 ) 23.14 ( 0.52 ) 2.73 ( 0.09 ) 26.41     \n",
+       "60 1000  500 0.7   0.50 0.13 1 ( 0 ) 21.58 ( 0.49 ) 1.57 ( 0.08 ) 26.01     \n",
+       "61   50 1000 0.7  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "62  100 1000 0.7  10.00 0.00 1 ( 0 ) 47.84 ( 0.72 ) 5 ( 0.09 )    48.84     \n",
+       "63  500 1000 0.7   2.00 0.04 1 ( 0 ) 47.31 ( 0.68 ) 2.9 ( 0.1 )   50.41     \n",
+       "64 1000 1000 0.7   1.00 0.06 1 ( 0 ) 46.57 ( 0.73 ) 1.71 ( 0.08 ) 50.86     \n",
+       "65   50   50 0.9   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "66  100   50 0.9   0.50 0.05 1 ( 0 ) 2.2 ( 0.13 )   5 ( 0.08 )     3.19     \n",
+       "67  500   50 0.9   0.10 0.53 1 ( 0 ) 1.28 ( 0.1 )   2.32 ( 0.08 )  4.96     \n",
+       "68 1000   50 0.9   0.05 0.67 1 ( 0 ) 0.93 ( 0.09 )  1.31 ( 0.09 )  5.62     \n",
+       "69   50  100 0.9   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "70  100  100 0.9   1.00 0.04 1 ( 0 ) 4.24 ( 0.16 )  4.83 ( 0.09 )  5.40     \n",
+       "71  500  100 0.9   0.20 0.36 1 ( 0 ) 3.54 ( 0.19 )  2.29 ( 0.09 )  7.25     \n",
+       "72 1000  100 0.9   0.10 0.53 1 ( 0 ) 2.53 ( 0.14 )  1.31 ( 0.08 )  7.22     \n",
+       "73   50  500 0.9  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "74  100  500 0.9   5.00 0.01 1 ( 0 ) 23.63 ( 0.45 ) 5.04 ( 0.08 ) 24.59     \n",
+       "75  500  500 0.9   1.00 0.07 1 ( 0 ) 24.29 ( 0.42 ) 2.65 ( 0.09 ) 27.64     \n",
+       "76 1000  500 0.9   0.50 0.13 1 ( 0 ) 21.42 ( 0.42 ) 1.49 ( 0.08 ) 25.93     \n",
+       "77   50 1000 0.9  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.00 0.00 1 ( 0 ) 47.48 ( 0.72 ) 4.95 ( 0.08 ) 48.53     \n",
+       "79  500 1000 0.9   2.00 0.04 1 ( 0 ) 48.09 ( 0.7 )  2.94 ( 0.09 ) 51.15     \n",
+       "80 1000 1000 0.9   1.00 0.07 1 ( 0 ) 45.6 ( 0.72 )  1.63 ( 0.08 ) 49.97     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.78 1         2.40   5.30   \n",
+       "3  0.62 1         2.96   4.29   \n",
+       "4  0.49 1         3.28   3.04   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.93 1         5.09   5.60   \n",
+       "7  0.72 1         5.39   4.05   \n",
+       "8  0.62 1         5.43   2.89   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.97 1        24.14   5.35   \n",
+       "11 0.93 1        24.48   4.25   \n",
+       "12 0.89 1        24.08   3.10   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.99 1        48.22   5.53   \n",
+       "15 0.97 1        50.18   4.63   \n",
+       "16 0.95 1        49.43   3.23   \n",
+       "17 NaN  1         1.00   6.00   \n",
+       "18 0.71 1         2.13   5.14   \n",
+       "19 0.27 1         1.43   2.59   \n",
+       "20 0.21 1         1.44   1.35   \n",
+       "21 NaN  1         1.00   6.00   \n",
+       "22 0.84 1         4.84   5.10   \n",
+       "23 0.52 1         3.77   2.76   \n",
+       "24 0.41 1         3.43   1.50   \n",
+       "25 NaN  1         1.00   6.00   \n",
+       "26 0.96 1        24.49   5.11   \n",
+       "27 0.89 1        23.05   3.15   \n",
+       "28 0.84 1        22.18   1.91   \n",
+       "29 NaN  1         1.00   6.00   \n",
+       "30 0.98 1        46.96   5.18   \n",
+       "⋮  ⋮    ⋮        ⋮       ⋮      \n",
+       "51 0.23 1         1.29   2.25   \n",
+       "52 0.19 1         1.29   1.30   \n",
+       "53 NaN  1         1.00   6.00   \n",
+       "54 0.8  1         4.35   4.98   \n",
+       "55 0.47 1         3.46   2.53   \n",
+       "56 0.37 1         3.04   1.27   \n",
+       "57 NaN  1         1.00   6.00   \n",
+       "58 0.96 1        23.78   4.93   \n",
+       "59 0.87 1        23.14   2.73   \n",
+       "60 0.82 1        21.58   1.57   \n",
+       "61 NaN  1         1.00   6.00   \n",
+       "62 0.98 1        47.84   5.00   \n",
+       "63 0.94 1        47.31   2.90   \n",
+       "64 0.91 1        46.57   1.71   \n",
+       "65 NaN  1         1.00   6.00   \n",
+       "66 0.66 1         2.20   5.00   \n",
+       "67 0.23 1         1.28   2.32   \n",
+       "68 0.15 1         0.93   1.31   \n",
+       "69 NaN  1         1.00   6.00   \n",
+       "70 0.79 1         4.24   4.83   \n",
+       "71 0.46 1         3.54   2.29   \n",
+       "72 0.33 1         2.53   1.31   \n",
+       "73 NaN  1         1.00   6.00   \n",
+       "74 0.96 1        23.63   5.04   \n",
+       "75 0.88 1        24.29   2.65   \n",
+       "76 0.82 1        21.42   1.49   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.48   4.95   \n",
+       "79 0.94 1        48.09   2.94   \n",
+       "80 0.91 1        45.60   1.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.4_sim_block_GencompLasso_binary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.4_sim_block_GencompLasso_binary_update-checkpoint.ipynb
new file mode 100644
index 0000000..b3900c2
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/2.4_sim_block_GencompLasso_binary_update-checkpoint.ipynb
@@ -0,0 +1,1003 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
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+      "[1] \"indx: 45\"\n",
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+      "[1] \"indx: 50\"\n",
+      "[1] \"indx: 51\"\n",
+      "[1] \"indx: 52\"\n",
+      "[1] \"indx: 53\"\n",
+      "[1] \"indx: 54\"\n",
+      "[1] \"indx: 55\"\n",
+      "[1] \"indx: 56\"\n",
+      "[1] \"indx: 57\"\n",
+      "[1] \"indx: 58\"\n",
+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1.68 ( 0.53 )</td><td>5.42 ( 0.1 )</td><td>0.58 ( 0.01 )</td><td>0.02</td><td>2.26</td><td>0.58</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>0.08 ( 0.04 )</td><td>5.65 ( 0.08 )</td><td>0.51 ( 0 )</td><td>0.06</td><td>0.43</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0.5 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0.5 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>3.43 ( 0.63 )</td><td>5.23 ( 0.1 )</td><td>0.61 ( 0.01 )</td><td>0.04</td><td>4.20</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>0.31 ( 0.1 )</td><td>5.8 ( 0.05 )</td><td>0.51 ( 0 )</td><td>0.03</td><td>0.51</td><td>0.53</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1.68 ( 0.53 ) & 5.42 ( 0.1 ) & 0.58 ( 0.01 ) & 0.02 & 2.26 & 0.58\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 0.08 ( 0.04 ) & 5.65 ( 0.08 ) & 0.51 ( 0 ) & 0.06 & 0.43 & 0.18\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 0 ( 0 ) & 6 ( 0 ) & 0.5 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 0 ( 0 ) & 6 ( 0 ) & 0.5 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 3.43 ( 0.63 ) & 5.23 ( 0.1 ) & 0.61 ( 0.01 ) & 0.04 & 4.20 & 0.71\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 0.31 ( 0.1 ) & 5.8 ( 0.05 ) & 0.51 ( 0 ) & 0.03 & 0.51 & 0.53\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1.68 ( 0.53 ) | 5.42 ( 0.1 ) | 0.58 ( 0.01 ) | 0.02 | 2.26 | 0.58 |\n",
+       "| 2 | 100 | 50 | 0.1 | 0.08 ( 0.04 ) | 5.65 ( 0.08 ) | 0.51 ( 0 ) | 0.06 | 0.43 | 0.18 |\n",
+       "| 3 | 500 | 50 | 0.1 | 0 ( 0 ) | 6 ( 0 ) | 0.5 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 4 | 1000 | 50 | 0.1 | 0 ( 0 ) | 6 ( 0 ) | 0.5 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 5 | 50 | 100 | 0.1 | 3.43 ( 0.63 ) | 5.23 ( 0.1 ) | 0.61 ( 0.01 ) | 0.04 | 4.20 | 0.71 |\n",
+       "| 6 | 100 | 100 | 0.1 | 0.31 ( 0.1 ) | 5.8 ( 0.05 ) | 0.51 ( 0 ) | 0.03 | 0.51 | 0.53 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 1.68 ( 0.53 ) 5.42 ( 0.1 )  0.58 ( 0.01 ) 0.02 2.26       0.58\n",
+       "2 100  50  0.1 0.08 ( 0.04 ) 5.65 ( 0.08 ) 0.51 ( 0 )    0.06 0.43       0.18\n",
+       "3 500  50  0.1 0 ( 0 )       6 ( 0 )       0.5 ( 0 )     NaN  0.00        NaN\n",
+       "4 1000 50  0.1 0 ( 0 )       6 ( 0 )       0.5 ( 0 )     NaN  0.00        NaN\n",
+       "5 50   100 0.1 3.43 ( 0.63 ) 5.23 ( 0.1 )  0.61 ( 0.01 ) 0.04 4.20       0.71\n",
+       "6 100  100 0.1 0.31 ( 0.1 )  5.8 ( 0.05 )  0.51 ( 0 )    0.03 0.51       0.53"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>1.08 ( 0.33 )</td><td>0.01 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.85</td><td>  7.07</td><td>0.08</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>0.14 ( 0.07 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.98</td><td>  6.14</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>1 ( 0 )</td><td>0.01</td><td>335.54</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>1 ( 0 )</td><td>0.02</td><td>223.07</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>1.91 ( 0.48 )</td><td>0.02 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.76</td><td>  7.89</td><td>0.12</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>0.27 ( 0.11 )</td><td>0.02 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.95</td><td>  6.25</td><td>0.03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 1.08 ( 0.33 ) & 0.01 ( 0.01 ) & 0.91 ( 0 ) & 0.85 &   7.07 & 0.08\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 0.14 ( 0.07 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.98 &   6.14 & 0.02\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 1 ( 0 ) & 0.01 & 335.54 & 0.98\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 1 ( 0 ) & 0.02 & 223.07 & 0.97\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 1.91 ( 0.48 ) & 0.02 ( 0.01 ) & 0.91 ( 0 ) & 0.76 &   7.89 & 0.12\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 0.27 ( 0.11 ) & 0.02 ( 0.01 ) & 0.91 ( 0 ) & 0.95 &   6.25 & 0.03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 1.08 ( 0.33 ) | 0.01 ( 0.01 ) | 0.91 ( 0 ) | 0.85 |   7.07 | 0.08 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 0.14 ( 0.07 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.98 |   6.14 | 0.02 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 1 ( 0 ) | 0.01 | 335.54 | 0.98 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 1 ( 0 ) | 0.02 | 223.07 | 0.97 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 1.91 ( 0.48 ) | 0.02 ( 0.01 ) | 0.91 ( 0 ) | 0.76 |   7.89 | 0.12 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 0.27 ( 0.11 ) | 0.02 ( 0.01 ) | 0.91 ( 0 ) | 0.95 |   6.25 | 0.03 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP              FN            ROC        Stab num_select FDR \n",
+       "75 500  500  0.9 1.08 ( 0.33 )   0.01 ( 0.01 ) 0.91 ( 0 ) 0.85   7.07     0.08\n",
+       "76 1000 500  0.9 0.14 ( 0.07 )   0 ( 0 )       0.91 ( 0 ) 0.98   6.14     0.02\n",
+       "77 50   1000 0.9 330.51 ( 8.59 ) 0.97 ( 0.08 ) 1 ( 0 )    0.01 335.54     0.98\n",
+       "78 100  1000 0.9 217.74 ( 6.88 ) 0.67 ( 0.07 ) 1 ( 0 )    0.02 223.07     0.97\n",
+       "79 500  1000 0.9 1.91 ( 0.48 )   0.02 ( 0.01 ) 0.91 ( 0 ) 0.76   7.89     0.12\n",
+       "80 1000 1000 0.9 0.27 ( 0.11 )   0.02 ( 0.01 ) 0.91 ( 0 ) 0.95   6.25     0.03"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 7 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td>0.10</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td>0.05</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td>0.20</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td>0.10</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td>1.00</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td>0.50</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td>1.00</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 7 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t3 &  500 &   50 & 0.1 & 0.10 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 & 0.05 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t7 &  500 &  100 & 0.1 & 0.20 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 & 0.10 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t11 &  500 &  500 & 0.1 & 1.00 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 & 0.50 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 & 1.00 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 7 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3 |  500 |   50 | 0.1 | 0.10 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 4 | 1000 |   50 | 0.1 | 0.05 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 7 |  500 |  100 | 0.1 | 0.20 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 8 | 1000 |  100 | 0.1 | 0.10 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 11 |  500 |  500 | 0.1 | 1.00 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 12 | 1000 |  500 | 0.1 | 0.50 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 16 | 1000 | 1000 | 0.1 | 1.00 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC       FP      FN      num_select FDR ROC_mean\n",
+       "3   500   50 0.1  0.10  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "4  1000   50 0.1  0.05  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "7   500  100 0.1  0.20  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "8  1000  100 0.1  0.10  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "11  500  500 0.1  1.00  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "12 1000  500 0.1  0.50  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "16 1000 1000 0.1  1.00  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "   FP_mean FN_mean\n",
+       "3  0       6      \n",
+       "4  0       6      \n",
+       "7  0       6      \n",
+       "8  0       6      \n",
+       "11 0       6      \n",
+       "12 0       6      \n",
+       "16 0       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.02</td><td>0.58 ( 0.01 )</td><td>1.68 ( 0.53 )</td><td>5.42 ( 0.1 ) </td><td>2.26</td><td>0.58</td><td>0.58</td><td>1.68</td><td>5.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.06</td><td>0.51 ( 0 )   </td><td>0.08 ( 0.04 )</td><td>5.65 ( 0.08 )</td><td>0.43</td><td>0.18</td><td>0.51</td><td>0.08</td><td>5.65</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>0.50</td><td>0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>0.50</td><td>0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.04</td><td>0.61 ( 0.01 )</td><td>3.43 ( 0.63 )</td><td>5.23 ( 0.1 ) </td><td>4.20</td><td>0.71</td><td>0.61</td><td>3.43</td><td>5.23</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.03</td><td>0.51 ( 0 )   </td><td>0.31 ( 0.1 ) </td><td>5.8 ( 0.05 ) </td><td>0.51</td><td>0.53</td><td>0.51</td><td>0.31</td><td>5.80</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.02 & 0.58 ( 0.01 ) & 1.68 ( 0.53 ) & 5.42 ( 0.1 )  & 2.26 & 0.58 & 0.58 & 1.68 & 5.42\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.06 & 0.51 ( 0 )    & 0.08 ( 0.04 ) & 5.65 ( 0.08 ) & 0.43 & 0.18 & 0.51 & 0.08 & 5.65\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 0.50 & 0.00 & 6.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 &  NaN & 0.5 ( 0 )     & 0 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 0.50 & 0.00 & 6.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.04 & 0.61 ( 0.01 ) & 3.43 ( 0.63 ) & 5.23 ( 0.1 )  & 4.20 & 0.71 & 0.61 & 3.43 & 5.23\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.03 & 0.51 ( 0 )    & 0.31 ( 0.1 )  & 5.8 ( 0.05 )  & 0.51 & 0.53 & 0.51 & 0.31 & 5.80\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.02 | 0.58 ( 0.01 ) | 1.68 ( 0.53 ) | 5.42 ( 0.1 )  | 2.26 | 0.58 | 0.58 | 1.68 | 5.42 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.06 | 0.51 ( 0 )    | 0.08 ( 0.04 ) | 5.65 ( 0.08 ) | 0.43 | 0.18 | 0.51 | 0.08 | 5.65 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 0.50 | 0.00 | 6.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 |  NaN | 0.5 ( 0 )     | 0 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 0.50 | 0.00 | 6.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.04 | 0.61 ( 0.01 ) | 3.43 ( 0.63 ) | 5.23 ( 0.1 )  | 4.20 | 0.71 | 0.61 | 3.43 | 5.23 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.03 | 0.51 ( 0 )    | 0.31 ( 0.1 )  | 5.8 ( 0.05 )  | 0.51 | 0.53 | 0.51 | 0.31 | 5.80 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.02 0.58 ( 0.01 ) 1.68 ( 0.53 ) 5.42 ( 0.1 )  2.26      \n",
+       "2  100  50 0.1  0.50  0.06 0.51 ( 0 )    0.08 ( 0.04 ) 5.65 ( 0.08 ) 0.43      \n",
+       "3  500  50 0.1  0.10   NaN 0.5 ( 0 )     0 ( 0 )       6 ( 0 )       0.00      \n",
+       "4 1000  50 0.1  0.05   NaN 0.5 ( 0 )     0 ( 0 )       6 ( 0 )       0.00      \n",
+       "5   50 100 0.1  2.00  0.04 0.61 ( 0.01 ) 3.43 ( 0.63 ) 5.23 ( 0.1 )  4.20      \n",
+       "6  100 100 0.1  1.00  0.03 0.51 ( 0 )    0.31 ( 0.1 )  5.8 ( 0.05 )  0.51      \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.58 0.58     1.68    5.42   \n",
+       "2 0.18 0.51     0.08    5.65   \n",
+       "3 NaN  0.50     0.00    6.00   \n",
+       "4 NaN  0.50     0.00    6.00   \n",
+       "5 0.71 0.61     3.43    5.23   \n",
+       "6 0.53 0.51     0.31    5.80   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.85</td><td>0.91 ( 0 )</td><td>1.08 ( 0.33 )  </td><td>0.01 ( 0.01 )</td><td>  7.07</td><td>0.08</td><td>0.91</td><td>  1.08</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.14 ( 0.07 )  </td><td>0 ( 0 )      </td><td>  6.14</td><td>0.02</td><td>0.91</td><td>  0.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>335.54</td><td>0.98</td><td>1.00</td><td>330.51</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.02</td><td>1 ( 0 )   </td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>223.07</td><td>0.97</td><td>1.00</td><td>217.74</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.76</td><td>0.91 ( 0 )</td><td>1.91 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>  7.89</td><td>0.12</td><td>0.91</td><td>  1.91</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.95</td><td>0.91 ( 0 )</td><td>0.27 ( 0.11 )  </td><td>0.02 ( 0.01 )</td><td>  6.25</td><td>0.03</td><td>0.91</td><td>  0.27</td><td>0.02</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.85 & 0.91 ( 0 ) & 1.08 ( 0.33 )   & 0.01 ( 0.01 ) &   7.07 & 0.08 & 0.91 &   1.08 & 0.01\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.98 & 0.91 ( 0 ) & 0.14 ( 0.07 )   & 0 ( 0 )       &   6.14 & 0.02 & 0.91 &   0.14 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.01 & 1 ( 0 )    & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 335.54 & 0.98 & 1.00 & 330.51 & 0.97\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.02 & 1 ( 0 )    & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 223.07 & 0.97 & 1.00 & 217.74 & 0.67\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.76 & 0.91 ( 0 ) & 1.91 ( 0.48 )   & 0.02 ( 0.01 ) &   7.89 & 0.12 & 0.91 &   1.91 & 0.02\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.95 & 0.91 ( 0 ) & 0.27 ( 0.11 )   & 0.02 ( 0.01 ) &   6.25 & 0.03 & 0.91 &   0.27 & 0.02\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.85 | 0.91 ( 0 ) | 1.08 ( 0.33 )   | 0.01 ( 0.01 ) |   7.07 | 0.08 | 0.91 |   1.08 | 0.01 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.98 | 0.91 ( 0 ) | 0.14 ( 0.07 )   | 0 ( 0 )       |   6.14 | 0.02 | 0.91 |   0.14 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.01 | 1 ( 0 )    | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 335.54 | 0.98 | 1.00 | 330.51 | 0.97 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.02 | 1 ( 0 )    | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 223.07 | 0.97 | 1.00 | 217.74 | 0.67 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.76 | 0.91 ( 0 ) | 1.91 ( 0.48 )   | 0.02 ( 0.01 ) |   7.89 | 0.12 | 0.91 |   1.91 | 0.02 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.95 | 0.91 ( 0 ) | 0.27 ( 0.11 )   | 0.02 ( 0.01 ) |   6.25 | 0.03 | 0.91 |   0.27 | 0.02 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "75  500  500 0.9   1.0  0.85 0.91 ( 0 ) 1.08 ( 0.33 )   0.01 ( 0.01 )\n",
+       "76 1000  500 0.9   0.5  0.98 0.91 ( 0 ) 0.14 ( 0.07 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.01 1 ( 0 )    330.51 ( 8.59 ) 0.97 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.02 1 ( 0 )    217.74 ( 6.88 ) 0.67 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.76 0.91 ( 0 ) 1.91 ( 0.48 )   0.02 ( 0.01 )\n",
+       "80 1000 1000 0.9   1.0  0.95 0.91 ( 0 ) 0.27 ( 0.11 )   0.02 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75   7.07     0.08 0.91       1.08  0.01   \n",
+       "76   6.14     0.02 0.91       0.14  0.00   \n",
+       "77 335.54     0.98 1.00     330.51  0.97   \n",
+       "78 223.07     0.97 1.00     217.74  0.67   \n",
+       "79   7.89     0.12 0.91       1.91  0.02   \n",
+       "80   6.25     0.03 0.91       0.27  0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.02</td><td>0.58 ( 0.01 )</td><td>1.68 ( 0.53 )  </td><td>5.42 ( 0.1 ) </td><td>  2.26</td><td>0.58</td><td>0.58</td><td>  1.68</td><td>5.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.06</td><td>0.51 ( 0 )   </td><td>0.08 ( 0.04 )  </td><td>5.65 ( 0.08 )</td><td>  0.43</td><td>0.18</td><td>0.51</td><td>  0.08</td><td>5.65</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.04</td><td>0.61 ( 0.01 )</td><td>3.43 ( 0.63 )  </td><td>5.23 ( 0.1 ) </td><td>  4.20</td><td>0.71</td><td>0.61</td><td>  3.43</td><td>5.23</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>0.51 ( 0 )   </td><td>0.31 ( 0.1 )   </td><td>5.8 ( 0.05 ) </td><td>  0.51</td><td>0.53</td><td>0.51</td><td>  0.31</td><td>5.80</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 ) </td><td>5.17 ( 0.13 )</td><td> 30.45</td><td>0.9 </td><td>0.73</td><td> 29.62</td><td>5.17</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.04</td><td>0.53 ( 0.01 )</td><td>0.53 ( 0.1 )   </td><td>5.63 ( 0.08 )</td><td>  0.90</td><td>0.57</td><td>0.53</td><td>  0.53</td><td>5.63</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.01</td><td>0.83 ( 0.01 )</td><td>17.45 ( 2.89 ) </td><td>5.37 ( 0.1 ) </td><td> 18.08</td><td>0.94</td><td>0.83</td><td> 17.45</td><td>5.37</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.03</td><td>0.56 ( 0.01 )</td><td>1.27 ( 0.25 )  </td><td>5.59 ( 0.08 )</td><td>  1.68</td><td>0.73</td><td>0.56</td><td>  1.27</td><td>5.59</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.00</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>5.98 ( 0.02 )</td><td>  0.02</td><td>0   </td><td>0.50</td><td>  0.00</td><td>5.98</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.86 ( 0.01 )</td><td>9.81 ( 1.05 )  </td><td>1.78 ( 0.11 )</td><td> 14.03</td><td>0.6 </td><td>0.86</td><td>  9.81</td><td>1.78</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.37</td><td>0.83 ( 0.01 )</td><td>3.64 ( 0.75 )  </td><td>1.7 ( 0.09 ) </td><td>  7.94</td><td>0.27</td><td>0.83</td><td>  3.64</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.81</td><td>0.81 ( 0 )   </td><td>0.02 ( 0.01 )  </td><td>1.75 ( 0.09 )</td><td>  4.27</td><td>0   </td><td>0.81</td><td>  0.02</td><td>1.75</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.81</td><td>0.8 ( 0 )    </td><td>0.06 ( 0.05 )  </td><td>1.7 ( 0.09 ) </td><td>  4.36</td><td>0.01</td><td>0.80</td><td>  0.06</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.10</td><td>0.9 ( 0 )    </td><td>16.32 ( 1.58 ) </td><td>2.2 ( 0.11 ) </td><td> 20.12</td><td>0.74</td><td>0.90</td><td> 16.32</td><td>2.20</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.31</td><td>0.84 ( 0 )   </td><td>5.71 ( 1.02 )  </td><td>1.72 ( 0.1 ) </td><td>  9.99</td><td>0.41</td><td>0.84</td><td>  5.71</td><td>1.72</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.80</td><td>0.81 ( 0 )   </td><td>0.12 ( 0.06 )  </td><td>1.7 ( 0.09 ) </td><td>  4.42</td><td>0.01</td><td>0.81</td><td>  0.12</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.74</td><td>0.8 ( 0 )    </td><td>0.49 ( 0.26 )  </td><td>2.05 ( 0.08 )</td><td>  4.44</td><td>0.04</td><td>0.80</td><td>  0.49</td><td>2.05</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.02</td><td>1 ( 0 )      </td><td>105.94 ( 7.57 )</td><td>2.04 ( 0.11 )</td><td>109.90</td><td>0.95</td><td>1.00</td><td>105.94</td><td>2.04</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.12</td><td>0.88 ( 0.01 )</td><td>23.82 ( 3.36 ) </td><td>1.9 ( 0.09 ) </td><td> 27.92</td><td>0.75</td><td>0.88</td><td> 23.82</td><td>1.90</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.79</td><td>0.8 ( 0 )    </td><td>0.24 ( 0.08 )  </td><td>1.86 ( 0.09 )</td><td>  4.38</td><td>0.03</td><td>0.80</td><td>  0.24</td><td>1.86</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.73</td><td>0.8 ( 0 )    </td><td>0.63 ( 0.15 )  </td><td>1.85 ( 0.09 )</td><td>  4.78</td><td>0.08</td><td>0.80</td><td>  0.63</td><td>1.85</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>1 ( 0 )      </td><td>154.21 ( 5.09 )</td><td>2.09 ( 0.1 ) </td><td>158.12</td><td>0.97</td><td>1.00</td><td>154.21</td><td>2.09</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.07</td><td>0.91 ( 0 )   </td><td>36.7 ( 6.19 )  </td><td>2.26 ( 0.1 ) </td><td> 40.44</td><td>0.84</td><td>0.91</td><td> 36.70</td><td>2.26</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.96</td><td>0.9 ( 0 ) </td><td>0.17 ( 0.08 )  </td><td>0.04 ( 0.02 )</td><td>  6.13</td><td>0.02</td><td>0.90</td><td>  0.17</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.98</td><td>0.89 ( 0 )</td><td>0.1 ( 0.06 )   </td><td>0 ( 0 )      </td><td>  6.10</td><td>0.01</td><td>0.89</td><td>  0.10</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>35.59 ( 2.41 ) </td><td>0.94 ( 0.09 )</td><td> 40.65</td><td>0.83</td><td>0.99</td><td> 35.59</td><td>0.94</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.26</td><td>0.92 ( 0 )</td><td>11.64 ( 1.38 ) </td><td>0.53 ( 0.06 )</td><td> 17.11</td><td>0.57</td><td>0.92</td><td> 11.64</td><td>0.53</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.97</td><td>0.9 ( 0 ) </td><td>0.15 ( 0.07 )  </td><td>0.05 ( 0.02 )</td><td>  6.10</td><td>0.02</td><td>0.90</td><td>  0.15</td><td>0.05</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.93</td><td>0.9 ( 0 ) </td><td>0.41 ( 0.14 )  </td><td>0.03 ( 0.02 )</td><td>  6.38</td><td>0.04</td><td>0.90</td><td>  0.41</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>171.29 ( 6.32 )</td><td>1.12 ( 0.09 )</td><td>176.17</td><td>0.97</td><td>1.00</td><td>171.29</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.06</td><td>0.98 ( 0 )</td><td>68.31 ( 6.57 ) </td><td>0.98 ( 0.09 )</td><td> 73.33</td><td>0.87</td><td>0.98</td><td> 68.31</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.93</td><td>0.89 ( 0 )</td><td>0.37 ( 0.12 )  </td><td>0.06 ( 0.02 )</td><td>  6.31</td><td>0.04</td><td>0.89</td><td>  0.37</td><td>0.06</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.98</td><td>0.9 ( 0 ) </td><td>0.13 ( 0.07 )  </td><td>0.01 ( 0.01 )</td><td>  6.12</td><td>0.01</td><td>0.90</td><td>  0.13</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>275.88 ( 8.89 )</td><td>1.39 ( 0.09 )</td><td>280.49</td><td>0.98</td><td>1.00</td><td>275.88</td><td>1.39</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.03</td><td>1 ( 0 )   </td><td>155.38 ( 7.45 )</td><td>0.93 ( 0.08 )</td><td>160.45</td><td>0.95</td><td>1.00</td><td>155.38</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.84</td><td>0.9 ( 0 ) </td><td>1.07 ( 0.26 )  </td><td>0.1 ( 0.03 ) </td><td>  6.97</td><td>0.09</td><td>0.90</td><td>  1.07</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.95</td><td>0.9 ( 0 ) </td><td>0.3 ( 0.11 )   </td><td>0.02 ( 0.01 )</td><td>  6.28</td><td>0.03</td><td>0.90</td><td>  0.30</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>15.75 ( 1.04 ) </td><td>0.71 ( 0.07 )</td><td> 21.04</td><td>0.7 </td><td>0.97</td><td> 15.75</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.44</td><td>0.93 ( 0 )</td><td>5.06 ( 0.38 )  </td><td>0.34 ( 0.05 )</td><td> 10.72</td><td>0.42</td><td>0.93</td><td>  5.06</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.07 ( 0.04 )  </td><td>0.03 ( 0.02 )</td><td>  6.04</td><td>0.01</td><td>0.91</td><td>  0.07</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>1.00</td><td>0.91 ( 0 )</td><td>0 ( 0 )        </td><td>0 ( 0 )      </td><td>  6.00</td><td>0   </td><td>0.91</td><td>  0.00</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>37.69 ( 2.33 ) </td><td>0.96 ( 0.09 )</td><td> 42.73</td><td>0.84</td><td>0.99</td><td> 37.69</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.26</td><td>0.94 ( 0 )</td><td>11.53 ( 1.15 ) </td><td>0.52 ( 0.06 )</td><td> 17.01</td><td>0.61</td><td>0.94</td><td> 11.53</td><td>0.52</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.96</td><td>0.91 ( 0 )</td><td>0.18 ( 0.06 )  </td><td>0.03 ( 0.02 )</td><td>  6.15</td><td>0.02</td><td>0.91</td><td>  0.18</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.97</td><td>0.91 ( 0 )</td><td>0.17 ( 0.09 )  </td><td>0 ( 0 )      </td><td>  6.17</td><td>0.02</td><td>0.91</td><td>  0.17</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>189.13 ( 6.24 )</td><td>0.91 ( 0.1 ) </td><td>194.22</td><td>0.97</td><td>1.00</td><td>189.13</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.05</td><td>0.99 ( 0 )</td><td>88.92 ( 5.92 ) </td><td>0.68 ( 0.07 )</td><td> 94.24</td><td>0.92</td><td>0.99</td><td> 88.92</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.85</td><td>0.91 ( 0 )</td><td>1.08 ( 0.33 )  </td><td>0.01 ( 0.01 )</td><td>  7.07</td><td>0.08</td><td>0.91</td><td>  1.08</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.14 ( 0.07 )  </td><td>0 ( 0 )      </td><td>  6.14</td><td>0.02</td><td>0.91</td><td>  0.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>335.54</td><td>0.98</td><td>1.00</td><td>330.51</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>223.07</td><td>0.97</td><td>1.00</td><td>217.74</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.76</td><td>0.91 ( 0 )</td><td>1.91 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>  7.89</td><td>0.12</td><td>0.91</td><td>  1.91</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.95</td><td>0.91 ( 0 )</td><td>0.27 ( 0.11 )  </td><td>0.02 ( 0.01 )</td><td>  6.25</td><td>0.03</td><td>0.91</td><td>  0.27</td><td>0.02</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.02 & 0.58 ( 0.01 ) & 1.68 ( 0.53 )   & 5.42 ( 0.1 )  &   2.26 & 0.58 & 0.58 &   1.68 & 5.42\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.06 & 0.51 ( 0 )    & 0.08 ( 0.04 )   & 5.65 ( 0.08 ) &   0.43 & 0.18 & 0.51 &   0.08 & 5.65\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.04 & 0.61 ( 0.01 ) & 3.43 ( 0.63 )   & 5.23 ( 0.1 )  &   4.20 & 0.71 & 0.61 &   3.43 & 5.23\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.03 & 0.51 ( 0 )    & 0.31 ( 0.1 )    & 5.8 ( 0.05 )  &   0.51 & 0.53 & 0.51 &   0.31 & 5.80\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.00 & 0.73 ( 0.01 ) & 29.62 ( 7.94 )  & 5.17 ( 0.13 ) &  30.45 & 0.9  & 0.73 &  29.62 & 5.17\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.04 & 0.53 ( 0.01 ) & 0.53 ( 0.1 )    & 5.63 ( 0.08 ) &   0.90 & 0.57 & 0.53 &   0.53 & 5.63\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.01 & 0.83 ( 0.01 ) & 17.45 ( 2.89 )  & 5.37 ( 0.1 )  &  18.08 & 0.94 & 0.83 &  17.45 & 5.37\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.03 & 0.56 ( 0.01 ) & 1.27 ( 0.25 )   & 5.59 ( 0.08 ) &   1.68 & 0.73 & 0.56 &   1.27 & 5.59\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.00 & 0.5 ( 0 )     & 0 ( 0 )         & 5.98 ( 0.02 ) &   0.02 & 0    & 0.50 &   0.00 & 5.98\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.14 & 0.86 ( 0.01 ) & 9.81 ( 1.05 )   & 1.78 ( 0.11 ) &  14.03 & 0.6  & 0.86 &   9.81 & 1.78\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.37 & 0.83 ( 0.01 ) & 3.64 ( 0.75 )   & 1.7 ( 0.09 )  &   7.94 & 0.27 & 0.83 &   3.64 & 1.70\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.81 & 0.81 ( 0 )    & 0.02 ( 0.01 )   & 1.75 ( 0.09 ) &   4.27 & 0    & 0.81 &   0.02 & 1.75\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.81 & 0.8 ( 0 )     & 0.06 ( 0.05 )   & 1.7 ( 0.09 )  &   4.36 & 0.01 & 0.80 &   0.06 & 1.70\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.10 & 0.9 ( 0 )     & 16.32 ( 1.58 )  & 2.2 ( 0.11 )  &  20.12 & 0.74 & 0.90 &  16.32 & 2.20\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.31 & 0.84 ( 0 )    & 5.71 ( 1.02 )   & 1.72 ( 0.1 )  &   9.99 & 0.41 & 0.84 &   5.71 & 1.72\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.80 & 0.81 ( 0 )    & 0.12 ( 0.06 )   & 1.7 ( 0.09 )  &   4.42 & 0.01 & 0.81 &   0.12 & 1.70\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.74 & 0.8 ( 0 )     & 0.49 ( 0.26 )   & 2.05 ( 0.08 ) &   4.44 & 0.04 & 0.80 &   0.49 & 2.05\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.02 & 1 ( 0 )       & 105.94 ( 7.57 ) & 2.04 ( 0.11 ) & 109.90 & 0.95 & 1.00 & 105.94 & 2.04\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.12 & 0.88 ( 0.01 ) & 23.82 ( 3.36 )  & 1.9 ( 0.09 )  &  27.92 & 0.75 & 0.88 &  23.82 & 1.90\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.79 & 0.8 ( 0 )     & 0.24 ( 0.08 )   & 1.86 ( 0.09 ) &   4.38 & 0.03 & 0.80 &   0.24 & 1.86\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.73 & 0.8 ( 0 )     & 0.63 ( 0.15 )   & 1.85 ( 0.09 ) &   4.78 & 0.08 & 0.80 &   0.63 & 1.85\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 1 ( 0 )       & 154.21 ( 5.09 ) & 2.09 ( 0.1 )  & 158.12 & 0.97 & 1.00 & 154.21 & 2.09\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.07 & 0.91 ( 0 )    & 36.7 ( 6.19 )   & 2.26 ( 0.1 )  &  40.44 & 0.84 & 0.91 &  36.70 & 2.26\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.96 & 0.9 ( 0 )  & 0.17 ( 0.08 )   & 0.04 ( 0.02 ) &   6.13 & 0.02 & 0.90 &   0.17 & 0.04\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.98 & 0.89 ( 0 ) & 0.1 ( 0.06 )    & 0 ( 0 )       &   6.10 & 0.01 & 0.89 &   0.10 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.06 & 0.99 ( 0 ) & 35.59 ( 2.41 )  & 0.94 ( 0.09 ) &  40.65 & 0.83 & 0.99 &  35.59 & 0.94\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.26 & 0.92 ( 0 ) & 11.64 ( 1.38 )  & 0.53 ( 0.06 ) &  17.11 & 0.57 & 0.92 &  11.64 & 0.53\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.97 & 0.9 ( 0 )  & 0.15 ( 0.07 )   & 0.05 ( 0.02 ) &   6.10 & 0.02 & 0.90 &   0.15 & 0.05\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.93 & 0.9 ( 0 )  & 0.41 ( 0.14 )   & 0.03 ( 0.02 ) &   6.38 & 0.04 & 0.90 &   0.41 & 0.03\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 1 ( 0 )    & 171.29 ( 6.32 ) & 1.12 ( 0.09 ) & 176.17 & 0.97 & 1.00 & 171.29 & 1.12\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.06 & 0.98 ( 0 ) & 68.31 ( 6.57 )  & 0.98 ( 0.09 ) &  73.33 & 0.87 & 0.98 &  68.31 & 0.98\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.93 & 0.89 ( 0 ) & 0.37 ( 0.12 )   & 0.06 ( 0.02 ) &   6.31 & 0.04 & 0.89 &   0.37 & 0.06\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.98 & 0.9 ( 0 )  & 0.13 ( 0.07 )   & 0.01 ( 0.01 ) &   6.12 & 0.01 & 0.90 &   0.13 & 0.01\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 1 ( 0 )    & 275.88 ( 8.89 ) & 1.39 ( 0.09 ) & 280.49 & 0.98 & 1.00 & 275.88 & 1.39\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.03 & 1 ( 0 )    & 155.38 ( 7.45 ) & 0.93 ( 0.08 ) & 160.45 & 0.95 & 1.00 & 155.38 & 0.93\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.84 & 0.9 ( 0 )  & 1.07 ( 0.26 )   & 0.1 ( 0.03 )  &   6.97 & 0.09 & 0.90 &   1.07 & 0.10\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.95 & 0.9 ( 0 )  & 0.3 ( 0.11 )    & 0.02 ( 0.01 ) &   6.28 & 0.03 & 0.90 &   0.30 & 0.02\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.13 & 0.97 ( 0 ) & 15.75 ( 1.04 )  & 0.71 ( 0.07 ) &  21.04 & 0.7  & 0.97 &  15.75 & 0.71\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.44 & 0.93 ( 0 ) & 5.06 ( 0.38 )   & 0.34 ( 0.05 ) &  10.72 & 0.42 & 0.93 &   5.06 & 0.34\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.98 & 0.91 ( 0 ) & 0.07 ( 0.04 )   & 0.03 ( 0.02 ) &   6.04 & 0.01 & 0.91 &   0.07 & 0.03\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 1.00 & 0.91 ( 0 ) & 0 ( 0 )         & 0 ( 0 )       &   6.00 & 0    & 0.91 &   0.00 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.06 & 0.99 ( 0 ) & 37.69 ( 2.33 )  & 0.96 ( 0.09 ) &  42.73 & 0.84 & 0.99 &  37.69 & 0.96\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.26 & 0.94 ( 0 ) & 11.53 ( 1.15 )  & 0.52 ( 0.06 ) &  17.01 & 0.61 & 0.94 &  11.53 & 0.52\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.96 & 0.91 ( 0 ) & 0.18 ( 0.06 )   & 0.03 ( 0.02 ) &   6.15 & 0.02 & 0.91 &   0.18 & 0.03\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.97 & 0.91 ( 0 ) & 0.17 ( 0.09 )   & 0 ( 0 )       &   6.17 & 0.02 & 0.91 &   0.17 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.01 & 1 ( 0 )    & 189.13 ( 6.24 ) & 0.91 ( 0.1 )  & 194.22 & 0.97 & 1.00 & 189.13 & 0.91\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.05 & 0.99 ( 0 ) & 88.92 ( 5.92 )  & 0.68 ( 0.07 ) &  94.24 & 0.92 & 0.99 &  88.92 & 0.68\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.85 & 0.91 ( 0 ) & 1.08 ( 0.33 )   & 0.01 ( 0.01 ) &   7.07 & 0.08 & 0.91 &   1.08 & 0.01\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.98 & 0.91 ( 0 ) & 0.14 ( 0.07 )   & 0 ( 0 )       &   6.14 & 0.02 & 0.91 &   0.14 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.01 & 1 ( 0 )    & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 335.54 & 0.98 & 1.00 & 330.51 & 0.97\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.02 & 1 ( 0 )    & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 223.07 & 0.97 & 1.00 & 217.74 & 0.67\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.76 & 0.91 ( 0 ) & 1.91 ( 0.48 )   & 0.02 ( 0.01 ) &   7.89 & 0.12 & 0.91 &   1.91 & 0.02\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.95 & 0.91 ( 0 ) & 0.27 ( 0.11 )   & 0.02 ( 0.01 ) &   6.25 & 0.03 & 0.91 &   0.27 & 0.02\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.02 | 0.58 ( 0.01 ) | 1.68 ( 0.53 )   | 5.42 ( 0.1 )  |   2.26 | 0.58 | 0.58 |   1.68 | 5.42 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.06 | 0.51 ( 0 )    | 0.08 ( 0.04 )   | 5.65 ( 0.08 ) |   0.43 | 0.18 | 0.51 |   0.08 | 5.65 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.04 | 0.61 ( 0.01 ) | 3.43 ( 0.63 )   | 5.23 ( 0.1 )  |   4.20 | 0.71 | 0.61 |   3.43 | 5.23 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.03 | 0.51 ( 0 )    | 0.31 ( 0.1 )    | 5.8 ( 0.05 )  |   0.51 | 0.53 | 0.51 |   0.31 | 5.80 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.00 | 0.73 ( 0.01 ) | 29.62 ( 7.94 )  | 5.17 ( 0.13 ) |  30.45 | 0.9  | 0.73 |  29.62 | 5.17 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.04 | 0.53 ( 0.01 ) | 0.53 ( 0.1 )    | 5.63 ( 0.08 ) |   0.90 | 0.57 | 0.53 |   0.53 | 5.63 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.01 | 0.83 ( 0.01 ) | 17.45 ( 2.89 )  | 5.37 ( 0.1 )  |  18.08 | 0.94 | 0.83 |  17.45 | 5.37 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.03 | 0.56 ( 0.01 ) | 1.27 ( 0.25 )   | 5.59 ( 0.08 ) |   1.68 | 0.73 | 0.56 |   1.27 | 5.59 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.00 | 0.5 ( 0 )     | 0 ( 0 )         | 5.98 ( 0.02 ) |   0.02 | 0    | 0.50 |   0.00 | 5.98 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.14 | 0.86 ( 0.01 ) | 9.81 ( 1.05 )   | 1.78 ( 0.11 ) |  14.03 | 0.6  | 0.86 |   9.81 | 1.78 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.37 | 0.83 ( 0.01 ) | 3.64 ( 0.75 )   | 1.7 ( 0.09 )  |   7.94 | 0.27 | 0.83 |   3.64 | 1.70 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.81 | 0.81 ( 0 )    | 0.02 ( 0.01 )   | 1.75 ( 0.09 ) |   4.27 | 0    | 0.81 |   0.02 | 1.75 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.81 | 0.8 ( 0 )     | 0.06 ( 0.05 )   | 1.7 ( 0.09 )  |   4.36 | 0.01 | 0.80 |   0.06 | 1.70 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.10 | 0.9 ( 0 )     | 16.32 ( 1.58 )  | 2.2 ( 0.11 )  |  20.12 | 0.74 | 0.90 |  16.32 | 2.20 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.31 | 0.84 ( 0 )    | 5.71 ( 1.02 )   | 1.72 ( 0.1 )  |   9.99 | 0.41 | 0.84 |   5.71 | 1.72 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.80 | 0.81 ( 0 )    | 0.12 ( 0.06 )   | 1.7 ( 0.09 )  |   4.42 | 0.01 | 0.81 |   0.12 | 1.70 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.74 | 0.8 ( 0 )     | 0.49 ( 0.26 )   | 2.05 ( 0.08 ) |   4.44 | 0.04 | 0.80 |   0.49 | 2.05 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.02 | 1 ( 0 )       | 105.94 ( 7.57 ) | 2.04 ( 0.11 ) | 109.90 | 0.95 | 1.00 | 105.94 | 2.04 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.12 | 0.88 ( 0.01 ) | 23.82 ( 3.36 )  | 1.9 ( 0.09 )  |  27.92 | 0.75 | 0.88 |  23.82 | 1.90 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.79 | 0.8 ( 0 )     | 0.24 ( 0.08 )   | 1.86 ( 0.09 ) |   4.38 | 0.03 | 0.80 |   0.24 | 1.86 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.73 | 0.8 ( 0 )     | 0.63 ( 0.15 )   | 1.85 ( 0.09 ) |   4.78 | 0.08 | 0.80 |   0.63 | 1.85 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 1 ( 0 )       | 154.21 ( 5.09 ) | 2.09 ( 0.1 )  | 158.12 | 0.97 | 1.00 | 154.21 | 2.09 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.07 | 0.91 ( 0 )    | 36.7 ( 6.19 )   | 2.26 ( 0.1 )  |  40.44 | 0.84 | 0.91 |  36.70 | 2.26 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.96 | 0.9 ( 0 )  | 0.17 ( 0.08 )   | 0.04 ( 0.02 ) |   6.13 | 0.02 | 0.90 |   0.17 | 0.04 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.98 | 0.89 ( 0 ) | 0.1 ( 0.06 )    | 0 ( 0 )       |   6.10 | 0.01 | 0.89 |   0.10 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.06 | 0.99 ( 0 ) | 35.59 ( 2.41 )  | 0.94 ( 0.09 ) |  40.65 | 0.83 | 0.99 |  35.59 | 0.94 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.26 | 0.92 ( 0 ) | 11.64 ( 1.38 )  | 0.53 ( 0.06 ) |  17.11 | 0.57 | 0.92 |  11.64 | 0.53 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.97 | 0.9 ( 0 )  | 0.15 ( 0.07 )   | 0.05 ( 0.02 ) |   6.10 | 0.02 | 0.90 |   0.15 | 0.05 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.93 | 0.9 ( 0 )  | 0.41 ( 0.14 )   | 0.03 ( 0.02 ) |   6.38 | 0.04 | 0.90 |   0.41 | 0.03 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 1 ( 0 )    | 171.29 ( 6.32 ) | 1.12 ( 0.09 ) | 176.17 | 0.97 | 1.00 | 171.29 | 1.12 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.06 | 0.98 ( 0 ) | 68.31 ( 6.57 )  | 0.98 ( 0.09 ) |  73.33 | 0.87 | 0.98 |  68.31 | 0.98 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.93 | 0.89 ( 0 ) | 0.37 ( 0.12 )   | 0.06 ( 0.02 ) |   6.31 | 0.04 | 0.89 |   0.37 | 0.06 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.98 | 0.9 ( 0 )  | 0.13 ( 0.07 )   | 0.01 ( 0.01 ) |   6.12 | 0.01 | 0.90 |   0.13 | 0.01 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 1 ( 0 )    | 275.88 ( 8.89 ) | 1.39 ( 0.09 ) | 280.49 | 0.98 | 1.00 | 275.88 | 1.39 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.03 | 1 ( 0 )    | 155.38 ( 7.45 ) | 0.93 ( 0.08 ) | 160.45 | 0.95 | 1.00 | 155.38 | 0.93 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.84 | 0.9 ( 0 )  | 1.07 ( 0.26 )   | 0.1 ( 0.03 )  |   6.97 | 0.09 | 0.90 |   1.07 | 0.10 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.95 | 0.9 ( 0 )  | 0.3 ( 0.11 )    | 0.02 ( 0.01 ) |   6.28 | 0.03 | 0.90 |   0.30 | 0.02 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.13 | 0.97 ( 0 ) | 15.75 ( 1.04 )  | 0.71 ( 0.07 ) |  21.04 | 0.7  | 0.97 |  15.75 | 0.71 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.44 | 0.93 ( 0 ) | 5.06 ( 0.38 )   | 0.34 ( 0.05 ) |  10.72 | 0.42 | 0.93 |   5.06 | 0.34 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.98 | 0.91 ( 0 ) | 0.07 ( 0.04 )   | 0.03 ( 0.02 ) |   6.04 | 0.01 | 0.91 |   0.07 | 0.03 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 1.00 | 0.91 ( 0 ) | 0 ( 0 )         | 0 ( 0 )       |   6.00 | 0    | 0.91 |   0.00 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.06 | 0.99 ( 0 ) | 37.69 ( 2.33 )  | 0.96 ( 0.09 ) |  42.73 | 0.84 | 0.99 |  37.69 | 0.96 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.26 | 0.94 ( 0 ) | 11.53 ( 1.15 )  | 0.52 ( 0.06 ) |  17.01 | 0.61 | 0.94 |  11.53 | 0.52 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.96 | 0.91 ( 0 ) | 0.18 ( 0.06 )   | 0.03 ( 0.02 ) |   6.15 | 0.02 | 0.91 |   0.18 | 0.03 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.97 | 0.91 ( 0 ) | 0.17 ( 0.09 )   | 0 ( 0 )       |   6.17 | 0.02 | 0.91 |   0.17 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.01 | 1 ( 0 )    | 189.13 ( 6.24 ) | 0.91 ( 0.1 )  | 194.22 | 0.97 | 1.00 | 189.13 | 0.91 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.05 | 0.99 ( 0 ) | 88.92 ( 5.92 )  | 0.68 ( 0.07 ) |  94.24 | 0.92 | 0.99 |  88.92 | 0.68 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.85 | 0.91 ( 0 ) | 1.08 ( 0.33 )   | 0.01 ( 0.01 ) |   7.07 | 0.08 | 0.91 |   1.08 | 0.01 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.98 | 0.91 ( 0 ) | 0.14 ( 0.07 )   | 0 ( 0 )       |   6.14 | 0.02 | 0.91 |   0.14 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.01 | 1 ( 0 )    | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 335.54 | 0.98 | 1.00 | 330.51 | 0.97 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.02 | 1 ( 0 )    | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 223.07 | 0.97 | 1.00 | 217.74 | 0.67 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.76 | 0.91 ( 0 ) | 1.91 ( 0.48 )   | 0.02 ( 0.01 ) |   7.89 | 0.12 | 0.91 |   1.91 | 0.02 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.95 | 0.91 ( 0 ) | 0.27 ( 0.11 )   | 0.02 ( 0.01 ) |   6.25 | 0.03 | 0.91 |   0.27 | 0.02 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP              FN           \n",
+       "1    50   50 0.1   1.00 0.02 0.58 ( 0.01 ) 1.68 ( 0.53 )   5.42 ( 0.1 ) \n",
+       "2   100   50 0.1   0.50 0.06 0.51 ( 0 )    0.08 ( 0.04 )   5.65 ( 0.08 )\n",
+       "3   500   50 0.1   0.10  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "4  1000   50 0.1   0.05  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.04 0.61 ( 0.01 ) 3.43 ( 0.63 )   5.23 ( 0.1 ) \n",
+       "6   100  100 0.1   1.00 0.03 0.51 ( 0 )    0.31 ( 0.1 )    5.8 ( 0.05 ) \n",
+       "7   500  100 0.1   0.20  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "8  1000  100 0.1   0.10  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.00 0.73 ( 0.01 ) 29.62 ( 7.94 )  5.17 ( 0.13 )\n",
+       "10  100  500 0.1   5.00 0.04 0.53 ( 0.01 ) 0.53 ( 0.1 )    5.63 ( 0.08 )\n",
+       "11  500  500 0.1   1.00  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "12 1000  500 0.1   0.50  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.01 0.83 ( 0.01 ) 17.45 ( 2.89 )  5.37 ( 0.1 ) \n",
+       "14  100 1000 0.1  10.00 0.03 0.56 ( 0.01 ) 1.27 ( 0.25 )   5.59 ( 0.08 )\n",
+       "15  500 1000 0.1   2.00 0.00 0.5 ( 0 )     0 ( 0 )         5.98 ( 0.02 )\n",
+       "16 1000 1000 0.1   1.00  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.14 0.86 ( 0.01 ) 9.81 ( 1.05 )   1.78 ( 0.11 )\n",
+       "18  100   50 0.3   0.50 0.37 0.83 ( 0.01 ) 3.64 ( 0.75 )   1.7 ( 0.09 ) \n",
+       "19  500   50 0.3   0.10 0.81 0.81 ( 0 )    0.02 ( 0.01 )   1.75 ( 0.09 )\n",
+       "20 1000   50 0.3   0.05 0.81 0.8 ( 0 )     0.06 ( 0.05 )   1.7 ( 0.09 ) \n",
+       "21   50  100 0.3   2.00 0.10 0.9 ( 0 )     16.32 ( 1.58 )  2.2 ( 0.11 ) \n",
+       "22  100  100 0.3   1.00 0.31 0.84 ( 0 )    5.71 ( 1.02 )   1.72 ( 0.1 ) \n",
+       "23  500  100 0.3   0.20 0.80 0.81 ( 0 )    0.12 ( 0.06 )   1.7 ( 0.09 ) \n",
+       "24 1000  100 0.3   0.10 0.74 0.8 ( 0 )     0.49 ( 0.26 )   2.05 ( 0.08 )\n",
+       "25   50  500 0.3  10.00 0.02 1 ( 0 )       105.94 ( 7.57 ) 2.04 ( 0.11 )\n",
+       "26  100  500 0.3   5.00 0.12 0.88 ( 0.01 ) 23.82 ( 3.36 )  1.9 ( 0.09 ) \n",
+       "27  500  500 0.3   1.00 0.79 0.8 ( 0 )     0.24 ( 0.08 )   1.86 ( 0.09 )\n",
+       "28 1000  500 0.3   0.50 0.73 0.8 ( 0 )     0.63 ( 0.15 )   1.85 ( 0.09 )\n",
+       "29   50 1000 0.3  20.00 0.02 1 ( 0 )       154.21 ( 5.09 ) 2.09 ( 0.1 ) \n",
+       "30  100 1000 0.3  10.00 0.07 0.91 ( 0 )    36.7 ( 6.19 )   2.26 ( 0.1 ) \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮               ⋮            \n",
+       "51  500   50 0.7   0.10 0.96 0.9 ( 0 )     0.17 ( 0.08 )   0.04 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.98 0.89 ( 0 )    0.1 ( 0.06 )    0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.06 0.99 ( 0 )    35.59 ( 2.41 )  0.94 ( 0.09 )\n",
+       "54  100  100 0.7   1.00 0.26 0.92 ( 0 )    11.64 ( 1.38 )  0.53 ( 0.06 )\n",
+       "55  500  100 0.7   0.20 0.97 0.9 ( 0 )     0.15 ( 0.07 )   0.05 ( 0.02 )\n",
+       "56 1000  100 0.7   0.10 0.93 0.9 ( 0 )     0.41 ( 0.14 )   0.03 ( 0.02 )\n",
+       "57   50  500 0.7  10.00 0.02 1 ( 0 )       171.29 ( 6.32 ) 1.12 ( 0.09 )\n",
+       "58  100  500 0.7   5.00 0.06 0.98 ( 0 )    68.31 ( 6.57 )  0.98 ( 0.09 )\n",
+       "59  500  500 0.7   1.00 0.93 0.89 ( 0 )    0.37 ( 0.12 )   0.06 ( 0.02 )\n",
+       "60 1000  500 0.7   0.50 0.98 0.9 ( 0 )     0.13 ( 0.07 )   0.01 ( 0.01 )\n",
+       "61   50 1000 0.7  20.00 0.01 1 ( 0 )       275.88 ( 8.89 ) 1.39 ( 0.09 )\n",
+       "62  100 1000 0.7  10.00 0.03 1 ( 0 )       155.38 ( 7.45 ) 0.93 ( 0.08 )\n",
+       "63  500 1000 0.7   2.00 0.84 0.9 ( 0 )     1.07 ( 0.26 )   0.1 ( 0.03 ) \n",
+       "64 1000 1000 0.7   1.00 0.95 0.9 ( 0 )     0.3 ( 0.11 )    0.02 ( 0.01 )\n",
+       "65   50   50 0.9   1.00 0.13 0.97 ( 0 )    15.75 ( 1.04 )  0.71 ( 0.07 )\n",
+       "66  100   50 0.9   0.50 0.44 0.93 ( 0 )    5.06 ( 0.38 )   0.34 ( 0.05 )\n",
+       "67  500   50 0.9   0.10 0.98 0.91 ( 0 )    0.07 ( 0.04 )   0.03 ( 0.02 )\n",
+       "68 1000   50 0.9   0.05 1.00 0.91 ( 0 )    0 ( 0 )         0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.06 0.99 ( 0 )    37.69 ( 2.33 )  0.96 ( 0.09 )\n",
+       "70  100  100 0.9   1.00 0.26 0.94 ( 0 )    11.53 ( 1.15 )  0.52 ( 0.06 )\n",
+       "71  500  100 0.9   0.20 0.96 0.91 ( 0 )    0.18 ( 0.06 )   0.03 ( 0.02 )\n",
+       "72 1000  100 0.9   0.10 0.97 0.91 ( 0 )    0.17 ( 0.09 )   0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.01 1 ( 0 )       189.13 ( 6.24 ) 0.91 ( 0.1 ) \n",
+       "74  100  500 0.9   5.00 0.05 0.99 ( 0 )    88.92 ( 5.92 )  0.68 ( 0.07 )\n",
+       "75  500  500 0.9   1.00 0.85 0.91 ( 0 )    1.08 ( 0.33 )   0.01 ( 0.01 )\n",
+       "76 1000  500 0.9   0.50 0.98 0.91 ( 0 )    0.14 ( 0.07 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.01 1 ( 0 )       330.51 ( 8.59 ) 0.97 ( 0.08 )\n",
+       "78  100 1000 0.9  10.00 0.02 1 ( 0 )       217.74 ( 6.88 ) 0.67 ( 0.07 )\n",
+       "79  500 1000 0.9   2.00 0.76 0.91 ( 0 )    1.91 ( 0.48 )   0.02 ( 0.01 )\n",
+       "80 1000 1000 0.9   1.00 0.95 0.91 ( 0 )    0.27 ( 0.11 )   0.02 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1    2.26     0.58 0.58       1.68  5.42   \n",
+       "2    0.43     0.18 0.51       0.08  5.65   \n",
+       "3    0.00     NaN  0.50       0.00  6.00   \n",
+       "4    0.00     NaN  0.50       0.00  6.00   \n",
+       "5    4.20     0.71 0.61       3.43  5.23   \n",
+       "6    0.51     0.53 0.51       0.31  5.80   \n",
+       "7    0.00     NaN  0.50       0.00  6.00   \n",
+       "8    0.00     NaN  0.50       0.00  6.00   \n",
+       "9   30.45     0.9  0.73      29.62  5.17   \n",
+       "10   0.90     0.57 0.53       0.53  5.63   \n",
+       "11   0.00     NaN  0.50       0.00  6.00   \n",
+       "12   0.00     NaN  0.50       0.00  6.00   \n",
+       "13  18.08     0.94 0.83      17.45  5.37   \n",
+       "14   1.68     0.73 0.56       1.27  5.59   \n",
+       "15   0.02     0    0.50       0.00  5.98   \n",
+       "16   0.00     NaN  0.50       0.00  6.00   \n",
+       "17  14.03     0.6  0.86       9.81  1.78   \n",
+       "18   7.94     0.27 0.83       3.64  1.70   \n",
+       "19   4.27     0    0.81       0.02  1.75   \n",
+       "20   4.36     0.01 0.80       0.06  1.70   \n",
+       "21  20.12     0.74 0.90      16.32  2.20   \n",
+       "22   9.99     0.41 0.84       5.71  1.72   \n",
+       "23   4.42     0.01 0.81       0.12  1.70   \n",
+       "24   4.44     0.04 0.80       0.49  2.05   \n",
+       "25 109.90     0.95 1.00     105.94  2.04   \n",
+       "26  27.92     0.75 0.88      23.82  1.90   \n",
+       "27   4.38     0.03 0.80       0.24  1.86   \n",
+       "28   4.78     0.08 0.80       0.63  1.85   \n",
+       "29 158.12     0.97 1.00     154.21  2.09   \n",
+       "30  40.44     0.84 0.91      36.70  2.26   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51   6.13     0.02 0.90       0.17  0.04   \n",
+       "52   6.10     0.01 0.89       0.10  0.00   \n",
+       "53  40.65     0.83 0.99      35.59  0.94   \n",
+       "54  17.11     0.57 0.92      11.64  0.53   \n",
+       "55   6.10     0.02 0.90       0.15  0.05   \n",
+       "56   6.38     0.04 0.90       0.41  0.03   \n",
+       "57 176.17     0.97 1.00     171.29  1.12   \n",
+       "58  73.33     0.87 0.98      68.31  0.98   \n",
+       "59   6.31     0.04 0.89       0.37  0.06   \n",
+       "60   6.12     0.01 0.90       0.13  0.01   \n",
+       "61 280.49     0.98 1.00     275.88  1.39   \n",
+       "62 160.45     0.95 1.00     155.38  0.93   \n",
+       "63   6.97     0.09 0.90       1.07  0.10   \n",
+       "64   6.28     0.03 0.90       0.30  0.02   \n",
+       "65  21.04     0.7  0.97      15.75  0.71   \n",
+       "66  10.72     0.42 0.93       5.06  0.34   \n",
+       "67   6.04     0.01 0.91       0.07  0.03   \n",
+       "68   6.00     0    0.91       0.00  0.00   \n",
+       "69  42.73     0.84 0.99      37.69  0.96   \n",
+       "70  17.01     0.61 0.94      11.53  0.52   \n",
+       "71   6.15     0.02 0.91       0.18  0.03   \n",
+       "72   6.17     0.02 0.91       0.17  0.00   \n",
+       "73 194.22     0.97 1.00     189.13  0.91   \n",
+       "74  94.24     0.92 0.99      88.92  0.68   \n",
+       "75   7.07     0.08 0.91       1.08  0.01   \n",
+       "76   6.14     0.02 0.91       0.14  0.00   \n",
+       "77 335.54     0.98 1.00     330.51  0.97   \n",
+       "78 223.07     0.97 1.00     217.74  0.67   \n",
+       "79   7.89     0.12 0.91       1.91  0.02   \n",
+       "80   6.25     0.03 0.91       0.27  0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_GenCompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/3_sim_binary_summary_update-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/3_sim_binary_summary_update-checkpoint.ipynb
new file mode 100644
index 0000000..52874ac
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/3_sim_binary_summary_update-checkpoint.ipynb
@@ -0,0 +1,1052 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize method comparison results across 4 feature selection methods in different simulation scenarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Independent results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_lasso = read.csv('../results_summary_bin/sim_ind_lasso_binary.txt', sep='\\t')\n",
+    "ind_elnet = read.csv('../results_summary_bin/sim_ind_Elnet_binary.txt', sep='\\t')\n",
+    "ind_rf = read.csv('../results_summary_bin/sim_ind_RF_binary.txt', sep='\\t')\n",
+    "ind_compLasso = read.csv('../results_summary_bin/sim_ind_GencompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "ind_lasso$method = rep('lasso', dim(ind_lasso)[1])\n",
+    "ind_elnet$method = rep('elnet', dim(ind_elnet)[1])\n",
+    "ind_rf$method = rep('rf', dim(ind_rf)[1])\n",
+    "ind_compLasso$method = rep('compLasso', dim(ind_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(ind_lasso)\n",
+    "dim(ind_elnet)\n",
+    "dim(ind_rf)\n",
+    "dim(ind_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>64</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 64\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 64\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 64 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind = rbind(ind_lasso, ind_elnet)\n",
+    "ind = rbind(ind, ind_rf)\n",
+    "ind = rbind(ind, ind_compLasso)\n",
+    "dim(ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <int> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t33 & 50 &   50 & 0 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t37 & 50 &  100 & 0 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t41 & 50 &  500 & 0 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t45 & 50 & 1000 & 0 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;int&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 33 | 50 |   50 | 0 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 37 | 50 |  100 | 0 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 41 | 50 |  500 | 0 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 45 | 50 | 1000 | 0 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "33 50   50 0     1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0     2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0    10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0    20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean method\n",
+       "33 1       6       rf    \n",
+       "37 1       6       rf    \n",
+       "41 1       6       rf    \n",
+       "45 1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind[ind$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "ind$FP_mean[ind$num_select ==0] = 0\n",
+    "write.table(ind, '../results_summary_bin/table_ind_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Toeplitz results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_lasso = read.csv('../results_summary_bin/sim_toe_lasso_binary.txt', sep='\\t')\n",
+    "toe_elnet = read.csv('../results_summary_bin/sim_toe_Elnet_binary.txt', sep='\\t')\n",
+    "toe_rf = read.csv('../results_summary_bin/sim_toe_RF_binary.txt', sep='\\t')\n",
+    "toe_compLasso = read.csv('../results_summary_bin/sim_toe_GencompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "toe_lasso$method = rep('lasso', dim(toe_lasso)[1])\n",
+    "toe_elnet$method = rep('elnet', dim(toe_elnet)[1])\n",
+    "toe_rf$method = rep('rf', dim(toe_rf)[1])\n",
+    "toe_compLasso$method = rep('compLasso', dim(toe_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(toe_lasso)\n",
+    "dim(toe_elnet)\n",
+    "dim(toe_rf)\n",
+    "dim(toe_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>320</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe = rbind(toe_lasso, toe_elnet)\n",
+    "toe = rbind(toe, toe_rf)\n",
+    "toe = rbind(toe, toe_compLasso)\n",
+    "dim(toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <dbl> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t161 & 50 &   50 & 0.1 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t165 & 50 &  100 & 0.1 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t169 & 50 &  500 & 0.1 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t173 & 50 & 1000 & 0.1 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t177 & 50 &   50 & 0.3 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t181 & 50 &  100 & 0.3 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t185 & 50 &  500 & 0.3 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t189 & 50 & 1000 & 0.3 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t193 & 50 &   50 & 0.5 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t197 & 50 &  100 & 0.5 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t201 & 50 &  500 & 0.5 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t205 & 50 & 1000 & 0.5 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t209 & 50 &   50 & 0.7 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t213 & 50 &  100 & 0.7 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t217 & 50 &  500 & 0.7 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t221 & 50 & 1000 & 0.7 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t225 & 50 &   50 & 0.9 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t229 & 50 &  100 & 0.9 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t233 & 50 &  500 & 0.9 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t237 & 50 & 1000 & 0.9 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;dbl&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 | 50 |   50 | 0.1 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 165 | 50 |  100 | 0.1 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 169 | 50 |  500 | 0.1 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 173 | 50 | 1000 | 0.1 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 177 | 50 |   50 | 0.3 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 181 | 50 |  100 | 0.3 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 185 | 50 |  500 | 0.3 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 189 | 50 | 1000 | 0.3 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 193 | 50 |   50 | 0.5 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 197 | 50 |  100 | 0.5 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 201 | 50 |  500 | 0.5 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 205 | 50 | 1000 | 0.5 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 209 | 50 |   50 | 0.7 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 213 | 50 |  100 | 0.7 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 217 | 50 |  500 | 0.7 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 221 | 50 | 1000 | 0.7 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 225 | 50 |   50 | 0.9 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 229 | 50 |  100 | 0.9 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 233 | 50 |  500 | 0.9 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 237 | 50 | 1000 | 0.9 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "161 50   50 0.1   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "165 50  100 0.1   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "169 50  500 0.1  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "173 50 1000 0.1  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "177 50   50 0.3   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "181 50  100 0.3   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "185 50  500 0.3  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "189 50 1000 0.3  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "193 50   50 0.5   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "197 50  100 0.5   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "201 50  500 0.5  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "205 50 1000 0.5  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "209 50   50 0.7   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "213 50  100 0.7   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "217 50  500 0.7  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "221 50 1000 0.7  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "225 50   50 0.9   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "229 50  100 0.9   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "233 50  500 0.9  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "237 50 1000 0.9  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "    FP_mean FN_mean method\n",
+       "161 1       6       rf    \n",
+       "165 1       6       rf    \n",
+       "169 1       6       rf    \n",
+       "173 1       6       rf    \n",
+       "177 1       6       rf    \n",
+       "181 1       6       rf    \n",
+       "185 1       6       rf    \n",
+       "189 1       6       rf    \n",
+       "193 1       6       rf    \n",
+       "197 1       6       rf    \n",
+       "201 1       6       rf    \n",
+       "205 1       6       rf    \n",
+       "209 1       6       rf    \n",
+       "213 1       6       rf    \n",
+       "217 1       6       rf    \n",
+       "221 1       6       rf    \n",
+       "225 1       6       rf    \n",
+       "229 1       6       rf    \n",
+       "233 1       6       rf    \n",
+       "237 1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe[toe$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "toe$FP_mean[toe$num_select ==0] = 0\n",
+    "write.table(toe, '../results_summary_bin/table_toe_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Block results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block_lasso = read.csv('../results_summary_bin/sim_block_lasso_binary.txt', sep='\\t')\n",
+    "block_elnet = read.csv('../results_summary_bin/sim_block_Elnet_binary.txt', sep='\\t')\n",
+    "block_rf = read.csv('../results_summary_bin/sim_block_RF_binary.txt', sep='\\t')\n",
+    "block_compLasso = read.csv('../results_summary_bin/sim_block_GenCompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "block_lasso$method = rep('lasso', dim(block_lasso)[1])\n",
+    "block_elnet$method = rep('elnet', dim(block_elnet)[1])\n",
+    "block_rf$method = rep('rf', dim(block_rf)[1])\n",
+    "block_compLasso$method = rep('compLasso', dim(block_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
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+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(block_lasso)\n",
+    "dim(block_elnet)\n",
+    "dim(block_rf)\n",
+    "dim(block_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>320</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block = rbind(block_lasso, block_elnet)\n",
+    "block = rbind(block, block_rf)\n",
+    "block = rbind(block, block_compLasso)\n",
+    "dim(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 27 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>  50</td><td>  50</td><td>0.5</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>  50</td><td> 100</td><td>0.5</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>  50</td><td> 500</td><td>0.5</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>  50</td><td>1000</td><td>0.5</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>  50</td><td>  50</td><td>0.7</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>243</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 27 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <dbl> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t161 &   50 &   50 & 0.1 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t165 &   50 &  100 & 0.1 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t169 &   50 &  500 & 0.1 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t173 &   50 & 1000 & 0.1 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t177 &   50 &   50 & 0.3 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t181 &   50 &  100 & 0.3 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t185 &   50 &  500 & 0.3 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t189 &   50 & 1000 & 0.3 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t193 &   50 &   50 & 0.5 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t197 &   50 &  100 & 0.5 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t201 &   50 &  500 & 0.5 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t205 &   50 & 1000 & 0.5 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t209 &   50 &   50 & 0.7 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t213 &   50 &  100 & 0.7 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t217 &   50 &  500 & 0.7 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t221 &   50 & 1000 & 0.7 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t225 &   50 &   50 & 0.9 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t229 &   50 &  100 & 0.9 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t233 &   50 &  500 & 0.9 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t237 &   50 & 1000 & 0.9 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t243 &  500 &   50 & 0.1 &  0.10 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t244 & 1000 &   50 & 0.1 &  0.05 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t247 &  500 &  100 & 0.1 &  0.20 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t248 & 1000 &  100 & 0.1 &  0.10 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t251 &  500 &  500 & 0.1 &  1.00 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t252 & 1000 &  500 & 0.1 &  0.50 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 &  1.00 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 27 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;dbl&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 |   50 |   50 | 0.1 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 165 |   50 |  100 | 0.1 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 169 |   50 |  500 | 0.1 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 173 |   50 | 1000 | 0.1 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 177 |   50 |   50 | 0.3 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 181 |   50 |  100 | 0.3 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 185 |   50 |  500 | 0.3 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 189 |   50 | 1000 | 0.3 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 193 |   50 |   50 | 0.5 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 197 |   50 |  100 | 0.5 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 201 |   50 |  500 | 0.5 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 205 |   50 | 1000 | 0.5 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 209 |   50 |   50 | 0.7 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 213 |   50 |  100 | 0.7 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 217 |   50 |  500 | 0.7 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 221 |   50 | 1000 | 0.7 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 225 |   50 |   50 | 0.9 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 229 |   50 |  100 | 0.9 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 233 |   50 |  500 | 0.9 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 237 |   50 | 1000 | 0.9 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 243 |  500 |   50 | 0.1 |  0.10 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 244 | 1000 |   50 | 0.1 |  0.05 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 247 |  500 |  100 | 0.1 |  0.20 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 248 | 1000 |  100 | 0.1 |  0.10 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 251 |  500 |  500 | 0.1 |  1.00 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 252 | 1000 |  500 | 0.1 |  0.50 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 |  1.00 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC       FP      FN      num_select FDR ROC_mean\n",
+       "161   50   50 0.1   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "165   50  100 0.1   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "169   50  500 0.1  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "173   50 1000 0.1  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "177   50   50 0.3   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "181   50  100 0.3   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "185   50  500 0.3  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "189   50 1000 0.3  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "193   50   50 0.5   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "197   50  100 0.5   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "201   50  500 0.5  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "205   50 1000 0.5  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "209   50   50 0.7   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "213   50  100 0.7   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "217   50  500 0.7  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "221   50 1000 0.7  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "225   50   50 0.9   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "229   50  100 0.9   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "233   50  500 0.9  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "237   50 1000 0.9  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "243  500   50 0.1   0.10 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "244 1000   50 0.1   0.05 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "247  500  100 0.1   0.20 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "248 1000  100 0.1   0.10 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "251  500  500 0.1   1.00 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "252 1000  500 0.1   0.50 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "256 1000 1000 0.1   1.00 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "    FP_mean FN_mean method   \n",
+       "161 1       6       rf       \n",
+       "165 1       6       rf       \n",
+       "169 1       6       rf       \n",
+       "173 1       6       rf       \n",
+       "177 1       6       rf       \n",
+       "181 1       6       rf       \n",
+       "185 1       6       rf       \n",
+       "189 1       6       rf       \n",
+       "193 1       6       rf       \n",
+       "197 1       6       rf       \n",
+       "201 1       6       rf       \n",
+       "205 1       6       rf       \n",
+       "209 1       6       rf       \n",
+       "213 1       6       rf       \n",
+       "217 1       6       rf       \n",
+       "221 1       6       rf       \n",
+       "225 1       6       rf       \n",
+       "229 1       6       rf       \n",
+       "233 1       6       rf       \n",
+       "237 1       6       rf       \n",
+       "243 0       6       compLasso\n",
+       "244 0       6       compLasso\n",
+       "247 0       6       compLasso\n",
+       "248 0       6       compLasso\n",
+       "251 0       6       compLasso\n",
+       "252 0       6       compLasso\n",
+       "256 0       6       compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block[block$num_select ==0, ] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "block$FP_mean[block$num_select ==0] = 0\n",
+    "write.table(block, '../results_summary_bin/table_block_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/4_sim_binary_hypothesis_testing-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/4_sim_binary_hypothesis_testing-checkpoint.ipynb
new file mode 100644
index 0000000..2357762
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/4_sim_binary_hypothesis_testing-checkpoint.ipynb
@@ -0,0 +1,768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hypothesis testing based on boostrap for selected simulation scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, 'binary_update/boot_toe_RF_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_block_RF_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_toe_genCompLasso_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_block_genCompLasso_binary.RData'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing based on Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>toe_rf</th><th scope=col>toe_genCompLasso</th><th scope=col>block_rf</th><th scope=col>block_genCompLasso</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>0.01</td><td>0.19</td><td>0.01</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>0.01</td><td>0.19</td><td>0.01</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>0.01</td><td>0.19</td><td>0.00</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>0.01</td><td>0.19</td><td>0.00</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>0.01</td><td>0.17</td><td>0.01</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>0.01</td><td>0.18</td><td>0.01</td><td>0.18</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & toe\\_rf & toe\\_genCompLasso & block\\_rf & block\\_genCompLasso\\\\\n",
+       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 0.01 & 0.19 & 0.01 & 0.18\\\\\n",
+       "\t2 & 0.01 & 0.19 & 0.01 & 0.18\\\\\n",
+       "\t3 & 0.01 & 0.19 & 0.00 & 0.16\\\\\n",
+       "\t4 & 0.01 & 0.19 & 0.00 & 0.20\\\\\n",
+       "\t5 & 0.01 & 0.17 & 0.01 & 0.17\\\\\n",
+       "\t6 & 0.01 & 0.18 & 0.01 & 0.18\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 4\n",
+       "\n",
+       "| <!--/--> | toe_rf &lt;dbl&gt; | toe_genCompLasso &lt;dbl&gt; | block_rf &lt;dbl&gt; | block_genCompLasso &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | 0.01 | 0.19 | 0.01 | 0.18 |\n",
+       "| 2 | 0.01 | 0.19 | 0.01 | 0.18 |\n",
+       "| 3 | 0.01 | 0.19 | 0.00 | 0.16 |\n",
+       "| 4 | 0.01 | 0.19 | 0.00 | 0.20 |\n",
+       "| 5 | 0.01 | 0.17 | 0.01 | 0.17 |\n",
+       "| 6 | 0.01 | 0.18 | 0.01 | 0.18 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  toe_rf toe_genCompLasso block_rf block_genCompLasso\n",
+       "1 0.01   0.19             0.01     0.18              \n",
+       "2 0.01   0.19             0.01     0.18              \n",
+       "3 0.01   0.19             0.00     0.16              \n",
+       "4 0.01   0.19             0.00     0.20              \n",
+       "5 0.01   0.17             0.01     0.17              \n",
+       "6 0.01   0.18             0.01     0.18              "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = as.data.frame(cbind(toe_rf$stab_index, toe_genCompLasso$stab_index, block_rf$stab_index, block_genCompLasso$stab_index))\n",
+    "colnames(table) = c('toe_rf', 'toe_genCompLasso', 'block_rf', 'block_genCompLasso')\n",
+    "head(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1732"
+      ],
+      "text/latex": [
+       "0.1732"
+      ],
+      "text/markdown": [
+       "0.1732"
+      ],
+      "text/plain": [
+       "[1] 0.1732"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.0097"
+      ],
+      "text/latex": [
+       "0.0097"
+      ],
+      "text/markdown": [
+       "0.0097"
+      ],
+      "text/plain": [
+       "[1] 0.0097"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean(table$toe_genCompLasso)\n",
+    "mean(table$toe_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1635"
+      ],
+      "text/latex": [
+       "0.1635"
+      ],
+      "text/markdown": [
+       "0.1635"
+      ],
+      "text/plain": [
+       "[1] 0.1635"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.14</dd><dt>97.5%</dt><dd>0.18</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.14\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.18\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1497.5%\n",
+       ":   0.18\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.14  0.18 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (table$toe_genCompLasso - table$toe_rf)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1782"
+      ],
+      "text/latex": [
+       "0.1782"
+      ],
+      "text/markdown": [
+       "0.1782"
+      ],
+      "text/plain": [
+       "[1] 0.1782"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.0079"
+      ],
+      "text/latex": [
+       "0.0079"
+      ],
+      "text/markdown": [
+       "0.0079"
+      ],
+      "text/plain": [
+       "[1] 0.0079"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean(table$block_genCompLasso)\n",
+    "mean(table$block_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1703"
+      ],
+      "text/latex": [
+       "0.1703"
+      ],
+      "text/markdown": [
+       "0.1703"
+      ],
+      "text/plain": [
+       "[1] 0.1703"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.15</dd><dt>97.5%</dt><dd>0.19525</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.15\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.19525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1597.5%\n",
+       ":   0.19525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.15000 0.19525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (table$block_genCompLasso - table$block_rf)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing based on ROC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### block 0.5, p = 1000, n = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/block_RF_binary_', i, '.RData')) \n",
+    "results_block_rf[c('n', 'p', 'rou')]\n",
+    "block_rf_rocs = results_block_rf$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/block_GenCompLasso_binary_', i, '.RData')) \n",
+    "results_block_GenCompLasso[c('n', 'p', 'rou')]\n",
+    "block_GenCompLasso_rocs = results_block_GenCompLasso$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0181"
+      ],
+      "text/latex": [
+       "-0.0181"
+      ],
+      "text/markdown": [
+       "-0.0181"
+      ],
+      "text/plain": [
+       "[1] -0.0181"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.0700000000000001</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.0700000000000001\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.070000000000000197.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       "-0.07  0.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (block_GenCompLasso_rocs - block_rf_rocs)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diff_block\n",
+       "              -0.18 -0.0800000000000001 -0.0700000000000001 -0.0600000000000001 \n",
+       "                  1                   1                   2                   2 \n",
+       "              -0.05               -0.04               -0.03               -0.02 \n",
+       "                  7                   8                  10                  15 \n",
+       "              -0.01                   0 \n",
+       "                  2                  52 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table(diff_block)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Toeplitz 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/toe_RF_binary_', i, '.RData')) \n",
+    "results_toe_rf[c('n', 'p', 'rou')]\n",
+    "toe_rf_rocs = results_toe_rf$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/toe_GenCompLasso_binary_', i, '.RData')) \n",
+    "results_toe_GenCompLasso[c('n', 'p', 'rou')]\n",
+    "toe_GenCompLasso_rocs = results_toe_GenCompLasso$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-6e-04"
+      ],
+      "text/latex": [
+       "-6e-04"
+      ],
+      "text/markdown": [
+       "-6e-04"
+      ],
+      "text/plain": [
+       "[1] -6e-04"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.01</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.01\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.0197.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       "-0.01  0.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (toe_GenCompLasso_rocs - toe_rf_rocs)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diff_toe\n",
+       "-0.02 -0.01     0 \n",
+       "    2     2    96 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table(diff_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/5_applications_results-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/5_applications_results-checkpoint.ipynb
new file mode 100644
index 0000000..fa46c90
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/5_applications_results-checkpoint.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results for real microbiome data applications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/data_applications/'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BMI dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/BMI_binary_GenCompLasso.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/BMI_binary_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>bmi_gut</td><td>lasso    </td><td>0.63</td><td>0.14</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>elent    </td><td>0.78</td><td>0.19</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>rf       </td><td>1.00</td><td>0.01</td></tr>\n",
+       "\t<tr><td>bmi_gut</td><td>compLasso</td><td>0.85</td><td>0.29</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t bmi\\_gut & lasso     & 0.63 & 0.14\\\\\n",
+       "\t bmi\\_gut & elent     & 0.78 & 0.19\\\\\n",
+       "\t bmi\\_gut & rf        & 1.00 & 0.01\\\\\n",
+       "\t bmi\\_gut & compLasso & 0.85 & 0.29\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| bmi_gut | lasso     | 0.63 | 0.14 |\n",
+       "| bmi_gut | elent     | 0.78 | 0.19 |\n",
+       "| bmi_gut | rf        | 1.00 | 0.01 |\n",
+       "| bmi_gut | compLasso | 0.85 | 0.29 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 bmi_gut lasso     0.63 0.14     \n",
+       "2 bmi_gut elent     0.78 0.19     \n",
+       "3 bmi_gut rf        1.00 0.01     \n",
+       "4 bmi_gut compLasso 0.85 0.29     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bmi_gut = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(bmi_gut) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "bmi_gut$dataset = 'bmi_gut'\n",
+    "bmi_gut$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "bmi_gut$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "bmi_gut$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "bmi_gut"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/BMI_binary_boot_rf.RData'))\n",
+    "load(paste0(dir, '/BMI_binary_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2997"
+      ],
+      "text/latex": [
+       "0.2997"
+      ],
+      "text/markdown": [
+       "0.2997"
+      ],
+      "text/plain": [
+       "[1] 0.2997"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.11</dd><dt>97.5%</dt><dd>0.41525</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.11\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.41525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1197.5%\n",
+       ":   0.41525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.11000 0.41525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0942798529453605"
+      ],
+      "text/latex": [
+       "-0.0942798529453605"
+      ],
+      "text/markdown": [
+       "-0.0942798529453605"
+      ],
+      "text/plain": [
+       "[1] -0.09427985"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.19020132947925</dd><dt>97.5%</dt><dd>-0.020967051775781</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.19020132947925\n",
+       "\\item[97.5\\textbackslash{}\\%] -0.020967051775781\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.1902013294792597.5%\n",
+       ":   -0.020967051775781\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.19020133 -0.02096705 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "### CI doesn't contain zero: compLasso is significantly different from RF based on ROC\n",
+    "### although very close to zero (now ROC has similar scale as Stability, Stability still better differentiation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 88 soils dataset application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/soils_binary_ph_GenCompLasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_lasso.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_elnet.RData', sep=''))\n",
+    "load(paste0(dir, '/soils_binary_ph_rf.RData', sep=''))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>dataset</th><th scope=col>method</th><th scope=col>ROC</th><th scope=col>stability</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>soil_88</td><td>lasso    </td><td>0.90</td><td>0.28</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>elent    </td><td>0.94</td><td>0.32</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>rf       </td><td>1.00</td><td>0.03</td></tr>\n",
+       "\t<tr><td>soil_88</td><td>compLasso</td><td>0.96</td><td>0.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 4\n",
+       "\\begin{tabular}{llll}\n",
+       " dataset & method & ROC & stability\\\\\n",
+       " <chr> & <chr> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t soil\\_88 & lasso     & 0.90 & 0.28\\\\\n",
+       "\t soil\\_88 & elent     & 0.94 & 0.32\\\\\n",
+       "\t soil\\_88 & rf        & 1.00 & 0.03\\\\\n",
+       "\t soil\\_88 & compLasso & 0.96 & 0.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 4\n",
+       "\n",
+       "| dataset &lt;chr&gt; | method &lt;chr&gt; | ROC &lt;dbl&gt; | stability &lt;dbl&gt; |\n",
+       "|---|---|---|---|\n",
+       "| soil_88 | lasso     | 0.90 | 0.28 |\n",
+       "| soil_88 | elent     | 0.94 | 0.32 |\n",
+       "| soil_88 | rf        | 1.00 | 0.03 |\n",
+       "| soil_88 | compLasso | 0.96 | 0.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  dataset method    ROC  stability\n",
+       "1 soil_88 lasso     0.90 0.28     \n",
+       "2 soil_88 elent     0.94 0.32     \n",
+       "3 soil_88 rf        1.00 0.03     \n",
+       "4 soil_88 compLasso 0.96 0.46     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "soil_88 = as.data.frame(matrix(NA, nrow=4, ncol=4))\n",
+    "colnames(soil_88) = c('dataset', 'method', 'ROC', 'stability')\n",
+    "soil_88$dataset = 'soil_88'\n",
+    "soil_88$method = c('lasso', 'elent', 'rf', 'compLasso')\n",
+    "soil_88$ROC = c(out_lasso$ROC_mean, out_elnet$ROC_mean, out_rf$ROC_mean, out_GenCompLasso$ROC_mean)\n",
+    "soil_88$stability = c(out_lasso$stab_index, out_elnet$stab_index, out_rf$stab_index, out_GenCompLasso$stab_index)\n",
+    "soil_88"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# testing\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_rf.RData'))\n",
+    "load(paste0(dir, '/soils_binary_ph_boot_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.4323"
+      ],
+      "text/latex": [
+       "0.4323"
+      ],
+      "text/markdown": [
+       "0.4323"
+      ],
+      "text/plain": [
+       "[1] 0.4323"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.37</dd><dt>97.5%</dt><dd>0.5</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.37\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.5\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.3797.5%\n",
+       ":   0.5\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.37  0.50 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_stab = (boot_compLasso$stab_index - boot_rf$stab_index)\n",
+    "mean(diff_stab)\n",
+    "quantile(diff_stab, probs = c(0.025, 0.975)) \n",
+    "# CI doesn't contain zero: compLasso is significantly more stable than RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0247120820917325"
+      ],
+      "text/latex": [
+       "-0.0247120820917325"
+      ],
+      "text/markdown": [
+       "-0.0247120820917325"
+      ],
+      "text/plain": [
+       "[1] -0.02471208"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.0838574423480084</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.0838574423480084\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.083857442348008497.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       "       2.5%       97.5% \n",
+       "-0.08385744  0.00000000 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_ROC = (unlist(boot_compLasso$ROC_list) - unlist(boot_rf$ROC_list)) # use all 100*100 ROCs\n",
+    "mean(diff_ROC)\n",
+    "quantile(diff_ROC, probs = c(0.025, 0.975)) \n",
+    "# CI contain zero: compLasso is not significantly different from RF based on ROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n",
+       "-0.24949 -0.04206 -0.02250 -0.02471  0.00000  0.03769 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(diff_ROC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/.ipynb_checkpoints/6_make_figures_combined-checkpoint.ipynb b/simulations/notebooks_sim_bin/.ipynb_checkpoints/6_make_figures_combined-checkpoint.ipynb
new file mode 100644
index 0000000..992fa99
--- /dev/null
+++ b/simulations/notebooks_sim_bin/.ipynb_checkpoints/6_make_figures_combined-checkpoint.ipynb
@@ -0,0 +1,1937 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate updated figures for both continuous and binary outcomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading required package: magrittr\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "library(ggplot2)\n",
+    "library(gridExtra)\n",
+    "library(ggpubr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### load continuous results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts = read.csv('../results_summary_cts/table_ind_all.txt', sep='\\t')\n",
+    "toe_cts = read.csv('../results_summary_cts/table_toe_all.txt', sep='\\t')\n",
+    "block_cts = read.csv('../results_summary_cts/table_block_all.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts$Ratio = as.factor(ind_cts$Ratio)\n",
+    "toe_cts$Ratio = as.factor(toe_cts$Ratio)\n",
+    "block_cts$Ratio = as.factor(block_cts$Ratio)\n",
+    "\n",
+    "toe_cts$N = as.factor(toe_cts$N)\n",
+    "toe_cts$P = as.factor(toe_cts$P)\n",
+    "\n",
+    "toe_cts_sub1 = toe_cts[toe_cts$Corr %in% 0.1, ]\n",
+    "toe_cts_sub3 = toe_cts[toe_cts$Corr %in% 0.3, ]\n",
+    "toe_cts_sub5 = toe_cts[toe_cts$Corr %in% 0.5, ]\n",
+    "toe_cts_sub7 = toe_cts[toe_cts$Corr %in% 0.7, ]\n",
+    "toe_cts_sub9 = toe_cts[toe_cts$Corr %in% 0.9, ]\n",
+    "\n",
+    "block_cts$N = as.factor(block_cts$N)\n",
+    "block_cts$P = as.factor(block_cts$P)\n",
+    "block_cts_sub1 = block_cts[block_cts$Corr %in% 0.1, ]\n",
+    "block_cts_sub3 = block_cts[block_cts$Corr %in% 0.3, ]\n",
+    "block_cts_sub5 = block_cts[block_cts$Corr %in% 0.5, ]\n",
+    "block_cts_sub7 = block_cts[block_cts$Corr %in% 0.7, ]\n",
+    "block_cts_sub9 = block_cts[block_cts$Corr %in% 0.9, ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### load binary results (not that should be AUC in stead of ROC -- code checked)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin = read.csv('../results_summary_bin/table_ind_binary_all.txt', sep='\\t')\n",
+    "toe_bin = read.csv('../results_summary_bin/table_toe_binary_all.txt', sep='\\t')\n",
+    "block_bin = read.csv('../results_summary_bin/table_block_binary_all.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin$Ratio = as.factor(ind_bin$Ratio)\n",
+    "toe_bin$Ratio = as.factor(toe_bin$Ratio)\n",
+    "block_bin$Ratio = as.factor(block_bin$Ratio)\n",
+    "\n",
+    "toe_bin$N = as.factor(toe_bin$N)\n",
+    "toe_bin$P = as.factor(toe_bin$P)\n",
+    "toe_bin_sub1 = toe_bin[toe_bin$Corr %in% 0.1, ]\n",
+    "toe_bin_sub3 = toe_bin[toe_bin$Corr %in% 0.3, ]\n",
+    "toe_bin_sub5 = toe_bin[toe_bin$Corr %in% 0.5, ]\n",
+    "toe_bin_sub7 = toe_bin[toe_bin$Corr %in% 0.7, ]\n",
+    "toe_bin_sub9 = toe_bin[toe_bin$Corr %in% 0.9, ]\n",
+    "\n",
+    "block_bin$N = as.factor(block_bin$N)\n",
+    "block_bin$P = as.factor(block_bin$P)\n",
+    "block_bin_sub1 = block_bin[block_bin$Corr %in% 0.1, ]\n",
+    "block_bin_sub3 = block_bin[block_bin$Corr %in% 0.3, ]\n",
+    "block_bin_sub5 = block_bin[block_bin$Corr %in% 0.5, ]\n",
+    "block_bin_sub7 = block_bin[block_bin$Corr %in% 0.7, ]\n",
+    "block_bin_sub9 = block_bin[block_bin$Corr %in% 0.9, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50  </td><td>50 </td><td>0.1</td><td>1   </td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 ) </td><td>2.39</td><td>0.67</td><td>0.59</td><td>2.69</td><td>5.30</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100 </td><td>50 </td><td>0.1</td><td>0.5 </td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>2.00</td><td>0.39</td><td>0.56</td><td>2.02</td><td>5.02</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500 </td><td>50 </td><td>0.1</td><td>0.1 </td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )   </td><td>6.52</td><td>0.45</td><td>0.63</td><td>4.52</td><td>3.00</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50 </td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 ) </td><td>7.64</td><td>0.44</td><td>0.64</td><td>4.74</td><td>2.10</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50  </td><td>100</td><td>0.1</td><td>2   </td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>3.48</td><td>0.78</td><td>0.62</td><td>3.82</td><td>5.34</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100 </td><td>100</td><td>0.1</td><td>1   </td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>2.02</td><td>0.57</td><td>0.56</td><td>2.29</td><td>5.27</td><td>lasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50   & 50  & 0.1 & 1    & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 ) & 5.3 ( 0.07 )  & 2.39 & 0.67 & 0.59 & 2.69 & 5.30 & lasso\\\\\n",
+       "\t2 & 100  & 50  & 0.1 & 0.5  & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 2.00 & 0.39 & 0.56 & 2.02 & 5.02 & lasso\\\\\n",
+       "\t3 & 500  & 50  & 0.1 & 0.1  & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 ) & 3 ( 0.11 )    & 6.52 & 0.45 & 0.63 & 4.52 & 3.00 & lasso\\\\\n",
+       "\t4 & 1000 & 50  & 0.1 & 0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 ) & 2.1 ( 0.09 )  & 7.64 & 0.44 & 0.64 & 4.74 & 2.10 & lasso\\\\\n",
+       "\t5 & 50   & 100 & 0.1 & 2    & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 3.48 & 0.78 & 0.62 & 3.82 & 5.34 & lasso\\\\\n",
+       "\t6 & 100  & 100 & 0.1 & 1    & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 2.02 & 0.57 & 0.56 & 2.29 & 5.27 & lasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50   | 50  | 0.1 | 1    | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 ) | 5.3 ( 0.07 )  | 2.39 | 0.67 | 0.59 | 2.69 | 5.30 | lasso |\n",
+       "| 2 | 100  | 50  | 0.1 | 0.5  | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 2.00 | 0.39 | 0.56 | 2.02 | 5.02 | lasso |\n",
+       "| 3 | 500  | 50  | 0.1 | 0.1  | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 ) | 3 ( 0.11 )    | 6.52 | 0.45 | 0.63 | 4.52 | 3.00 | lasso |\n",
+       "| 4 | 1000 | 50  | 0.1 | 0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 ) | 2.1 ( 0.09 )  | 7.64 | 0.44 | 0.64 | 4.74 | 2.10 | lasso |\n",
+       "| 5 | 50   | 100 | 0.1 | 2    | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 3.48 | 0.78 | 0.62 | 3.82 | 5.34 | lasso |\n",
+       "| 6 | 100  | 100 | 0.1 | 1    | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 2.02 | 0.57 | 0.56 | 2.29 | 5.27 | lasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1 50   50  0.1  1     0.03 0.59 ( 0.01 ) 2.69 ( 0.14 ) 5.3 ( 0.07 )  2.39      \n",
+       "2 100  50  0.1  0.5   0.11 0.56 ( 0.01 ) 2.02 ( 0.15 ) 5.02 ( 0.08 ) 2.00      \n",
+       "3 500  50  0.1  0.1   0.28 0.63 ( 0 )    4.52 ( 0.29 ) 3 ( 0.11 )    6.52      \n",
+       "4 1000 50  0.1  0.05  0.39 0.64 ( 0 )    4.74 ( 0.22 ) 2.1 ( 0.09 )  7.64      \n",
+       "5 50   100 0.1  2     0.02 0.62 ( 0.01 ) 3.82 ( 0.16 ) 5.34 ( 0.07 ) 3.48      \n",
+       "6 100  100 0.1  1     0.07 0.56 ( 0.01 ) 2.29 ( 0.19 ) 5.27 ( 0.08 ) 2.02      \n",
+       "  FDR  ROC_mean FP_mean FN_mean method\n",
+       "1 0.67 0.59     2.69    5.30    lasso \n",
+       "2 0.39 0.56     2.02    5.02    lasso \n",
+       "3 0.45 0.63     4.52    3.00    lasso \n",
+       "4 0.44 0.64     4.74    2.10    lasso \n",
+       "5 0.78 0.62     3.82    5.34    lasso \n",
+       "6 0.57 0.56     2.29    5.27    lasso "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(block_bin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 1: MSE/AUC vs. Stability (evaludated by FPR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 9 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>243</th><td>500 </td><td>50  </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>50  </td><td>0.1</td><td>0.05</td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td>500 </td><td>100 </td><td>0.1</td><td>0.2 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td>100 </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>249</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 )</td><td>5.17 ( 0.13 )</td><td>30.45</td><td>0.9</td><td>0.73</td><td>29.62</td><td>5.17</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td>500 </td><td>500 </td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td>500 </td><td>0.1</td><td>0.5 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>255</th><td>500 </td><td>1000</td><td>0.1</td><td>2   </td><td> 0</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>5.98 ( 0.02 )</td><td> 0.02</td><td>0.0</td><td>0.50</td><td> 0.00</td><td>5.98</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 9 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t243 & 500  & 50   & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t244 & 1000 & 50   & 0.1 & 0.05 & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t247 & 500  & 100  & 0.1 & 0.2  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t248 & 1000 & 100  & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t249 & 50   & 500  & 0.1 & 10   &  0 & 0.73 ( 0.01 ) & 29.62 ( 7.94 ) & 5.17 ( 0.13 ) & 30.45 & 0.9 & 0.73 & 29.62 & 5.17 & compLasso\\\\\n",
+       "\t251 & 500  & 500  & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t252 & 1000 & 500  & 0.1 & 0.5  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t255 & 500  & 1000 & 0.1 & 2    &  0 & 0.5 ( 0 )     & 0 ( 0 )        & 5.98 ( 0.02 ) &  0.02 & 0.0 & 0.50 &  0.00 & 5.98 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 9 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 243 | 500  | 50   | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 244 | 1000 | 50   | 0.1 | 0.05 | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 247 | 500  | 100  | 0.1 | 0.2  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 248 | 1000 | 100  | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 249 | 50   | 500  | 0.1 | 10   |  0 | 0.73 ( 0.01 ) | 29.62 ( 7.94 ) | 5.17 ( 0.13 ) | 30.45 | 0.9 | 0.73 | 29.62 | 5.17 | compLasso |\n",
+       "| 251 | 500  | 500  | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 252 | 1000 | 500  | 0.1 | 0.5  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 255 | 500  | 1000 | 0.1 | 2    |  0 | 0.5 ( 0 )     | 0 ( 0 )        | 5.98 ( 0.02 ) |  0.02 | 0.0 | 0.50 |  0.00 | 5.98 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "243 500  50   0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "244 1000 50   0.1  0.05  NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "247 500  100  0.1  0.2   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "248 1000 100  0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "249 50   500  0.1  10     0   0.73 ( 0.01 ) 29.62 ( 7.94 ) 5.17 ( 0.13 )\n",
+       "251 500  500  0.1  1     NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "252 1000 500  0.1  0.5   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "255 500  1000 0.1  2      0   0.5 ( 0 )     0 ( 0 )        5.98 ( 0.02 )\n",
+       "256 1000 1000 0.1  1     NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "    num_select FDR ROC_mean FP_mean FN_mean method   \n",
+       "243  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "244  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "247  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "248  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "249 30.45      0.9 0.73     29.62   5.17    compLasso\n",
+       "251  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "252  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "255  0.02      0.0 0.50      0.00   5.98    compLasso\n",
+       "256  0.00       NA 0.50      0.00   6.00    compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block_bin[block_bin$method=='compLasso' & block_bin$Stab %in% c(NA, 0),]\n",
+    "\n",
+    "# composittional Lasso perform poor (often select nothing) at Block Corr 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 21 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>13</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td> 0</td><td>0.62 ( 0.01 )</td><td>10.42 ( 0.22 )  </td><td>5.56 ( 0.06 )</td><td>  9.86</td><td>0.95</td><td>0.62</td><td> 10.42</td><td>5.56</td><td>lasso    </td></tr>\n",
+       "\t<tr><th scope=row>89</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.61 ( 0.01 )</td><td>69.03 ( 7.45 )  </td><td>4.13 ( 0.19 )</td><td> 69.90</td><td>0.96</td><td>0.61</td><td> 69.03</td><td>4.13</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>90</th><td>100 </td><td>500 </td><td>0.1</td><td>5   </td><td> 0</td><td>0.57 ( 0.01 )</td><td>70.64 ( 9.13 )  </td><td>3.91 ( 0.17 )</td><td> 71.73</td><td>0.91</td><td>0.57</td><td> 70.64</td><td>3.91</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>93</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td> 0</td><td>0.59 ( 0.01 )</td><td>103.61 ( 10.54 )</td><td>4.34 ( 0.18 )</td><td>104.27</td><td>0.98</td><td>0.59</td><td>103.61</td><td>4.34</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>94</th><td>100 </td><td>1000</td><td>0.1</td><td>10  </td><td> 0</td><td>0.56 ( 0.01 )</td><td>103.21 ( 12.75 )</td><td>4.15 ( 0.17 )</td><td>104.06</td><td>0.95</td><td>0.56</td><td>103.21</td><td>4.15</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>161</th><td>50  </td><td>50  </td><td>0.1</td><td>1   </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50  </td><td>100 </td><td>0.1</td><td>2   </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>166</th><td>100 </td><td>100 </td><td>0.1</td><td>1   </td><td> 0</td><td>1 ( 0 )      </td><td>5.09 ( 0.21 )   </td><td>5.6 ( 0.09 ) </td><td>  5.48</td><td>0.93</td><td>1.00</td><td>  5.09</td><td>5.60</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>170</th><td>100 </td><td>500 </td><td>0.1</td><td>5   </td><td> 0</td><td>1 ( 0 )      </td><td>24.14 ( 0.56 )  </td><td>5.35 ( 0.11 )</td><td> 24.79</td><td>0.97</td><td>1.00</td><td> 24.14</td><td>5.35</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>174</th><td>100 </td><td>1000</td><td>0.1</td><td>10  </td><td> 0</td><td>1 ( 0 )      </td><td>48.22 ( 0.62 )  </td><td>5.53 ( 0.09 )</td><td> 48.69</td><td>0.99</td><td>1.00</td><td> 48.22</td><td>5.53</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>243</th><td>500 </td><td>50  </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>50  </td><td>0.1</td><td>0.05</td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td>500 </td><td>100 </td><td>0.1</td><td>0.2 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td>100 </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>249</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 )  </td><td>5.17 ( 0.13 )</td><td> 30.45</td><td>0.90</td><td>0.73</td><td> 29.62</td><td>5.17</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td>500 </td><td>500 </td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td>500 </td><td>0.1</td><td>0.5 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>255</th><td>500 </td><td>1000</td><td>0.1</td><td>2   </td><td> 0</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>5.98 ( 0.02 )</td><td>  0.02</td><td>0.00</td><td>0.50</td><td>  0.00</td><td>5.98</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 21 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t13 & 50   & 1000 & 0.1 & 20   &  0 & 0.62 ( 0.01 ) & 10.42 ( 0.22 )   & 5.56 ( 0.06 ) &   9.86 & 0.95 & 0.62 &  10.42 & 5.56 & lasso    \\\\\n",
+       "\t89 & 50   & 500  & 0.1 & 10   &  0 & 0.61 ( 0.01 ) & 69.03 ( 7.45 )   & 4.13 ( 0.19 ) &  69.90 & 0.96 & 0.61 &  69.03 & 4.13 & elnet    \\\\\n",
+       "\t90 & 100  & 500  & 0.1 & 5    &  0 & 0.57 ( 0.01 ) & 70.64 ( 9.13 )   & 3.91 ( 0.17 ) &  71.73 & 0.91 & 0.57 &  70.64 & 3.91 & elnet    \\\\\n",
+       "\t93 & 50   & 1000 & 0.1 & 20   &  0 & 0.59 ( 0.01 ) & 103.61 ( 10.54 ) & 4.34 ( 0.18 ) & 104.27 & 0.98 & 0.59 & 103.61 & 4.34 & elnet    \\\\\n",
+       "\t94 & 100  & 1000 & 0.1 & 10   &  0 & 0.56 ( 0.01 ) & 103.21 ( 12.75 ) & 4.15 ( 0.17 ) & 104.06 & 0.95 & 0.56 & 103.21 & 4.15 & elnet    \\\\\n",
+       "\t161 & 50   & 50   & 0.1 & 1    & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t165 & 50   & 100  & 0.1 & 2    & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t166 & 100  & 100  & 0.1 & 1    &  0 & 1 ( 0 )       & 5.09 ( 0.21 )    & 5.6 ( 0.09 )  &   5.48 & 0.93 & 1.00 &   5.09 & 5.60 & rf       \\\\\n",
+       "\t169 & 50   & 500  & 0.1 & 10   & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t170 & 100  & 500  & 0.1 & 5    &  0 & 1 ( 0 )       & 24.14 ( 0.56 )   & 5.35 ( 0.11 ) &  24.79 & 0.97 & 1.00 &  24.14 & 5.35 & rf       \\\\\n",
+       "\t173 & 50   & 1000 & 0.1 & 20   & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t174 & 100  & 1000 & 0.1 & 10   &  0 & 1 ( 0 )       & 48.22 ( 0.62 )   & 5.53 ( 0.09 ) &  48.69 & 0.99 & 1.00 &  48.22 & 5.53 & rf       \\\\\n",
+       "\t243 & 500  & 50   & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t244 & 1000 & 50   & 0.1 & 0.05 & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t247 & 500  & 100  & 0.1 & 0.2  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t248 & 1000 & 100  & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t249 & 50   & 500  & 0.1 & 10   &  0 & 0.73 ( 0.01 ) & 29.62 ( 7.94 )   & 5.17 ( 0.13 ) &  30.45 & 0.90 & 0.73 &  29.62 & 5.17 & compLasso\\\\\n",
+       "\t251 & 500  & 500  & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t252 & 1000 & 500  & 0.1 & 0.5  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t255 & 500  & 1000 & 0.1 & 2    &  0 & 0.5 ( 0 )     & 0 ( 0 )          & 5.98 ( 0.02 ) &   0.02 & 0.00 & 0.50 &   0.00 & 5.98 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 21 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 13 | 50   | 1000 | 0.1 | 20   |  0 | 0.62 ( 0.01 ) | 10.42 ( 0.22 )   | 5.56 ( 0.06 ) |   9.86 | 0.95 | 0.62 |  10.42 | 5.56 | lasso     |\n",
+       "| 89 | 50   | 500  | 0.1 | 10   |  0 | 0.61 ( 0.01 ) | 69.03 ( 7.45 )   | 4.13 ( 0.19 ) |  69.90 | 0.96 | 0.61 |  69.03 | 4.13 | elnet     |\n",
+       "| 90 | 100  | 500  | 0.1 | 5    |  0 | 0.57 ( 0.01 ) | 70.64 ( 9.13 )   | 3.91 ( 0.17 ) |  71.73 | 0.91 | 0.57 |  70.64 | 3.91 | elnet     |\n",
+       "| 93 | 50   | 1000 | 0.1 | 20   |  0 | 0.59 ( 0.01 ) | 103.61 ( 10.54 ) | 4.34 ( 0.18 ) | 104.27 | 0.98 | 0.59 | 103.61 | 4.34 | elnet     |\n",
+       "| 94 | 100  | 1000 | 0.1 | 10   |  0 | 0.56 ( 0.01 ) | 103.21 ( 12.75 ) | 4.15 ( 0.17 ) | 104.06 | 0.95 | 0.56 | 103.21 | 4.15 | elnet     |\n",
+       "| 161 | 50   | 50   | 0.1 | 1    | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 165 | 50   | 100  | 0.1 | 2    | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 166 | 100  | 100  | 0.1 | 1    |  0 | 1 ( 0 )       | 5.09 ( 0.21 )    | 5.6 ( 0.09 )  |   5.48 | 0.93 | 1.00 |   5.09 | 5.60 | rf        |\n",
+       "| 169 | 50   | 500  | 0.1 | 10   | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 170 | 100  | 500  | 0.1 | 5    |  0 | 1 ( 0 )       | 24.14 ( 0.56 )   | 5.35 ( 0.11 ) |  24.79 | 0.97 | 1.00 |  24.14 | 5.35 | rf        |\n",
+       "| 173 | 50   | 1000 | 0.1 | 20   | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 174 | 100  | 1000 | 0.1 | 10   |  0 | 1 ( 0 )       | 48.22 ( 0.62 )   | 5.53 ( 0.09 ) |  48.69 | 0.99 | 1.00 |  48.22 | 5.53 | rf        |\n",
+       "| 243 | 500  | 50   | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 244 | 1000 | 50   | 0.1 | 0.05 | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 247 | 500  | 100  | 0.1 | 0.2  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 248 | 1000 | 100  | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 249 | 50   | 500  | 0.1 | 10   |  0 | 0.73 ( 0.01 ) | 29.62 ( 7.94 )   | 5.17 ( 0.13 ) |  30.45 | 0.90 | 0.73 |  29.62 | 5.17 | compLasso |\n",
+       "| 251 | 500  | 500  | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 252 | 1000 | 500  | 0.1 | 0.5  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 255 | 500  | 1000 | 0.1 | 2    |  0 | 0.5 ( 0 )     | 0 ( 0 )          | 5.98 ( 0.02 ) |   0.02 | 0.00 | 0.50 |   0.00 | 5.98 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "13  50   1000 0.1  20     0   0.62 ( 0.01 ) 10.42 ( 0.22 )   5.56 ( 0.06 )\n",
+       "89  50   500  0.1  10     0   0.61 ( 0.01 ) 69.03 ( 7.45 )   4.13 ( 0.19 )\n",
+       "90  100  500  0.1  5      0   0.57 ( 0.01 ) 70.64 ( 9.13 )   3.91 ( 0.17 )\n",
+       "93  50   1000 0.1  20     0   0.59 ( 0.01 ) 103.61 ( 10.54 ) 4.34 ( 0.18 )\n",
+       "94  100  1000 0.1  10     0   0.56 ( 0.01 ) 103.21 ( 12.75 ) 4.15 ( 0.17 )\n",
+       "161 50   50   0.1  1     NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "165 50   100  0.1  2     NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "166 100  100  0.1  1      0   1 ( 0 )       5.09 ( 0.21 )    5.6 ( 0.09 ) \n",
+       "169 50   500  0.1  10    NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "170 100  500  0.1  5      0   1 ( 0 )       24.14 ( 0.56 )   5.35 ( 0.11 )\n",
+       "173 50   1000 0.1  20    NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "174 100  1000 0.1  10     0   1 ( 0 )       48.22 ( 0.62 )   5.53 ( 0.09 )\n",
+       "243 500  50   0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "244 1000 50   0.1  0.05  NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "247 500  100  0.1  0.2   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "248 1000 100  0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "249 50   500  0.1  10     0   0.73 ( 0.01 ) 29.62 ( 7.94 )   5.17 ( 0.13 )\n",
+       "251 500  500  0.1  1     NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "252 1000 500  0.1  0.5   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "255 500  1000 0.1  2      0   0.5 ( 0 )     0 ( 0 )          5.98 ( 0.02 )\n",
+       "256 1000 1000 0.1  1     NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "    num_select FDR  ROC_mean FP_mean FN_mean method   \n",
+       "13    9.86     0.95 0.62      10.42  5.56    lasso    \n",
+       "89   69.90     0.96 0.61      69.03  4.13    elnet    \n",
+       "90   71.73     0.91 0.57      70.64  3.91    elnet    \n",
+       "93  104.27     0.98 0.59     103.61  4.34    elnet    \n",
+       "94  104.06     0.95 0.56     103.21  4.15    elnet    \n",
+       "161   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "165   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "166   5.48     0.93 1.00       5.09  5.60    rf       \n",
+       "169   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "170  24.79     0.97 1.00      24.14  5.35    rf       \n",
+       "173   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "174  48.69     0.99 1.00      48.22  5.53    rf       \n",
+       "243   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "244   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "247   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "248   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "249  30.45     0.90 0.73      29.62  5.17    compLasso\n",
+       "251   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "252   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "255   0.02     0.00 0.50       0.00  5.98    compLasso\n",
+       "256   0.00       NA 0.50       0.00  6.00    compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check other method results at Block Corr 0.1\n",
+    "block_bin[block_bin$Corr == 0.1 & block_bin$Stab %in% c(NA, 0),]\n",
+    "\n",
+    "# other method: Stab = 0 or NA (if Stab = 0, then high FPR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "ind_cts_mse_fpr <- ggplot(ind_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_cts_stab_fpr <- ggplot(ind_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_mse_fpr <- ggplot(toe_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_stab_fpr <- ggplot(toe_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_mse_fpr <- ggplot(block_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_stab_fpr <- ggplot(block_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "fig1_cts <- ggarrange(ind_cts_mse_fpr, ind_cts_stab_fpr, toe_cts_mse_fpr, toe_cts_stab_fpr, \n",
+    "          block_cts_mse_fpr, block_cts_stab_fpr,       \n",
+    "          nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "ind_bin_ROC_fpr <- ggplot(ind_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "ind_bin_stab_fpr <- ggplot(ind_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_ROC_fpr <- ggplot(toe_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_stab_fpr <- ggplot(toe_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_ROC_fpr <- ggplot(block_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_stab_fpr <- ggplot(block_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "fig1_bin <- ggarrange(ind_bin_ROC_fpr, ind_bin_stab_fpr, toe_bin_ROC_fpr, toe_bin_stab_fpr, block_bin_ROC_fpr, block_bin_stab_fpr,       \n",
+    "  nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Feh1tIPbAsBQZpptwZRCwesyuy6kpjXCLU6dk7vWtBMrBw2Iq7rMctyuvuYWYgD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\niPM1Ho/X1taOGDEig1c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iPb2xmSYNA4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5sY5EsiPja69ILfuuGEoVzchI0kOADJgE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\noc/bw8PjuOSiosJv7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AASmQJkkVsNBX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Y5Hq21I7yPGVWlB0IQbk8mk20vEAG4gi+15K7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\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VoRU0JsGGCjTdpOyAZBAO2kRcKxSTr2QMYZkElkdWtpdVf1F24rV1z/fAyFXopt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\ncJtExw/OHS/hsLCfY4f0CICjnTm3UfXmtCGQUNKGDs79XF3ybZ1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YQKNjZzSNCwBoa72z0xWWNVXq2zwzndedWev/UDoPTjqyFKXmmF0x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\n4Wxh0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umA3WhEJ1x6NQ3TEbrAmF6o5H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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1_combined <- ggarrange(ind_cts_mse_fpr, ind_cts_stab_fpr, ind_bin_ROC_fpr, ind_bin_stab_fpr, \n",
+    "                           toe_cts_mse_fpr, toe_cts_stab_fpr, toe_bin_ROC_fpr, toe_bin_stab_fpr, \n",
+    "                           block_cts_mse_fpr, block_cts_stab_fpr,  block_bin_ROC_fpr, block_bin_stab_fpr,            \n",
+    "                           nrow=3, ncol = 4, align = \"hv\", common.legend = T, legend = \"bottom\",\n",
+    "                           labels = \"AUTO\") \n",
+    "fig1_combined\n",
+    "\n",
+    "ggsave(\"../figures_combined/fig1_combined.png\", fig1_combined, dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S1: MSE/AUC vs. Stability (evaludated by FNR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts_mse_fnr <- ggplot(ind_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='MSE')  + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_cts_stab_fnr <- ggplot(ind_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_mse_fnr <- ggplot(toe_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_stab_fnr <- ggplot(toe_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_mse_fnr <- ggplot(block_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_stab_fnr <- ggplot(block_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "# ggarrange(ind_cts_mse_fnr, ind_cts_stab_fnr, toe_cts_mse_fnr, toe_cts_stab_fnr, block_cts_mse_fnr, block_cts_stab_fnr,       \n",
+    "#   nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin_ROC_fnr <- ggplot(ind_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='AUC')  + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_bin_stab_fnr <- ggplot(ind_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "\n",
+    "toe_bin_ROC_fnr <- ggplot(toe_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_stab_fnr <- ggplot(toe_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_ROC_fnr <- ggplot(block_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_stab_fnr <- ggplot(block_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "\n",
+    "# ggarrange(ind_bin_ROC_fnr, ind_bin_stab_fnr, toe_bin_ROC_fnr, toe_bin_stab_fnr, block_bin_ROC_fnr, block_bin_stab_fnr,       \n",
+    "#   nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zvfkes+les/4y/Ww7EAoqI8sWCxmLuA8guG2kDF6MTtmRQb3Qdh0W0ZgKfhNJ18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\nfvZXu+wiqugFr6fLzc3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6eT0hsiJxiv\nnp5zamP69/a00kQdh5D0QEC9YU73uO8vKxkgqeKIM87mOgCKyuctsF5/hadnQFGIJCJhXjaN\nT5t+pHuWohuDG3bnnHOO2iVAofOtbduHsV+HBwaWlbOMN77NND8IxRHf5HB6kzs2Ai2TjKi+\n6cEWb81+e/bxSqoG6KEyRCWnsTLZxWIUaktT1EaPt0pVPZalS6mAdEBISYEmEEESP36WcvFy\nZA9JQP8ADFMuQt58mGF48qClFmEPG/Pnz9+5c+dtt9129tln5+R0y9mMRqM/+MEPACxYsOAI\n9W4s4tPzz5px+6KS66sC7+5tWtcc3hG3WprC2wDk+o+PGo0xs8mvFw3dpANgObFQvHbuxC+f\nP/unVwh/cVX1/dW1BaqaphxEUpopqc40z8hIe3BqyaSxXY9rnM11AAB+0mmKZcnN5dTYACI+\ne65y/megjl3r+dhkcMNuy5YtA7wdd7hyPt+gPp7r1FjcXxrMNIQ9Iu46m3ts7k+IeFHtvibL\nKhzDj4ljnPERRNxB6+KTf3/cCfn7q05rbpCMA6RLR5EyuSZrqqruckHhcu8e64//J844a+jr\nsJ14CuBJuYoOMzfddNPatWt37tw5bdq0a665ZsGCBfn5+S0tLZs3b/7Nb35TV1cnhLjxxhuP\ndDfHHD49b1bRF2YVfQGAI6241cKZ4taypLTf37tmc/UfWqN7vVquEIMYWLY0okZjlrfspNKv\nLyq5Lln18fYpE4/3un9Z27ApEs1TFc9gxV5tombTanOcb00oumVScZ9FY8cU42uua0cIsfRc\nseRU2dLMvD6WnjEU2aYUo8xAht3y5ctHrR+jRoLUbe7lxeH/sbk3N+a1uBx+m4wcU2QSWEKI\n6eFQ1BmBNo9ZOgOKxwVro4m1E6bllB5fHAmds3/PtVvKI4oa03QbzGQsxnm+phZqGvOCwkHn\nD09g2XK+5NQj3esUPTn55JPvueeeVatWtbW1PfDAA713uOuuu5KRdin6Q3DVp+d3vj657KZZ\nxZdtqFizfvf/UxW3pvg1xSO4xjqyXAnSdgzLiZl2JNNTsmjKdYtLb/C7Cru2+cXcnAuyMh+r\na3ymsWl7PJ7GhUcROuOiw5ZIqhPHpIw6MuI4/56f+80Jhcd7PKN43YfO+JrruuF28+LB67yl\nOFIMZNi98MILo9aPUUOAVepn1jofFMT+7rfmORiuEcbJMnmGzb0AHC6yTUOkHmCOGSKOU6Ip\nADy2fXrt/jqPL9YlbkEDNZiWzniWqjDdBSHsl/+kZGXzaTOOXJdT9M23v/3tpUuXfu9731u3\nbp1lWcmNiqKcfvrpd99995IlS45s98Yjaa7i82bdtaRs5a76v+5rWd8Y2tIQ2pz0xoGIIAvS\n507KPmVi1snT8i5Mc/edKp4mxE0TCq8qyH2tpe2foXB5JLoxHOEdkioOyVkezwlez0npaRdm\nZU53j8WCsCkOB0QIVsAKI2Mq1FS+YneGlDwRjUa93vaEpvfee2/dunWapp1yyiknnXTS4ezb\nYcGvCIuwM+Nm3Wn2mpCHpF3XCSPb5l5DyUvm41sM6ZaZNuSYkhS9GV9q7MnIbkb05e2bc+PR\nVt3d9VMGpjI0WVaaIhTGoKjM65PvruMlU1M1s8cgCxYseP311x3Hqaqq2r9/f1FRUUlJiZL6\nSw2PNFfxwinXLpxyLZETjNckrFZbGgrX3VpWmquo3c4bjAxFuTwv5/K8HALqTbPZsuKSFCBD\nVSZomjY+817H11zXFUlUb1pZqnIEM44bP8C+18EUNJdj+pUQKZO+C4PMWa+88srNN9+8f//+\neDwO4L777rvllls6P125cuUvfvGLPuUoxywTdM0mGNy3PXOVKZ7z2g6GULi6TxjZkrkSIp86\nFhcYwaUqWdpYFMMcL4xHNfayYOuZtZX7felEBIaupdA5Ywkp22y7XSLV7aaqPXL3Dj5j1hHr\nbooBEUKUlpaWlpYe6Y4cbTAmMjyTgEnDagQo1LSjI4h5PM51AEyiG3dWPN3YvMTve2ha6Rzv\nkVn4jtRA8UJxIVKDeHOq4mI3BjLsKioqLrvssng87nK5ADQ1Nd16660AFEVJT08PBAJr1qw5\n77zzDlMoXiKR+O1vf1teXh4KhaZNm3b11VeXlJQMv9mJur7A72u2LK5MrPXOyDAqOYUlUw7S\nvCNOtsV9CZFPXcrjMCmlxzO+LN2xxvDV2KWUTz/99Lp16xzHOe2001asWCF6hVE/99xzTzzx\nROdbIUQy8GAox/bmhOaGmFBjRMliwQpjKmed5p1gLOrInKS1zxjpLqrci5RhN5YoLCzsc7sQ\noqSk5NRTT73ttts6Vy36ZPRHXYrxznic6wC8Fwo/2xSY7nHvjCeebGi6t3TyIfR8+Hjy0boV\n0oSnAK7sI9KFsctAht0dd9wRj8dLSkruueceAH/5y18sy+Kcb9y4cc6cOd/97nfvvffeNWvW\nHCbD7s4772xtbb3uuuv8fv8LL7xw2223rV69OjNzEOHyQVEYm+/z/Kmp1aPxgDvD4ZmmUDUn\nQODUf5XDrjCyiamGyDZFNnU3BxWSLn9KlGJYDF+N/emnn37jjTdWrlypKMqaNWts277uuut6\n7FNTU3PiiSdefPHFybedtvhQju2NLxKOMQaAMwbAISIJnbe3yQCTDqz4M0WhYNuhXVqKw0R9\nfX1/H9XU1PzjH/946aWXPv74Y71/+YzRH3Upxjvjca4D4OWciOKOY5H0Hrml2PwlUH2wIsic\nDsU9+P7HFAOZMp988gmARx999KyzzgKwfv16AGedddacOXMA3HTTTffee+9hUmOvqanZvHnz\n3XffPWvWLACrVq36j//4j/fff/+CCy4YZsuhePVC++1NkU8KeDSsWT7Tn3DzhMgVlFBlmMDB\nOPVR7pUYCCQZpCkyTJ4lWc+1AAK5bCt9ygi4FVN0Zffu3a+//npVVdWUKVPOOeec6dMHEsOM\nx+Mvv/zy17/+9cWLFwO4/vrrf/rTn15xxRXp6d0M7pqamtNPP72HMtkQj+0BAdK2uub8M8aS\n5Sw7C5zILoYdMQbLHOrFpxgVrr322j63O46zffv29evXb9u27aGHHvrud7/b526jP+pSHJWM\n8bkuyUK/78clk14JtJ6RnnZ90RGTYuICOXOO1MnHOoMsxQJYuHBh8u37778P4JJLLkm+TTrP\nGhoaDke3VFW98sorp02blnxr27ZpmlIeegarI60ttc9tr31xe/3LinAtdrgF0exO051zXHYb\nMQmQwz0SgkFysjiZXRZnSTLNYbrD3Rb3Sdb3U7vjyHTp+I47/pA7maI3N9988y9+8QvHaa/k\nK4T45je/+bOf/ay//aurq2OxWGf9gLlz51qWVVlZOXfu3B67lZeXP//884ZhzJw585prriks\nLBzisT2wiBoVTaGeWTjU5UU3wWrHYd5UBYmxxa9//esBPv3BD35w1113/elPf+rPsBv9UZfi\n6GPsz3UHujqh6OYJQyp4neKIMJBhl5mZGYvFduzYsWjRosrKyvLycgAnnnhi8tOtW7cCKC4u\nPhzdysvLu/LKK5OvDcN46KGH/H5/Dx2p888/P1n9wrKsgQMUKhrfWF/x88rmdzx6drZvKmcK\ns+3KhGF72bbswMRwWkyxAHAyBdk29yZELjGVUfsPjJgimUIYKP6AiHy25ZROY/kpMdkR4777\n7ksmjuXk5JSWllZWVjY2Nj744IPFxcXf+c53+jykublZUZTOcChVVb1ebyAQ6LpPMBiMRCKK\noqxatcq27T/84Q+33nrrww8/PJRje4w6AhRgV1qmx+lZiKXTlJMgj+jiAzZNXpQK9B1PfPGL\nX7zrrrt27NjR3w6jPOpG+PJSjAHG/lw34pccrUVTORQdeYuhpfRKRpSBDLsZM2bU1NTce++9\nt99++89//nMAfr8/adhVV1cnM3qSS6WHCSJ66623nnzyyfz8/AcffNDn6+bn8Pl8yYebWCzW\nnzOPINfvfujNrT/06LlZ3rLO8IJ0RUkTdlTKfxXWXB3IiQubGBE4MU1QwmPXJESeKTKHnlHh\nkCywTN+pZ6RkuEeQ3//+9wBuueWWu+66S1EU27Zvv/32u++++7HHHutvsiOi3skrPcrf+f3+\np556yufzJfcsKytbsWLFhg0bNE0b9Ngeo44BnLFP8wq2+NJyTDOqKAwggDPWEWJHDuGACL5t\nwzT5tOMO6ftIcWRIytpp/WdijvKoG8alpBijjP25bnjX1xNpofIVGG0gG2YEpZeMbPPHOgMZ\ndt/61rfefPPN55577rnnnktuueyyy1RVDQQCEye2q073N+aGTzAYvOeeexoaGq666qozzjij\n9yh8/vnnky9eeumlzgXirjjSevXTVeX7Hs/wTFG617RhQJGuVSWMnektn+Q2zGjNDmlG8iOC\nIMZdTiMnK9EhUDcwNlGhZSozZvPjZh7Kpaboh9raWs75j370o6SWmKIod9xxx3333TdAqHtW\nVpZlWbFYzOPxAHAcJxqN9qj+yTn3+w88Ifp8vvz8/EAgMGvWrEGP7THq2t2zbvdzxVNu376p\nSngcsKS1l8yKtSRlKYqv07ALh8XpS5E1YBKXZVEoiFiMEnHmcjGvD35/qhrjkcK27dWrVwM4\n7rh+zfFRHnUjd3Epxgpjf67r3QFZsUu+/y9EIygoUs44G2kHERVqxxGthSsHZMEMDf24FENi\nIMNu2bJlyXhhwzAALFq0KJkem0RV1Z/97GdnnHHG4egWEf3oRz/KyspavXq151Drw7y786fl\nVU9keKfwvtJdXZwXalpFIvHa5D3ZhjsjrsfVzucVJpmmyTZyhCFyeh/bFYco07J8uXnuC5el\n3HUjy9lnn/3HP/5xy5YtnYGe27Zts237zDPP7O+QSZMmuVyuzZs3J0sFbNmyhXPeQ5OsvLz8\nqaee+uEPf5j0Acfj8aampokTJw7l2B4EbTvdcWoM83cTSxe0BS5qrK11exwwm4gRAXAJXqRr\nAECEcJCVlIql5/bdVjwud22XFbuoZj/V1zHOiHGQBBHLL2RFE3jpVD79eIyTcknji/7KhTmO\ns2fPnmQk8YoVK/o7fJRHXYqjj7E/1/WAGursxx5hPh8Ulepq7bZW5csrMOQkWdWPnLkIbAYR\nCk4e+mlTDIlBBD5uuummG264YevWrR6PZ+rUqZxzAB6P55lnnjn99NP7E38aPps2baqoqLjk\nkkt27drVubG4uLjHI8UAfFrz7Lu77sv09G3VJUlTxGRdr0L85Sm7b9i0gBgSygFftGSK7jRL\nplu83/V/iyjTtgvMuH7BxSwnd4h9SzEAL774YufrZcuWvfbaa5/5zGe+8Y1vTJ06taKiYvXq\n1V6vd+XKlf0d7vF4zj333Mceeyw7O5tz/utf/3rp0qUZGRkA3nzzTdM0L7roopkzZzY0NDzw\nwAPLly9XFOWZZ54pKipauHChEKK/Y/tjkd//XiLhEHSh3Dp7ob1VfL62KqC7QopqSfIrYqKu\nqQAMg6IRPm2G8tnPQ+8lkW5Zsnyjs3ED1ddB15mus9w8dPVRWybt2GZv/pjlFYgFi/iCRSkH\n3sjyj3/8Y+AdLrjggquuuqq/T0d51KU4Ohhfc10PZMUu5tLh8QKApsrtWynQzHLzhng4Yyj5\nLHLnQ+jw5A/9tCmGxODKbZqmzZs3r+sWt9v9pS996bB1CQD27t1LRD2qcV933XXLli0byuEJ\nq23DnjV+V6Hgg9z/slRF46yGh34zs/yifWVZhiusdkpRMGKK5rTY3NM7c4KILKJC20zLzNLP\nu4KXTRvipaUYmN6yiKFQ6Lbbbuu65cknnzznnHP6a+Gaa67hnP/kJz+RUi5ZsuSrX/1qcvu6\ndeui0ehFF12kadoDDzzwm9/85v777+ecz58/f9WqVUlxzv6O7Y/zszKa3e59CSMmpcmVm2ct\nfCsr7z+qK05qCxhceFXVZcTJsljxRHHGWWLBYqg9q5JQU6Pzlxdk5V7m9bH+lmiFAq/CvF5E\no/Zf/8y3bBbLLmH5h+ux6hjk3HP7dqNyzqdMmXLqqaf++7//+8DC46M56lIcHYyvua4HjMCI\nyDTBGFOUQyjNyTj8w6pCkqJfGPWSaehkiGrAra2tI9efQyEZAXDXXXd9//vfT275564H3tl5\nd7pnSp8zMQExRxokHSKbiIMRKOJIT9h16d7ppeH0iGoZot11x8lKiFxTZHU5nGxJLseeYJna\npMn6suW88LCkBh+bDKVux4oVKx577LFR6MwAJEfdj//nf1688GJTkgTi0rGJBJjOMDfc5m1p\nWZnpn5eTw7Ky2YSJTPTxECUrdsm/vSJbAszrO4h1/GgE6RninAt5SlvnGKP3XJdi/DK+5roD\no840nI0fyHfeQksTtRdQZKygUP3GKqQqpowNBvLYtbWNS318y4lvrX3eo+X1/tHEpWyx7Kjj\nxKRkAAMYAwFEIMDwxh4/ftO8xoKTmooKot64sC3h2FyoMmTwDAKTgGLbXsfJlrbIK/AtXMQX\nLsFRUbVwmEgbTIxMhGEikRh0n7FTcEkwlqOqexOGX3AXP/Br2p2Zs9Pl/eqcWSKt33V8ub9K\nvvoyRcLMd5C5/l4fRSLO73+Lq6/jpVMPufMphoiUcv369c8//3yPNYQUKYbD+JrrkshNHzv/\neFvuq2QgcAGSIABE9bXW079TvnxVn0+wKUaZIf0NysrKFi9enJ8/PlbC97f8qyH8aZa329qo\nSdRkWo2WJRgEmN4jxpMBAIEsIdcX1GzNaZrVllPallEU8efH/AQnQ4QEuEIUzs71TpzoK5vO\nj5uJAWtHHt0kWhCqRLQOZivMSLsar1ChZcCdC28x0iaD91x1HBI9qjZJKXft2hWJRDq3tLa2\n7tq164YbbhjWBYwc52Vm3L2v2if0rmZti2Wfkpa2wN+/EHEo6Lz6Z4SD7UEqBwlzuQByXvsL\n+9KXWXYupBx62HKKIWLb9jvvvLN27doXXnihrq4OQMqwSzGCjLO5jsh581Vn3ZsQyZI6HAwQ\nHWXWSdLWzfLZp/ill7PeYcQpRpeBDLusrKyWlhYAFRUVFRUVU6ZMWbx48ZIlSxYvXrxgwYJD\nTlY93FQ2v6MJf9e7bMRxag0zIaXepTR7bxiYYBAMUdX6V05tRVFTMXe5DJVFQ7Ozl8+f+p/w\n+b0+P3Md06M2sh/176NlC5gCBoBAACQIYByxBrTtABHSp2L6FcM91+7duy+44II9e/b02L5i\nxYqxMtkBXy3Mey8UfqstmK0qOuM2KGQ7k1z6bZMnav37MO3X/kIN9exgBAK6YZpk26xmv73m\n/yFZBYgx5vMjM4sVFvOSUpaXEso+REzTfOONN9auXfviiy92lWwdQMcuRYphMvbnOqre57zd\nxrKyqaUZkoAOZTsuAIBxkHS2fYpXveJzX0gJRBxZBjLsAoFARUXFBx189NFHzz77bLJcsaIo\ns2fPThp5V1999Wj1dki0RCtUcaAmcKttVyYMlTF1qC4NJhg4Q4NpmQpN9EhTi1dl1SyYcqwL\nECRaUPdPNJWDCTAOJwHG2p/W2le0k6U6CBAIVY7AGX/wgx/0nukAjKmcwTRF+e2MqY/U1r8T\nDNUaZpZQzs3M+Fph/mxvv08+smKX3PQxsoea4n0AIsRiFI+SaTHGiDGKhrmqQNdBRM1NVF+H\nTR87lslnzBJLTmVl01Iz7BCJxWKvvvrq2rVrX3755VCop7LWvffeO4DcSYoUw2Tsz3WyZj+b\nehqEgJQAgTEQoWuMPgNzeZ2NG9hxx/MZh7FyQYpBGWQptqysrKys7IorrgAgpdy6dWvSyPvw\nww/Ly8vLy8sfeeSRsWbYRY2mTomTsONUJgyNMX6QtzcGpnO02TZnyBdK1Gg6DD0dTwT3oPrv\niDeBMTgJMAHefewwdC2uC2fw6JHBeffddwG8/vrrd955p+M4jz/++MMPP/y73/1u1apVI9D6\nyOEXYtXE4lUTixNSuobw/CDf+yfzeA9u8ZQIRgKRCFkmGGdK+7fPAIrHWXI9VwHTdcALKWlf\nlb19GzthDj/9rFRyz6Bcdtllf/3rX2OxWNeNCxcuPPfcc5Pinf/1X/91hLqW4phgHMx18Xh7\nUr9Q4DhExACwzkmMQARVMO5zPtxw+Ay7SDXiTdDSkV4y9MpQxxwHcWuxbTsej8disWg0GgqF\nepQfGSMQybjZyrkAYEhZa5jqwVt1HTCV84BltzoUM5tHtp/ji8aN2PEU4s2w45A2uDKYG4h1\n+b0Pg0AgkJ6eft5551166aWbNm0qKyt74IEHHMdJlrMbgwzFqkNLQG77FG734Ht2JRySLQGy\nbQilm0XIGYwELKvbzpzD60VOtty9Uz7/rNxcfnDnOvZYu3Ztp1V3yimnPPjgg5WVlR9++GHK\nnksxOoyDua7jYTKqqg6RwxyLSQNoj7CWBFWDpsPlou1bqe2waGVUr8O2x7DvNex4GnteQv+S\nHsc6g3jsdu/evWHDhvfff3/Dhg3l5eXJEhQAPB7PaaedtmTJkqRu9diBMa4Il+lEATSYliHl\nkFdg+2oNUBkL2nYu0wff+yilZQsqXwFXYMdGLPV1iOTn5+/bt+8PbeKEAAAgAElEQVSDDz6Y\nN29eJBL5+9//Pn/+/EQisW7dutHrxJCRRHWmZZKcqOtK/1+TU7ELqjZ0dx1JiWAr4gkuFOqj\nWQbGYBi9FfLAOPP5ZSwqn31KBJrFmeeklmWHQkVFxY4dO3bu3FlUVHSk+5LiWGEczHVCALCJ\n9nClVGFumzkMksiREJCQkmVkgjEwRkKgJYCMIcmlDZ14M+rehZ4JJgBC4FNkz0J6ShWgLwZJ\nnujUqGOMzZgxI2nJnXTSSSeccMJYS8PuxKvnRIyGiOMEbLtn9uvBwxmTjlVt95/eeFQTrUPt\nPyFUWDFwMdqu7/POO+/RRx896aSTAoGAqqqf+cxnvF6vZVlpaWmj2o/BcIgerW98uqHpg3CY\ngc3xepZlZ900odDX52+kthr6UMPwCYRgK0skSBHU37fPOFlmf38ZpussK9v5+2vQdXFy34Wz\nUjz88MPPPvvsu+++K6VsaGj41a9+9atf/SojI2MAbdgUKUaQ8TLXWUQOY7W+jKxE2G3DZTuO\noghNZ15fp/IXY5wS8RG/V1ghgIMl51QGrsAYl4Jso8FAdk+nVVdaWnr55ZefeeaZLpfrk08+\neeSRR77+9a/f0MGo9PMg8Op5jjQDlq0MRf9xCCiwPzXce4agOXSU4VjY9zqM1sGsOkpWNG13\nyY8gP/7xj+fOnSul9Pv911xzjWmayTH57W9/e4TPNDx+XLX/lj2V9YZxnMdznNvV5jgPVtfc\nuGuP1ddSgQyHDkLGMxRGPC7FgDY1A6QzQBskBMvIdF55Se7YNtTzHmOsXLly3bp1tbW1Dz/8\n8NKlS5O1E9va2tauXZvc4YYbbvjkk0+OaB9THM2Mg7mOCIDGmJtzC7zJnbHPn741LSOalcMy\ns7rquTKScI+8aIaeBZKQFoD2F65+KvWkGJKO3Z49e/pM2Enyy1/+cuT6MwIUZy76tGYtrPQc\nWzUUGVetwY8ZEIXiLdrst1qDpYXHltBJ4BNEa0AExvuyK3rZc4T26LoRCbADUFRU9OGHH27c\nuJFz/vDDD5966qm7d+8+5ZRTzj///JE5wUjwSST6s/01pS63ytszhD2MTdL1F5tbPpvdcllu\nr7knHmND+4LISCASglAG0OgBADBIOeAOgKIyf5rz99dY8YSD1kM+ZsjPz1+5cuXKlSsbGhrW\nrl37xz/+8Z133pFSAkj68Aao05MixXAYB3OdIwEIxtJZIm4FOCwJrgu/X+lmw5HjsJzcw1E5\nXc/A5Auw7zVAgBwULEHalBE/yVHCQIZdQcG4ksIiopr9smLXjIpIZs05RK78mLfRE3U4hVRj\nb3rbnoy2Gl+EDtKtxOAIMoKuhW8HQ9cUjg+J5hHBjqOpHEyFHe6ZAAuAZIe4Cetu8xHIAWhk\nbDvHcTjnyThOxthXvvKV5EbHccZOJMCLzS2csRrTjDmO1X7nZwqDYHi8vuHzOVmiu+OYOU5f\noXK9IEIkQpwPye88lEHtclFTg9zwL3HOBUPY+5hmAAsvRYrDwTiY62wLQKvRJo1KlQkQEwzM\nbmyIWsW+yQd2S8RZyVTmT4tL+efmlj2JRIainJOZPu1g08X6In8x0suQaIaWAc8xdDc+aAYy\n7JJi6+MF+clH9iNtpGqqrrtkpkWxJndUEFdsFFq+yaGMC6vE9szAP4urK9IPImFHc0INnnOg\nFe+MxSXRoSbYjipmGOFKJAKwY7Bi4AoUL1QvfMXwTeiIURiMlm2INYKhDxOt3arr85toFyEf\nVv87URTl5ptvfvDBB7tuXLhwYSAQ2L9//8icY3j8rbXtX/uqbSkJSW0nzkAEaRIjwiuBtrM/\n2fLLaaUzu2raebwIh4DB6nLE4zAMKEP5a1G7RuhgMF+a3PYpn7eQHYKE3jFJVwvv+eefP9Ld\nSXHUMvbnOni80jAiiXowhZPD4BBxyRRmtwTNzHQtDcmn1liMLz65zjSv3bF7fTDsEdwmPNHQ\n9M3iwivyRmDacWWnVmAH5+gp60bBNmTnJt0bJmXCCBLTHEgw2JAJYYeJTQz7r/50zkd5DX+d\nUhEbwhItI0ehYJX/coVxm6jNcbKUsfuNESG4G03laN0OoYApyWIaAAES0oE04S2EvwT5i6AN\nFpIb3A2hw472yqSkvqy6pMeocyMblm33yiuvrF69Ovn6hRde2L59e+dH4XB48+bNPUrxHEE+\nDEd0xkyAt+v4tS9Ht3/M8G5baNnmrU8dP/2U9PZvnHl9NKjvh0jGY2zAQilddsZQc2wVhVpb\n5KaPxVnnDWn/FB3k5+ePwXjiFOOdcTTX8cklFGzTFNMSjMEhcIAYSQJP2PF0LQ1SymCbOG0p\nL5165649H4Wjpe724KVWy/p5de3iNF/pYavb5BC9HGjdEA7XGma+pi70+ZbnZg9Q++foZuya\nKQeNpnXaIDbzSJ6mygixAxdIjKKqFVPs2c25GQn9pbLdTZ7owE3qTnON53Nt2gkK0fZYotmy\nx6xhF6tHzbto2w7FA1dWvyuhVgyNHyJYgayZKFjSbzlXO45ECxjaVeu60tNio16vWZf/HxL7\n9+9/9dVXk6+rqqqqqqp67HDiiSceeusjispY0JFIBhYzArF2TXYARCrnnGOfYV65bec/58+Z\nkEyGzc3D1k8x8LqEbTMjgSEONiIoQ67L63LJXTvE0nNT0icpUhxxxtFcxwqLYqfNz/jrSxHN\nMoXVMdkBIA5ORoJFImLRSeLcC8OO80EonKsdmL68QuxOJN5uC5YWuKKO/CQarUokclV1ttdT\nMBKV+mKOc3NF5bONzWmK0Bi3SP5vbf2fAi1rppVmjNVb9mHl6LxmBsR4rp8sDpPQbZWKGAX1\nRGHU98WdM545bmvAHe+vEc1pDWvTK9K/BoAYI5A6Vm+FLVtR+w8YQejZYAzShmOAZEesG4fQ\n2k09rkJT4SRQ8w5idZh0IbS+IukTTUg0Q+h9uetktwoTfUDDVUU57rjjkuWbfve7382cOXPx\n4sVdPy0oKPjGN74xrBOMHEHbliAwECWjN5MTXft3ZBNxMI3x/YZx7Y5dr86ZBYCXTpVv/HWQ\ndi3jQL22QSHJhvxYzzQd+6qosYHlj6sI2hQpjkbG0VwHIOuMi37R+PhpFWZZW54lLJs5gGBQ\n/LbkRWnsuJlIz3Q2fZyYUro1Fp/ePahOMBZz6NWW1p/uq/k4EtU4s4mmu91fzM3+9sTiYYZk\n/6K2fm1Tc4lbF+1zpshR1Dda2n5SVX1v2ZThtT0uOeoMOyKYZloi7rYsxjMIIVuYdi/nREQz\nsw3XJRXT/2/GlrjSRwkNzWkzRN62zO8YIguALel4jye3twbsGKDhfex7DWoaNC/sBOw4HKOn\nQcYEuArFA6EBAFehZyC4FxVrUXoJ9F5CkkYQTHS34QCgu9L3YUsQXLp06dKlSwFs37798ssv\nH0Pa670gtNc1IQYcyJlkjIGBE8gmcogEY/8Ihj8KRxf4vaxoAiZMprZWNsCqhGUPNf2EJFQN\nQ3/qZYwUhZpShl2KFEeecTTXJTnj7DuecF86NbBhYqTAZ3oYCVFw9knTLqDyjc5H7zNFJcfO\nzC+8rmzWS5ra6S2TRDHHkaDLtmwv1PRpHUu0Ucf5n33VKuM3TSg85C6ZUr4caM3TNNHldsUY\n8nV1XTDUYtlZ6lFn5wzG0XXB0ShiMbIS6dTuNiK4ba4bwghrtsO6WSJhxZwUTjt335Q/l+7u\n3gq5nKawWrYz46aI2i5rbZFMF1rferNHlJbtqHoNWjoYQ6IVjgHG+0hiBeAYsONQPVDTkvLg\n0HyINaHyFUz9AkR3G8NOgPFhFGwZttMOQDQaXb9+ffL1e++9t27dOk3TTjnllJNOOmm4TR8G\nGJLmXFcIHckkDiHmOHdU7Xtp9vHgnC840f7zCwMYdtLu/TDS364SPv9BrasyRTlMBX9SpEhx\nCIyjuW5O1owT807aYj1e7avjDB73pK8v/Ro9/5psqGNZOUjedoPB7+3c/OzxC1u8Hi8XNqHJ\nsi7PzdkYjuaoWnqXhDAX58Wq+n+NTVcV5B7ymmm9aW0MR2b0yrrVGd8ei1caxjFo2I2Q4NhY\nIB6Xba2wjKTfpD3Qi6BI7rXcuTGPxxLdQtEZQppxcu2ECZGOqHZAlSGPtT+gL9qcfWdQm9m5\nb9ShBT7vKF7MkIg3ofYd6H6AYLRBWuBKv46epMFnx2G0dsTJMWg+hPdj3996ud+ovVTVEeGV\nV16ZPn16Tk57CtV999138skn//d///d3vvOdk08++cYbbxxncmKMMcYIeLM1WJlIABDzThST\nSxCP9XcEJ+q3zkRXpISqMc9BjkzOcOypbadIMQYZd3NdOFG3q+6ZbG9Zursow1sCJ1axc63c\nurmbOqbbndFY/2Kaa5Hf7+Z8gq7dMrH4JyWTtkSjftHz/uQWYns0vina72Q4KApn6D8saIDq\njkcxR5Ela5mMQRIxSurEInlvTP5VFRJZhtc2ZUIxY4ptCglAMoop1ik1RWunNSgUVSjWqs/d\nn/mdRveZ1N3kDUv7zIyxVdoFQN0/YQYhXDDbQM6Q1u6YgDRhBqFntH81WhqaP0H2LKSVHtiN\nq4DsyKg9BIbxU6qoqLjsssvi8bjL5QLQ1NR06623AlAUJT09PRAIrFmz5rzzzlu+fPmhn+NI\nwBiLS/m1nRUvnzBTUxR+5jn2Y48wRe2jxusQIYJ0mC/zYA1wAuAMVKkiRYoUo8B4nOsSVisY\n15U0XUkD4DiGGW8BQ89ZSPD5qvrMzOOSgSgAQrZNQJ96YZwj1n1GqjXNda3BD8ORJssOOXa6\nouSp6uI03xnpab2TLQpU9dR0f2XcSOsuDhWT8gSPe6r72CorkOQo8tgBIGJdrLmO6PPOmHYo\nxD2WKzfu9Zu6IJOTmVCi85oK3E5hjfdzH+U88FHOgw3us3pYdTEpZ7jdZ2akj/r1DESoEs2b\nobhhhoZq1SVhArYBqyMhmDGoHtS91y3dVfFCOn0o3vVdgmJEueOOO+LxeElJyRNPPAHgL3/5\ni2VZnPONGzc2NzffcsstANasWXN4O3E4IOLAv4Khn+6rBsDLponPfp6CrbD7CPGUg6oSE8Gx\nWVo6DkE+gOggYvJSpEhxeBiPc12mp7Q4Y1HcDABkOQnDCWcVLWT5hbDMAztJyWwbOTkAOuXZ\n/YqSrghD9nQVSMCScmJH+le1YX5/T9X09zZ+c/fetc2B90Kh7bHE+mDoj02BG3ZWfG7ztjsq\n99ebZtcWOGNX5uY0WJbR5R5mSaozzC/m5Xg4b7PtV1pa/7eu4cHq2icamjaGI0e91PhR5LFD\n0kXXcUPsDPNiDNSuMMYInMFiwme5JPM3u1Riwm0lQKt2ZpT212yjZV1fVDDWhE4Cm6C44ZiQ\nBthBdo1z2HEIV3s0nnAhtAeRffBPad/BlQmSECp6rwgy1pFUwfrx5w3P8ktW5Hz00UfPOuss\nAMnQk7POOmvOnDkAbrrppnvvvXfnzp3DOsdIMlSXZnI8+hVx776az+dkneD1isWnIBqV6/6G\ntIwelhbjAmT164ojCceBz49DKw4mZaqqWIoUR5zxNtcBgCJc8yf959s776wLbvJpObOLLptS\nfB4WZchXXiSvD6oKx0EkwhedzIsmdD2QARdlZf60qtrrcnWd2JpN68KsrOM9bgBvtLbdUbl/\ndyJR6nZrvOvsxwHkqkpc0i9r6//e2vbDyRNVwde1BrfF4gHLarXtdEVsi8VVxnQmBCNDym8W\nF30pL+eOyv2vBFp2xRO64IJgE8WkPDU97WtF+cuzs8ZFxYFDYGwZK8Ok29+oqyRHh5FHDAxQ\nSFpcpJmWIbQ2XYurcnZzw+uT+jbs2mx7jte7In/kK98NB8dAtBZChxk6JK8rAzlwEuC+9rdc\nQ3DvAcPOnQNfEawIhA5pdXMHtidVUD+23cBKKEOgoqICwMKFC5Nv33//fQCXXHJJ8m1mZiaA\nhoaGQ2z9CEEdT64JhxSwr2zb+fmcnFxVzTthwVJ/WsaLa8njZh7vAZ1hRSDRT9CIlJAS6ZnM\ne6hBn7aFrJR2e4oUR5hxN9dFjMYXP752R90rBMmAsNHw8b7HKwP/uHjO6tJLLnM++oAq97AJ\nE/mik8TJp/d+Lr2+qOCjcOTV1rYsoeiCW0Qh2z7O4/7RlImcsWcbm6/esatQ1yf0v56gc1as\nadtisQs+3aYzZCiKhwuFM4UhTQhF06OOjEknIWmR35enqVdt27U5Es3V1LIuC7JEtDue+M/t\nu/5RWPA/UyZ6xl5O5PAZB4bdu+++u2TJEu2gFo96FEJo33RgoAmSDuNpphHWNIvzzETfkZtx\nKetN85fTy9LGmLsuXIV4AFoaHKPvHNjB4XAMqL72d0JHuKqrmxP+SWgqh+JCd7d3ezULsrvY\ndr0ZRq3YzMzMWCy2Y8eORYsWVVZWlpeXo4tK59atWwEUFxcfYusjDQMjooEXTgkgImLMIQo7\nDidsjsYbzDrGmCUpIXH9hcuvqdhWtnsnPG6m6VCTgXfdDTsikISUcLmZ3w/1UNdSHYfl5LGi\nsfIFpkhxzDK+5rrm8I7fvru0KbxdcI0fCMqRLZHdv19/8WnTV531n7erDoPWr6amT4jfzpj2\nREPTu22hqkQiV1Pn+bxXF+QX69p7ofBDNXUTdH1g6YmQ7TSYZlQ6gnFGyFAUd+fDMIOL8ywV\nAIiwzzBWVVTqnJfouqt7YR7GWIYifML1eH0DgPtLJx99fruxZa/0pr6+/v7773/yySeHatj1\nZW0k67J3fkhgnMhh0B3ba1mGEJmm0buluJTVhvHg1NLzMjP6O1e4GqE9SLTASUC44MpCegl8\nEw97IFqiFVyF7H+xblAYg2OCOgLphAI7DjsBpSNnPPN41L0HVya4CtldVY0xQAE5fRnQye+a\nD7UibW9mzJhRU1Nz77333n777T//+c8B+P3+5GRXXV2d1HmaNWvWAC1IKZ9++ul169Y5jnPa\naaetWLGidxXtRCLx29/+try8PBQKTZs27eqrry4pKQHw3HPPJeNdkgghXnjhhQHOJRgkYx1L\n0zLf3tUqig3m69yBiNqtZSIGqIxxzohI5zwpi2gT/mTbj5XNvqZw8rWB+pKWJmqsh1AABsdu\nH7DJ2hK6m7nchxJU1wVKJMTU6cztGXzXFAfDaI66FEcH42uu21zzh8yYLrgGIodMAoEY41wI\nzbbNDXvWRBONn523ZuCJ38X51wrzv1aY33WjIenHlfvrTDN7AAcKUZPlVJuGypnOOBiziGoN\ns9Tt6n0PZAwRx+GATVRlGIWaltar6LbC2ERdf7Su/tQ0/xdyj7YVjLFr2O3Zs+e999574403\nDi7fO/lH7qGjxtrfd90miCRjLttKKIrbspKOl85Pg7ZTbxr3lJVc230IdpIIoPottG4D19tF\nRojQtgu17yJzBorPhPtwLt7aMTA+rHqsSUi2W2DEkWiCFT1g2PkmIHcuWndA9SLR2rMOAmNg\nCkh29KHDwmN8uAkW3/rWt958883nnnvuueeeS2657LLLVFUNBAITJ05MbvnOd74zQAtPP/30\nG2+8sXLlSkVR1qxZY9v2dddd12OfO++8s7W19brrrvP7/S+88MJtt922evXqzMzMmpqaE088\n8eKLL+64zEGvhGmMmUSSaLb5+oWx+/cr857x35ccc53KO8l2WEdSmAAitsxVAUBhyFCVDFV5\nXtUe8fhvWnjSrUyqoaD88D1ZvR8uNwRnQkDVhloQduDuJmLs+IFuFSkOjdEddSmOBsbXXKer\n6RIxKW2CBJKBTUTSJjicM4+avaXu+Wzf9NOn33Kw38MzjU3rw+GBy8jWmVaDZWmM8Y5AeoWx\nsOO02XZmL3Mw7DhB29E5A5hFVJGIl7hcvXXyFMZyVe3XdQ3Lc7LE0fWjG7tZsc3NzcFgcNq0\naQdxTHuNzi7mHZKeEta5gTpeMCICUyRxSUHd1WnVGVLuN8x8TV07+/iVRQV9/rUj+1DxAkJ7\noWdD80NxQ+hQXND80DMR2os9f0K48lCueog48S4XOAwOlEoAwDua7SB/CewEmArNB9mXPkZS\nG6/rf0wM11u5bNmyhx56qLP09aJFi+65557OT1VVXb169RlnnNHf4fF4/OWXX7722msXL168\nYMGC66+//m9/+1swGOy6T01NzebNm2+88caFCxdOnz591apVRJQMcKmpqZk/f/6CDubPnz9o\nhxljOmcK50B7VbFO0K5hl3TYHahtxxgzSPYQgM5QxBSX/nBjyzU2C89dKC67EiDmdjGPF7pr\nRKw6ikVRMpUfP3v4TaXoyuiPuhRHAeNrrhNMI7IIksAIjFjyfsoIJEkCzK8Xba19vi1WebDf\nw0uBlmxFGeC+EbCtBtPSGO+xZqowFrT7uDOFbEd0uCIEYxoX9aYZk304QtIF/2co9FFkkKrx\n446x67FbvHjx4sWLd+/e3SnJ3YPt27cn7511dXWdC7VEB1YnOwy89vcdth3riLejpBdFIdmm\nuyySEUdGbKfE7boxL+f6ooK8fgTGEi3Y9zrM0IEAta4wDtUHM4J9b6Ds83AdHhcvV4ccx0bo\nNCGSQ71HJkTXPXn3K/bko+h01P8LejqkAzsB3p83bkSfdm666aYbbrhh69atHo9n6tSpnHMA\nHo/nmWeeOf300wsLByo+U11dHYvFOiepuXPnWpZVWVk5d+7czn1UVb3yyis7nxls2zZNU0qZ\nPLy8vPz55583DGPmzJnXXHNNj9P1GHWdhrHKENKmr6drm/kUF8w4dNb9EZixdiFNdOSc2N1/\nfhZRTJJHEc83BTZFo2dnZCybc+KSLeV2WrpHiBF4oJQS0ag49QwcjcHCR5ZRHnWjdFUpDj/j\naK5DR2BJDwhgIDBShas1tveTfU8VZy4iyPy02WnuCb1270lVwni1pW0Atbm4I/clTI334VLk\nQFxKm6iHCnFcSt59N4tQkzBKPe4ecx9jzMPFlmhskb+v2/m4ZewadoOyYsUKu0MDrKCgAACS\nq6mdkXS9xgEBlPQgt78nE0zY9mbdvTtufC4768yMtAuyMkoG9AnXvotEC9QB9SIUF4wW1LyD\nsuWHJd5O8XYYdgM67Yg6IuGSrsyOZdOk8nDXMhUkAerDVC06HUYr2nZATwfjsKJgYjQqUmia\nNm/evK5b3G73l770pUEPbG5uVhTF25Exqqqq1+sNBAJd98nLy7vyyiuTrw3DeOihh/x+/+mn\nnx4MBiORiKIoq1atsm37D3/4w6233vrwww+7uxSr6THq6pK9pfjp0V/MNl4mMEaySSl5z/WV\nrdrZnX97ItI4Z12eMRJSOgSFgYCgbbfZTqttM4AzMLAtkXi1Ya7LK/5Z1d5JoVCNy+0VPFNR\nhlPUjkJBvmARn3rcIbeQoj9GedSNyjWlGCXGy1wH2A4YI6KeYU0gMLtDoG79noccchhYjm/6\nnAlfPqnsGwc6HNlZvu+JpvBWw45keqZMzTt/ZtGlFYmEytkA9SEaLFNhrM/8BsaYIWVCyh4T\no9PL/lQYi0nZbFn5vfw1ChCw+hATHdeMY8Nu+fLlySePqqqqzhgF4EBeZ7e37XZeu9WfzKRQ\nQF6XSzXiX16y5IYZM7OE0rIDwY3Y3gquwp2H3Llw5XQ7aawBzZvgygIAaUOa7WFqXO2Znap6\n0fIpCpbAWzTilw7VB+lA8YIpAxVm7WrVAV3MOwYAQj1g2EkHnvwDAXadcIHJF8COIlINLQ1c\ngRUD2QAfxLw7UoVw+sxRtfsSASait95668knn8zPz3/wwQd9Pp+U8qmnnvL5fMkWysrKVqxY\nsWHDhmSV7iS9Rh1xOOdH7pxqvM0hk992gb3rc5E75ip//th1yU7tTIeYwllvlxsHQo7TZFph\nRwpGOj8g3CQ4grZje3y/mbd41Ufr04xEGynNlp2lKHma6j74ZVkKh/jkEuUznztideKOakZ9\n1KVIMfqjzi2hcC4Z2Z2zOwMInEAWcdOOxMxmhbtz/TMAJKzw37bcmuUrm57/GQCVze+8vuV7\nbbEqXfGB8ZbI7k+rn90X+Fe44LsDWCFhx2mzHa3/GY8Bdq+bDe9LY1XhvNW2sxRF7f6lScDT\nq9DZeGccG3bf+973ki9eeuml//3f/6UBnFeUVCnuobLGmKJ6dI1lZJYcN4OEsv8N1G+A4m63\nlsL7EarAxHO71doK7QVXwTjMMKxIl2Vfgubv7vHi4CpClYfFsPNNgLQAgKtwjC7dcLqEzbF+\nbb6k7LDokpZOFrylfaeyKh6UXYqq19C6DVoahAtWFE6i3bxrj2Tt+CElJe6I+rARR4esrCzL\nsmKxmMfjAeA4TjQa7SzF2EkwGLznnnsaGhquuuqqM844Izm7cc79/gOeWJ/Pl5+f3+MJuMeo\n08g4J3z3VONtTrLdbiYwRgDLlbuXRe+eYr7/ru/rgCvP3uaTTW7ZpslIgvkMnm5HCyqpjAlF\n77XCzRgToEbL2pmZ8+SMOT98bx13eyKqGnKclphdrGv9xQn0ARFFwjwnT/nspdCPxeo6o8Ao\nj7rDezEpxgmjPuqKHe5i0pRMZ8klHoAYJ2KSMSZcCauZQXAmvAlds0TYzW0959Pq56bnf8a0\nI29t/1HEaPC7OlZ7hVdX0zZW/TZXmcVwQn/X2Gbb4uCTiVTGDfSMveOAISnoODndsygSJCfp\nB+6FBGyPxfbEjUbLDFi2m4tsVcnX1AU+X3qv1Noxyzg27HpAyfXVrpkTrGPhlbWHtYMoGc7e\nboZ4vYjF2MIlULXgDtRvgJ55wIkldFhRVL+F6UVQOm6IRhuECisKKwLeJVGAEcwwGIfSRUeC\nq0i0HpaLdWUhfSpiDVC9sOPtwXPkHPDGJa2rvkk+ZGndbC87gbQp/Z5O8aDkc9AzUfsuFBdU\nL1QvpAnHhLTbl3GT0Xucg2sQWs9wvVFj0qRJLpdr8+bNS5YsAbBlyxbOeWlpN/VpIvrRj36U\nlZW1evXq5JyYpLy8/KmnnvrhD3/o8/kAxOPxpqamzvS0PrsecqAAACAASURBVJlvvDwz0caZ\n7PQTJ//lkG6KGPCeYP1tUuhTA75cZ7cNFUxICEYOhyNgLRLTa7U5u7Wl9crxPVpWwAKWnaeq\n7xRP/u5p51376Uf58Uib5hKM1RqmIWWRpg0eeCclhdrYlDLx2UuRmTW0rzDFQTPKoy5FChyJ\nUUciy5FxAdthol3ZikgwK4acfKElTHtW09QFTbOLwrnEEPTG69Ja9mlBAPta/lnTujHLN7Vr\na5wpbi3LCr5p02wAutOcldjosas1pxWAKTIjSvF+msXZQIHqBPRexvUJHrTtPhdJIpbd1bBL\nEE1zuU5J9wPYbxj/19D8djD4dltI50xhPLkeZoEsSdPdrvl+3wWZGZ/LyRpg4XiMcBQZdoCE\n5Ix1JLwy0AGZiS5GDrWXydJ0pmosM0ssOUWaqP0XANgxCA28IzpZuBBrQHgvMjtuu44BAHa8\nm1UHtMv22vFuhh0YZB8CeSND5nEIV0LJhOqFHQN4F6sOfdWE6ApB9R7YWVrw5CG935pqAMAF\nJixF1vGofw/Nm6DoEDo0fx/6MuTAMWHH+2/rcOLxeM4999zHHnssOzubc/7rX/966dKlGRkZ\nAN58803TNC+66KJNmzZVVFRccsklu3bt6jywuLh45syZDQ0NDzzwwPLlyxVFeeaZZ4qKijp1\n4fvE7QR4P79xTpYbQQbKtvcazN+qTOr8i0giAIIxD7XNTPx1fuyPn7qWfeq+xIaqU1SjmMk8\nJvM1Iy/iaC7OP8wvavR4b/zk/XmNdUGXJ6woLZbtECa79P4mGJLE4jHEo/zEk8T5y9jw1O9S\nDMwoj7oUKXAkRp1P9dQ7uT4noCGeTEN0oEdYDlNyXJwf3zDj1L2z47oZ9MUJEJKXNeYVlutY\nHA3GqxXeh+Ccwl3MqkuTu2e2vDAh+orkmsNcRAIAYw6XiROksc11Ybn7smZR1rs/SfeF3muh\nNkNRApZtE/WIzBNgceqWbNFkmNcXFwC4d3/NC02BvYlEmlCmu929Z/U4yddaWp9pbD6rIX1l\ncUG/6rZjg6PHsAMgmWQkOmXDuumugXWu1TJAMoLfw0Nt4qzzLPJW/AHhKhBB2jDD3RZVmYDR\nJX9ccUHakFYf9R56SP4Ch3dFMnsOAp/CaIHmh7TbLc7uHerHtiMIDcLV+Q5WBPlLupuk/eDJ\nR+klyFuA1u0I7UO0Flx0xNtRe66GKxu+CcicMZyLGxbXXHMN5/wnP/mJlHLJkiVf/epXk9vX\nrVsXjUYvuuiivXv3EtEDDzzQ9ajrrrtu2bJlDzzwwG9+85v777+fcz5//vxVq1b1FvzsCmPU\nn2nVIb0DADqFvI4WFTkgdMqg5ETmzWi8IiMxyeGh6b435+f+ly1aHKaCGGMEOAFREo5Pst0L\nGt2n7fMX33rK2efsr1y2d+f01kBYVYNS1nFW1CNHkgi2TUaCJeLsuJni1DPZlAEN9hQjxGiO\nuhQpkozyqIs6jgVPq3ApZAjYBG4xnSA0ItVQZzWUhrWoFIxDAWAxy1CsvGC688F6daqbeq2N\nAiA4jtnwb9GvJpg/pkyU3aueO4wcmCXWP483XvuX59qP3VfI7gJtkpAuhNrLf6Ywlquq+wxD\n667PxxgSUlodhl2zbZ/g856flfFv23Z+EIrkaeoEvd+yGTrjusqzVbU8EvnCp9tXTSr+3sTi\nAYL/jizs4OR/xyQvvfTSJZdc8sMzl/zXyQsUKRj6utV2rMwSCGARLaFLXTtlqf6ZL1T+lQU2\ngxw4VruBIh24c9oXE80gJp6HvI4nmcaNqHoNTqLvQl7Shjv3wEdmEBPORv7iw3HRABDYgj0v\nQM8AGBItkGaviLoelSGSznMBV/aBTjpx6FmY/mWIg1VRIMSbkWiFHYVjgAkIHa4MuHKGZCOO\nd5Kjbum/sdO+MKRfEBGL84wQz02m8eRF51+4/VFTCTg8xKBodnZT2mubJ18TFBkxnpk8QiFb\nIOFBXDjxqrQv7fNdEVcK3bZ5Rs3+JfXVE0Ntk0NBXVM1RQAMRORIOBYrKGITJ/PjZ/Gpx41O\nqkS8EcEKWDG4spA1s1vsZoqRJTnq7rrrru9///tHui8pjhWSo+7sq3JKL81uD6kmtEuHJWce\nYElb3uXbZyb8XJJj2mEwaMLn1fNcUme5eS2Xnv7IusWZvqmii+lGRIHIDgkH2uQ6i/U2kmwi\niyQDE2T6qXGrdtHb3m92Nf4MKSfqerbat3+q3jTrTUvtnlFrSFnmdnm5aLTMUpfr3wvyHq9v\n3GcYeYrS52xpSYo4TkxKU0qbSCZjuQgGyYV+3z2lk09J84/BimRHlccuSd9ZFIxJJiUkBzeF\nrTjK1uy99YX4HC6L7IPqgR2HYwACYGAMjgGuggiOBW8XeYG0EpDdLhfS24riKnjnM4+EY3VL\nvBhxsmciVov69+HKhJ6BROBAuFuyP8l4wa4FKrgKPaOLVWdC8WHieQdv1QFgcOce3gIb4wbW\nIX7d/w+cGHdRSEoe5jkMmF/3FVNpspRktLIZ1xJ5bRdnZ1ziZDwnwRM8HWA2U22o4GlC2MXR\nv2QZH9e7z6n0/9trk8tem1zmN82ctsAcy/xOTqZOkqkavF6WmcXyC0dTqa6pHJV/BtfABMhC\n8yco/Tz09FE7f4oUKUYD2w5p8FlwAd0LOTHGSHoSMZtzn3uSyrkjTQIJrjMwWBbFYnlpM0+c\n8rWP9z2R5i4W7fHX1BrdY5OR453GubtNJkxJPaLiqGNKdZgWQuFM89U4z/zEtVynWIL7o/D7\nhMjsx6oDUKBpGuNNlpWQMpmBkWw9YNv1jrksO/MrBbl3VVU3mHafuWgxx2m27JiUhqSkVFW7\n809KYiCi90Lhs8s/PS8r8yv5OZfl5vR2HB5Bjh7DjiVF//uX/iCQIO5wh0v29oT9r0+sSNv/\n9rwJXyY6BQzCA8eAdLpJu5kh5M7tltbqykLBEtRtgHS62HAd+6vuA0aVEUb+iXD3TFEaURiK\n/z97Zx4fVXX+/885d5s1+0ZCgBBAgyCrIChWhVZxqbsVpV/R1rrgt7X9ytfa1lpbu32r1Ra1\n2lq3uv601aqtWFFxQUXAUjaRfUnIvs565957zu+Pm0wmk8kkgWSSDOf98oUzd84997l3Prnz\n3HOe8zynI9yC1t3QMqBmwvCBme0GoCPnCFVACKgCyQlJ6bxAVgRGG0oXwjNcykyPSAg6xoG7\nKS92MpyAWVDcvJlxWWPco48yqS9GrcySAh59UjXkDFZrEdUgTtjReJwwooSlApkFytv+7DH2\n78i+xaCZPlX15Re9o+u5Y8fcVJIskengEW5E7adQMzvWynAE61D5DsovGhJzBALBYFHrPHWe\n9bFO3WHitUj7sDyFpbCAk7VWeb7K4KwxzFJNk2jMoL1lEbcHwKIT7qZUXrf3IUXSCKQIC1im\nnukeJ0suAEWKuiccpnEzp3bYFCDzkMqDhFvzgo/OCT7BCW2SxpmccrW4zZzV5JjtUyYk/OXP\nUeQMWWoxTb/FIpwxDgr+leysa4sKprhcV3+xq1KP5Hfz6iKM1xqRBsOUCZFA1LiAO9tCSmRw\n3WLr2nxr29qeq224efSorwybwLv0cewAyEzmPQ+byFwyiVXlqV9btPOzIhOQfKb39V33fiVn\nnq+SKC6omYj4YOkAh6XDDKNgJkoXxfc36lQEquE7CMvsrPPEGWRnR2QehxmEuwjFCwbzbAEA\nVMH4r+Lgm2jYDC0LUg4sHZyB0PYRR7t4a/caFUYQmhdjvoycyYNuZBpDAHvJCulIi935b3yI\nIydgDLKH1VOicGJRSBxGTFeSRcOcUMYlN2tslUp4V+VZRAvKxfmh91TWuDX7Dl3OByH5ivJc\nXcOyUYWuoYj28B1EuBFa9G5GILsQrIEZGrJ8NwKBYDDwq5Nfzbup1P/SaHNrnrWXE4lwRrm1\nWz1ll+Os7KIFZ1SudobDQUXpvBdxjlCQlE8EoEius6b830llN9S1bdON1o0HH28K7HEq7feO\nDFkqUaV6vU2BCaIwojGiUEBjfjdvURBioJxTDmoStVUqATfdlCvGF/mRfx/XsrLWdeZ+75JW\nNUEVbJmQPEXJ63DedoRCd4wdXeFy3bHv4CZ/sFSLn6vymebhiKFzrlGSOKyrE6JS2myaxzsd\nW4OhS7fu+N8xJbeNGT0chu7Sx7Hbk3WowTExN5xFAHDCCbd/YSknnICBHXbX/6fg8815O1pl\nF+OjJAJTyjzcsEqatsPaWWEvKVA8AIdrFIpOhrswPjuxjeJG2fnY/w/4DoLKHQmK1faUKNyC\n4YdrFMadk7jm2IAjaRh3HrQcVL4D2QHZ3UupMUuHEYB3DEoXDkqOvWMKjq5p2G1/jnT4eV0g\nFIyDETCLW42eD0sbrwqph233jzKHbHkavO8DYERSeEhjvjDNoCSuMhkNK0XZ+q6ZdY9vKPqO\nQTWPJG0JBN9pbjkvdwiymTAzXmyEIFQPnqiysEAgGNGozvJPyC2mGcjjlU7eZsDRTApbae54\nhybL8ssTT1ix/oMIIU63m0gSDIMH/HTceOmkk6M95LjH57jHVzavO9y8Psfdmf0kFGmiel02\nCzNOCLhFVIu6CY8ozG9Bsohqp6G1ONV4UOFhUBchxCSaCS8klh3eUBh854D3st2ZN1ikx2fK\nCOOTXa4STdsZDN1XebjMGR8O3GCah0JhhdI+OmeEEInzOsMc69A81HHvocMHwvo95WVDnvEu\nfRy7Q97qh6c/VxDInVk3eULrGE/EJTPZpGZY0ndnH1xXtLne1cTBCZgMyR7Yk4gUgmO9+cp5\n36yo24BQPSQNRXOQPytxzFmgGhEfXAVw5GLCpaj5GIfXgsigFGAw/O0LZkfNQ9F8KO7UnTuh\nKD4V2cejdh3qNrZnkrNnXe1wQM7ALLAIWASuIhQvQN60xOs/BP2i/a+fd512RYxXFzNLS8AI\nwDmVYO4c9Ru3PjHPd6opBSiTJebZOvr7rc7N9k4MkpO3huGNcw8zguXH1/xXdnBSVnDCQk/1\nztzMdybU1yqRj9p8Q+LYufLBIl0KNFsReMdCTqH4BQJBapAJGefQaiO00ZoUYsxBqUbJBEXx\nShKAf40ZX0ulb1bunW0ZvK6WjiqhJ5wonXZG97zo++rXKLKHdDwUho3WtkCtJ3KcS2IBdX+E\nRwg3NavRHp/r3K09Uy11IhQhsbcYGpFyTJox2v93p3l4R/atYakwof0+y5qb4cmQpF/W1GXK\nktb1qbTZMA/pukqlnjJYJb4mlDaZZgFTnJSOc2ivNDQCeGhS+dCO26XVbzsHr3XXv1H2HgGn\nnHoj7qCsRyQjbl2ownWFBbnkAWBR186GD9xTbi87v5euD7yB2o2gEpiFsvOQPwOjF6JwDlr3\nQW+EGYbkgCMXGeOgZgzyefaAMw/jzkXRPLTtg28/Qo3gFkL1AODMh+aBpwQZZfCMOaKlEoKe\n4YiWHwa6TMa2x4nwzpbErpht0YYNZUsLW8/1hicZkq/R+36rc0tnh4QqPCRznZPOe2JWaNJJ\n++5066MMqa3NecBt8BmHXcVtY/40bf8mX2DwzzIB3rHIn4H6/0Bxg1AwE0YA40XdMoEgTVEI\nGa2pHDAYlwjiljt8XFy6Z+z4NRUTUF9nHdiHhnrzjddIbh6dPIUUdC5CrPNtV6XO1Anu2jmT\nqr+VFZgNoNn70b6ie+vcbwEAOGmvk8RJe1VQAlCJJ0gPy4gckkdl65umNN61OffuiJTgQddn\nWadlZUQYW9valtl1hVmAsVrTUGny6a4EEEACfJblpFQipFRz/LWhcbSm3TluKHOMp5VjZy9N\ntIXGCGvVfOgcSWnXH4PEQTXWEpY8ABi01mAlByNI9oW27ELdZ3DkgFAwA/teQ9ZEKB4oXuSd\nOLin1F8cOXDktOdnscIwgpAUyK7E5cIEA0J3NyZ+S1fXj3AQwiyqH87+W099clCVBw3aOa1w\nQuV1br0wrDTYb01qWbQyL1B2/u6iFzIqg5blSn3yM4LSs6Dlom0vjADcuSiYlayEiUAgSAMI\nEL+kAADAAZUSXlNtvfEqr6uFqoIQ/vlWtvpNesnXpBmz7WZBvYF2ZC1xtk6esPuhiFITdOzk\nIO7QhPKqO4NjDgYcXwBMgcHAiR3JzDkHBQhFpCe7dCk/w9hZ0fybLbk/YaTLTKufsQqX85yc\nnHU+/6ZAYKKz89ZqcV6l6wbjR1ZSghLiMy17aS0lGK1q9x6qmpfh/UrOkK2lGKbp9Y6YnpbE\ndvxLLMgMVOV+yg0ABJIa2RWINCXvNtzUXiIW9iJTCXovewwLJAccOVC8wqsbPKLl6nppFatM\nTgjpLX8kB5G5LnXs5owUlrScoSutMQ0kmQd0uaXU5zzUajSZQxPXJikYNQ/HXYUp38KES4RX\nJxAcu0QsVg5mvvEab21BVjZcbjhdyMhEVpb1t+d5zWG7mcnCUd8jt/p8Q24yJZ890RqRm936\n2JLmq+zbKufMLoHJQTioXWFK5mHNqic93Hd1KS8/vHas7/m47fW6sbQwP1eRPw8EXbSLW9po\nmiGLHXGhMEoQ4Zx1mKNSkqcoDxyujjCWdL9BJH0cO97zuXSsUCQW5I4ljISyIABQiQE1wfrk\nnWuZ4FZ79hBugVtQRKYugQ3tbenUEd0uOIiEziJxmpHNweOyt3MiSby1qM2RZUghJhYsCASC\nocTH2MVN9bzmMJxdk9SrKjQn2/of+51TzeWw83JB0QuZFJ1a5QC3aEiLFNvDMd3WpRIAHESz\nmjSztqMNi3XyOKgu5ReF3naYNZTrXuOLwtC73taXz8Nr041Vn1f/vd63RaVmtL3JeLNhHk35\nVwISZsyMeVbPVuQPWtr+1jBkwz/pMxXLO77yhI48B7HQkcONwBM6Pk+fX59xIKAeABAkvZRK\nyJqErElo3gEigxsoXQgWQeNWeEqgZQ/8uQhGEqx9CXYssZlPOE+wSJZwlnDpbGwTis67jyH7\nKCcElKPzKZBDUliIgPskpvU7OEQgEAgGjCBjE5yO2f4GqF0yw3HLIgCVJF7XPoCS6Rhd1bTO\n9j4spcXBykzJRzqiVQjXIrLdsjPlQExCUDBQgKqsWTaCHJSAAeCQOJEs4jCJixGHO7JvZv13\nOVU8kd0WUQiRMmTl3a2cwwyb4XOk8qBjcr1jQYNzXpPFwowdZXEwAhicq52R1chVlGdq6y/P\nzx2SuhTp49gBAAgB57zDt7MLmBJCwFn7WB1AkB2YeerOf1lUb3Mc/HT896sdtMCReBFNZ78S\nyi9G616YfjgLwCxsfRhUhpaL8osHOQuxYHjTUZi4y4xs/DLZRFF4MQ2oNzzBpZeZNOB3fqHL\n9R0PKJ27BdXD1ZmfZAUnRuTW2G5UK+uL/D2jsuUkGdgFAoHgKAknjR4xOQ/4/b+W4amp5KEw\n13UwRjhnjFHG7OdbRD43H3+EFI2a7Dl+qx6CBgDNhW9n1S+ypKAJPwCZuRUzqybrr0BnGrmu\nvh0nsAhMgFCuM6IoVq4nNEm1ckLqPr+2QyMN9u3Ta+4OyqVtyliD8XKnI6MjBNkfiSiRQEHw\nvZLAPxq1kz5WL6qTZ+GoIUChX5t7MCfPrwY0a3t+218zazcHgtM9Q5AjIM1+DDg4j/WPOUFH\n/YXOFRQ5/vmm1BZU6jx6aYFvWp03mKf2Xt6SUGR1pN05/AGoBtWLcBMClcKxO9YhIIy3F1CM\ny05MANaRDYQjWtWmPWoEgDs8cXLVTwrbzmI0Qjj1a7trM1ftLrqXk0BsVURO+I5RT3xpxx8I\nwCiTLafENMnSLCnASfVJTQUeOvRxlNyE3gYzCDMIqkB2QnIOZHkxzhCsQaAaehOMIJgBAJIT\nWgacBfCUpChzpEBwDOKzrGbD7P4ASTifXl25aN8X8wL+oqCPWwymAUKiN0COaMInwhrqUF01\nSg8vcZy3u+DwnjHNvty11RNWZtec4/aXA2hzbt9Z9NMW9/q4SdiOe2q7d2dHXlHQstpvFzdf\n7g0fx4lFuNLkeW/XqJ80uzbaleE5C6qSo8ypuWMWlskgJlEJdUSQ7TG+WBz+frl25oeu5SF6\nxGsdOAcmtLn/Z+2EkGxFJC5znHQoa9Ro5zavPs7tphoUFxRP6tK2p49jR7kF8PgJr44XMgwT\n4JAA+B27JMujARJz+rVthnNWf+fXnflgERgBcCNxEmPBMQLp9iJOgQlLjaHj/qSZ+dMP/t4T\nnhRw7LbnWCXuKK+/Sebuz0v+15ZrlLqMDRvH/WJa5XfcoTzbezRo0KThssYJFQ1ZuxpQcjrc\nQ1FajFto3Y22/fAdQuBwR4ViE5yBA6oH3rEo+RJcRb111DNmEPX/RstO+CtBFRAZhHYMwXNY\nJpgBzlB4EsaePTAnJRAIYrm6sGCPU9vkD2RLskuSZALO+cIDe676YuvxvmbKOoZUOm55XW57\n9oNvKEglGR4PPN6MgDVjrzK+oeiL0bW7i19sznvXbHRZPNjq+E9Yqu1e8oF3enWdj8fjar87\nseaHYaXar+2xh3G84SkVlb/bPPYbfm0nOPewxkzN7Za6+FMKidYeJTrJDBBPuf6RlzW+5/7v\nRqnsCK4M53ASet4XRQHVDCrMYVDFopSRr+4rMiv5LqU9laxdWj2jDFkToXqP4Dj9IH0cuy6/\nsQnGjIkMw+SEE1qX8a9N467R9C81era1uN4ty/9mfw+VfRzGX4hgHbxj4Bl9dGYLRjK9r4cF\nOsrJkhiNck4kAKUNV2WGTgiqldGmjESCSuW4uusqs59o8NTF9lLYOre87lIGq9W5G5A4MRkx\nCJgOl+bKCFTR/a9h9JnInICUwTladqL2U/gPgqqQNDhyYYWht4JQEAkEMAJo3Iam7Siah1Hz\noPZzAM8y0LAJjf9BsL69ap9lgEXAGTgDSHvFPEkDldG8XTh2AsGgMM6h3Tul4vm6htXNLXtC\nYbW66r7PN01vqG0v0R4NK45NGktiXxDCGG9rIZEIcnIcrvxm7neE6LztZTltro0TDzVkfJLd\nekpJ49cVI09XDzW6P/K5NnX01OnVkfZgemhGYXHzZWG1yiI6wAk4AQza4g5PKKv97tbS/wYx\nKGe+0D6JjHMqnbHwTkliHWZxgBHqowU51t4v+375jufWOnlSf68MA4pN7fh6T0C18vyqwggI\nGLhJeUA1M7PaIw6ZCd8BtOxE/QbknICCWZDiMzcPGGnk2MWWAYiBd66nIBJMEyqBdTD7zZCy\nXWW+Wjrj+lGnhRrQtA2Z5X320ghypyB3QI0XjEQICAcnICQuyC62SeeDZucm+5/s0KyI5Ivb\ngRPTosHswOyajDXRjRmh8SdWftth5OhKY1xziZAMWZIUGAEcegeKF65eQkYHBjOEg/9C4xbI\nLkhOWDrMEJjZfh+3vS50uHeWibqN8B3EqPnIndLXQ+gtOPAGWvdBcYLKiPjAjPYRwejwAGMA\nhxUG56JArUAwiHgl6bpRhdcV5utrVmPtW6R9JX5MUHtCoj/MhICB60E0cWTnZLhKW7C/TQpO\nqip0h127cGN2y6Wm5OfEoFydZHn2FP1qT9GvOazojzrpWE5BgKzgDLc+IajtA4jEFcXMlZlX\nstwEGNPwTU/4+BbPh1V5T4cc+wLhWomqqtQe6+ai1CPRCOOxKU9CJNvDG04LPvCG+8cBqX/T\ncBbn4yIOmZGMkMwpt2i7tV1WugFUAnVCdsIMo+p9NO/EmK/AU9KvQ/WV9F9J1+7eAwAhnBNu\nEZg6zaDgitXM8v5rbmZW3UYc/gCH3x9SQwUjEUIokTgYjyk2AUTjSuzbEI2bj23PycQhWS5O\nEqQp4cQiPId1lNMhnEw+/A23PsqQu3mBHBqV7fzvkgORNhz+oK8DiUdDuAm7/4qmzyE5YAag\nt8LSAd6ldCxn4CaYCc5BKQiBFcKel1H1PnrL4gcAvkPY8zf4D0F2IOKH4QcYqNw+RNd+d7d9\nagoigcqwEmSkFwgEA4ceNl95kbz9JmlP0pZ4gqz9kzh4Ryo6PYy2VpmoWc6xVNJatbbR9fkz\nG3hY26Ur1RG5PqxUhbR95bX/W9x0RcxYXRd3ReJuO/2TZuU49fGqmU+5wqhuUZ3RsGYWjm68\n5pTt68fV/TfjzBeqYjEVrL2SZGcn4THhWyGSmWvs/VLw9xI3+n498nTlm3vG/M9n5SAgIIQT\niRFiV8oAiUgJUtlRBVom9CYc+Acat/X9UP0gvRy7nkZMOr45TkBhRYjbohmq2bBbPW3phEtU\nSjPL4ClJ6RyWID2QqMrBJSpTItvLYwkh7UN4AAiVqEKIhFgZcs7s/OkEulIjsQT13SSu+jVf\ndI+s4PFjmr6iy81xzThAwd1y59If2Ymmz+GvGvgzjcUIYN9rCBwGASJtYAxU6gx664R0rEw3\nAQ5ugarQslD9Aao/7OUQgWoceAORFgDQW8AZiJQ0OYx9wPS6nwkEwwvLMl9/mW3aSCyLt9f4\n6udDJOcghHOOYIAH/LLkyHaPcymFEWRkW5+X6p9FGzISich1o5quIJzErpCNEpGrKVc1o8Ch\nj6Zc4sTgYAAnnDJiWNQfVg4H1QPlVT+aWPUT0wwHIw3RffMUxUEp4zxm3AcA/FLeuMjHk/TV\nfTmV0QHHf38+7i8fzPj6/pKMsGI/2VNOKCcKIyqjHDw7pOgtMEPddiaQXTBD2Ps3NO/s1xXs\nE+l1I+zpvt+ljicJSQUab2uSx3lKf3B2Tg6ArEmouAaFc1JhoyCdcCo5Oe5yp5InSw6ZOgDC\nOSeEUCJJVJGpRokcrXVNAAIOSkBkW6p1GasVK5t0XSQhW542x87qrM3RLQW+WSYNdh/bs+c8\n1ZhIDUIhqfAdGJyztQ9q4sAbCNaAWTBDoHJvGZhJ+8ysPUpHZaiZqFqDpp4fVSM+HHwThg9W\nBBE/qCSKzwoEQ4/13ttsyyaYJueMJM4Y2wdsd4pQBIMwTUpkl1xEuVsnrtLw9myjJtrQJAGH\nWSSx7O5eHYBW14aAY7fDKGYkwknnwBjhkknb7NeMZbkXZwAAIABJREFUhkPKwTF1NxY3XxmO\nNFusfShOJiRblk17DUXX1bchkj1Vf13lvVTf/srh/Ic/OfG02lyLckZgUW4RbkqMEfv0wAHK\niWJRS4fegkhbgk6oCtWLqvcQrO39svWL9HLseqJjqIQQYkLWeDBA8ury/vfuSXOGJHmgIJ3Q\nZG+GsyTPMynPc1yuZ6IquSlVJKpGiyESQmOeLTigRN/UZL9amfOsSx+rmJmUaRJzqkaewyj+\nfNRfdLklegh3uNii8eUROezcjFyVu6TXlhToLRg8ajegZScIwEIJStUl+XtiBqwIABAJSgYO\nr4XhT9CMcxx8E8FacAtGEFTufaBOIBAMNryp0Xr3LSJJsIzOENej6A+Wxf0xsSVUNYmj1Ngp\nEwV2BAshAGh89vd2LMI51RkxCGRwChDCJcpVQ2oz5M7BOU4NXakd3fANGJpudrpXeYrslSXG\neVxcSIS686w9x+v/SmL6kn3F/71jXL1DpxyqRVlHHXAOWJRHi0zaqzM4A5VgBGCFE3RFNRit\nOPQ2+ICWH0sfx67H+M32SHXOQS1OCeEt0ph9+Xf8ZPLi0VqCWTCB4MgghGqy1+ssZszgMX+m\nBIQQCeAcnFJVoVJ0AoPD2lq6Ymvpra3uf1tSQFfq6rP+seb4Kw/mrontWWburmG49iQskQlz\nqtmUdM3zTgYx1MwIoHELJAcigR4KEBN08WOj5hIQCYa//SNJhd6Emk8TdNDyBZp3QFLbx+oS\nwMFZe2W/9v8GP6ZQIDjGYQf3E5ebh0JdEnUeBYRSBAPEsuw/cwLJpFpWpDLPrKRE4eASc0aU\nelNK8JzKwXOC8z2hKQF1ryE1c2IAzKKhsFwdVg9EB/Ds4TiL+LzBqXm+RRGz04+khJRqmrO9\n4ESXkwnDW65/SHpwtc6sybtyb0mtUwdHpqEw2mVfyrs923JwBkJ7vC3LLrTtRdP2Hi/UEZA+\nq2IZJA4G1u26cgAwoRBAglkvldUU/9+vJ84aknzQgrRHkzOy3OMC4VqLG9FBO0qoxS1KVYnI\nAFRKDcbsZaOcmIdynz6U+zRlxKKISHkGjc9xFJFbCe/0cWyvTqXEYpZD6VbSjkEatMWh9Z8h\n1Ag75qWngTQ7GI6zzrtldLkD02GG29euym5Uf4iCmdBi0oJyC7XrIbtgBBMF7aHDjYv7UWHg\nBETM2AoEg0dDPZcVWNZADNe1F4UCCNfDxOVWXDBCEgcxqCMvsqdRLaOWrJoFu7N+wQmP2w/g\nElWzgyeb1M+kYFgKAki0jINE/29Sf1Zgbn3O64xbtOORVCFkjEPbFQobjMXeOiLUNdr8dxY7\n1CyNjbO6IKRddLCo0RGJgJWYDosme6iMZvOzHbseq3kTKG7UbUTO5AGLEk6fEbv2lYaE8K7Z\ndDjAOZVgGUQziMZLfvToCScLr04weDiV7ExXqUw1i0UYtyxmUKLKkjsazUEBlVIK0uH8cMoN\ni9KwPKq7VwegzblXZg4AHGCATIhGCeemJmc4lIy4xpYBV/6gnBfnaN0LyQEr0ssNiFBQGURu\nX6zaue4h5rGVyqAK2vZ22bFlJ3wHAcDSExyCWx0TFqTbf7w9JbJAIBgUZBmhYOcs49HCAXBK\nua4DULyQNUhcs+DM0fe7w4pTH3uw4IHqvOdAYKcd4GAgoESWqZMSWY0UcBKJ67DHg9GIZhSZ\nLMxYlxWvDkqLNRUEnHe6aBzUhFpgftG9n/MrC4qCWkhiLiK5DJn1fNAuH5BeZhUkB/yHEDic\nrE2/SJ8RuzDJaKNOlQVUGpB4ezYtxqlBHBHJoxOXg/ldWfMenrGUiLVzggEiFGkKRGSH7JVo\nl6p0suRwqvk8Um9aAYk63GouoXJL8AABJR1p2VUKizHGLZOoupRn0iyeeHYTtZmfSMwBplJq\nyIRIBABnzPS4CuIGtbgFFkFm+eCcbC18B6F6wEzQPtw5uo/qEQJmtKeWBCCpaN2L/JmdDZp3\nQtJghXv26pLmyuI9PRMLBIKjRJZhmQMa8MoJAUwTAKHQskDClt/fqJqwsv/f5qJPq5V/SlSh\nkDi4fdeIXelAQJI6S/ELaQkkgDBuxrXLkeUWSfZbFgdY+1FgEjXTqo5tZnHuNOn0xkyfYmqE\nuAype6UrdPXn4nJcJYcqaNs/YPUOhq9jxxh79tln16xZY1nWqaeeumzZMklKVg3TgZBJ8wzq\n9bfPVVkMtCPtBFTWlu0uu3n2b4RXl/b0RTk9temv6o4bdf7Egqzqln/X+z4nVJaIzLnFuMm4\nVV7w5bK8LxVnz2rw7dhbv2ZH9d8pqMnClLTfp2TJ4VA8iuQNElfEIiGLUc7a/b4O7HtNWD68\nq/ip8prLTWf7kjGLRTyOQlXuUhuVAxE/iuZB6zY9OyAEqkFlcHYUM54EzAS3YM9RSxpCDZ1u\nomUgWAMqI9IW7zhG60x0vE/Qc+e/Q0EqVScQ2KRUdZJEmJXAlzkyuF3YnXLOOeOEEhCoTkUl\nJOxrhXN3m2cjibQfy84QF9dBRGqgXOnWLzp26fqWK7pcR0BYt4c/AuSrii9kqYQwQizOGcAg\nO1hDhDGgveqFV5JO8+WMCzobPBGAKBZNOD3ACWekW6QdT5bvz4aqCFYna9Avhq9j9+yzz65e\nvfqmm26SZfmhhx4yTfP6669P0r7AW5HNdvtIlkndEmCfGgPAI17ekus9/soZv8319LtaiGDE\n0Rfl9NSmv6oblTntghk/0E1fS3B/MNIYijTJ1OFxFHm0ggxn+8PXuNzTZo/7VkCvbwrueWvr\n7TWtm1xaPiWKRBU7CM8D5AMhxoKWpTNucM44B0AJUSlxUOqi1Bj/Qjg82dl2vKE1WCziULI8\njvi6sIYfnhIUnzJQFzIeI9Du2B0lnT1QhOpgBKBlAkC4HsE6yO4EjmMvXh1i6hcNEalUnUBg\nk2LV8fZ/+p++Lg5CwDk4OAFMg3Ar6oq4HYXBcETxm5RQzpO5kS3ujWW1PYVUxU8WSJa7zf0Z\nB4/GPceSIUk5stxsmiqldrJ3mdNcaox1aHZuKoVQlZJJutuQ209cYglzsAD2wlgGicekgyEg\nUkfYcc+hyQmzBBwZw3T4KhQKvf7669/85jfnzJkzc+bMG2644a233mptbU2yy7SSCy+Zft84\nz+g8VulhDS7W5GGNWVZ1Hqs+teyaG+Y9Pzpb5KlLf/qinJ7aHIHqbDTZW5gxtSzv9MnFF08q\nOqc4a2bUq4vi1vJLs0++dPYzY3NPC0daZEmNvcUQwEVpnqKUaOo4hzbe6RjvdIxzaMWqmiPL\nDkotue3QpN8EMrYpwUIHzc1wjo69r1gG9BZ4ilF2HromPxlIzED3G+aREPXb7KIRZkfGKL0F\nRAK6T6fGrpYYlgtgh0R1gmOcIVDdwMx3xTyi2a5iTLcExKnlZsslIaM1+V97i2dtq2ujbMUH\nGXefhJWYK+jc1ZTxLjinPQSRlGiqR5LMjodOQohMlSxZzpQltySplADw6HI0swm17e+GHQZN\npS5BxnayT5Y0CJhSmHrPCyz6yTAdsausrAwGgzNmzLDfTps2zTCM/fv3T5s2LdrmlVdeYYwB\n+M9//uPxeCiRZo299sTRVxxq+qSubVsw0qDK3kxn6bi8BV5H8dCchiDl9EU5PbVxuVz9VV1/\nzctwFl8y+8k3t/7v1qoXvdoopT9eWEDZ9Z+x/3XSqAfzm84JN0qSAlCAgZlw5CB/GgrnQh60\nqtLoGDY7+pWncb8O0ZudGQKhCQLp+prNZOgG7Ya56gRpyRCoTpahH2leYpvuSStkhdAudwRK\npDE5cycWNm+r+hsFIz0MP1k0eCjv0akHHrZoiJPokogEoXWaWXio8BFdqZPhlEji2VuJkNGa\nuj+sRziXCSGwDBo/HEjtIUa7W06659ezn0BVQqSOQOr+3ZE4OAMGIgpjmDp2DQ0Nsiy73e1X\nVlEUt9vd2Nil/PmvfvUr02wPhMzJyWlvKbnG5585Pv/MVForGD70RTk9tQkGg0emun7hULLO\nn/5QQcaULZXPNwX2urV8uevCi+4YVjig1+V5J82b9t3ZZedaIdq2H3oTzDAkDY4cZIwbxIG6\nKLILYKAaks6QJINzSGpMAjwOzqF03D+ZCUrAhuWYXHKGv+oE6ccQqE6WQPr+pNUrBJxD7ubI\ncEY19yWz/1LdutkXqrJYJLZ+T8zOtDrrhczgjNEN1+nqIUaM9pS1sW247DBKarJfPpT/Z84t\nTfYkCbLXKB3n0A7pkYBlObgRloriGgTUzsA6Rrls0thULPZzZdSr6w6Vk02RcgbZCanHoMH+\nMUwdO7suU9zGqMhsvv/970efJ37729+mzjjBMKYvyumpTcpUR4k8r/zbJxRfun7/w7tqVzX5\n9zoUryy5JKpGcyxxWKalR6xgxPDnuMefWP7t2WXf8mgFAGQnciqO7MhHheIGZ6AKJBXMOqKh\nOwYak2OPMTjzOl1SSQHjIzIX3YhQnSDNGALVyepRDYvHHdEO1VPjywRw0yTeDIU6K4q+uqXy\nOYDqZmvEDFAqcRBil+PmnINxwr4Y9UNT8pfXrDDkFlNqZR0JUAhXZCtDNXMOFf55d/HdnJic\nMVVJkEwqFo3SModWpUcsPXyYjIk72WZXRGbtrplJeNRu27mjgEIo7eHqcN5LaUTGBvLhfJg6\ndjk5OYZhBINBl8sFwLKsQCCQl5cX2+bCCy+0X8iy7PcPXNihYCTTF+X01MblcqVSdRnO4oUV\nP51TduOu2lWHmj6ubdta0/qf9kI6nHHwUVkzyjJOL82ZN6HwLNulG1ocebCTQMlO6K1IFIWc\nFA7O27MTt28woGZD6hivlJwgHESKj13pa1HKofMIR5DqBGlDqlXHGNE0KApM42jH1bk9ZUq4\nZRG1W/iIZdHcfAAnlFy6fv8jOa5yj7PQMIO65eeWaXGDc0aJTAjx63VUlvaX3NOctaakfllG\n8ER3aBIjFoEUcOxqdq09nPdUU8b7ADhnsuTU5O4BefFIhJSoCMqTSrJmr/aFsiTZq0gaoQD2\n5ARUi1BGGOX2aB3vGCSUCZGTP5J2faZNcEkMOAcu/+gwdezGjBnjcDi2bNkyd+5cANu2baOU\njh8/fqjtEgx3+qKcntqoqpp61Xkdo2aOvWbm2Gs4Z369Jmy06KZflTxONdvjKOpx5dVQ4CmF\nqwhmELITZgjM7F8sNWdQ3F3ymFgRZJR1vtWywEzI3eelY1dsHPWCvMFgxKlOkAakWnWmBUKI\npjHTJEcwIRuXx4lQcE5UlcSN2HGOSISUjAZQmnPy9NKv76j+u8cxSpFcihQ/osW4pZttBLTV\nvaHVvUGxsl3hMtnKNCV/SN0fUeqjLS1ueNQi2kOi0DjCRuvkUV/93okn/bOx+fWm5k0+/xeh\nkEZoNY2sHdU0pSGjTTV8kpnNFQouESJ1H/zsBueJ7mwx18PSu9wMj5Jh6ti5XK5FixY9/vjj\nubm5lNI//elPp59+elZWVu97Co5tkijn7bffjkQiixcvTtJmCFVHCPU6iofzQh8qwVOKxs2g\nCtQM6K39yGnHLVAVSkzcP2fxjp0zH65CGEFwDtJ1FoRQcCsmWV1PeeyGiJGrOsHIJdWqMw0A\nxO0lYR2MwbL68YzV/TZBCCwGryvuI67rtKycFLbncpo/4Zbq1k2hSKMqJ5hFdao5oUgzkST7\nAdiQmlvdzd2bMWZostep9ik4lcEMRZpPHL1EJuSreTlfzcvxWdZmf3BfONxgGLLGsv+heBWq\nUEINaCbty52HW5BdoD2XprdMOAvgLe2LgX1imDp2AL7xjW9QSn/xi18wxubOnXvdddcNtUWC\nkUFPylmzZk0gEFi8eHGSNkJ1ySmcjdp1kJ2gChQP9GaA9j5ux612XzC2pRlE3lS4Y5LxURmu\nUWj+ArJdtayrY9e+ZCx5IuKhG8wTqhOknpSqLjOTh0NwOOF0wueDRGH1LTlHnFfHAUI4AE0j\nrq6DcJwjGCQLpkZ3yfVMOm3S7S9uuDLTOVaR4idtNdnrVLN1s430HBfCuCVJWlx+qCQEww0n\nli4Zk9uZDtQrSadkek/J9ALAaBxqRe0nspYF04VIa++dcgYidy4RS9QCZgBFc5J5fv2F8AFb\n4TJkvPrqqxdccMGUKVMqKoYipFyQiMLCwpUrVw61FYPIMau6YC30ZlAFADgDM4Eklb44OEAp\nqNKlDedgEWSOR9y92vDBVwkqwTISjQVy8J6CtzkA5HoL/986oTpBSjlW7nUTJhynSlA1AIhE\n2lOucSR7nEo4ns/bl0BAVeMfCplFFJWMKwPtMmfaGNhd27pFkZzd0wszbkVMX09rXTk4AU24\nY0IYtwwrWF6wMFk0Hof/MCJtkBQwI2mdQ7TfAKVuJ9qliQWqwDu2f5EtvaiOj3yam5tvu+22\n2JNyOBwVFRVFRfHLlQeE0aNHV1RUyPLAD3ZqmlZRUTFqVHxFgQGhuLi4oqJC7bYE6ehRFKWi\noqKkpCR24/jx44daF4OLUF1fEKobWITq+oJQ3cAiVNcXhpXq0mHEDkBbW1tDQ0P07Z49e26/\n/fazzz772muvHfBj3XPPPZ9++ukjjzySnT3AJTkrKyu/973vnXHGGTfeeOPA9gxg5cqVH3zw\nwcqVKwsLCwe257q6uptvvnn+/Pm33HJLdKOiKKWlAxcyMCwRqusVoboBR6iuV4TqBhyhul4Z\nVqobvjF2/SIjIyMjo3PsNBwO2xsHY3GZndRx7NixcYvDBwqv1zsYZtupw0tLS0ePjq92dZQ4\nHA67/2NtKZ9QXa8I1Q04QnW9IlQ34AjV9cqwUt0wrRUrEAgEAoFAIOgv6enYOZ3OkRgBYAcu\npEcEwDGIUF13hOoGG6G67gjVDTZCdd0ZVqpLkxg7gUAgEAgEAkF6jtgJBAKBQCAQHIMIx04g\nEAgEAoEgTRCOnaCvfPDBB5FIZKitEBxbCNUJUo9QnSD1DKDqRmSMHWPs2WefXbNmjWVZp556\n6rJlyyQpvrhvT22S79uXnsPh8GOPPbZp06a2traJEydee+21ZWVlAF566aWnnnoq2kySpJdf\nfrm/ZvfUyVGa/dFHH/3qV7+KO9bChQu/853v9Gq2TU1NzfXXX//00097vQlq9h3Z1R5ZCNUJ\n1aUeoTqhutQjVDfSVTci89g9++yzq1evvummm2RZfuihh0zTvP766/vYJvm+fen5Zz/7WXNz\n8/XXX+/1el9++eU77rhj5cqV2dnZVVVVs2fPPu+88+xmpFs1lb503lMnR2n25MmTf/KTn0Tf\nmqb5u9/9bvbs2UmOGGXv3r2ffPLJ6tWrkzwDHNnVHlkI1QnVpR6hOqG61CNUN+JVN/j1SAaY\nYDD4ta997YMPPrDfrl+//pJLLmlpaelLm+T79qXnysrK888/f+vWrfZb0zSvvPLKVatWcc5X\nrFjx6quvHo3ZPXVy9GbH8eKLL/7ud79LcsRY1q1b99BDD/3iF784//zz29ra+n5qR2DYsEWo\nTqgu9QjVCdWlHqG6NFDdyIuxq6ysDAaDM2bMsN9OmzbNMIz9+/f3pU3yffvSs6IoS5YsmThx\nov3WNM1IJMIYs3fftGnTNddcc+WVV959993V1dX9NbunTo7e7Fhqamr+9a9/fetb30pyxFjm\nzJlz4403XnbZZT11eGRXe2QhVCdUl3qE6oTqUo9QXRqobuQ5dg0NDbIs21VHACiK4na7Gxsb\n+9Im+b596bmgoGDJkiV2EkJd1++//36v17tgwYLW1la/3y/L8q233nrbbbcFAoEf/vCHoVCo\nX2b31MnRmx3Lgw8+ePnll9tVSno1uy8c2dUeWQjVCdWlHqE6obrUI1SXBqobeTF2nPPus9Sm\nafalTfJ9+9JztOW777779NNPFxYW3nfffR6PhzH2zDPPeDweu4fy8vJly5atW7fu9NNP73vn\nXq83YSeqqg6I2QDWrFnT2Ni4cOHC5EeMmt0XjuxqjyyE6o7YbAjVHSlCdUdsNoTqjhShuiM2\nG8NGdSPPscvJyTEMIxgMulwuAJZlBQKBuGrBPbVxuVxJ9u1LzwBaW1t//etf19bWXn311aed\ndpp9ZSmlsYtZPB5PYWFhrFPfl8576uSEE044erNt/vrXvy5evDiqhl7N7gtHdrVHFkJ1QnWp\nR6hOqC71CNWlgepG3lTsmDFjHA7Hli1b7Lfbtm2jlI4fP74vbZLv25eeOed33XWXy+VauXLl\nl770pej3t2nTphUrVvj9fvttKBSqr68vLS3tl9k9dXL0Ztts3769qqrqjDPO6PWI3fdNwpFd\n7ZGFUJ1QXeoRqhOqSz1CdWmgupE3YudyuRYtWvT444/n5uZSSv/0pz+dfvrpWVlZAN5+++1I\nJLJ48eIkbXra3seeN2/evGfPngsuuGDXrl1Rk0pKSiZPnlxbW3vvvfdeeOGFsiy/8MILxcXF\ns2bN6pfZPXUiSdJRmm23/Oijj4477jiPxxO1qlezk3CUV3tkIVQnVJd6hOqE6lKPUF0aqG5E\nJii2LOvxxx//6KOPGGNz58697rrrZFkGcMcddwQCgd/+9rdJ2vS0vY89v/LKK4899licPddf\nf/25555bX1//6KOPbt++nVI6Y8aMa6+9NiMjo79m99TJUZptN7vppptOOeWUq666KtaqXs22\n2b179/e+971nnnkmOrB89Fd7ZCFUJ1SXeoTqhOpSj1DdSFfdiHTsBAKBQCAQCATdGXkxdgKB\nQCAQCASChAjHTiAQCAQCgSBNEI6dQCAQCAQCQZogHDuBQCAQCASCNEE4dgKBQCAQCARpgnDs\nBAKBQCAQCNIE4dgJBAKBQCAQpAkjxrGbN28eScSCBQuS71haWkoI+d3vfjeAxpSVlRFCioqK\noqVCAPz85z+3TbIsawCPlZzBOLvLLrus+3UuKSm58cYbfT7fAB5o+CNUlxChukFFqC4hQnWD\nilBdQkao6kaMYzcMqa2tveeee1J5xDVr1jzyyCP//Oc/U3lQm8OHDz/88MO33XZbry2H0Mhj\nAaG6hAjVDSpCdQkRqhtUhOoS0icj+Qjh5JNPBrBgwYKarjQ2NibfcfTo0QDuv//+ATRm3Lhx\n9tVzu93V1dX2xrvvvtveaJrmAB4rlmXLlgE4++yzo1vq6upqamoCgcAAHuXSSy8FcNxxx9lX\nuLq6etOmTVOmTAHg9XqPwMiRi1AdF6pLOUJ1XKgu5QjV8TRS3QgbsdM0rbArOTk5Q2hPIBC4\n6667htCA5cuXX3HFFW+88caA96woin2Fi4qKpk2bZovJ5/M1NjYO+LGGOUJ1cQjVpQChujiE\n6lKAUF0cI1R1I8yx64nq6upvfetb48aNczgcpaWlV1555e7duxO2bGpq+u53vzthwgSn0zl+\n/Pjrr7++srLS/ogxdu+9986ePdvj8UycOPHOO+8Mh8NJDrpgwQJVVR999NEvvvii+6fJe3vq\nqadmzZqVn59/9dVXr1+/vqKioqKiwg4d6Olc5s2b98QTTwBYtWpVNM7g448/XrNmjX0K1113\nHSHEdvxt7Ln800477QjOLg7OOQBZljMzM/trZPdD9/24wxmhOgjVpRyhOgjVpRyhOows1Q3M\n2OLgYw8Ud8fv9xuGMX36dPttRkaG/WL8+PGWZfFuA8Vf+tKXABBCiouLCSEAJk6c6PP5OOdL\nly6N7quqKoDTTz/d7iQOe6B42bJl3/72twFcdNFFvNtAcZLeHnroIfsjSimACRMmRHdMci73\n3Xff5MmTAYwdO/aWW27pfnbvvPOO3X7v3r2cc9M0s7KyADz44IP9Ojt7oPj4449vaGhoaGio\nr6/ftm3b1KlTAVx66aWc8/4a2f3QgyCQQUGoTqgu9QjVCdWlHqG6dFJdOsjuk08+sZW0bt06\nzvmbb75pf7Rjxw7e9Ys5ePCg/dE///lPzvmePXtycnI8Hs8//vGPtWvXxnaye/dup9MJ4Lnn\nnutuTFR29fX19nVfu3ZtrOyS9NbW1mYPbi9dutTv92/ZsiUaT2CaZvJz6T65Hnt2lmUVFxcD\nWLlyJef8o48+AiBJUm1tbb/OzpZddxYvXmzHW/TLyISHHihVDDZCdVyoLuUI1XGhupQjVMfT\nSHUjbCq2e2iny+WaM2eOaZrNzc3BYPD++++Pfv2xK6VtVFWVJAnA1Vdf/Y1vfOPjjz/esmWL\nz+c755xz3n33XQA5OTkvvvjiihUrHn74YVsc69atS2JPXl6evYxlxYoVnPPo9iS9ffrpp01N\nTYSQ+++/3+12T5ky5cc//nF0x76fS3copVdccQWA119/HYCthoULFxYUFBzZ2cVRXV1tjy33\ny8ieDj2CEKpLglDdICFUlwShukFCqC4JI0l1A+HrpwL7eWLRokXdP7Is68Ybb1QUBYDH45k/\nf759whs2bODdBopfe+212bNnx16XJUuW1NfXX3fddQmv9eLFi7sfMfo8wTkPBAK2Fz9r1ix7\nF9M0k/T2xz/+EUBubm60t08//TS6Y/JzSf48wTlfv349AE3TfD6ffcUee+wxznm/zs5+npgy\nZYr91jTN1atX22Pat99+e68XPM7IhIfu33c/dAjVcaG6lCNUx4XqUo5QHU8j1Y2wEbuEPPvs\ns3/4wx8IIW+99VZbW9uTTz6ZpPF55523fv36vXv3/uEPfzjnnHMopc8999wf//jH0tJSALNm\nzYr7DnpNaeNyuexlOxs3boxuTNJbUVERgObm5mgqwj179hzZuXRn9uzZEydO1HX9xRdfXL9+\nvaqqF110UXJ7eu1TkqSFCxfOmDEDwK5du/prZMJD9+ukhidCdVGE6lKGUF0UobqUIVQXZaSo\nLh0cO3u1iNvtnjlzJoAHH3ywp5ZPPvlkaWlpRUWF0+m84YYbXnnllUmTJgGor6+382tv2rRp\n69atAGpra+fPn19RUfH000/3asA111xjxzNGSdLbrFmzVFVljK1YsULX9d27d//kJz/p17kk\nT0595ZVXAvjRj35kWdbixYvt6M6jOTsbOyQbSSniAAAgAElEQVQzEAj018iEh+7jQYczQnWx\nCNWlBqG6WITqUoNQXSwjQ3V8hJBkoDgaWqgoSkZGhn2hAbz11lu861DqgQMHcnNzARBCxo4d\n63A4ADidzk2bNnHObddbUZSKigr7Ks+ePdvv93c/YuxAsc2rr74aveL2mp0kva1YscJuKcsy\nAK/XG90x+bl8//vfB0ApnT59un2U7skhd+zYEbXk+eefj27v+9nFDRTbnH766QBOOeWUXi94\ndyO7H7rPX/sQI1THhepSjlAdF6pLOUJ1PI1Ulw6y45w/8MADZWVlubm5l1xyyaFDh2655ZZl\ny5Y99NBDvNsXs3379q9//et2epjCwsKzzz77o48+sj8yDOOnP/3p1KlTnU7nxIkTf/jDH7a2\ntiY8XHfZcc7tZDZR2SXpjTH2q1/9qqKiIisr68ILL4yO1to7JjmXqqqqU0891eFwlJSU9CQ7\nzrkdi+ByuWJV1fezSyi7m266CYCmadu2beuvkd0PnfC4wxChOi5Ul3KE6rhQXcoRquNppDrC\n0yIIYAQRCATsgdaJEye6XC4AL7/88sUXX1xYWFhTUzPU1gnSE6E6QeoRqhOkHqE6pMfiiZFF\na2vrjBkzpk+ffsMNN9TW1n7++ed2dvIzzzxzqE0TpC1CdYLUI1QnSD1CdQDEiN0QcN99991+\n++26rke3zJo1a9WqVXl5eUNolSC9EaoTpB6hOkHqEaoTjt3QUFlZ+c4771RWVmZmZk6ePPmM\nM84YaosE6Y9QnSD1CNUJUs8xrjrh2AkEAoFAIBCkCSLGTiAQCAQCgSBNEI6dQCAQCAQCQZog\nHDuBQCAQCASCNEE4dgKBQCAQCARpgnDsBAKBQCAQCNIE4dgJBAKBQCAQpAnCsRMIBAKBQCBI\nE4RjJxAIBAKBQJAmCMdOIBAIBAKBIE0Qjp1AIBAIBAJBmiAcO4FAIBAIBII0QTh2AoFAIBAI\nBGmCcOwEAoFAIBAI0gTh2AkEAoFAIBCkCcKxEwgEAoFAIEgThGMnEAgEAoFAkCYIx04gEAgE\nAoEgTUjm2G3bto30gQMHDqTM3CFk0aJFya9DZmYmYyxl9lx22WX2cffv329vufTSS0f6N/Lm\nm2/2qrclS5YMtZmpQ6huSNi2bduKFSumTZuWk5Pj9XpnzJhx2WWXPfPMM6m81EOIUF3KePDB\nB6NXtbGxMe7TjRs3Rj/961//OiQWpgyhugFEjNj1CcbY+vXrk7eZM2cOpam7nrY9ubm548aN\ns7esW7cOQGFh4dixY+0td955Z2lpaWlp6YYNG1JmmGCgEKpLPYZh3HzzzdOmTbvnnns2b97c\n3Nzs9/s3bdr00ksvLV269IQTTvjwww+H2sbBRahOkHqE6gYWuY/tvF6vpmkJP0rltR4qfD7f\nt7/97ejbJ554orKyEsDVV19dWlpqbzz55JNTZk99fb390HDSSSfZWyKRyLJlywCMHz8+2mz1\n6tWVlZWSJFVUVKTMtoEiMzNTUZTu271eb+qNGRKE6lKMaZrnnnvuW2+91VODHTt2nHfeeWvX\nrj3hhBNSaVgqEaoTpB6hugGG98zWrVujzV566aUkLY818vLy7MtSX18/JAb84x//sA244447\n7C3Nzc1bt27dunVrY2OjvSUSiTgcDgBTp04dEiOPgFWrVkUl98knnwy1OcMLobrB5rbbbovK\nb8yYMU8//fTOnTt9Pt+///3v5cuXy3L7Y/CkSZOG2tLUIVQ3qDzwwANRyTU0NMR9GjsOdEz9\nBAvVHSXpP9g24Ozbt6+hoQHA+PHjo/qLwjl/6qmnFi1aVFhY6PF4ZsyY8dOf/jQYDMa2qays\nXL58+Zw5c7xe75QpU5YvX97W1hb99Pbbb58xY8aMGTP27Nlz5513zpw5Mzs7+6yzztqxY0e0\nTXTUOvo8cf/990+ZMmXKlCkff/wxgJtvvllV1XA4DGDLli2EkG3btgUCAVmWE4Yv/PnPfx74\nKyUYOITqBpvKysr77rvPfn3SSSf9+9//vuqqqyZOnOjxeKZPn/7AAw88//zz9qc7d+4cbjMv\ng4RQnSD1CNUNAEmcPjFil5Do/f1rX/ta3EfNzc0LFizofpHnzp1rGIbd5qmnnsrIyIhrUF5e\n3tzcbDeYNGkSAEVRFi5cGNsmOzs7+qxw7rnn2hsPHz5sbznnnHPsLTU1NZzziy66KCsry95S\nUFBQUVFhWdZ7773Xkww2b96cimvXG2LErieE6gabO++8M2rY2rVrE7a59dZbly5dunTp0ldf\nfTXF5g0JQnWDjRix645Q3dEjHLt+8z//8z/2NbnnnntitzPGopr76le/+txzzz344IM5OTn2\nlscee4xzvmrVKjsk8cQTT3z00Uf/9re/nXXWWXaDH/zgB5zzpqYmQoi9pays7Pe///1zz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ngoGxa8wixlia6oTnjdzbiu/zYPErhC5hFA\nIgaKRQBxgGOXOngw4pxyKmCG5hS9jpNUspoTj13syapBhQC49Xm49awMgTjto5FijMkWZjwx\ngIMYgEEKwiUva3CsktrteYd1CaTgOrDJhfnrAHkH6Sqi8fVNxTGvG+g267n11y8VdqCrWDjW\nz4ZcxQRy3e0i13TVDbhx2LnCbnFx8TOf+cwnP/lJuorV++Mf/7gx/aSusbGxazi1ATc2y8vL\nWutyuVw89TyvXC6vrKzEcXzF7Zvfe+mqe+G4e9MbuRQiSaTVQtxFFJFSVKm6ao2efUZMrn/6\n3SiVruUBDtiBbOWqkwuKwzFtCpwjgZAAGyME6BufHGhT1uq6U3Lj2lo8LvTg+iCBQJjQzydw\nkCJkzwEQMITBGlksvgLBkbIUqKTniTOqrqRrKASoH+jWD+ADXD/FoW9HE4G4/mywbhwrtilk\nqJV7Z1xpxNLERiSc2AtnhhjSA7j/Ec6A9QUX6gXRuvnppqPub3BX1mgb54cuy6jYPOa6y7uX\nYCtX3YAbhx0q7Jxzn/rUp372Z3/20KFDx48fv+KY3/7t3y7uJ5588slPfepT13aCA25gROTy\nuwVjzNW2b3566aorl7kUSrcjzTYVP0ftFpgEisbGaGpajj1nKzX1rp/e1iMasPPZylVHVKRD\niJMLmRPAujAjEghdiKUTAoEvxPuvUygSt26RuiC/ipeJ+l7MwqTV9026C0F7JAKCVgp58bFE\ngB9wmnpmUUluuCob3sr1xFLwesoqQYgJVpj6M7BFei6I11WokPWHdb4MGiVSKKThphB/Z0H+\nukWwULEbuk2w+dxsTo+4yEiJTXbLzacSFxVAIYHwZdpOLtrtDmQrV92AG4cdKuy+8pWvtFqt\nhx56aH5+/vz58wDOnj07MTExPDy8Mebnfu7nigda606nc+UdDRhwGSMjI3mex3Fc+Bestd1u\nd2xsLIqiK27f/N5LV532JEmwuoo4dpD1HyOhoIK4i/oQjY7b73xL/cgbMT19jQ9zwI5iK1cd\nEzMcHG02sKEfVVdIjeLRRfJl0+/4ZjPWhfwBKexrF8EEVxS527CqsSpsWcQCD4oUsp4VXyyx\nB12CcX6QL+WICnFWuFkvfDStfyQDDsTiRBUjiQEGE5wrzIJQAZwX+r25cGSaeFgcxEIHF/Zm\nU5TGEI70j5r15gou67r0ita7TXVMNrJlL7LkrZsVGf19XqGY3VXMeDuHrVx1A24cdqgV+dy5\nc/Pz8x/5yEc+9KEPPfLIIwA+9rGPffazn73e8xpwM7Bnz54wDJ9+up/ccOTIEWbev3//1ba/\nzO7abel0YQ3lBsYgNzAOvY6srsIY+IGEoX3h+W09ogE7n61cdcQilJarqZAVlwpyYgNxF6VG\n0ubAf1ckFWyUalsXOYWkKyxWanOBOwCAIoGAFBcRfSTrY1n1I9iYiYmYEAT+EKsAJgHBEmVK\nW1YA+qqOFVj3MyRAYB/EYHasnAo90nAWpKFCsA8wyIPyoYoKJrrkUQuAy6FD6PWYVRHYBP4w\nWMHm2Di2Cz7ndZ0qAtf3LffPybpVErRumbso6XXd+LdxruxlWac3RKmGLb7WDbhB2KEWuw99\n6EMf+tCHisfHjx//zd/8zc9//vODOnYDXg//+I//mGXZww8/HEXRu971rr/4i78YHR1l5s98\n5jNvf/vbh4aGAFxt+0vRiyGWROBpWAsGgSW3cF1JE9IVLgWytHgtjnDAzmM7Vp1lPl2pNhcX\nx32uxwSXOSm8s8JEECgikQte2nWDlPDF3rfNNqrigQKkn3NQSBsqrIMi4ghiLIjB66P9wFkC\nwAxdJjUOOJgErmvdmq9sLFEVqrDaQSyIoYN+jJ2zAMDk8vKYinzKkXXWw+4UtL9ejs5AhyCO\nPHRzhzxGbR/8fmAYshZqtyEcRXUflp6CV+3n1ZIPJMB6tbmNkDuSC4Y3Wg/mu2CxvPjU0EaQ\nIgEA46L+E5f7tXcU23WtG3CDsEOF3U2LtZL0kOWwBgCYyfMQhLhSYckBW8vjjz/e7XYffvhh\nAO9///uZ+Q/+4A+ccw8++OCv/uqvFmOutv0lcGnGDkLAeuV3YiEFJ47yHIB4AWX5S+5jwE3L\ndqy6laR32janaVXMqmXRFo7BDkTsxPVD6jZZlNYNVFRUa9vMhlbpVz8RAW0YvEQEsCJKFfIO\nUMSACJhAjID7FW1Th4CJAAWvDFHO5uRS+FUH5sLXmbfByoGFiEDMDi7NVblcmgiDEWUSNE9A\neQiG+7MxXdgUyi+yaJWIy9tWB6o81Z+r6SIYQjQFZtT2o30SeQwikAIcoPpSstCRuEzCblA0\npeVLTsfG+SH00042bXfY6U7YbbrWDbhRoJug9u9XvvKVnd7JzlpprEmzgTSVudPQHphBECdk\nDKzh/QdRqdLQ8CB98kahWHW//ws//7/fdZCSRDZClIpfPZAj8K7dNDLm4pjrNf2L//P1nvKA\nG55i1f2vv/ju/+nn7xhaMGmeH2iYWuY5IuUgxLzhYoTw5qCyy/qfYv1VWTfd2aKzFvXteuKE\niMAMZlFMgFjbD9tjJdqjMABRP/SuXqdK36Mi3S6Sno3z3B/nQLPkYlLk1rVjZoDhVGSd8nyD\nqGT3vtH51bwDUkhXYC28CljBWdgeTA/sA1Z043Rr8p1Dh0O/CnHI2wiGEU3BrwGA6eHF/xvd\necDC5nAWVPSKvaxM8UUUlkwNydHXw8WWi+Vd3+x3FRNdESv44O+/ji91x3MD/MIOuJiBxW6b\ncU5WV2R5Sc6eRTmisES7ZjdepI0xraY7d456Md99DyYmKAiv03QHvDqEiFhtlHQlovVQbCEh\n8jwAlKcSll92VwMGvEKa/spqknSxsi8VA9+SUo4tCUtRR3G91odsKvxBAOAI7C7KkNjwxhY+\nyQs5nkVyLRExwVlWSghFJWRRuu9wSAo7IMHzsakbFRFERHlA2Uoc99vaKsXlkjNkM1EsHve4\n1zFhzbRMrqUyS8EQ8i6659A5BfagvL63NG1AaQorXD/oACSrsAnq+1Gagl6/TOoSpt+Cc99E\nZw5ctLXICq0K4qsEwxUaTgEC9uCy/rlycqk1rm/wu0zVOay3b9uhkeoDbl0Gwm47ibtubg5z\np6Vep6mpqw5jRiniUiTG2JMnVKctI6M0MbmjgzgGAChsHqqIa4IDyAkTCDCAYnLaY8D1Yj05\nqAI1YMtYxFqt0Z3s2GrGPS8JnFe1ESCMfg07KUrGFQY8gev3EZOiasfGHeXmZIn1cDTq6yAB\nuO9/BbN4nrCiJBU4skaUoiAsOpgJQHmKPMeGs4EY1iLwVXcV2rP1qKhjAoBD6EJXGsArq6GS\ntierMzM8NAZWfhVeBeVppA24FGJRUmAPKhC14sxuDR86gK70W4ptJhrH6D0goHUSzkL5kBSi\n4Uzfqiib7XCFeuNNelaDcsgVKxVfbOe8kE08kHQDdioDYbdtrK26hXPSbNLk1NVqLF8CaU2j\nY5Km8vSTfPud2LWbBl2ZdzpCYSSdDuAYECLnhJgUgCAgrV2v583sloN3Xe95Drh5MCpcDuem\nO6XEo/HeKJEIQa1XqivUSVHHbuPCUzxhoChgzOt5AW7dUUub2siSiCuK/vYljILvAyQSCwlZ\nQej17XLWUhCIIep2UKmCi08AkgTWIHm+9BEAACAASURBVIpQhOFt/p1xAmeR9Gh2D8ZqJM4t\nLwhLcStLBL8K/5I0OWMxtbe0X7+EkGIflRnE81ABmi/AdKACIIUrahevh0kUx1TUVQH69jxS\n/RorsP1htFEwZXME4mXJswMG7EwGdxzbgiydt9/9jsQx1Wqv2vDm+zQ55U4ck5MnxNqXHz/g\n+iFZJp4nWtNGpl3xbWsPni/WYnVZ9uxTg7rtA7aOcmJC04697kha104HJsg4d+Q2FqD0Y+f6\nzSMuqmbHAoKTfpW39cqLIFaFPa+Iur5Qi9cLoBRYkXNULpOI+D6cg7NwhjwfAEURlBazniHU\nV3Vl8Kb7UmuRpohjNNawsoyiRVXcAYjrQ7K4IGurVz3gNIPnF6rxJfDrGD0Mr4zxw/CGN1zL\nYKzn2xYHxuvl6wAQuDAK+tAlkAdQ3yFbZEgAAyk34MZjYLHbeqTZcD/4PiYmyA9efvQVUYrG\nJ+XMKfJ8zO4Z+GR3LnEPvYSjMtJUkl5Rfh+eh3IVWSILi/ymN6m3vPV6z3LATcVULD2vF7gR\nAJ5Vlpx2+qKI/yKXk8Std4ilfr3evlmOmGQ92K7fdkKK5AEBCExgBXHQHggIQhgDBqCkWqdu\nDGtdlnMQiCIyDr6HPO97Kns90Qr1OjbPp5B0ut9QQsKQqlUxOZY6qCZUqVKtJidfpEoFV7pm\nul5Xzc5evv1ySuMYuxdLP8DoG5A10D4DpWEzmBgiYG9d9tJ6s4mN5hsaxGAfeRcw4PU+ZwPT\nx82AtYi7MEaCkG6NprcDYbfVpImcO4uRkdeu6gqIaHTcPf8shyFNTG7R5AZsMfQjP0KVils8\ny6UIzJxnAiAqUZqKNfqBB9W7/gsG/vQBW4plLmfBTGccJInOAuvTuvOQBA603oTiQvYEgZyg\niAaljVxZKfyzAmIqbHXFAxCJAzOUgu/DOgDCGp7HfhlaS7tFQs4YioGhGphhHUwueYoook6G\nsIQk6dvYejF6CZSCBUyKapWGhou8ImgPaeoaDdq1G6WSNJs0PnHp0TpHSUK12is8OeEIph5C\nvIBmhuoskhWIIBhB3oVkgFrvTlGcAAdnwbqfZhFNIGshXYPLAR/IAXvB8nc15PIyeAN2DNJu\ny9l5OXMampHlfOddtHvPy1p/b3QGwm6LcefOupVlHh7Zgn0pRWPjWFnG0NAVb2QHXHd4fJJ+\n7K38w6dkfk6SHpxQ0SR9ZoZGR/mt7xhUKByw5TgQCzsQkbCw59RG7maR5urgWEBFc1MBAEtQ\ntNGhdd1aVSTQOrh+JWL0+zYwww/gHAhIUonKCDwuV0UppKkYQxPT0mpwr+dsTs0mfA9DI1Sr\noRQhTYo6xtJpEwG9HjodaI08FWNQKpHnwW2SQp5PRGi3qVpDksDaS26EpN2iA4dQehWGFq+M\n2m0IhpAso/k80i7SBphAAfIUJgNR/8BJAwLHCCpgDV3C0B0QxvnvIlkElwADm6/3012vdbxR\nQaaAB7+iO5Y8x/xpt7BAUQkChJE7/jxpzTO7r/fMtpfBktxKpN2S55/j6Zkt26Pvu+UlrlRp\nds+W7XPAlsLj43LgEA7env/7N9NOm/2wdPc92L2bgpCiQZWTAVuPEFlWOaeVvExFpwiBFMXW\nQMK26aeBUUo0s2bHpKAdnAMTi2IBURCQ70meI8mELDlHBKF+frdon5yQWPEjBIGa3g3N7txZ\nKI/qQ4jKWFkWa2lomNIEgOSGlEK5Sp6WboeKOp2ej0YDxggrGEvlEgUh/ADOorkm5QpKUT/r\nVnvS68HzEXcwOnrRX42xaLfpjfe+2nAUYoSjCEZQ3o28g3QN8TJsA1kPJoFJwQQiqAC6DK+G\nsAavhtIYvBpYY/QuzP0LOi+gtwovghFQDnEXYvX6zW09aFUYNAfsRGTpvPnhU5QbeB6IYHKp\n1WlxAVMzN7fRbiDsthJZOo9qbWtdbzQ05I7+UI2ND2oX70xofFLW1uT4sdw4m1urbDB/hpzj\nh34MevD3NWDrYYdY61xbnTJBrZfWLbIDxAo6XtLTUAi7I0FcGSn53vSphYqrUSkSP+A8l7Qn\nnbhIrnDOEJic9Kv1CNgaBIHUx1iz8z3bbZO1pHwM10kpOCujo1yvkh8iy0kcDQ/J/Lx02uh2\nZW0FUYQ0l7gLa0gclSvwfARBX5wpDaURxxCHcrXvKQ4CWVmi0THk5sJxOpGVJb77Hiq/xhsk\nInhleGVEkxjGBRubyHo+7FUqD3sRbnsY+TJO/RviOZjzcAqk+g3SmOEsnIPN4AT1g69tdgO2\nGefc3BmK44tqh3U77pkjfOddKN3Md92DH54tQ3o9OfY87dq1xftVGqVImms0EHY7EiqVoJQ0\n1rxOSxFRloGALHvlUUEDBrwqSNDRfLq8XEsqoSGhi/qEGW1YyMJ5wgqU1aKer0eGgpKuq713\nmtXV9NxZU63ZqBq1m8rTnrEOAgbaHbCCVqI9vv8hiko0d5qckDgaG5fVFUzOIPQpLNHiApwD\nK+SpC6J4fFzK1Wx03GcJn3pS+7401nh6SjodWVkmLwAESQ/YqAJHYEKnK0pT4WNlBitY028i\nCwBwqyu8dx/NbN0VdaP4C1367xXxxnDwZ5Hn6JxA+ySSNZgEzkILyENYhZrEyEFEgxDoHYk0\nG3J+oW+r20D78JxtNNVA2A14RXTaEgbE2xApH0XSatPVKxwPuJ6YHKdOklI0MuI5JwDCEqJQ\nujG9msCgAQNeIY2oF5k9C5XTw2ltT2cMNtdOM8hBMpXn5ADRUJnnSKjHq612ZzrPF+uVNY3R\nZnt1Yrrm+9V2q61V5tykVrUsBWsKyrAGQ0M0MkL1uiQ9esNhxQoisrbGQSiliGpViAixONMF\ndZut5Yno6UZrLMnOOvirzcNrjbJiDXLGBsSh53nNBntaAZSmFiLKs9wvjWyzrDcxxVp5RCHB\n5vlqbrpxLzfWW11Jpqez2kgp7k363rjvXy/Pmedh+A4M33GdPn7AayZJii6dbnWVKxUQS56j\n08bkNKfp9Z7c9nJzCTuTSxwjTWGMWEuKhZiCgEolFHXStxPpdincFqMahYG8eBz7bhtE4u9E\nivqnSuWzexvdOPS8umICv/JkubyL3nn4NYSj2znPATcLsWeH82AtKM9VzpfzoGx8LV5gPMs2\nJ+TKsOPABT3PzdeTrl3N8iDN3Ipbaq4c1QhWxYV5HDZWMj8Y7nY6oFIYenmOXtd5fr660ilV\n9LHnoje+KSxuU4loeFhOvijtNsRSqZwHwer86XM27dUrmde+zTQ56ZyLPbva7Ckv6jSNc9yL\nLcFmxjFRnnt53i8tYlPjBSCws2STtNeNw1LHWu71TG7PdeKKtbV2e37P3vMT01mWxb1ew9oH\nqtXdgb83DGNnEyepcwIETCFzXalokHh+jRFBnotztOFh34kImGl8gpWSuTkCaHwCtx0AX1TY\n8abk5hF2srrqnn9Ozp6F74EVFItzcM4ZQ1lG+26j+hCNjG6jNkoT6O3ZOSsojSwdCLudiNY4\neDuOPdvrdF9U3hShniRSil555kTWQvskKrNbKexMD7YHvw4AaRM6hB5YD28WEkq+PZ289dxw\nJ2h0/TxX+Vgy1PNScpwrm6rckdZijw2np1XiuxHtxPmuVx1J2ucCoyqmtNxdHkqV2CocdZVf\njWNPK5umMStJkm6j0ahGveapGc9VWIiUwHncTvfvWosXl5YWeH5plRanbMnmvjqnXii5F4bM\nRPPpcb0rj6SyljSiqspNrdtmwPi+ZSYizxpHTAwNZ5UGgZzTSS/1de4wDDLGpK1mrzLbu3MW\no2NDkouk0GoPwmfj+KsrqxO+v5bl04HnsyIgF8mcS5x71/DQdODvCwP/po6I3wmICJbOy+qK\nnD5JAtmzl4aGaGJqJxZ18gNJUxoapokpGh2HtYVbVlaWcbN3Y7+JhF3cgbE0c2lGar9JYqct\niws0PkFDwzQ+sfVR7SKwlrztOp+ileT5jr0zuqUh4pnd7oVjlSg6kNsSE5crfO+P4hVHfAfD\nqB+6rI3S66Mzh84cRt8AEFaeQmU3hg4OaujfJOjyvd26/220dnfUcpnGuzRfWQ0MBTaw5DwJ\nMnbPjp49EVUMSt3EzvRwYqgrJlFmnhPn2DdUV+xZaYZZ6nHQ1EqXZ5wtGeek6s3vai3QnDm7\nctqvjfuBJ+28d7bS8o+E+hS5TJbNjKeZQjubG9XhUNeq5bBkG47PrbZMt4tO2EmqRoJcYs+5\nPLHkMcAKECErIqm4HCyUG9frerIwoqrVrnlq/8zp3cHJcnM4PTd1PuPek0QUW9vx3zCfuygY\nWzUTK64+Gwa3hYFaNxRlIseT5Fut9psq5YNRaX8pHIi7beTsvHvmKI2M0K5ZAZAm7tlnVJLQ\nvv07zXRH9SGenkGriUoVSvWlZ5IgjmlL6pHtYG4eYUd+AH2VmwYiKkUoRUhT+9wz3GzQrt1U\n3dLYduewqT/0lkPMGLQX26lQtare/q5weWmq26EgoKFheTUXDh2istVllXSIygx0CBAqM1DB\nQNXdPNwxeliGDi26r1rvxabu7NfeG5Z3p9p0VY8dn44W1qJsKdrnSzqSZjqnb0yujUj53sbx\nzGusVXYZUyZOm14jMtW1sg8Rz/GcPrsyuXsq7vbc4rf9eZNX72nTindqQWvl7ZpIdx0ZXlrM\nn2E9mXtTZcrFxUn+rcz1hEfjdgWxd5wPdAP70CoFTpbLnTylcVti0ZagAC1CzikIRKwFQRkw\nWCWev1QJvNws1N3Xa0+2O4+lyZ7zwexxnrkzuj3w/BMmrubdSTLonRXbLEdvfGZtOrW3312p\nFNrOJ5rwvAnPO5/n31/svG2o9kCtGg5Md9tBHLsjT9P0NJRGcUXxA5qYdC+8wEPDO04tac27\nZ90c3OI5CksAIcsQd/nNDyIcWOxuJnyfJyal1ZRTJ/lH30xj41u7e9q2H09xL5m+NeB6IwTJ\nM4hImsEJXe8vqzILcf1qW/VD662TBtwU7C1F77795788v/f08tcMfbERLZyqnZqIR3fFY7FK\nY8Ww3mRPB7Z8sn7q5FTUpNVlF06X8ijhVklPLCELuKMSa72a8ZVNWpFmEb/9fIfl+1P5C34U\nyMmg0rl7aZLYKCw+MZZntZy8XXFyrkyrIM5QbvCU8qClA9LnTVDFkbVo8T8mDxAmlQSNQMqZ\nyVlFRmnrPOuEiqZjgJJE5cqxdhyTaSt/Vxw/MzkfRyz6vtAllJ1QnP1jOh351QnPc9Ad0ppK\npWAqNMtT2dOn0jlPHrijNr35z2xY61pZfa/VSZ08UKvWr3afP+C1Ip0WSmGh6i5AhHIZ7TZ2\nmrADUKnygYM0MoqkJ04oDDE0tE2h8DuKW0zYAQCoUhXPcz/4Hh++lya2KNe0uEF0Ftieq4m7\ntCD7gB2EiJw5I8vnqVqVPJf/+C6/5a1Ufa2+VUHWAml4ry8ff0PMDVTdTQYBh6vVkX1v+RRN\nhl5Flj7dqMTN6uK8S2wSecY4UmmptlrepV0XOKr9e6zpzgULP9a6/ZQXdCYRdfIqzJBLl30h\nyitS1qGfZqeP1Fa/E5XrvWPKxYtlWQrXIoQdsqTExyyyZc92QGTU0JJVuVgHCEqROT8kDedN\n9bw9S9VesrSSerXhuNzSqBjjC0GcZacdFwX3WMg31ijT0yib5NBK+6nJtecmh3KtASQU5jxl\n8vN1ytJsao3GK0ppIiuymMuwjmr+oVF79vmlf4vo/j3VfZvPjCI6FJWej3sA3jZUG9jtthhr\nL1V1BVrBmCts3wl4ftGT85ayi9yKwg4ABaGMT7onfsBvfoiGh7dijwTfl7hL25PfQMZs054H\nbAFp6p49ugYkJ08qzx8tR367hdcq7PIuzn8fItj1E2Bvayc64OZhNgz+j9sO/Efjf/m+O4/G\n3zEFvXy1U0oz8smlvXBXxCueHm8O/dczeVBKn+GSaoxUp9pBtprNl2zFnj9TqVuvqkznDn9k\n2KRnJlZ/WKHQvshIhJWB73vUs01ADE+Z5KwS63sBnMuSOcO7WVUUiKUbyppzlJkULLHWRyfc\n3tW1c6Gr96qjmYLLtSiS4r5UGAQHYk7Y5pyxo67XawSrKVvBeOzQs1aAnOoV9Ibz767yg8t2\neCrwI+YISKxbM2bCG6ujtXj2syN7f7kSXhrHsCcMftiNA6Yfr9fU9bad31QoDXslAWesaLVD\nTrSsrcrqCvk+Tc3Au0UvoLeosANAvi/DI7J4jkqlLfG4U1hy6cK2dJHKc5rdc9Mn8ty4yLmz\n2emT3W6clULO8ijw9dgYj45R8Fo6/LKHyi6QAl3dRGti5DF0BJdBLII66Nb9U751mfL9/zQ2\nWXH/9fvPnVjNVmATH124FN6YCfZEyqj6T0XlB8O4N1KaOJSfJrtSGzmYVHT1fGvP6kjOJZV6\nLOM07r2g5xZkydJMOT8HVRIqB0QsmYNlQe7aJbvmuAwrucBKOurOdGifJVWyZw0iUdp3q85V\noEoL5Vxn/lCyvBZ4u7oBOy5qS1BRGUiECIEgE9fWvfnawmqUzzbHz1RfWPIbmd6nSXWt9Ygt\nSqLGVH428IOG4RIzQAIh4EyaDqkooplK48lDo5H2LnUC7g/Df220pn3/UHTz+92uGVStopfA\nmIuyD0XQ7WxxzPprJsvk378tw8Ou12MQ36qtOG/pXwMql2V5SaKI9uzbgt2Vy9tU9lCSHtfq\nA1fszkRWlt0Lx3r7bsuefmpVEDjnaiPluEtzZ2j/gdcQGakC1A8BV3eh5l0sfBs6QG8FugQd\norwL9f23mLNhAAAgZH7b5H1T9Gv/9Mzvk78f5Jy//wxN++LO0Z5c3dWNe/vC8L2TbzzE/9uR\ns1968fy/6HI13z/9g6Fzoagkfb5a3TNSyerRHZVG4iUZSy6oMzEgVowVsIhv1kBEFBBpIyIU\nCnTk5lNUHYUCDcDBNyYOVKnruROV7KfX6rU8aes8zKsACkEG9FcpCTW87MjY3JpvNGW+Tsq9\nsINjcL1VvcehnIkjIiDUtoH89JI7UFI6ZEWAECCYz1JNgTROdC1PjbxdK6WJfOaQWQGxczWt\nv9FsiUhN61BxTSkeWO9eJ6WI7nmDHD1Cw8MoItWy1DUafOAQhrbC8fW6EWOEiMoViNAVjYu3\nBre0sANA9bp75hkeGaNK5fXuqlaX6RmkW19tTrpd1Otbu88BW0ZjLa/VjoTR6bHJ/Y3VU9Ua\nj40N1UdGXzgmMzOvrfnES0fF2Ri6hHAEySp0iNI4Wi+iOgse+OpvSYj49smfGq8cOtv4DyvZ\n+eaRQ+U3rkjN5DOWyz8xVDtYCu+MSorueMuBj8yOPNBOzp9Ze2KuWi+VDuwJ3z5bHg+9qBmf\nacQn6+mpjh42EIFTrkM2Vi4nVtaB2XdMFi4XENgSsYiPtZRGUNR7JfbRJqfhKPb5ibHeW7Nw\nKp6xDIIjIQgxIH1lJm2/txrmADt4Pe5pEyY84pmzdduyan+PRy2RdUJSGc7PDPNwUyYq1Ldi\n5xBN3DH2B3l5Mf7mWreSefsixT3nhrXnMcXWDnu6a93ROK4rnTv3lqH6hOfNBP6Uf4u657YE\nntktfiDLS/L8s6IUHzjEUzM8ObVTcvtKJbr9Dnn+OZrdsxOTOa4Vt7qwg9KoVbB8Hq9b2EEp\nqg+548/R2MRWzKyPxDHv2k0DYbdTkSw76SReXZ0tV/y4O5HnWZqejntVrcIkxTZ0FVMlmB6S\nNYiDTZGsoLZvEI13qzNc3jdc3mdddsfkf/F0mcDvdg6gSPHGT66vq/vH3yni7px+90LjiTNr\n3x2tHCxy+Rk68Icib6XTyz2XGqesTYS0RcAOmlOCg1kFyBMFEFEosLkEAEiMliSQLsMQW3FK\nw3giS6WJ4aReyX0nADkWFpAADgC5jFMrxIAltuj5hsl1AdGut9t+d03N9rgq5KeIuiizXSYa\nSR1bSOxc21gUxUOBeRntdV84oYaEPAA+EwE+c1mpXYHfNe5wOawpXs6y43FvxZh3DNXuiqLx\ngbx7rdDYOILQnDyhZnbzwUPQO+hMEhHtvQ2T0/A0ROT8ItIEAoQhDQ3Bfy2xMTcit7ywA6hc\nc88/q2Z2v35LG01MUKspcUxbWCan2aB9t105F2nAdUfEtporzz/3hsaqy01bMMSIFuYWmmtx\nvRa6bfEF2AQ2Q3kC1d0gBdIIhgZ+2AEAoNhX65bb8lWCN4g40NWZ4fuNy+bXvlsOxhX5me00\nu6cOjtwPri91X9TpHJQGfBGnpMMCcT0QA6RB5BzDkaTCdZFeIC0HJaLAlqAyhD5lDomDLERL\no72K7xSJJJ4oy55jRy7hbClMAAAWzpA4T6ySHsSW0E6oPG6fz2wFRDmFCVW0sSagJbcvhycC\nIjiBAE6kR5VJ89x52t9SE0QQIGAGqG3tk+3O8bh3rJv+57HabBDsDYPd4j8bJ/+01vz58dF7\nytHAOfva+GGaLzVaSS1+mHjHnUEilErSbsn8nFtYoDAAgCyj8QmanqGd4TLebnauXDhz5syf\n//mfP/fcc0qpe+6555d/+ZfHx7e47FwfreD50mnTyOvu6KQ9Gh6Rs/PYItO0tJo0u2cLJnYr\n4Zx77LHHHn/8cWvtW9/61ve9733q4l+4b37zm4888sgl73rnO9/5G7/xG1/4whc++9nPbmxU\nSv3t3/7tS3yWrK7YkyfHV5ZdVEq0F+eZsA48f3xlyeu23fw8G0vjE3jFZRfEvlTOREEegxXW\njqE0Dpth+segBk7Y6821XHVbgqdK+8Z+olbaFWcruYnr5dlWb64ezUz25mGizD/U6Z4kskqU\ntT7EEDOcAWmWwu5mhTxPep70AA6kAWEyjiQOMcTEThktqu23lkurQ2m5ZAN2ygFdbbpeHnud\n5VLMkmnJLZhd4Gg5kJYTVnABeg4+weSIFEzZrfjS3Z/+P0PqtibPLun9sdScgEiYSICYqxWs\n9WjSQaxI5pwAHlOkdMfab7dbp9JeTas7y6UZz69qHTD/9/nF/zw6/J9G6qUbuSTK9Vp1zwN/\nsv/Oh4bqP6N2SjLsRZhc5uak3eSJC94z6XZx7ixKJboF0hB3qLDLsuz3fu/3Dhw48IlPfKLd\nbn/xi1/8wz/8w0996lPb9XlhiDjGVugnGp+g2/a7M6d5dOx1ajtJelQf4t2zr1wWDADw2GOP\nfe1rX/vwhz+stf70pz9tjPnABz6wecDdd9/9yU9+cuOpMeZP//RP77//fgDz8/P333//u9/9\n7uKll60zLHGs9+4dO3HcmLyS9IrkVBeGic1l712w1h15im47wLN7Xt5hIejMYe051PahMgt1\ndadBNAmbonsOpXEkq8g70IPMv+vNtVx1W4Vib7x658bTUNePnv1b49KQlbI9p7TvjWQ2abmM\nCSJGARYs5AClJXPwlWRADsBSIKw1jCMvcGsk0lVeaNVqmMxXlnq6p61XzysWqhEkBHe2vGS4\nrcUISMFUTHDOb0McCYiccimTDcQ6agEkUI6oZFu77JMzeGqWR5f1/kV1xyrvZuQeDDszKsdi\nlEE6R2g4yuAbERFHRE7cmTStG7WUZ3XlBcx7wsBn/Onc/HO93tuH6rdHYeXGTE27Xqtut+8d\nqFTuqFSK9yTONYwd9/QOKS4jjYZbPMsXF6mlKJLl8zQyisktKl67g9mhwu7FF19cXFz84z/+\n41qtBiAIgk984hNJkoTb0wmEPA/ZFiW0MvPsXjLGnV98XS1WshTLy/TmB7Ed9VNuXnq93le/\n+tWPfOQjDzzwAIAPfvCDjzzyyHvf+976piDFoaGh++67b+PpF77whQcffPDHf/zHAczPz7/t\nbW/b/OpLQ0FAUeTVa1hctMwZWEj8TrsWhlFUpiDA5LQ7fQrW8f4DmwW6y9FbhliEI9ARAGQd\nNI+jPIV4CTpC+dKmxxdQAcpT6JxB2oBNoG/++8+dzjVeddvEePWu6aGTp1a+2csbzuVKBwA0\nkSYyoggOcBADYoKw5MIkEBYr8FkMCOQsWCw8LbFRcnxobqYztBo2gHw4rS3rGPDI8WJ5uRWu\neCAIEUzgvJXSctPvaDhHzLDoO/hYQIAiGBLFIgmXNeUlae3PvrcP323xBJMkVHFQFsGYO5ki\nEmBR32Go1KaRjprKJPSJPEjHWs3aikSKY+uWc7s3DP6l0VxM858Yru32/TvKpRuroPF1XHWH\nSqX3TU9OeBqNNen1jq41vtVLf3LP7nu2yav2akmTK5ea8gMkvWs+m+vADhV2t99++5e//GVm\nzvN8bW3tn//5n++4445LVN0jjzzinANw6tSpkZHXlf8izMjzLbvX0Jpm9zBgz519bXY76Xaw\nusr33b/juu/teObm5uI4vvfee4unhw8fzvP85MmThw8fvuL4hYWFv//7v/+zP/uzjbc/8cQT\nX/rSl9I0vfvuu9///vdPT09vHn+FVddqhcaqSrVFFOcm1KpEFMKh3cTUFIh4bNydOUVBQJuK\nKnXOonkMHCDvYuggSIEYInAOsC/fK8KrYuJHkcfwInivshCyOORtsIYe3DJsEddh1W0DWoW3\njb3j9Mq/PT33JU+F/dbXxB4pJ8YBxBrwnLCW1IlP4gAAJCAiJpcCDHjoS0BZDleijCIbnI9W\nW0HXc+yAVJlUZ4AjQaEMh5L6s6MvGE5YwCCQkGhLGuQ0jMAJWMGARCNRzvmuA2YSO+pOE9Ci\n8YSrgSyLFQd9Tt9ZsqsATdGzS25fi6eaataxL4KVLLcecsjuwJ/0veU8bxrzQ+sOReEz3Xgh\nz3+0Wh67cUraXsdVN+Lpd1TL7tRJ9+ILKJWqeb6v0RpbOCP33U/TV78lvXYUNROBJJFOG8ZC\n637F+J1hU9xudqiwI6LCMvyJT3zi6NGjtVptYzlu8OUvf9mstzGpvL6cVmIF515igHPGwUKg\nWNPLxkABCEt02wFWSk6ewNDwq2hOZ61rNnh0nO64i2qDTNhXzfLysta6XO5rFs/zyuXyysrK\n1cY/+uij73nPe4p7hmaz2el0Y+rliQAAIABJREFUtNYf/ehHjTF//dd//Tu/8zuPPvpoqXTh\n67vCqut2xDkvDIc77ZITz1gvKpNiWVnBnhRhCCIeG3NHj6iREZQrAMSicxrxeTDDNFG/DaSg\nIwzfhayFYDfCVxAU4Nfhv/oFIoL2KbRPQRxG34DSzrjBvtG5Dqtueyj5QyOVQ/XSzFp8Ak7g\nESACo1lZZ0UIrOCcJU3iqLDSFZfr4v0EcqkCizAIqZcsllfuW7yzEXa6XgxAwAQLFEkOUE6P\nJPUT9TPLpcZ6q20BiGA1rEAB5MACIhCL8ZEAAIPgLHkEEaDiVjzkOXnaZQq9XflRwJ3x3rhG\newLE+/Nvrbrzy2p/qich1LLWOsmcMw4zgRcyn0myJzqdnxoePtVLjHP3VMp7wxsjd/I6rrrY\nurnjx8bn5+pTM2C6TWRqdCxy1h15isPwOtojZHUFWYYgkF4C1ZLjx/rNbY3B6ZM0PoHb9l+v\nuV1Ldqiw2+DjH/94o9H4u7/7u9/6rd969NFHo+hC8Yi//Mu/FBEAX//613/913/9dX2MyOVx\nbKlpt5NzcbaS5i3rsrm1f989/AAArcJA1yJvpIZhnZNkGawBiDwN7SGK+jJOa963X8oVWV1x\n5xe4PvwyWbfOSbuNdotvv5OnprekGcaNzpvf/OZf+qVfes973rN796Vdg66GiFweLGKu0sfw\n8ccfX1lZeec731k8rVarn//85yuVSrGHAwcOvO997/vOd77z9re/feMtl64659BsIDewpuV5\nzdyEisdEuN0m1q7d5uJ7VBq1qpw/T7dVAJgEeQus4HKEE/1KJUQoT6M8jW3FZWieQGUGpoe8\nPRB2W8O1XnXbhnVZIz552/hPVtqTy51nMpMyoDgMVeB51ThZDSlo2JxsTEhARDAAAwSxBCci\ngAIEIkwkYpp+9/tTRyfjscl4OPaShI1lx4588Ut5WDLhM6MvnI9WBVLUwoMw4ECEIvNVCCQM\nxzAkDiQOSoqPgDgwQYS5JA2NcEntd/AUssD19uY/KKvmor5zRe0ru9XD5shzeGdD71NEGaQs\n9Fyvpwljvrcn9L/RaI17/r3V8qqxf35u4VdmpmZfU8+Y18MNcK3bxKlWc/HJJ1t7Zu9lIoCJ\nykpBKanVsbpyvQrISZ67730HzOpNP8pTU+65Z6le3+gqJsaAmeqDrNjrx9LSUrfb3bdvX61W\nq9VqH/zgB3/xF3/xqaeeeuihhzbG3HlnP+z32LFjWZa9rs9zdnNfhzhbWWo/e3zh/wu8WuDV\nPBVpFcwM3WddLmKzeLW3enS13ZpcK0kQaK/GWsNaEacsaQM6cDAc2a3Gd5HWNDlFQ8MURbK2\nKidfRFRCKYIfkNLo3wwbyVKkKSUJHbyd7nnj5c3jxVoCwHyLmJE3+N73vve9733vox/96Nve\n9rb3vve9v/ALv/CymdEjIyN5nsdxXNwDWGu73e7Y2NgVB3/xi198+OGHNy6OzFzddPIrlcrk\n5OQld8CXrDrptqXZpHotb661jQOQWtdLs/LIGLJMXjwuQUC1GgCqVKQXI8vI95WP2kH0zsMk\nqOy5ppVKWKM6i2QFLkdpK+st3tJc41W3LccAADAuUeRXw5nl7vOhHh2rjDsRJ1kjPh2quhcF\nnfR8xbmMLZxYZwEtZAED5IAUPyiKnGMnxdWK0PbiuD7XCBq1rKqtt6szAchc9dxSqbEWNBOV\n9H2+QGHxKx4VqoXIseRMTkBgcmCCK5y/DNc35om15GlJRuzpZbXfwo/Z95DuNk/6Lp733xRz\n3VJwMHv8NN6y4t0lIqm4CvOZNCXCmOeNe/6/t9rjvjcb+AdK0VPtbsRq1LumP447/1q3+aVJ\ncRxFOgguuXRRWJI0JeeuS8IfeR7f+SPIUqnVyRocySRN4PsESJ7T6KhkCawBbgyL7Othh8aK\nPvvss7/7u79b3C4AyPPcGKP1dv2lSZ4X5rTcxnOr//6t4392rvHE1NDhseod1XA69OqaQyat\nRJVW86EFW53v6Th7Tj15NPvGE+2/O5U9taQXV/2VxdL5udLcqRf+36XvfeX8d7/Uefob7sRx\nd+K4e/aoLC7AWVlekRPHafm8NBtIEyiFcpmmZvjue/gn38EHDvZVnbVoNNz8GXnhmDxzRJ57\nxj33jHvmiHvuGTn5opxflF68TadiR/Frv/ZrBw4cEJGvf/3rH/7wh2dmZn7mZ37mr/7qr5rN\n5tXesmfPnjAMn3766eLpkSNHmHn//iuY348ePTo/P/+Od7xjY8sTTzzxsY99rNPpFE97vd7S\n0tLs7OxLTTFJMTTcS9NmalSW1Xo9L8/jLG2nqQwN08ionDiGuAsASrtz59DtAGAP1d2o78f4\nmxBdW3VFCpVZVPdi+M5ttw7eOlzrVbediEg1nJ4deotWgXGZpwJfVSvBRG66mkv10u4oHAM8\nxWXFnmLtUCLJIJB+YJNzYoV8EhEwgYjEklkuNU7Uz74wdPKbu77/jdnvHq+dOlc+n+jk4rsa\n6YvBCzmawmQFRBARWheA671nUahAJnE5wrpbrLmF4m05ghZPjrsXZrPvB9LLKVzhffuyb1bM\nKUXUMoYZqWAhM2vGRIojpf610WwYO6zVmTT7frudv2RwzpZzA1zrNjGi9cEovG3nuZVoz146\neDv5PsISRkbpwEFMTcvkFO0/SNO7aFCg+Ppy+PDhJEkeffTRhx9+OM/zv/mbvxkfH7/nnnu2\n6eMkSbkUddPls43vL7WPTtbfqC9rz0SpCRaadG6xGbQb/z97bxpk11WejT7vWmtPZ5+hT/fp\nWWrNlgfi2RgDBjOkuNTHvcANuTGBpAiOPyhCYidxQRJwhYRrDCYEuLj4cXECGYoKCQHiEAL+\nQvDNl2Dwh7GxsCVLsjW0uls9n3lPa633/jjdrZbUktu2ppb0lKvcZ5+9dy+dXufdz3rX8z6v\nGHcRFvxBImlsFmXzUroFf0hJDy7IZ9tomb07W3M/Toc2+xsu99eNLC5fwdZykiJq8dwcbSxR\n/wAVS0dScVrb6SlU53n8EPwcux45ClIAAIOjmKvzSGJEMW3dRr39nWzQ+Yr7778fwN69e7/7\n3e9+97vf/cEPfvC9733ve9/7nud5b37zm3/1V3/17W9/+zF0P5fLvfGNb/zyl7/c09MjhPjS\nl750yy23dHV1Afj+97+fpumb3/zmzpk//OEPt2/fvlw7cvnll3dqsd/2trcppb72ta8NDQ1d\nd911Jxui47RAjVbTcZ0mO8000UJ2u66t1+bDfMXzmC1X5ynIgYhcF1nWuU6FZ612QQUX7VFO\nMc70rDttUMIHseGsJ791XXb9nsMPiWBYqTBwuy2bVDcdmSs4xczjKJ0U5DCnnhDWqI5PCQtJ\nTEJIy4aJCMwEsF3KyBlhDQwWmuYtrNtBAC/I7hbX8h1wR3HXydJ17sGdlrMEBpaSdhIacDLy\nu/hwhK4MAQCGrIv+it1nM+c59xWWnFm5/tL4e7vCX9LUnRq4BIdoIkk3+P5kklwahgfjuBTm\nhj33p43miO9fmjtz35M1EOuWgUFoNdFsIgiOamIeR+ipnAv+XJQLxfB6xBHlFxKT3GrT+g1Y\nvd59LeMcJXbFYvFjH/vY1772tT/+4z8WQlxxxRUf//jHT5PXCZgpTeqyenh+Tz0a7w63Hn+K\nbKXuZDWZHj1M+12bz7t9S1RMCifweqJ0zrIpeAO+8Z16JGsR5/JmqJA2pmb2HuzZ9Iqu4tbO\nIpSEQOAj8FHu5vl5PrBPbNtO60YgJc9M88wMT05QqYsGh48fBikF+CgUYS1PT/Fze2nrdjEw\niOB8nqxbt2794Ac/eOutt37jG9/4kz/5k/Hx8SRJvvWtb33rW9/asmXLf/zHfwwNHVWHddtt\ntwkhPvGJT1hrb7zxxttvv71z/OGHH261WkvB7qc//Wmn7H8Jrut+5jOfeeCBB/7sz/5MCHHN\nNdfcdddd8uQGV9ZG1XkhnVaW9DZbDltDFGdeFubM1FRWLDpBjqenUShRoQApWJ+64utFpDW0\npwAgHFi5QlZHaI3DagSVVZVlXMSLwBmddacNUrhbet8419oben3D5RuMSWrtQ61k0hE5zymA\nbaKbjgxyTtDQobAtAGwNSangMgRDaLLMKcAExTBgCwiCtVBEC8SNOvyMFkgcAUwL+7EMWrYv\ny0y0yP+WWODiChlH3urc1MBxbauIyVm5YfEmVBP9Q+bnTd07o7ZpBA3R3509G7ldCVtfSs0c\nStnQBkQFKXa2owHX7XOd9b73XBRv8LxAnlGOck7Hug6Y7cQ47/iZzTQ9+QT19oIIQQ7d3aQc\nrlXltu2n5rN4iXAc6uu3P/lfKOSZmUBoNuiS7ecC6TwDID56ibQW8eCDD771rW/9v+/4nT/8\n77e/iMu52Ujy8hH8S0+4xXdXUFaKVDsHp6P50XlM+G63OEFVbKpb+TTXP5NDmDPeImNmplZc\n95r5Db9Qym1gWMsGYAEhpOcInxg8O0MDg0zA6EGUu19ACa01XKtTd/d53CnlmWeeefDBB//5\nn//5hz/8oTGmc/Dyyy+/5ZZb/umf/mlsbOzuu+/+0z/90zM/sM6s+9P/47994NWvaoInjYEx\nXdrERJHnlaXMJ3FXqeQKgUKR1q2j/kGenxVbt9MptcdkRm0P0gZg4XWhtG2Fcxr70Z6E8JDM\nYug1F7vKrmF0Zt0999zzR3/0R6fpV8y1nnty9Kv9xSuJqNYePTj3I98tpVlTm6gWHUp0I85q\nodebUjgXjTmUGh2DNYRgq8GwMGADOIYTcAYmgImIoYgtk8YCLVvYuO0UwXZycwAW1EHMnW3Z\nTsYPAIh4sTwWbC3JTnWFXaycBZBRQLAAJuW2lI7kw320pE2fCt6SIC/AvXbfc/4b5sW6Qddt\nGzPsuy1jAyFeUSpa5j7XuSofAtjVit7S233ZGUzaneOxrjPreG7WPvYT9PWRlHZyknc+hTRG\nb5/I5ZHPi6uuoYFzSOHBzYbZtRN799All8rLLjsdnbvPTZyjGbszB2ZTnZns0iVn/Yqsjiyr\nidlkal/Vreack5nS5dMgP95q5BG44dJJTJT5yNXt5HP/3958lvf7iRSBGMay6clvc2WY8/PB\nricpZdp2Cb2gfrVCUrmMdsv++Efi6mtOLWM4F3DJJZfs2bNn6eXw8PCtt9767ne/++qrrwZw\n++23X3PNNTt37jx7AwQ8L3PdJE1JSENUlY4lKKKI2ZPCBjk4jm02xcQEcnnoo2p0Tgk6BhFC\nLmxjMa8wQ5kBASGO3+q6iIs4FuXcxpHuV041nu7KbSgGQ32FS+fa+0q5dQyUw03MJjXtRDem\n6k8Vne5qe58jQ2trsCkzBHmKlLUZhGADgjIwBAkGyDJZwkI/sqOVdcs3ZRf+v0DoaGm+MsEC\ncsFcb5HudZJ9DAewtOB4l/rcWE7sUoRF1Lqz/Yedl1lQU/aWzViNBi2YiAzDFyKxlsEFKZ9q\ntTcHfkHKXtc5FCdnjNitgVgHAOD5OXSVyHEAiP5+bjV5dpqDHNatF79wFeVeGHNiRmsMViO/\nDuI0kBHKF0RfH8/Pir7eC4fV4SKx40ZjpicZp/Eef9OKJ8jZph3dP+vOBk7PSaoXVcZdEyYJ\ng4hrSJ2c18NApttp1minM8rxyjU3cTMnzBWChQUNM4yJG8lca2q+Ky16TsGbnRT9w/RCc8VB\nDv3KPvm4uPYG6lm5JGqNohPpSqXSL/3SL73rXe+65ZZbxLIP57LLLvvgBz94IjfOMwMCkRSZ\nNYGUkRUGVpEAOmkEVlJCCApyyFLs3Q0/OPXBheD3IpoCCEHfyuuOoBc2A1v0vOxiV9mLeB4Q\nieHuGwynM429XbmRSuESw1kjPpxzK0IoQPnCY9itfW9iiKen/2M+aTpuWcf7JUkQLJtOxSox\nAyyhIB3DBGRkDQAwg8SRJcbyklgcoXF8FPs7slIGiKnTkQIABGwGr5PJI7aWFDG73O6QvKVr\nEir0mj1VNdKiYoJCf7ZzTGy0PCwAy/AFTWptGIKQI1HXpiBllxT/s1p7RalQOCPb4ud+rFuA\n1rQk9RMCfX0wmnp6xPZLXyirA2AzzO8CCfjdcE+PXFwMr0d5wUD0wsEFTew4TeO50ecGnu4J\nr1nxBKFNOnOgKg7nvMpJWB0x/LrNPFgFh8NGPKGkl+l2K512ZBC4XQAZ3xbTrunmXiX9wC0D\nIIIQXqFlnKSQ5LieHSqN1j2ZBH0riPxODnJdrvTanzwqXnnz8W4paxdvf/vb3/Wud73lLW/x\nVrKV8jzvC1/4wpkf1THwPT+o11Kpek3mJIkVlPg5nWWqUOqInQkWrs++jyRFbR7hKS6a8Lvh\nlxcflyvByaO0DTjxCQCsRnsCOoZbQNB/ofnqXMRR8FRhpOdVUrgHZx4pBoM9+S1EYr69P+/0\nZTaK0vme/Jae/NZq++CWrium4/m5ZLolStJMp/EoYAhSCF9IR2cN6YbGpJKkYQFowIKEAAFW\ngDqVrqYjkTsy5ZaScR1Gd9RcXM7YALZQiy8XqysgXLQV0gxHNNmaPAEO7WxLFkGUiGIXz1ms\nW+KXEpRaBhAo2TQGgBQiJ8V8ps8MsVsTsQ4Aua6tzo8yzWRZIMWWQlElidx26YvTAkkXlath\nMzgvnXfVqnbyMEmJgSFaHmOlROF8LjFcERcwsbMGM9NzmzzfX7diMwkGmtOz0ejuVrlYS7UF\nM1iAJEgKeCQ8IRxBzBBNo+qmnSNiSwCRqEVjgkTgdC1FJe2KfNNGQTFK53ynKDREqlU18qYb\nNnC8JnlUyHTc2PfDosulrq0v1NyMPJ+7yjw+Rlu3nfL9vrOFf/iHfyAicXQKs6M+OVvq8uMR\nBkGzVObZ2WIas4DUrIyJyj3dpcVoojWFkpOY1o/Yp3YIzz/1iVV6HipG9DwTKp5FfT+cAlqH\n4IQvuE3ZRZxn8FRhY+W1XbkN1fbB56b/nS2DeXT+keHy9et7XlH0B4lkZhJH5dbl84GkmWgq\ndTdn8JFOCkEgwSaWssBWCBFYGxETQTBLQUfaVXQsiSXD0pKIoJOqW9DX8ZFZ29l1PcLqOqWy\nBvJIxo87jiukOBGcgXwsvmcgDahXPzMj1gm4KYW+rbcX76vB3Y7TIZQOUWwWjE58sUDyzgDW\nRKwDQJXe9q5dPwZRkIutCZvNkYEBvISAFpySWGitHR9Ds2G1Jsu07ZKF48ZcgP6vuHCJnbU8\nPZ1srOzJvj3oHZuuY2AyTQ8nWTS2p+QIa0nAdr5bGmxhjeGUOTK2pKQLEVZ1TZBOAYA5cfUc\nyOb99cSsFv2YCDCKAu3V69Ooe2FNspIySolBmSVmMLuWgrZTs4/YwWrXwC/QC6wCpkLBjo1S\nuYv6zhOxnVLqzjvv/OxnP7v84HXXXTc7Ozs6Onq2RnU8+kqlWQExPl4jqcjIIDfY06MW/vLM\nWcaWRU8fdXWzH2JqkvN58s4tCyi2IAXhAgQ+owZeF3GOQpDsDrd0h1vWd79Cm8iyaSVTtejQ\n+PxjBPJVF0MLKCmdSjjiCjXfPuQ6qs15bWJmTsh3KBOw1iauUEIqY9nCsGHDmgBrF8ojiLhD\nZwxIgHhBLLogsjtmQUILOlJrlxJ1x4A6ueljGjCIvJ1tiD6fW4CXkS+RKWQaShBSw6EUhhmA\nArLFHWGXKD1TotS1EutQKM5dde2+nz+15dAoMR+87IoN60demDT8dKAzZRwXRGQNz86gXuc4\n6kwWcl3kC9Tdg7XTCPgl4oIkdmnKM9O0bXstPxPM9xzTlWU204fiZFc7GrDxFYcnq33FY3aw\nmEkzW0bGdiazfoL1kUpC4QmwzbJ0BiKwnNayaE6jS8mcFE7HtYngzZoeGzS8WiPoVYnJxVnm\nO4KtJFIkHEVQQWiD1sQub6oWbL6Kyt0vKP1GpTLPzVF3D9QansHf+c53lvYdvvnNb+7atWvp\nrUajsWPHjhV3K84OFneNwrAwUypXGrVWWJDlkrM0qeJE5AsodVGlAiIKfDMzLfMFrB85i6M+\nHn43dAv1/ejadrrELhexRuHIwJEBgNDrrRS2V/KXNJLDjfjw/pn/KPrrpHQBNka70pdECjp0\nNztO0IgOx1lVStfoONENQSoF4rQmpBCWmElIIhYMy52yWYaEXayHJTB4hTwLM4QFdaooBCwf\nu4MLdBJ+zMs5oQWBYEh5FKeAZtlvdo3zDW0oAhJwj5C645pCsItkjgBzmondWop1S+jufqRv\n6Fu5woZc7v9aN0jngnxNSuqp2CceE30DttXE2CjlC+x6EALWcrOBQ6PU10+9fVS5ILooXmDE\njpnrddTrnRrS1tTuwCktf3N/kvyo1uhx1CbfU3OHrSNx9C5tbG1kuGW0RyIvJYCSRVPYzLIH\nkGmR8IgkWYdgfSnaxlS1KTsylwkTGb9uxvrI2HkvC7tilSqpmZlh2RpmA5SVLLS02989pccH\nxnJesoF6e5+nyewyUODbw+PUU6HeNdwuanR09Lvf/W7n5wMHDhw4cOCYE66//vozPqiVwVna\nUQI5JFAu7wtDzbzZXxQRpylLQeUeqvRiMUBTqcs+/XNRqdCZqtJijawFAE7hhDI76aG0BYUN\nIHVGW5xdxNqCIFUON5XDTcYm1sZRWicSkqQgR0p3vPpkIx41VjeiMWZDBAklnYKUnjaJYe1I\nRcKH4Ey3QMSd5DAvqOksAIIAMYgJ6PgSLxE2ZoAMKSz4nrDB8sm6XEPKILF8FgswGJpcl7Ok\nY7JHkmALQmSW+x3lENSiabJYXJVZQJ7mXbw1FOuWwAwSVMiHh4w9d4rsqX9AXH+j/V8/RrlM\n/YNYnrBxXYR5RJF9/CfiZVfR4NBJ7nN+4DwidicXQ1jDrRYaddq0pdOPVdt4/8z/HOq6duFq\n5t3taEerPeL7TmdGZHVWR62WWsbOZplPIlyWRQs0IMkw19JmDpErfQBEAjAEuFIIi2YzC+ow\nnoBDAUQqPV+n+djGvlILO7UEkGW0jI2tkbFxc+WmipzGnGCLSu/qt2UpzHO9tqaJ3fbt29/z\nnvcA+MpXvnL55Ze//OUvX/7uwMDAb//2b5+dkR0PAlfnqRDGhqN6XRorBaVCcBCg3YIQtG6E\nhoaxbNlNSnGQQ71+ZsrvdYTmKFoTACM/jPwI5IlSAHTR4u4iVgspvMDtMdb4y9bGjnRDr88R\nfjEYqEfj1dYBV4UMEAlBkogn67skXCFlM5kSLDXFmYmWUnSSFIM0A2CC6djKLmsjJixJQCzT\n2y1/fnU6mLGBI6EtHXmLAQnbFBUmR8BasAJJYs2QghK2ReVFxnRCsWY4i2QxZfZOM7FbS7Fu\nEZZ4NksHfR+cdYjdbKans6xLyYGzuCcbRTw1Sb0ntjXxfeobsD//mfB9Knef2cGdaZxHxE5K\nnhiD58NxSUmGIAIbDZ1xkogkFVsvwfbLqNTVkVKmuiVICVIAmHlPFO1stUc8TywoeDXplJes\ndRgNY6pa54RYvobrmGYaAQkjbCMmX1hWgkDCmIaUxdQiScxIQ7RcJgFDncWmlFlqhXOMqFMQ\nPCIpRTXTZLx2NuUVi2G7RbNA/yBW1yqX/ABJAq1Xef45iFtuueWWW24BsGvXrl/5lV+58847\nz/aITggaWkcbNtoD+2h2ukgqESQsc62aeZ53yXYMDFK5+3jpLgU5rtfOjO9gNIlkHrl+AIhm\nIAPk152BX3sR5z9cGWYmWk7sBClmAyJHhnm/r5lM57xl2njmojdYiw+5KHoqz2wVBxJOaptg\ngpRgK0CS2Fgs7qQuClUhiJY87ECAOVIPCyxq7wDLpDR7ltUyUzwQtCWvs+VrAUUswQmksnaj\n73tEDUZHPpFYu9RtIjI2f5oLF9ZQrFtCTojri8Vex6n5ethz57X+f0bHuhynpvV/HxoY8s4O\nt+PJCZ6bo/JJi3Mdh8rdPDVJxdJ5U2K4Ik727J+cnATQ29srVnJWi+P4r/7qrwC8733vO02D\ne0GgSq+4/ka025zEyDRZC0EkJbkegoDyeRwtVzc2W+ohcSBJftY8wuoAsI2T5BBhIWfbtLaq\ndU4e219GMLqbmM8DJiJSiqhlbJ6kJAAUW5MYVFKROiBJhq22ZIBOis+IE6wFhQiIahZz2pXR\n3IZiWdZrEJIGBldV3SMljx3CyAbk13xl4yOPPHK2h/A8IMcRW7ZGUv5MqLa1RUZCHENc7Tv9\nm7acsNVb4PNze7FxE5zTHgRNCrk48ZUPk5zuX7iGYa19/PHHe3p6Nm7c2Dny6KOPlkql7dvP\njS5J5xhCry/Ofljwj6xPlPCMTTs/OyLM+73WmiOteog8t+hmoTYxQVqrhRBK+poThmW2YAsh\nBQNExnRoW4fMiQUut8jzCJZxJL1Mi74nEhmzjEVB08I3iwEBKKRtqkhiw8IBBZTNuVeA/CHP\n7VLSAKm1eSUBxHaBzGnLMdvuMyW3P/dj3RJ6Hef/7O35H/PVW7pK23O5uUwXpNoa+PvjeCbL\nXiKxSxsA4L7QZ1cS8zO7aHgVa9ZcyGOj3NtH3edzd8WTEbuBgQEAhw8f7u/vB/Abv/EbAD7/\n+c8Xi0UArVbr/e9/P84ZYgchqNSFUteqU+cdEyXMa/1IrTHie3LZlZY1pJQMAJGx81kWHMfq\nAIhOvT5bmAZkIEBKcMYsibTlyJhK5gQZEgcAJJFlRGwdEsgaLE80eYkYroBQ/qHWYd8pD4f9\nPDWJfGG1HnWOw2m6RoVSn//85wHcfvvt//qv/3ro0KETnXbHHXecwUGdDGwMzc5MFopppmeT\n2LXsS2GF4KhNJyJ2QpAQyPQZIHbSRTK/sP2qY/gXhHT4xeCnP/3pu9/97p07dz7wwAO33XZb\n5+CXvvSlBx544Lrrrvu7v/u7rVtfsMHk+Y28P5BmDcOZpAX246hQ27jzsxBKkpeaWVcdEdc7\nIidIulLFusEwgCKSSgSZaRNJIhDIUiZIGJYC1gKdJmOdaGYhABawGi7jSB9ZwDK5BGvIEzAZ\n+ctZIcAOUiMC10ZCuCBrmedNAAAgAElEQVT43J62XX2e3+M4AGJjNwS+Q2SYI2u7lAIwr/Ut\nXaXwdPaKXXOxrgNJ9Oqu4lX5XCClQySAhtF7o7imdWU1PNhars5zq0Vaw3UpzKOrq/OOiTH1\nKBgYetWR5eiKYHOU+p0bDet5K2agVkAQcrNx4RK7Y/CVr3wFwCc/+ckOsVvrEKRAlplH46Tb\nUc7RyTBmayWEgWXEln0hVszbGoG5PIRJLXXWlJBEmbUAMrZ5Ft0ttBclTcRQQBsmMCAmppWF\np8Sd1tdQAnkV7mnOFrxKIReiXkMYrqqHsZDQ+vlPOyfR2Yy49dZb77///ocffvhEp507wY6I\nFFF3mrhT07GUmRAFKQux5elpBLkTcTuWknV2Bsh30A+TojUBZhSGEVwkdithfHz8jW984/z8\n/IrvPvbYYzfddNOePXu6Fp9AFwHAU4WtA2+aqD7eldvQOeI7xY5mrhMMA7er1j7gyHBJyK6k\nVwiGWvG0J/PNrCGES0RKuNpEDCOFTyC2VpKCFMakAmwXbmcZkgCC1XCYxFIjMgIzpIWQrFPy\nmBFTAYuDIILLUZ36Nbwc1Q15imB1vZi/us9zRcev1JoNygNQN+YXwjAnBYCpLHt11+l90q25\nWLcEAoqLUp+yo35n/fBqNXbG2AP7sO9ZzoWsFLRGsyW2XUIbNoCEUMivBwA6KTFJ5jH1GLov\nQzi8cITTlFa/SHYUsmy1J69NrFUZ1kuHIwNrs6k03dmONvnH6smJRNsxc10iTU3ENjjBtqkl\nMEDGsDiS7xOEtjGeDAMtM0l2cVUpLWoBCSn0YlfPFe9JbHmRvDnSC9lOJlGQy6uZaZRKtJoN\n1jWarAMArFu3DoAQore3t/PzuQ4p40rv1id/Nsq2nGhLIM9rVyo5azA7w4NDtJKYg7GYjzjN\nUAGKmxe6jZ2kKvYCx7333js/P18sFj/3uc/deuutS8c/85nPvOY1r7njjjtmZmY+8YlP3Hff\nfWdxkOcg+oqX7518KO/1K+kD8FShN39pO53znAIAVxVLuZF2OuupI1HLU8UGJjy3YJG2kzlH\nhkIooTyjI4IkgMHMkJBWKOZUgsyCqR0RrCWXIdApi2UQQbDR5AnoTPgSaVNWUg5AC4k+CXYR\nVcWIAhSnKYUBca+Istxw56vQMnbY88pKWcacNjcUXADzWl+dDzce91w4tVh7se4E6HFUj7Mq\nLsETY3zwAPUPHalaLhTtc88Kz6PBIVIobQHoecKUSSAkdAwAbNE+DDnHzqqfeiTOUOw9i7iQ\niV1uXfeNP52f7F7J8o1IWWnaipNa5gYnm2WZQhAbsyyRZhhgy+woAy2OpOUci5ZiASRsrAgJ\nK6fshGWzlP8nIbLJ6aS/4Hj9nocoWpVyju2qEnvnJJbcOP/+7//+7I5klWgYM87co7MrGo2O\nbNNvNaaEnC2Ve+ZmqVRasaENWU1nSr4jFLyLmaaT4jvf+Q6Ae++9tyM4WUKxWPy1X/s1rfV7\n3/vef//3fz9Lozt3ETjly4beunfyoUrhMkECoNDrm23tdZ1Cp8whdHurrf1KeFIsJFSkcLqC\n9bVozHcqljnJagQpmaTMaU4VeUo4xlpBQkJqSwBLsIEAORYSJCQ6GbyODsYYcgAYeBaCIJpU\nOdJmheFwvSaGrQhz3KqqzaEKfTPJhZulKgHIgLrR23IBgLlMXxnmuh3V2cZ5ZanonuYouuZi\n3UuFMdxooFzG8kSJEOgqcbXaEZGv1ATqWPgV9Fy50IUsa6D6DGRdVUpmldSOMyPWss/rarBW\nH/8vHURCU/HpxmRhJRWFEB6zmXazUkqSNdsYNmIbs83AR/mqxIodLZY+ScOcMQuyyqp8jKW5\nJpiURaIAQIEzdFybjgNz4jtaLZ/dqqBsQ+tUOYhjrMYwU+sLx2L7rONwq+3vezYftaUULkGA\nIET/7JSzZ5euVXlqamUjHmtZvoBllc2QNU5x6YNJkTVgz/NNiVVhfHwcwE033bTiuzfffDOA\n3bt3n9ExrREMFK9c33NTtb2v8zLvD/aE25Ks1nnpqFxf8ReibM4uC5ueU8r7/dpGgdPliIAg\nPKekVCDIyUwMJsBYtgQWQoINIJT0lXAECQI65mmCSBJ3lFaGHEOOx+26GIbwVOc8god2KnvY\n7fUIIZqO20PQ/aIuvC0ADDCXZpfnwrwUbWurOhvxPQAHk/SmUvH4bZyLeKlIEhw6ePyeKXk+\nH9zHabrK2wgFv2dBN6xCFDejeEVO6NU9HAFOIuTOkIfo2cKFm7EDkMgez9bFiqWmJDx3aFIf\ncAp+pRk1Xd3xmuOO5MPpAXkgH8KJHevoRLC0BACGIQFpjWZlxZFdUVfbuZC0BADBukV+aaVZ\nqDQnvuTlVJNIgue0yUs5MDOFgYHnERNYRqbPtY5Vq0dHUPy8OEd0JxzH8sC+7pkZFgQmYhYE\nYisYTr3KWca1qm3UUekT/f1H2HaS0MjG1ffhiecQT6M5ARiUL0N4Kvw121OY2wFIwKDnygtd\nezcyMrJ79+49e/Zcc82xDQYBPPfcc1gsJruIYyCEWle+wdh0svbz7nCzIFEMhmaau1wZCqEA\n5Lzubt4219wTuD1y0SkxcMqWbSuZlsJV0gu9fstZYtpxOpdkDQFFggGHWFoSxliGEiQcQLMl\nggUEWxDAnIjAsuOhWZdDiexetLkj3zaNLFnVD0gf7bazXqneER7XudcaVc6AuSzbHAS9rsoY\nE2n6+u6ugpQzWbYl8K8vhKfbmhhrLdYdg7oxs2nW4zrFlaUmGE8Swxj2llUlEjp/tJXxoj5w\noVDYANi8NRu5XqdwBcbG1lLUZhIUBGwMRRGVzvMtjOcndm94wxucZemfpZd6zcrzl5DKvnzh\nZUY35DIJCJizbCZNJxrtva14phoOb6p2xV5mRKdzIcAMG7FtEGdw+rUIJorNnrjc9KxlZMwK\n2sqco50lW25p4RhqO0d+g3E9blvC0V8JhsxMWjqek1FOiJa1IIKxOGkyjpNEbNyEc7ARzeqw\nSiencyTY8fycardi18k1Y8MAIK2xIC0lQLAWlQraLfv0DrQaNDRMhSIAjtpU6V3ldrnNMPM4\n/ArCAbBG9Rm4BTjHbchnTUTTsBmkh1z/89SU6QhzOxD0QTiwKWZ3YPCVz3PJ+Y1rrrlm9+7d\nd9999+tf//pK5ai25K1W66Mf/SiAa6+99iyN7lyHI3Mbel6thHdg5r/K4ebQ6xsuXz9RfaIY\nrO+UTeS9ASnUVP1p3ykqGRJAJFwZJqhKp+jIXGbargpzwvNUPkrn4nQ+MW0Jo4QLUiRlbNqW\nHUAoEoDVnIGEoQDSU4YdbjXlYKoGwR1zFOvZRqIqkejNWLnEnq0Kf3tI89q7NPG2t62taXNZ\nLuh1nJR5LE5vLBYGXHc6zQ7E8Zu7y8UzYgK6tmLdcsxl2edHx0MlW9rcsX7oeFOYJxqtb8/M\nSaKbu4o3L9WgeD6NjCCOjrEe46gtNm15SdIUIai3lw8egO8da1BnLe95hqenwIyNm9jz5RUv\nwwvsw77m8PzT96mnnjrJyzWNGE4u2JhUH8qpBZ8qa9M4ejZqP6OcsvS3UDzX8M3u3mjLTDBT\nSBc7CBLgknTBFnoO5NZ94+m2ykQirGQITozKCYYlBkgwchkmizCd+caGZDEjX5gEiwWwHTiZ\nbhc97RzzvGfBwtd6ttlOs9SdOsyuR4JYSHIcuC55/lEUIWphoP/FrX7OBawtETEFQUqUWpJC\nKAtpjZUqI2JrpYADgAE/IMfhQ4e4XhWXXIZiCe32ap1rAJOA5MK+AykIDzo+ltiZBK1xpDUI\nD/EsTIbSJpxEraJjCHehz4RwIRRMckETuzvuuOMf//Efd+/evW3btttuu+3aa6/t7++fm5vb\nsWPHAw88MDExIaX8rd/6rbM9zHMXjgxGel7lO121aHS2uSfv9FcKl861ni14AyAiQs6tDJau\naaXTjXhCCs+aNPT71/XcBLLtdGa+ua+tq4EsSXICtwdAFiUkRIbUlwVByiMYk2ibMoHZSspb\nGRgmwSkTtdVIU3QbBrFVaLscN+SGtigrkg7Qgzmbu8xTUqvBuvuyqpb9ntzk+0Ul28ZOZNlN\npfyI543GyabAf3NPeeBMWeyurVi3HFOZ7lLOtsDbX29OpdnxxG4yTbcEvi/pobn5G4p5v/OE\nEgLlbn7qSfT20xJ1zjLMV7Fpy4t/ZsUx16owRmzYYEZHRV/fcm7HM1O87zn0lDk19Mwu8erX\n0uDwSW52fuBkxO5tb3vbGRvHWUHGHAQboV+WppOO021MK4meS5JDrr+eQAxr3BG2s1Ml4Woa\nqnnzoT7iUcIGnIAzNrOZjRqqvL7Rm7mpREokpakKa8n6gp0gpdmQYnfhQrYROb0Qci7v9qU2\ncxemoNScek7iH/UXkYZVpMOs5bdnATLWoNGEmzAY1kIb6Iy7KxwEotgF14ExaLepaw33S1kS\nFK8NEDWMlcS9DGUNwMKwhLWAIQFJSBI4DkmFQh4QvGc3Nm4SIxuoWHr+mwMAhAM2YA1SAMMk\nkMc9d7IWoin4FQCQLlqjCAcWxMUrQjpgDbYgAWth9YXeTOymm2761Kc+ddddd1Wr1c985jPH\nn3DPPfd0lHYXcSIIkgOlK7vDzaHb10omdStxZTjd2FUIhpVwrDXMxlU5T+U9VYzSeUf6JCDg\nFv1BSc74/BO19KAkJ/T7S8H6YjBcbx/MTOw5RYKQ1tHUFjqSKtBWg5Th1BEus2ucQYXA5TTV\nsVbdlvprKBlyCkJ4Qvi2lqlhC7dJPXNiW5/btV3KslIGOJzqIc95Q5hzBT3Vim4sFW4o5Ivq\nzDUkWGOxbhn2t1v79j2nsviymanK5VfYSoX6B5aX/xeV3NFqKRKv6Sp5y/IOon+AjbE/f5ID\nH9LhLBNJLK665kW7ynG7xQf28/Q0BKHdEutGeGwUxS4KQwjBzIgTVCpotymO0NcvSud5z4kO\nTkbsvvnNb56xcZwVWLAQjh9e2mz8RJBI4gNpOu24/QvvMpPqpviQlbnxcsICG+b8uTDTwsJE\n4AwcgxSJAqAaqjqZD3qa+aqbWVFgSJCROnUtz+bR8txFp8yMVAEyD1Dbk6qVaUcwkTDspLpZ\nCJfqYYVlP9KluRbIh9uGEK4xpFNKEoYlx4XvQ0gArFOaqvPoAYxsYjbikksRhmfpE73wwFZq\n3ZNlhpkFEWSnJtkKAjMbRqvJ7Tbyefg+pSl7Hk2M0ZVXr75sWXooX4b5XZAeTIbixhX2YYHj\ndMMnXf06IYpbUH8OwoVN0HUJ5AmslC8c/N7v/d4tt9zyB3/wBw8//HC2aHOllLr55pvvvffe\nG2+88ewOb63AVfl13TcYmw52XV2PJyaqTxyc/aH0enJeRQjHEf6G7pt9p5joVpJVU9s2JjM2\nC9xKJb+9GU+30ql6PN6KpxhcKVyemmY7niZS5dwgwKmOjI0FuQaZNUnoD/lOXolACkWkIrgN\n601kVgAtY6bTzNNzcLoCIbLc9p7cNeudYiAoYZ7Jsrox1+XzBSXmtH5ZmLuxWNgc+GrNbnSc\nSYyl6T888eRrpg7v8bxLhteXrbHP7BTG0PqRpXOuKxRKShnmzYF/1GdKRMPrRLkb7RayDI5D\nhQJeiiJ8doar81SpAGAvgFLi5Tdheoqf3cuuC0HcqMEY0dfHhSLlQlar90VZw1iVkqDVaoWL\nXOFHP/rRww8/7LruK1/5yle84hWnc2ynHRJkmVxvuLvnvx0e+38JUN5R+mgmV3ubbHKwJUu1\nop4n/bLDQdttt5wGC0Xkyo7ojhSEnHcnbZD1xH11QSD4WkHIqXwUywg2DxECBBtBlgEBZi1F\no+Tn2qkRwkl1rTvU7sLD3ol1oR4HccbGsOMLa8EsrdWuZ9MYSQStEebZdUWYJ+Wg4CDI8dgo\njBGXvewsfJSnDmvLjZ2jKIjbrJSTJE3ldjbWJdhl01ReIAQ8H1IiSbjZRJhHo4ahdS+04UQ4\nBLcIHUE48EorkDa3gFw/knlIF1mE/Aick1d9EQoj8EowKaQHt7C2vQ9PFa699tqHHnrIGHPg\nwIHR0dGhoaFNmzapNdtz+SxCCrcYrCsG64bL12/pe/1sc89E7WdFf8hTxY7qzneKvnOsDRCz\naSXTh+s/LwbD863nfFUUwmknM81kKvT7BYSxUT2ezEwzr/pLuXW+U17OxIpAL6OsdV2bg3Fz\nRM2kMsykOOi/Fs5WGG/GpgnbDZ5blGrQcxvWXBWEw567JfC9s+EPtbZi3RJmWq3XjB10hoaL\nxpIgcl2qVPjpn6Ovb4mieYKuWKmOoQPK5U5ZXWp2xDeKHEXGdBpQYd16ESesM+6b5gPPUaUX\nllGbFxeGx/jzxKzvfOc7d9555+joaBRFAD796U9/6EMfWnr3Ax/4wP33309rbZVjOWsl06lu\nZdFso91w3VyajgHWmFTajBY3pWJra8bM2aJPvX42a2VxPFdvDB/ub8nN1b5IZi1HZ8qSYEXK\nwmrwbE5b4k3VwAjeU263Mi0FQfgwbYYlAKoXcoEiEyj1HL+dgmm+ku9I64iRb8SVyUbmyNRR\nRqSO41khAQhjjHLI9QDAA6ylWhVpykFAYR5CgC36+81PfyKuvZ56+87KZ/vSsbbc2NnYquvX\n3EymqW+0gRBAKmVTOIatdn2vQwscB0pyow7PRZBDu/VCs6pOfuWtVZshrcMa+D1QAUwKvw9B\n76qImrva3eALC1LKzZs3b968+WwP5HwAgbrDLV25kVJupNo+MD7/mOeUPJV3ZCBIgQRbYzjN\ndBRn9VTXNva+dtvAm4v+sOGklcykuhGn9enm0wdm/sv3ewv+ZRtUQQjZjCbHaj/JTFOJnJSe\nEi6RYAbD5pHATI2YycG+N4WF69zg0gaFzUxrwBGUE6KsZE7KgpLdyilKcRafX2sr1i1hK2E+\nH/6PJNPgzZ0qBKmgFJLkJeXeloGzbLW1FEHAzSb5AYi42aBF3kZ+AD8ggHp6uLubG3USkrZd\ngvO9HraDkxG7Z5999h3veEcURb7vA5ienv7IRz4CQClVKpVmZ2e/+MUv/uIv/uIakuLVo7Fq\ndLCdzB6uPaFk0Ey5GWdSZHG0x+iWcrtbzSc9b711equZnsyy1HBRKZL9yKxjZomzWCX7u9yZ\ncCKfuPnU2VAtaAJIS9tNrA4WmpPFmadLQcHIvqZvCLlMRJ6FdMnUWZZJFQGAWRibtzYwmOst\nFOfbhlNjNGlTbGTled3yyEoARoiAxEJ9KxmD5baKQiDIsTWYmWat2bIYHKLefk5T+8Rj4urr\n1ii3W1tu7C1my5YYDd9vaZPLEgIJsLJWu96cENJaXwiAKc1QKMBaqs1zvXZK/jomQeMA2pMg\nCROj9xrkyy/9rhciBgcHVzwupdy0adOrXvWqu+++O7yocHixEOT0FS7vzW8fKF7ZSqbjrJqa\nFrM9NP/ouvLLPVHwcoXA7c57/YG7oA9W5JeCha//+p4bLx3436caO5+b/ndmzrndPfmtXbkN\nUVZNsrq2kbYJs9U2zkwzM/HmymsHy9f1F69wzm2FwdqKdUsIPe8VYbiu0lt2nbJSAJgZ1rBU\nL50j88w0Zmfs/udoZCOVuqh/4OSSOOrrF0lsd+8Cg7ZsxeAQOgOq1zlqo1FDmlFPj9iybe0W\nFL4InIzYfexjH4uiaNOmTZ/61KcA/Mu//EuWZUKIxx577Morr/zwhz983333ffGLXzxNxC6O\n47/8y7984okn6vX6tm3b3vve927atOlF362VzEw3nn5u+gd5vz9Q5YHSVQCQZrt0jXgCgB9u\nY5s6bl892lttHhDuSNnpamvDYEAZp1fpGalnjSgDaDpZw8kM867CjG81cwEicHGorbyY2lZ2\nM8tyPt3UDF49WpJGMxJ2eqyFNFUhisrweOgg77ZzFIlGTbV6xxvGp3wiy3Oy7cESw7I1iZA+\nkRDCk3Darue6xy1ihESQ48nDNDCArm4Qkedxpc/+9Cfypldh1Qr9cwcv3Y3dWvvVr3714Ycf\nNsa8+tWvfs973iOPCw1f//rX//qv/3rppZSyoyhdzbXLkVrb73nVdrueccDWY1gwAZKQk4JI\nTKbpoJSu1sjnKZeD1ogjnpzE+g1YtY/diRDPIppdsKAzMaIZeBeJ3YvC4cOHT/TW2NjYf/7n\nfz744IOPP/64d2IXoTM569YoiGQxGC4GCzWJxmbb+v83IiFW0W0g51U2ejdXCtvr0aFWMn1g\n9j+V8ASpzn9KcGriLb2vzwcDXcH6nNu7JvaR1lasO4IgJ7Zu7R4bcxdZOGpV2rSFXsLuatYE\nCcjmpH3yCZTLGFqHJLa7d1KWiZENJ+NkUtLGzWJwiJjZ84kIWWoPHOB9z0Ipu28vSUWuJ15+\nE23Z+qKHt+ZwMmL3s5/9DMBf/MVfvO51rwPwyCOPAHjd61535ZVXArjjjjvuu+++0+fG/vGP\nf3x+fv5973tfoVD45je/effdd3/hC18ol1/Mg2u2ufeJA3+TDwYGilcJcWTulhy10XOrzapU\nRQAk3FQNHValIo2rdDeb7pLoTdmXpMi0GVY7AzKbscLXkNpmCplV+QbljQgNSJpyYOcUsxZh\nQrlqTu/MNayMhmuy5Zc8EbiA5Wnj57VbmBLZkKhzvE/KQLuBHsoPTduuOicBWHR8N41UBSly\n1qbGtqT24lyhfHzLWmM4alH/ADOoOofe/gVu191tx8ZEELxQOdc5iL179z700EMHDhzYuHHj\nG97whksuueTk53/1q1/9t3/7tw984ANKqS9+8Yta6/e9733HnDM2Nnb99de/5S1v6bxcegys\n5trlcIxpJYkGlXQmLDeVAwFmSKJclgXMBSlaKud2dZHrg8BEkAq1qh09KDa/hAp/AIDVkItU\nXyiwAZuTuZxcxInwm7/5myseN8bs2rXrkUce2blz5+c+97kPf/jDJ7rDmZx15wfkC6/Eznt9\nea8PwMbKq+OsYWxi2QiSUnieyjsyXBN87iQ4l2PdElJr/6vQdUhMDzyz67rucomZNmykdetf\ndDRrjqK6B2RMT37G7aksmLBKSZVe7N7JPRXKn7jCHwBg4Sd1eCVIl+3oQZ4Yp6Fhbrcol6dy\nmatV+/hjcnAQuQsl6f48W7EArrvuus7LRx99FMBb3/rWzssOx5qcnDwdwxobG9uxY8e99957\nxRVXALjrrrt+/dd//dFHH33Tm970gu7DzBO1J3ZNPFgpbl/eiLoDAUhuzzZ2l3ObwUisOZAk\nReUpZzNML9KDLtmmFSW7T+pqp+7QqjzrpgUEcpkoW+kzJAAJsMy3bepxlTix7Lkik7Y9Whxe\n1+xreM4EmVDJAAFEwwhVNLNkG8qtdHrjuZqDhI2A1ICCJstWCycPgiAltIwdjjGttTSiW5ID\nAGw5SShNqX8AYV4AOHyYHY/KZQAU5u30JBULNLz+FPw9zh7uvPPO+++/3yx25ZJS/s7v/M6f\n//mfn+j8KIq+/e1vf/CDH3z5y18O4P3vf/8nP/nJW2+9tVQ6Knk5NjZ28803H2M5u8prl2NG\nqpYQxSQup2nDcTxYWBCgLDxCorUOik3X85QbdoKesTCWBgaw71nu7qEXtVBZgvShI6gcQMha\n8CsXWd2LxJe+9KWTvPvRj370nnvu+da3vnUiYneGZ91FODJ05Pn2kD7HY90SZrX+r3Zy9aWX\nTgz0jzqq3F2mfOFFdydng/lnEA7AtlOz9wCuPlJaCyGt48o00e18awJs4XbBtKFjuEWEy9QT\n7cOYeRxeBaW+lrd/jxheByLyAvT08NwstEZfn52ZESPn25w5EU5G7MrlcrvdfuaZZ2644Yb9\n+/c/8cQTAK6//vrOu08//TSA4eHT4vXnOM473/nObdu2dV5qrdM0tfao7qrvec97Ot+BarV6\nom4/M81dzxz+dn/xCrmoVGOgrs28zqqZSdnubExNZ3KyHTFBEoFhmAWRlAXyt3vpRKTWOQhz\nvNOoMhhtpkjAp0TZyDMTZFImB5BMBIYVHqxSZqogNan1qVgfifzjQ+LKCdgCtbSBdHLpAcP7\nAm9QqQUVp9IYnOZMQechLJwMTmLgFgDJgLCwiiLXKTlBputta31RUFYg09RToXyIpaq9fAHN\nBgr5jhSPSl325ztEd4WCc1pochJ8+tOf7hSOVSqVzZs379+/f2pq6rOf/ezw8PDv//7vr3jJ\noUOH2u32UmOoq666Ksuy/fv3X3XVVcec9sQTT3zjG99IkuTyyy+/7bbbBgcHV3PtMbPOel6z\nVK5pa4w2RJ5laS0AI0TmuVKqOjOTiNiEnW7CSYKhIXI9FIs8PUVdXS8laRdUYCJU90IQ8iOn\nptXYRRyPX/7lX77nnnueeeaZE51whmfdqf8XXsTZxrkf65aO54TI2I6l2bzrX1fpXrGL1+pB\nEoUNiGfBqcxJhrXLOaKwBlK2JxHPQLpI5mBiuGVUd8HNHzF+sgbWgAQaz8YSrtsJqkpS/xB1\ndbPjwBrE8UsZ59rCyVj2pZdeCuC+++7bsWPHPffcA6BQKHSI3aFDhzoVPZ2M2ilHX1/fO9/5\nTtd1ASRJ8rnPfa5QKBxjEDo+Pj42NjY2NlatVle0JKhHYztG/66S37bE6qra/LzZ/s7s3I5m\na05rzdjsyk1B6ErhkWgZmzBPZ7qqddtaS0o6/euyp4yez1TFiFKbCnXOSRlq0R2r4bp3ZeRs\nTuRAJstGFI0sGtVXlZsThMYZydSQEQWAJkN+po/7m5QHxbqe6XlP5nLqCNnKR8wEJyMrKJO2\n5bTb+XzmeBpwIu22U2tBmQ4TGyQOmo2IGqaYo5ER6iph+T/cUVyvcbXaeUWOgzDkmelT/cc5\nc/ibv/kbAB/60IcmJiZ+/OMfj42N/eEf/iGAL3/5yye6ZGZmRim1pHN3HCcMw9nZ2eXn1Gq1\nZrOplLrrrrs+/OEPt1qtj3zkI1EUrebaY2adBCLLNSFrygmMcYzWgjIhBCEXx8pql0izTTtr\nEmMRRaLSCyLkC5G9Kp0AACAASURBVHxwH5qNl/L5kEBhA4Zehb4bUdwMtVYJ/LmOjq2de2JN\n5Bmedaf4n3cR5wDO/Vi3dLyk1O3Dg1flw7f0dG/PnYKgk1+H/DoUt7retZfx/BwvenJyo4Hh\nEcoXwIAAKbQmAAEhwARelufxSggq8EogYvCypbKSCENyXTABKzRnP19xshjxu7/7u9///ve/\n/vWvf/3rX+8cecc73uE4zuzs7Pr1Cxt8J1pMnBIw8w9+8IO//du/7e/v/+xnP5s/eqP9oYce\n6vzw4IMPLm0QL8Gynmo81ZXb0MnYG+Zno/ixRrNLqU2+v6RVyzgNpRtasT9OSkoRACLDPJvp\nUIqCdFxZKsU/b/tXW+a6Np44kmBhuFocG+tjYV1Krer39aSW3SCHCQdLnBFfNaFrzuysKlRg\nBbjjSCEtKrNsgUwi04kVrlU9VasEwc84DZ3IkW2JspLCdT0pPaeQ/P/svWmspFd19/tfe3im\nmuvMp09Pbk8Y22AbDGGKSSBcQO8FRSjJlZILCooSLCFQRBIpgwKJEgmIwwcHQpTwhugNugJk\nEHoTQibEBW6IwQYb47F9eu4z9DlVdWp45r33uh/q9Dy47W5wd3N+Oh/qPPVMVfXUrv+z9lr/\nhdSoQUM2zv7wKIyQJnB27F2MahWjIVtLV2cu9tLSkhDiIx/5yHhYUUp9+MMf/vjHP36BVHdm\nPjvP5oy+xrVa7XOf+1y1Wh2vuWfPnve85z0PPvig53nPue3ZV13BDloxgZiZyAJMMCQySUFZ\nIoxS61LLsMb1+2J+DsbysVUIwca6zrqonenm9XyRAa7KT/cqwRhz//33A7jpppvOt85P/qrb\n4hrjqhjrTrDgewu+h6Lg9TXUG5fYmlyFqI5rgosFB8MHD7DnwRjato22bYOUwSScATtMvxI2\nw2A/mjee5tPuN1Hdgf4i6gu+OlqA+cyZkCKj4LQe0Nc2FxJ2b3/728f5wnmeA3jlK185Lo8d\no7X+y7/8yze84Q0/pjPr9/sf/ehHV1dX3/3ud7/hDW94vlmx3XjxaO/h6dpLAWTO7U2yvWm6\nKwjk6bshgiT4QjjAMqtNg1mKBOWOmW2FtM9Jv+yXsqkFiQv6gxXMkZAgvS63T8umnz/mRM2J\nBpNcruZ2dn1y6G1LIos++1NEGoCXMzP81PR1VooqUSigagy/cIPISwNVKiqsLZReJc5dOeV0\nXYZp2RWpakY7z3xXtOJuB+0JqlQBkOfzkSO0sB2XrB5eFH7u537ui1/84uOPP34i0fPJJ580\nxvzsz/7s+TZpt9tlWSZJEkURAGttHMdntHUXQtROadVarVZnZmY6nc5LX/rS59z2bByjJ1Sk\n/JYocqGaZV6SsIJZUJAXXSl9diqJwQGyjIsS3Q4EwTGyhP/r2ywkZufokitkt7gUztcuzFq7\nb9++cSbxe97znvNt/pO/6ra4xrgqxroTcBLz0hIfW0a3R7fcKq7b83xf77nxPHH9DZiecWme\ndJVBXQ9VVIFXh1cDO5AEGLWdEKcrF5Ko70Z1O4SsOf86Xlul5inpy0WJ4ZBaV3GnzefLc0T1\nP/CBD7zvfe974oknoii6/vrrhRAAoij6/Oc///rXv/585k+XDjN/5CMfabfb999/f/T8i6iZ\nXS/e3wgXiKhkfjpJD2XZwrlbOytH1jrMaN0zNhA40VXGI7LgTplPi6hG8UFbrYgLOl8we0KE\nAlKQFNGAmlFlSpsVL3vCiUC4UVcPhhOtssLVYTrTHZSaclFQwpnRjBqJekihNpDWDEJ/UNGZ\nliDkjhtK1aQAIQJG1qwVPO1V8uRwoBuhd6bdIns+pRkqx6Obvo80vYqE3Ve+8pUTj9/+9rf/\n67/+69ve9rb3v//9119//eLi4v3331+pVO69997zbb5jx44gCB577LFxD6jHH39cCHGG2ewj\njzzyuc997o//+I/HMeA0TdfW1rZv334x256NA/esDcJwPksI3Nfad853LnCu0F4tidOgylJx\nlon5BUxM0PFwMQc+QHb/PpEmtH3nZbNi3+L58+1vf/vCK7zlLW9597vffb5nf/JX3RbXAFfd\nWHcCXl7iYyucZehviOeTw+2YC+bgApUWRGg0ihKDdeg6RstQIfwmQMcrw+hMVXcCoQCQ2LHD\nseOjRxCEEISiQJKIO+66in4EL53nTtfwPO/lL3/5qUvCMPylX/qlH9spAcAPf/jDxcXFd7zj\nHXv37j2xcNu2bRd5S5EU64c6D25r3cHA3iTdn2bz51Z1EEIba9ZN2VbKFzSyLmf2j2s7CZLC\nFdaTZrWu2glLn/jsoJ0DSuZAilAIyYXzduVOVD3KXMjedUbNK3PEG/2XEIFn962r4XrD9V1v\nSuykQrtRsFBERjnGIBc29nThVUulxwfJmUMhqlKeOGYghE/oGVOloJetB7pOdNqXhJSCPSWi\nrjXn+VXkAXC2LeJgMPijP/qjU5f84z/+48///M+fc/Moit70pjf9/d///cTEhBDib//2b++5\n555mswngP//zP4uieOtb33rLLbesrq7ed99973znO5VSn//85+fn5++66y4p5fm2PR/SWknk\nmDva31etN4ucQZEtPcvK5KXvG6kmBr3Q92lunhpNnGJYQ0Lw+rpY2M7drgPE7j0X5WxXlpyl\nKM3mp6w0tKIw+mnobP3j401vetM5lwshdu3a9drXvvbXfu3XLjBp8BO+6ra4Nri6xrrTsA5a\ni0oF03M0PXORG+1PsyeT5Dv94RuajZdVK9Nn27Iehx1IQPogcVou3UXhB2LP9dyeRJbAOvg+\n6o0LeOyxRRlD6GsqR/k5qmIvZhe9Xu8yncxJ9u/fz8z33XffqQt/8zd/8+1vf/vFbD7MViKv\nCdCRLH88TrafPwNAyErpUokaAb4Qgii2dmCdT3R8WlYY5yy8CmVKRgNjNUEd11kMGLBhVKUI\nhCBAcOZEXQtZFeqIKUBCiaA0caAqoRsJb45olzG9jfCmRLeOleUdUh6UVCmQehpswDHsGmgG\nspaDfKKqlGdPH1elSK23khzVujUVTpz+kgSMPbmyFGRPy5y4vLhyM/sBgFCQ3ovfdfS9732v\nEOLP//zPnXOvetWrfuM3fmO8/Bvf+EYcx29961s9z7vvvvv+7u/+7i/+4i+EEHfccceHPvSh\nsTnn+bY9H0aqZp4TkBOtBwETzaXxZJFlJFcrtUol9JKkboxPAkXBZUmEk/UuRBDEzlKjwceO\nuSAUO3ed90jWcq/Lgz6yjJeWoAQLSQBbS9bQ/AIqFao3qNn6qTJYv1z8+7//+yXu4Sd51W2x\nxZgX66qjqSmyxh06IF5+10XeUvaM+ezKsT1hcGeteiDLDPPPt5re2c6sAACviWgWgwOoXwf/\nBdj+SEUXFwOyOTo/wvoP4DUx+zOo/lhMPl4E6EQFyjmeu7hfiAvs4SfDOLXzz/7sz37/939/\nvORg59vrw6e1N/XDUTwyNpLnDfwy22P9R46m/drxrukMpM5lzsXWaSKPR17+7IbTgW6TN2+Y\nc8epc2OxZRiRFCdUIABlVvPgtg5N7vR9B6wURZEd3pN/XalIyupY9bBZX5E3eTJqKXXDKub6\nqOTIT9y9sGOXFKIW6Imq8vX5g9Z5GYd+e09jT3hqZLs00Jq2H3cDSmK0JsRlNd1mi2KIcgST\nYHgIRJtijh2qCxAevAp0/QXeAI0TOi+MlPJFrw0cX3X/56/93//XW/8Pf211RchYKTBuiAe1\nsiQhGtbOmqJa5GkUhZ4XKo0ohLGi3eYwIt8HgdfW6faXk1KwlpeOije88RzeNMzcWefOOi8d\nRVShMII6ayQtCk5TSmLs2EXTM7RlgXZZcc595zvf+dKXvnTGreZPnrPHui2uXq6use7Uq25o\n7cH+wKXxjsGgVhSiUacduy5sZbc3Sf93p3dDuNlM9tFR/BvzMzPnn6NghishLyH3mB2SFdgM\nugISyIeQHqLZ02Zy02NY+S9Awyao7cL0XaAX6Md3ZXFRV8yePXvuvvvumZmLjbi+6BibSfKW\n8vxwmm8LLnRpEEmImnBHgU1hR0AkhC9EIFzh3KggOMPwc1fCORyvt9AkCnaREAHRiUpZYkMu\nK2TDGO4aI4hu1D2bPpzDOaqO51MZXFizwdwW6BmzVnKlFMpSIiEAScQgR1GINMKGRBs4b7za\nk+GoTJfydEdQ0SeLdfnULxhbFufXtc8Xdsi7SDtIlqEjCB/R/MmjMcMZlAmSFbgS286b9Xsh\nzuja5Jzbu3fvaDQ6saTX6+3du/d973vfC38Zl4/I9x6p1DylRBzrvLghHVwXDx0wacuh5xda\nF0oPA7/u+0hT8gOEipMUGxtotlCt0tQUjRM3pUQYYDjAGcLOGHfoIO9/lpotmjm/gZnnkeeh\nXufuOh/YRzffIhYWrpEh6sXDGPPNb37zgQce+PKXv7y8vAzgRRd2W1xLXF1j3Qn2b2z8P489\nHg/6ty8f5VF/vlZvlQW98lVi93XUap9vxkALURx3onUMw6wvGDkiuiRVByDvYWMv/Dr6i3Al\nojmYGEIhOnUclWCGS+AySO/aGTIvJOza7Xa32wWwuLi4uLi4a9euu++++1WvetXdd9995513\nvoCahp8YxuVC6m5uW95zK1cdzFL/IaB1qoSSQCREJERN1jOasWZk4HwpAhJEUEQCMMy5g4Eb\nlnZ8NXu229MvCSkIJO8Kghk5SPo/EMFEDJuRGFlDIOuyPqYsqZKdICG00IKEAzNyZguWQF3J\nUFbYphYbQk+c73IjIXSxtppOh8Kb9zdPnq2hU+/wnMFluuErBkhWES/Ba5z+3ThxPgRSEAo6\nev6JEefi2Weffctb3rJv374zlr/nPe+5QgY7AWTMfa17lZoJXNfXNw4H7aIg5mqeVzkH8bQQ\n0tOgsT0B4HnwtBsMKM9pZvZk1p3nc5adNtRlGR8+yCtLNDt/UROsRFSrI6zw3me4LLF9B+nn\n3bVpi6Io/uM//uOBBx74yle+cqqz1wV87LbY4hK58se6McWx1eG3vjlp3c5WS83MrNvyaFi5\npxJ5/Z77/kN03fVi+w6ca9iZ8/SdteqPRnFNya4xP99qNn/MkUhnWJYbXr9r13K27Cmiwuf1\nGibbJ34TgxYm70C8AuWhcZlKe68ELvTOdjqdxcXF7x3n+9///he+8IVxu2Kl1K233joWeb/+\n67/+kzrbi4UghsY8nRR7jgd+L4CW1Sy4LSr3QZ8jJClJSBEq7goZKdCps7qSyBcAREMpZjBn\noG0U7lbCWy3KaVVydkTraWN6gfIUZOlc31pr0lw2CGQdl8SJYMOSHQjwBSkiQVQyr5dlQ3ow\nA0Bob+LsE9t8paRr0u1NkpaubU7IWnuakjMlvEsyGQIARrKG7mPwWwinT2oMtnAl2G3KOJIQ\nEkIDdHluff7wD//w7JEOwJWTWn40L25Q6kBaSqIG3MsGg6o1TlJX+pLIOdtwzityTghE4mQy\nClEUob/hGg1p7WaSihAYdxNKUx4NkaXu8GEe9Gl6+vmdk5I0M+MOHyQC7dz9glv9/LSRJMnX\nvva1Bx544J/+6Z8Gg8EZz37sYx+7gN3JFltcIlf+WAeAj60WP3jo+2F1jZA4eNrjenNnv5vt\n3BVMTkNKPnqInaXde85OvPOFeF2zPuvpxLqmVnvCQPxYs4Hj2Hv2h419i6Y3qs3PsPbSwzV/\n2vOW9js5TzMz1J4AQAL1Xajv+jGeyIvCc0jmPXv27Nmz51d+5VcAOOeeeOKJsch76KGHHnnk\nkUceeeRv/uZvrkBhp2TQL9LwLPfgc+IRcjXPIiezDnmObxGLSNoEcqI8j3W1BADD5RpVXwER\nWMACrlgy5ZrWk6ZYK5xI2DigJWloCui6YNnQUpFASDVCHbBSiOP794gU0dDaSAZeuS5kJOV5\nEtZICHaRlJ2yXBgH9osC4clgKuX5qf++AJgRH0F/EeE0xGaXWpQxbApXIl0HaLNewjHAqExD\nhpenwuhb3/oWgH/7t3/70z/9U2vtP/zDP3zyk5/87Gc/+6EPfegy7P1ysC3wh9ZkzILolkH/\n9kEXJJxzzLDgQAjr2LFVaULt9hlql4koiV23I6amAcA5lAXvW3R7n4YfoL9hN3oyiri7jqlZ\nNBtUeY5O2Cchoskp3r+PPe9qbxb8k+Fd73rXv/zLvyRJcurCu+66601vetPYvPN3fud3XqRT\n2+Kngit/rENRuEe+703P+hvDld7aFK2C3XI0tdG6/sb52XE0gSam3NEjwvdp+86zdxAJcXv1\nJ9KtdTiw3/8eHzigZ9tqvknkUKZeuS4mrqfmLNLYPfxdcdvLaPaabcL4PO7mjTFpmiZJEsfx\nYDA4w6X6ikLJIOP8wlP4J/BJLASVTO+AbMGNzl5ByFohJyRyMM4zwei4PEbRrdCzAHLndug8\njx9TqjX+d7UsS8e+IONGpd6uZCSJKlJWpXAVOjYhhlUK7GmqUQC+EKnlHL61o/N1QyEQ4CpS\nPpvmhWMuDbUnKDgep8xzLOygyiV9l5IVbCwimILQYEYxRLyM/iJGS0jXAaDoIx8g76McgIDk\nGAYH0TtvX83nQafTaTQab37zm3/xF3/xhz/84Z49e+677z5r7bid3ZVAXYptnhcIMZmn1w02\nhkL2pexrXXEmYJaAx1wa55QaN/A9ARe5aLXQbOPIIc5SANztuscfc6vL2LYA3+ckltOzqNdp\nYhLJiPc+7Trrz6NQiQiTU+7xH2F0jkt6izN44IEHTqi617zmNZ/4xCcOHDjw0EMPbem5LX4y\nXPljHY+GqNWs9paLomaP5sm+ND1s8oM31iq1U+aIqD3hnnwCSfyinWhRuKee5AP7xcws+Z7Q\nREpSGFKrgWEfxiAIaXrWPfYodzvPvberk+eI2D377LMPPvjgd7/73QcffPCRRx45UcUTRdHr\nXve6V73qVWN7wyuNyJtI8w0lLs6QkFCRcr0MQ2878sMwXajTfF4UCUseuSwxeUOrM7WwK2FW\nKbwZ/mYhamrdDHWVahDJwnHsXEikBMGVzqbstx1QlUISATACI81k4ZUoTo9ejydnUycp70hZ\nk/Ic08oMBkgRPEJsbTNN0Jg7MfvGo6GYnr6Uybi8h96TCKchJJxBto74KEAw6WY6HQl4teMW\nJwznwPakAcolMjMzc+jQoe9973svf/nLR6PR17/+9TvuuCPLsm984xuXYe+XBYYBJrSaGOQD\nrZolgWgkVUkiAFel8Iw1WgulJAjWgpmdAzukqWu2hCCEAUaxLfJs/+LgtpdTGLUd6+EAlerJ\n264gIE/zkUMQAhftn05Kca3ujhyiySnOcyoLNpbALARpD76PKKKosjVXewaLi4tPP/30M888\nMz9/zd7Qb3GlceWPddxZp1o9Y2fAO33hZIuFLtNDIX7mtPWk5CjkjQ2Kzh9QcA7OnnGve7lw\nqyu8dASN5hnGW6S12+jJZgu1GrRGawJrx9BoXpMOoM9RPHHCo46Ibr755rGSe/WrX33bbbfJ\nK/jtqPmzxo4g7EWW/TakTB3XdFOFPrL9KI9AtnF8JlfCVZQc6Lt0+jTcDpzsP+FgBrAbqNwF\nb9tY3ZSMSS1Cm5CsMKNXmsxRIBhsret15J5AeAVzVZycluvUsZxhrgdlYc56Uz0hUiM9m0Tn\nEnZgNy6r9ElkRUH1Bhqbs8lcFDQxSZNTz+ONOx1XIjmGoA2hYHOkS4jXwCWED10BCMxgA+cw\nDiQJAVIQ3qVWM53gzW9+82c+85lXv/rVnU5Ha/22t72tUqmUZVmvX0Ee4o5RKfKX9zoDqarW\nhs5YEk7QCNRQyg8CL02InUtiiocYl1CXJXs+rSxzs0kkys76aprun5zdgDD9wY1FvquzVmmf\nYU8oqdHkg/spjBA8d+YoAB4NORm5H3yPdt9AjTorDUEMgJmN5SKnohA7dqLRoMnpy1Vhc5Xy\nyU9+8gtf+MK3vvUt59zq6uqnP/3pT3/6081m83zesFtscXm5CsY6pSAEWSsAVq0o+0FY+lxc\n31rrMASd4npLQchxfN75snjkDh/ig/vFS2+jbdsvs+9mWfITjyEIkWcAHKNk1rTZ7oekZGNM\nDFtAiEgdOiInp2jiGmzfd6HR/ISqu+666+6+++5xFuejjz766KOPnrraX//1X//4zu+F4ev6\nTPsNa+uPQ1+U4WAgxY1heCjP2ipCeBNUA6aLcpVFhWTIdkjBTX3s9Dw7bQ5Dz4AtXMJ2iPAG\n0ndAnvzu9U25W+cmO+AHO4fGdk3ZlD7KLnPs9G7nmgQy7PQp3clyhfUqtq/DLzE4Kx1OAEp6\nmc3Cs/oaA2AuCRqAB/BggLnZkyWx/Q3ccCP8ixIB5yRbR7aGYBKuxOgQhkegq5BVEMAMLmBz\nlDGINieKieEYfgPSP97+5dL4kz/5k4ceeujRRx+t1Wrvfe97P/3pTxdFAeC3f/u3L8PeLwc6\nS3d2VtvHVqeSOFQeA9q42SIRYAUOpFQMURZkSgJQqZLSgEMYUVQhU/JgyKYsux3XaDVuuS3Q\nCkR5r7smlMfQZ3zcUiIIXX9DBOf3PWHmsuBuF9117nSgNbSHeAitICWTADtyFtYROzDss89w\nlou5OZrfhoUd4qdV3t1777333nvv6urqAw888MUvfvGb3/ymc25jY+OBBx4Yr/C+973vt37r\nt172spe9uOe5xbXKlT/WjVOEQyFaSnvpvjJ5spnviUTQNqXbvyhrd5zw1ySlYcrz7catrXGv\ni7lt7onH5cTkJaaAnwEPh6x9Irg0Wc3LkTVLebEt8OtSTmlNzhUjOdqA9MEGvo6C7lD9tAm7\nE+zbt++cBTtjrkBhB2CydnO58nXmGaKLeo2Tno6dja2NhIK3ADUF1YYZIH8GphdUmm234pQn\nUEHyKIe3wV+AbJE6bV5sZN205zVEkgmfmWPrIiHZMpl1Cl9mREuwNcw1KfzT57/Wanh6Drce\npaDg7Kxwl4Qsim6qp6IzYtfOMayQATOqabwxNT3f2Lxt4tFIzM7R9PkVwHNhcxQDeA2wxeAA\nRkfg1zF+L5lRDlEOAAFnwIAAGJvyLu9BBtAXneh/Aebn5x966KGHH35YCPHJT37yta997bPP\nPvua17zmF37hFy7D3i8HXhI3rVkJI5RlR3t9JRfA03mmrQWRzgsBtkq5IFBSoSw5HlG9CT9A\nErs0ISFcaXLn6kms9u3tLews6rWp7vqhqNo0tqnPUsdBQEWOE4W0Z5Am3Ou5A/toNOQgoiBg\nQQThkliWxqUj7g9ErebCiqhVoD04BjMpiYMHzI8ekzfe6F7yUpqevUhn8muPmZmZcyo8AOMY\n3otuxr7FtcqVP9aN0USvr5oHj/5nZNPGaLTLHK2lJc1Mge1mGSEA8IUaEAmCMWQMM19+47iy\nIKWMUZ3e6EAUNLWa973C8bN5Ct9MFYWxgVffjDsUXV/28mvyXvZCL2p29oXLghed6Wibrb62\nyJ7yw3OU55yNJsx63sODofK0RwThw1sgDxCa/N0yvMlmcae0s7UmlSs0/DbIh6ydugcu7Y7M\n7nTWz0dl7DvhqCx8MSq9yRHtiEWLiQDkzI2zLIOZcLiNwPD1K+wbOvNaI5Ikcmej082KLee+\nv1NA+qPBsNnOmi2XpaIokMRYW3cvvZUOH4SU5PkUhqhUn9d0W9FHtg5/AqNDGB2C1wJJMMAF\n8iHKPoQHAojgCrhxoFuBBEAwMWx68Yc6L9ZaIcQ4j5OIfvVXf3W80Fp7hWQCsJC59Aobj3/w\nLSgRsq9UBRQZE2tPaulIBuOUwzyD53GeolcSg6SEsVyv56bUlcgfDltHDybZlCMSQrhz1cuQ\nlNztYHL6zAYVZeFWV7F8lEtDRY5GC0odbx1jcewYV6oIIzE1DecQD5ndOMGOSEJrBAG12m5p\niVaWxUtvx/U3kOdxWaI0cBZCkqeh9DWZjHJOLqDwttjix8GVP9ZtmjEBEzJr6HBy1J4qG14g\nKQqQFW4wEMdDX1yWonbe5jdiZs6VBqYU218B5/joYWq1cYGEvOcDO5cPRG9QWx62F4pePkEs\npSZMEZbX1prXvyRd8fwT0RgFNtfmrdqFfunHZutXKTOevn3yFesbZVksae+inMCqUtxarTwe\nJy0lAyEAsFmj8CWovhKi4lDuqopj1s1UdyK8hbO9NPh/WdZI1SMTVnt2YWUwEfoi1IVLuShj\n9Bom871dKqn7abCe9tf9sOe7RqCCc6WrW4lnZ0CO7tpPnSqnp3vPCRIjW9Y4UKfcCDmbeGou\nGPSH7Unj+dX1NR71HQsqUuy6TqQpJzGcc6akoqDZeUSRmJhE7aKSNvIBZIhigMFBeHUICXaw\nCfIRbA4VggFXgi1IH6+dYLgC0oPwLk/xhFLqgx/84Cc+8YlTF951112dTufw4cOX4QCXzEhI\n0+sys2Am5kZZKOsykj651SDUAiFJCYRCwRSo1SkK2TJ11nlqkjwfSsnS1LMsJyHANByoonQC\nRVjxz1fTIAScBTCO/3EcYzTk/ga6XSaiPHdKIUkorDgthXWcjOAYYUhq0y2PwojzFN11brSo\nXofWpJQgwsQEx4l77BGsrIhWizvrEAJSwVpyFjt2UhhRvY5646cnIe9UhfelL33pxT6dLa5Z\nrvyxDs7CWChZU7PXu5sbxzaUrvgi5DSFUrI8OffKaYrjVgyOOWM+tT8TfP9Ei0t3YD+efQZ7\nrqfdz9sduIxhRiAB3TiZ1U1Sucy6GjZoIrKaRBxt9OA4nphcmtt23UQrciiGkCHgYFMnw5ND\nmUlQDEECXh3ykr1fX1yuqQGamRHHyFJYq8tye5yM4nk3+C4a5KKLqiGY0Opl1WitKAfWVbFB\nxTIab4GoWObMmjc0W4ey/FhZtlWNKnci2KPylenVgzsPrRq95pp6KIlhpa47VAuaKkXkhAdg\nGDRVsrQQewgUNVS3Bj5XoNoIPDWPXPP2DiZH2IhgT/64OwIZ59TxYB+VWcM0Xa2ZRFpaF6yu\nIPRRqdJo/Myd0wAAIABJREFURLfcSvUGjkfDNw9lDS8vu71P4/qbxMwsLtg4xBYYHUQ4jcFh\nQEB4YAuTwGQQAtAAgQ3YnpZLRwRI2BwkLynE/tWvfvX+++8fP/7yl7/81FNPnXhqOBw+9thj\nZ7TieRHZK1Wj1Z4ZDqbL3Gc3UeQFyaYpUyGUc8LBwZVa59rz6zUwYxRTtYIwEiThB7DWZamU\naqi0T0RKcZFTUd5cb4bnE3bOcV7wYBkHFmGtSxIqS7CDdSQIlYikAjOcpX5s00REERHYWYKC\nc8gzLg2nCRFhZZl7Xfg+mk0E4bhWl3s9jGK37NPNt1D1lJh0Ubh+H/sXaXaeJidpYvKnqpx2\nZmbmimoAsMW1wVU01tHUNA82UKttLB0L+zNWpIKC3HnSlWJlmWdmqSxZSjJGTk1Tq20ZTybJ\nwSz/7/7gFbVqIMS0p2FsDW7O9ytBACGo2eQdO9n3kaXkBxdfSJF20HlkU5+F06jtPC7FopBc\nxikhV/HUBLfq8eQUExmlNtLME+RNgIH0GADU2qk3s5mFn/ew9gPIELAIplDbDnXlttZ6bq4d\nYceDPj/zFB86AM9nKUjqXcYudTpzdCeWji5NLJrGbFwP7XM1Tm0opQXJ+NARO1Vpvb4qWwR0\nS3NHrTqptSeIEywXxZRS2obtjl9fryXbd00EwicLkCBtivXB6NFOWW0e7zbMsjrQ877sVU24\ncwX7DZabp4q2U14FYd801qu4eRWzPdKWUw0nYBlOSGGcsixNKU3Z8/u2dYPmpNpLyrCehGG7\nyGSzRTt2IjiXO7BUqDWMrLpnjuAHT7kb78LkjFAQGrp6Zq2DzQGJYoi8BxWAHUwCW4AUyuFm\nuTDzZmHspucKjjsVi+dIsXhODh8+/LWvfW38+ODBgwcPHjxjhVe84hUvfO+XFUfUj6r7tG+B\nlwwHUtrpIh8JZQUcCERWCCdkBihGJBV8IMsh4EYDEUUwBWWprlanpMyc6zpX973QcTgaUrW6\n2TfWuU2fFCmJCEmMvU8xM0xJfkDWghgOXIyIFfICHqAUrOWyEGHElilPKU2YCFmOLIWnN2dy\nAx9ZDikxinlpCVqjVqdmi9OEhaRel5U+6YmoNWmNep3T2D12VCxsFzt2XWR97hZbbHFOrqKx\njqo1mpjs7Fvcu7pqtD+jdWSkSRIBhKZ0+xep06HJKTvYwPYd8uiRvab8usFke+LWSuX7/eHh\npaW72WwX1FheMp5q7t4z3WxQpUpCuMceY7C48SbasevC+R4MHMry5axwh2RYVfN17RFlHegq\nKvMAQJWq/9JdU8v9Xkse0dmEUEZ7mXMreX5rtRoKAQ/RLMJJEFy5EnfCkMuyJmXeEX5zU8zl\nPXjVLWF3ZcDdLo9izC/Q8dzvBrArDPYl2eTETKv/NC/uDScnk9bkqB4QszBOWkcEJ4SVdELw\nGbOBfGlP48654Nb1wj/S7VSshS1nJyYzKUiped8bWvt0t397d/W6PNPbttXPmJkSgeUSx1NJ\nS+bY2slgSqfLqfZXtFzoQFkcnmArzi1/BhEe3ompNk/3MTNAIawTLYMyptRIk2M9DVIp6+14\nSSe2q5zXscP6Nt2aKibr/lmqjhk2RRnDFsi6Uqo2XEU//Eg+9RJTX7ClqMxDBfBb0LXNWyZX\ngghlH66A9GESmBzSAxuQADHYwZVwxz2qCXAA8WYzMS6BS7Aouummm8btmz772c/ecsstd999\n96nPzs7Ovv/973/he7+sZM7FzubAsh++dNDPpep6SI6XPAvAk6QhSubVopzxEEkFk3FewPfZ\nGDjAMaLAz3OfqDHuLVEU8HyORwymwYDyzI5GNK49DgIUBVotKg0qNTfYwGBE1rAt4RxrSabk\nPKdKxElCWoOIJLFSSDNOM5ISpwUACL6P/gb7AaRkU4qy2FTonTWX5zQcYHIaWkNpCnwoDSIK\nQpoJuLPunKP5bePY8BZbbPECuIrGOijFU9O9/+9bG4yRHU06kSD1CjUQWrfbXr2JZpOPrQqC\ne/xH+XrnWePuIazf/NJHGK2jR+7e6BxUnmq3hvPzG5ZHq8feNuxXlo4iimjXbpCwBw/IIKS5\nC5lHPjQc/WunNy313MFqtznqCu+WSiSUOPFLxKDSn3bdQ9ua08753x0OUudWy3LW04UdSsJ1\nQRBJYRUvLS//a625ujGijbhw7n8MZnYqfxyQkB7seYt6rw6uHWFHUUSndoa1BtbtdNaW2Sgr\nKnp3MdHW8cr0kSeTWiWtVKc7BszETIATtDQXjrxs5I30xO21+t2RnN/R7epR11tZOmjdNt9v\nbXRcWXZ3XWenpl/SqotB12dzqNHwSfDpRiRKN7S3QOUwdzJ3rq7Ugq9qUlp5SzZ4vBDNbk3P\n9WEkHW2de04WgBVYaaBbxUoDtVF/oR9a0UlEwMp3OmrYycqo5uVcAhuRn1aw7CWT9tkDR37U\nrt8xWdkx4dfrShJgc+QbSFahQkgPwWbVrI/abNh/xtXYzu5wJSUDDPahtgvhNHQF7FBmKDYg\nNbiASTcD3ezAFiYHBDBuEeuOx+3GcbpxApgAXUKO3T333HPPPfcAeOqpp375l3/5yvFePxvD\nrIUk61b8MFNyxzDuHG8VPxZI1oHhIiVBOFaWC0IoP0CeU54TM+CcVhRGGA1BAr6mvGCArHWr\nq3COrXF+kJMw1sHZ8PAh+KHyPFGtcppgbY2CAEqSKaE8a40tjNUej2IS5I3jsAxyYDAVBTwf\n1uHUoLWznGdgUBCQs27tmPA8KA1nkYw4TTkvKAhgDMoSs3OoVKlWgxDUaPJoiKNHSEpcfK+z\nLbbY4hSuorEOQBFGPyA5EPktG4ulEJ5TqY66CEJj9dEjNBrSxKSTApPTZjiIoMrpqalHHha1\nxk7wWntyOUl7cRIJYRkVKffp4LZ6A70uH1ul6VlbqYg0ucBMz9E8/5f13q3VUJPwdvJ81z+a\n5RWS29PwRAfLbB3dw+2wfbNe2VtDq17XM03cXqtEQjjmg2leOL4lCg4fO/aoH03t2j3v+QBy\nx4+sD8sN3DzrE6iMUb0on7Qrl2tH2AEAMw8H6PXcaITOMTbGc25PlneYKYp861QySqqTGOWJ\nn7q8Y7RgIjB71m0Ts6wqQs/0/N2lq4aDw5XOel6rH2xN3FSJbo5CAmAtj4b40RLm5niwUSws\ntIuiV9onkyQgoQUkBAALjp1OytGkF7a1qko5DsxJ1QrCPWm8CN3uVcSuNco01msXekGFMMei\nY6t4aql1M8zstNA1k+XpRtvOdIQcTXpWC6ulA3wRBp5mZ5aHzxzsPWTCO25u3TBbBOqI0iH5\nrU3pyRamAEo4J2Gn5JNPFXO+a8xKH7qJdA39fZh6GQhwOYSEBWwBoQEGG9gcrgQJOANXgMd5\ndSc0HIEdwHAFLq5P74WI4/g73/nO+PF///d/f+Mb3/A87zWvec2rX/3qS9315SMQIiASoJFS\nBZF30g6DcTzP0AG5swwERBvGtJQSlQp6PR4OHENUqhgN0GojS2Esj3Pghn0i4rKwtfrQucQ6\nLWWYpoXvOzi5sRE4piwhrSEUyhxS5HAZsxIEa2RZZkE4LMuW1podeZqLAp4Ha1BkCMLjgVmH\nvCCtOc84z8jTJCVLCc8DEdIEzSYxUxTBlJwKXj6KPKdKBZUqVassJQ/6bK284SZcMZlAW2xx\n1XFVjHUAYmAVtOGv36SWhGkbQjvz+lKlLmgoaSqVdGWlMz1TUsbK0xu90FMuy+ZJjKame6Xp\nG7sjCAJBANZK82yW3iKlqFQ2lpdX8nKxNJNhpRknN0WhOleyXac0bU9pEgDKKaOdmjnqp32q\n3cbBxOb6xQB+kyjYXnrsnvnhjX7NaH/sRyGIprRa2xgdOLb2cKMxsXuP9TZHLV/Q7IJ89MCw\ncVg1pGregOCC3nY9Y9ZLE1sbCjGh1aT+sbTQuBSuIWFX5O7pJ93+RShNUiLLUeTwtCdFU+le\nlpG1ELKSFCLPvaiSTNyU1CoOTEI4KL90XpbGUbu9tFRdW43bU72pmaWiuDGKrg+DzatGSmo0\nIYX9r2+L214WCLErCHYGuD4KYmsLxyUzAZqo9HdWy0XfU/L0C7Tmz6bWmWyfUs2NiteK0Y9Q\nnpFUwA6cMhvYFHadTJf0bOLXYs/l6ER2Ynp0YzURWagqhXGlcGSGQBTqUGsrtS+n2bWK4snO\nUa/f3TnZ9id9VSXhCpQJ8h6EBBSEAEg6f0of/EEy/9pM1G2OcAoqwPoPEU0jX4dXhzWbha42\nh82AsTVxCWaQBNwp6XS8+UcKDLB94Z/kV7/61Q9+8IOHDx9O0xTAxz/+8d/93d898ey99977\nV3/1V1eI15phHhorAcUuFWpN+1VrYqkcaNPGmViALCN31kIwSgIqUgaex1rL6/YgjChJAHZH\njyBPEUQEgrNgpqJMrE0dR4AscklgqUiKERGyNOx2UGuALcrSSJlZpwUBUuc5C6HZQajMWemY\nhCApAIJUKEsICd8HM/KMspTLkgBWapwdyWlKngcIJoEk5V6Pel0uchISUkAIZCmvrnKjTvUG\nGWv3LfKgL2+/g6pbcbsttnh+XEVjHQCh/e9PtH9h5du+qW348WxSW6r21/TM9UGIZnM5iNTK\nKsVDpydS59a9YProwdBy3GyDcSDNFwI/OJ59VJXySFYM61UsHT1k3eS+vTO33nGo1vjmWuct\nE+07a+dwPxl3zdl8HHCxvcymuKJFbS447Q0igESvMf/oDO+U/drqPudpJwQZ6J4VdtuS3MEz\nDadPEz/kg3aYLMxfUlMXKIll4Iej+EvH1utKBUIUzBum/B+TE3fWqldUKdm1I+zc0lE+eAC+\nj40NpCmYicC5Zk8H5WBWyEG1lpdlWOaQ0stz2e2x8rNqfaxLrI/M86ONDkDW82WnExt3+8LC\ndZXwDHHmRjG127x/HypV8jwCalLWTk/5ZH/bcnxzL1mK/NPazkpBlWB2hWWt2FuIYDKuDkOx\nMk5SYoBHsCO4DGaNnGMXE0pwAUgR9yeSWr1cuG0QzMamV5UyNw4kYBxzndGMS+9Y3J2opBWv\n9HTF3uCvwjWWe2768EDc5Cr+hpCalA8IkDhRLeFxteGVK5io6SqZDFkHfgsbz8AakAAMhIDN\nYHMQweQA4Ma+kuNA3Yn3ZvxAAACJF253sri4+K53vStN0yAIAKytrf3BH/wBAKVUo9HodDqf\n+tSn3vzmN7/zne98gQe4rJTHLWs180yRxVKOhJrJ00SoUggQwHBgQaRJlOxylrnjMosnpVRz\n82Jhx2ayMLOYneNDB9HrMhh5DiktkGRZRQjhnPF9P47zKCTnPJB1DkLCWnZERBYsTljOEFkp\nlTFO69xy6JxUAoaNK5xzllnmGzYIidkbDgmAEEREec7OEgQbAzEECTIZj/0JHVNUGb8WsGNm\nMPjYKooS7QkxO4ennrCdjrj95WLbwk+PE8oWW1wiV9dYB6Dtqcm5qdl9lZxGgXG5yC2074pK\nWQ6D8FBZ7pycrG30bKUmPG/O02AqTbkMQUUeSXFqZKtwHAgx8sMHZxd2w8VxbJrNsFK90fFX\n1jt7wqChzqyimNCqV9ptnmOiDWNG1h7Kixtl8INhHAqa8byWVl4No8PwmygLcnaic1OrG04l\ncZyUmVjzvbyyMalTJ2ZXfdlgM2EBMPOGsX1rjhalEEPnuZZVc75XPVcZx7Np9r/XujdXK4mx\nA2tLdp4Qn11e7RXmnnZDXzES/BoahcsSztFohDxjonFnLbKG44I8n6yt9TeMUgOpJJF2zh8O\n68ylH9jjSVFMMFLVlo4emZqut1qvSOPGaEDV02tjioKWj6I94ZKE+huYOsUhz1oQxp1kiWgq\n2r7UfyzUVRKnxWmrUtb9iUxGvusNyyPVOJAN3zrDpoviKIkQMoBsgrsgyZAkatWiXksmZvph\nURE5jq6HU5n2jzebpcTalta5FIXj6ihvdZN+o8mDyaKWGXPML/1aMtdNbBUUKaJxPpyDroIU\npAfn13R/v61M2qA9tqAzKfIBij50FSaBCDZV3bgqlksAm7/yoOMzvKcIPHaX1P3vwx/+cJqm\nu3fv/uhHPwrgn//5n8uyFEI8/PDDt99+++/93u997GMf+9SnPnWFDHaWuWD2BOnSxUpbBd+5\nI2GlZkzDFAXJXBADlsHM2kHYUtlSCtmbnZ8uCmTpZoIaEff7MIZm55Cm3OlQHDMJo3QehU5p\nfzTarO9hFpJiYyLtCWvG2zrerEgmxzAGnh7Lbu2sE5KK0hZFDiJBwlk2VhQFOS6ZhacFKUkA\nmIQEgRyQpszjNsQCYYSxQqXxESQAKJDvIY55FGNmmipVCj23+Az1N3h2loKQlIL2oDWfqw/e\nFltsgattrBtz9+zCUJPP3Zn+JLHwHMqKL2cXcq1964pqtba+7o+GRbU2mWWtJNaN5qvbjRXH\nxjnDmz8UOXNszZTvAbCeb0M/CUOXZwezbGTd4TT/+kb/1ii8LgxOjaps87zXNetfXOtIYLko\n2PF84JfsHhwMU+v61ry+Wb+hGk7e4psBQktLOilr4lDOP7Jc1dEuRGVE3aTIrdtbpjvX/R1N\nmYMPptnjcaKJVvJipSieTBLLUMCvzk3PeZ5hNsyBEG2tfCEW09QX9HSc7EuzihCKCESxsf9r\ndbVg95JKdF14RRgFXEPCTkoWAnlGzhEJlMeDKQRmJq1EnntOtDxZEKygmLVOEuqsj6ZmGLCA\ncRwmSa1auY4o0oL8SnnskJBOTs3RcVs2TlN4GkKIIESWwVrOM8QxsmyzpRYJBAEq1Zlo2kR3\nFvmP/GDbqW4igtBQar1UQs2i2pjr9Vbi9ZF5kGSVRY1JCFc60yU3IhAxGuXOG1YmDwXqYF1V\nC942Ep2gZ7NMqyaJwIFrUkVjO2VBeaAKrZrHykwfs66i0+2qFypZSt8foBRKVaVku1kn6yxk\nBOWT8BsyXrN+a9yrQIXwGkjXkXc31wTB5Jv9xOAgxGYuHcYRohOqjjcjdudqmnCxjDsRf+Yz\nn3njG98IYJx68sY3vvH2228H8IEPfOBjH/vYM88888IPcFmRRB4RgyNwIUwhzfyoWPWqqe8P\nlfat1c62y2IsihI/zIX0paBmS1ZrcJazjMbCLk2wfJRabQhBlQqEYFOgWjFCC8+X1qoihSBm\nVs5mKoiYhVIwBqaA0kRjpU3EzinJYwlmnSoK63vWshFCkiBrpXUsBDELGBbEpXGKIaQEYCyP\np4CVhjWQEsawsbCWgnNZTAUBihyrqw6glSXRmrCHDiIMUW8wdDm0ZnIPgoqYaIY7a/KKGO62\n2OIK4uoa68bcSkcO+fExr+eBo8LPvKjV8quRP3Rkma3Wo4nJuNV2Sjkp/YnJ2SC4uxI9Y9xD\ng+EgjifhhONMiLlK9a56tamVBQNIhTgcJ4/0RxWtBs4eSLMn4+R1jfrPNGonwmCH8iK2ti1V\nIGje0xUl5z3vhJH7uDbivzYGP9dq3rm7KooyWzX/c3ljR+Dv9n0mGsKajBJtZjwvTvk7o8F/\nr1njeN73JpT6/nCUM+8WgQClzj2TpX+2/3BdqeujwBdUOL69WomU+l8rxyxj3vN2Bifna5tK\nLqbZgSx/eBi/fbJ1Z636ot/MXkPCjohMibKEY8AwmByDBDPYlE4IgFCWSgrlhxBUCoksm++s\nVVtNU6krgmQXpIaocIPDQ9Xp8xo58p98wpU3jPyolHWIWtRNlCVkuSSqrq/51gT9DfZDeJqk\nBBi2dMMBHT5UX9h+e+P6fUOL/GlPz5I4+VaHgnb4/pE8r4sgUzJKRsPWXQQGl8TW2QFxCdUy\nJm0Xt80fqz8VuVJSVVDbUOmRlJFCScWhRM05UYkkp86FYtPZm4xvXN0rRs2OV5a+0TLHMQ+z\nIetBbIUUsjwuUxm2i1LB1CqVdF9Z28beZpqUlPBqSDuAga7CZBDqeG0Ebcbr2GwG7QDwOJrD\ngAMcmHFxHXrPweLiIoC77rpr/O93v/tdAO94xzvG/7ZaLQCrq6svcO8/HhwgOI9cl+T6yBdz\n+dy6rsZSjqQE85ofSICJJKFli4N+lCuvUZQVQdUsH7/jbjhCEJ6w/KUgRHtSHT3assWo0NIa\nCEku16Y0flBAREUOPyAwSjjjpJYlM7FjEiwBZiZSaTLSnlSabKEZgg05y1LCOWkME0FIArO1\nzoEEk7WkNKxlW5LQIEBKcoYB2BLitMQTdo6MgbGcJuT7Ls/RmhCz85wmIJ3RbKFID4fUPWaf\niZOV66NbZ+TERbU82WKLnxKuurHOsZHlcltxXr09LoeRqwShUnbF2e01VZ/QqjAW7Eb/P3vv\nGmvbdZUJfmPMOddrP8773Kd9/cZ2knIcEhuqkhRq0Ujp0ApVjZAitUREQOHxAyHyCxoQQiCB\nEhERwR8QtKqTiLTcRIpolUokIkDRTQJUOXHsxI7te31f557nfq7nnHOM/rH3ub52nJDOwy/u\nJ8s6d++19ll7rXHmGmuM8X3f1nYwdjw6Otk0BOTMbyrc2fHR/GhvyjyJcsZQwthe6Tlj/k2W\n7ZTlXPRIcTZ1+yIP9Hq3ZykB/89kumbNm/o9AF+p6k/u7t+Z5w8Ne7UooDnz9enDKkolElVX\nrfm/9g7+78PRyIevlNU4+rKSSqIjmls8VK6e6CWrakPEl/vTx8fze4r8XJ5+tapa1TvyHErT\nEA59mIc41VhYy6C783wu8Yl59YXZ/L9OJg/2B6RtbszQ2sGimwEAWHP2ZJr858NxxvyG3iss\ngvcaSOz+7u/+7uGHH06Sb4Fm6aNGgSoxqWpUECJAErX1HUMBNl2nYEqcJTbWgnlzXmJ1tfbj\npj3qDi7HfurIwod+/7RXKbWrrh3tmJ3Ctyxt2m2zZKHqByIZH42abnBie83aFWuPh8wMO4ei\n0N1rt66ufZnPbBeuKb/o3Bab5y/20JjTaXptvjPQq86cWNg9EQANpJ2YlYZWnd+4fb9/vheV\nOWdKFGlAZxZD+S5yvxd3rL1V1Rx53zO2b4wjMl2iLoofpnMx5qh0aywpWm9CmjJNJGwklheS\nc4qlGcERmTSjan49sVOCsWAg0pIGi+MergpIoQQY0DFDgm4kg35nxoZra2tVVT355JNve9vb\nLly48Oijj+IGlc4nnngCwJkzryIyugKdKMivyLijdpyku86l3m51TWNMIBbAQNMQU5HL/aHp\n9SdREpFLQaaj8a3rG7cnzrQN0mWE1yLzKD7NsyJvyOwwn/HesJW8B9HaJj5GR0xMsA5oSCIH\ndc61Cgvm6I0IeV9nKWwSRRNjOLSkpGwAsKouWqsAQGQMfIQorFMFVGHs8zVYZgpB2w7G0XWr\nieDRefUdjKMsh+84yzE60jynwdDvjaMYd/oUkGue02AtXNnvDp9OH/w+PnsW7jvmS9/ETbwu\n8Jpb60Jsnz38u/VQJqEMaRl8O2iH6mpFSJm2mOfl+Kmtk3PR2jcP9vurowOokjF8cLA+HW+c\nPi2AV3HE1LU0GiHKnePRyrWdv2EbTp19tvN3FvmdWbrowJ5NkyudfyNwqW0/ubt/T54dBv90\nHZ6qagD35NmKdZYwjfHxeemYLYiZLtfNFR8S4FSanMnSoDKPoRJsrbn9QX3Kp0ccyn7odXSq\nS662rQJX23bLuZHv9jt/EP28C0yUGrPTNWMfDr2/2nWO6Lm23bbuq1W9nRhHplU5lyW3pqko\nzqVpSpwQ3V1k/+fewS/dcnr1FZ02frUndteuXfvQhz70sY997FtJ7EQCE4t4DYuaxcJlDKya\nxg4KIYVa1KENydy6PrFLUkyP5ofVlI8S7vdtz5h0Ibw7Cn6vala9nDqqB6tZXayoUeMnGvcy\n2c4byso5DYbjprlMfDZNtxPXM8eadsy0slKMRj8wXP3n4sz2sPDdta69at06LYRACEOUUZ+b\n01ACH8+oqXZXtb1QuTu02/j3u8XlQiKTIzgiF2izooNCAQRowsZRn2UOdoVxrYpGHWiaVwUg\ntkki2UQm0nJDpzR4cZ5hnXDXwhHkWICRHcigrbLwXG0cXA+4LkdngA6xhcmhCkSIBwRqwIDy\nse2EvGDSjug78hO79957r1y58nu/93u//uu//gd/8AcABoPBYrG7fPnyQufpDW94wzcNA/nE\nJz7xuc99Lsb49re//X3ve9/Xu2g3TfOnf/qnjz766HQ6vfvuu3/qp37q9ttvB/DII4/8p//0\nn65vZoz51Kc+9U1+V1D1KkSYc1HRekQnoH3XC7aYJEkegxNVqBC1aT6ytmPTiyGI7rbcQgtj\n/vPh0RuseejabnFiuxXZ7/z5pk2JHWHoknkxQDU7SjNhW5AOppOUac0mpm0AgBlZBu9NlsGY\nINL4LgvRSKiT1CRpGzUzxFGWuTgAESzN7I+TcRGCioJUWSKYrg8eLDdZ5nYtsgxE2rUoK3Lu\n+RTNGMSIPNeDfRgj6cDu76DpSzYAACIMhrHo6eWL0nV8yy3fLcPvVxVezqi7idcHXltrnWjc\nmXxxKkeT4tmiciHBUeF9WW0cVIf1P0l+24pdz++40w1WIlAYswrFeKALJfadK1jbAIGBdLG8\npKnOZ2ibNITB1ok3Xzh/4a5iuLqy4Z6XkugZ8/fjycPD/mOz8vYsu9J1X57XW87emaVR9ULT\nXe7mBeHOPL+jyEjRKQ58mEk85dzTdb3mnIRQxXgqSS7U5RuKbNCjOToAe52/3LStSlBtRDZd\nYggjH3Y6r0BhDKmWIXwthCrGL5XzW7K0jvJc0xIg0Enph9YQ6Erb/hOVDjiXp5bJMq8YkzA9\nWdYPr3xTJbPvMV69id2zzz77D//wD5/5zGdUv7WJrRClaRCDqBCWQv1QYoICLFCCUVaVSEhC\nZyWKYE7RShfnTb6xSYQ4UAoiwMjHaYgnvWQ+tr0kGBMck6JoTZ0NQ3eALvNJmrTdiepab31r\nEuzuob8zyftsFpkNJzC94Zmj/ap37mlsbPc3rFsL/qhpLxkuiJLO7xbJSo8SVV+11apv89ix\nuFoPOgXZAAAgAElEQVTe6OPKnXM7SzDmCLBjZsBFBEIERDVjdkwMhtSIBnYjIQqq40Z7oonP\nlEAcvfZ79Si61Y4zCsqRmVBD2LJxx7mYQgUKp007u4TBGbgB2CJ6mBTSQQJYltLEC7uwhfOE\n6pL6en34asmoiDiekf128Eu/9Euf/exnH3nkkUceeWTxyo//+I875w4PD2+55ZbFK7/8y7/8\nTT7hE5/4xGc+85mf//mft9b+0R/9UQjhAx/4wIu2+a3f+q3RaPSBD3xgMBh86lOf+rVf+7WP\nfvSja2trV65ceetb3/qjP/qjy2/0L03+L7rfCh1z+mRvcHt75cBuNNQjokNO2CUgxBvi1wB1\nlIzYMlbZdEQ9Y748nhVN0ysrrxIVJ50jAqnkRPMzpwfT2eozT87YuKxYWaG0beAc0vR6Qk2s\nIBiivjF5ZEhAmvSLnlcxpCxivI/WGh+UhRdDkc830ZWigEhVSSKIXkKBUAHn0LYwrKJoakqT\nF1xgY9E2CCkNV/XaDlbPIemjnCDtX0/5yRja2NTRoUikM2dff34VL2fU3cTrA6+ttW5cPffl\ny3/eyqRN5hvTM/t8SEDdq8ueDKzZSWb3nHvP2mDr5PUdRKQsqSy1rrWqqOgtPRKvwzmIqPdO\nkQc/2N6m5AVEw6D60HDwTFV/tao3nHt0Vt2Wpww0IlfbbrfzA2vGPnSqZedL0ctNW8ZYRrkm\n3ocw3N+9W8IQOGD+okm+kia3pRqgc4mX6vYo+DXn6igXmzZhWqzSlcjQGEOwxF2MASqgURcm\nPhBh3TpHNI1xomEc1BILNJCsW7fm3J4PPearTXvkgyptJu72LOVX6G/5VaW98gIcHBxMJpO7\n7777W9xemypqG0iwYH4SGMAiveNFuWJBGwSBSUSoUwpox533PIvcBRCpM+JD6b334eykcVG8\nM96yEYWoQutewqBE86hNk8Qucz4tBjtHm3vduk8ult3Ex4U3Q3OEbkqC4vYQzqbpNY8kPVf0\n3zQc/ts8ux0I4ifeT4adua3L/9186wd3zt2+e+etV5Nz++v//or9dxc4b/TszJzuyClFQEQ6\n0oSob/g6JxacajhSqQFYolxcDKwK7hKASWxEkca59Sk8lFQXHdTFJByWA3NkQI4JUQNml9CM\noASbL08kE2KzzCKIQYDEZWKHRVZ3w3/Xczt8u3In7373uz/ykY9ct75+29vetqCMLeCc++hH\nP/rOd77zG+1e1/Vf/uVf/vRP//RDDz30lre85Wd/9mf/6q/+ajKZ3LjNlStXHnvssV/4hV/4\n/u///nvuueeDH/ygqi4GXK5cufLggw++5RgPPvjgv3jABERVC0+8txrrNblc6EyPq11Rnz89\nFrDMjsgwTUI89N1hlFEXLGMcwhNV9ZWynsbYqQIwnZ9vbockLdfXmxOniqLP5awkIE3QHisK\nYsGXWPjJerQNKzhNTd4zRKJgVY5RmJWNGMtRKUYQK6CqoqqqIOii0a5KN4ytPM+AcQ7EZJ02\nDdU1JelLpO1sEAKMQZqYUHY+1f29OK5jB1X4GiYHABquaDnXq1dQ1//iiX0N4eWPupt4HeC1\ntdbNmp3crQdNd5KjK8W1vDU+1m2sZjQb89GRXLk6/WLrp8tj60az8/+99aM2EakqdJ1eu6rz\n+Y0fSMTU6/HpMynz5G0/uDN48cPeKMSCedeHM2kyCWFgmYF5lMttNwpx09mEqFP5/HR2qWnr\nEE45u2HtwHCf+e3To7fsXPSTyXQ+2zg6+KnzT+4dHT1V1xfb5olZdeQDQLutPwwBwCzGgTEJ\nUc4cVOdRZlGmIo3AMUBIDHvFoQ/7ITCw6qwhGlizZm3BplW91vony2oe43bi7s2zL0yn//vO\n7t+Mp7PwHQi6fgd49VbsHnrooYceeujpp5++Lsn9IkynxzFU18wc1Hds0igEXVQgjo0RltK5\ni9EiJbBEIYawqu9YE4lxXk4Pa6w7hklqz2rWO1sX1hMHUQHyeesTlsQWICMxa7zCVlRSO+Ww\nHsnkfi8WJ2HTy76znPSsYQsNqMskS7r7tzJH9NWq2k5c5jbhVkOcrbj7+pWsj6rZSk9dHk8b\nX+8elcwW+QzPFdFH2RDuVTTtY55Swtyz9HWpOBGnkAqcA8jaPGkKKJEyR4awwOZ+1JmtyCmp\ngohBGhGhZGmR1QEAmEiNESVu90EKX2HZVnaAXzJeVSC67LnSSxZSF68zvsUy60viF3/xF3/u\n537uiSeeKIrirrvuYmYARVF88pOffMc73nHq1Klvsu/ly5erqrq+SD3wwAPe+wsXLjzwwAPX\nt3HOvfe9773+zBBC6LpORBa7P/roo3/xF3/Rtu3999///ve//0W/7kVRJ4CoGsQ13U34UhYM\n21jIUTCZRxKXpwREYBATMcCgVjRAZyFMkqxVsUwqMo8xJz5fN/MQTqXpya4tNzYBKHOX5cKG\nesXVqkoY/dZrM6cosFZjVO8py8AMx9Qb6GS0uDYCMMDeB7aqGgyzslPRGEmxkKSzMQoRNCqg\ntBwJOO7lCKwFFHxsWlLVKF7sRLwEH5PCk0wPD6F5U6cxVG1SuAKrd+F4ehNU9HR0JGzo9jvo\n1afY/u3hZY66l+Mr3cTLgtfQWpe7taNu8vToMTXZpd6h1xOr3Ubpxl2ooLo9/L796RO9dPvc\nxtuPymcOLn8+79KwlnS6e+ZoWIhDMdTda5TfjuNmsfqOslS7zt53f//kmWdH07sLKo7fHYVw\npW0fGvT/8uDogUHPS2eJ6iiHVWXa5qRzM3IHMYx8OJUm1wfaLCEotqrybQe7jw9WG9WUeTfh\nk4U+UM3+sddbtEQmMbYiXsURlVEYOAzBqBomUUxDyA1nRF60U3XLehA6Uaeq1jhQZE6ZHAhA\no3qlbe/Is6fK2hKtWJsZvrtXPF5VjchbBr0T3wpD4LuKV29i9y/iR37kR0JYev+ePXs2EhMp\niyqElEFQ0kXpTgXCIAUpooECHAVAY0NQz8KNc8mYjwrxHFMbNpuszFGpnUfPIAaYtAxxBjEq\na144atVLNCm0rEFdTBKNmWvLaJM+8zTEhNkRwcANrL+0ZzdP3ZXkaelHB7OpbxzNstn5zdqU\nmY4N7SdxrBK8zLraGGcUaaQ0ZQElzJ6wFnBmjDKFeckyGCUaDsmsOF/0ygGAQJFJjVgSQyCh\n1FIZyIF1UTryUMO0pK8KyAIkYBJhkyPWaEYgWVbpRAALCCQeM2LpuJzzjcrM+m13YpdIkuTN\nb37zja/kef4TP/ET/+KOBwcH1tpebznF5Zzr9XqHh4c3brO9vf3e97538XPbth/5yEcGg8E7\n3vGOyWQyn8+ttR/84AdDCH/+53/+q7/6q3/4h3+Y39BBeFHUsWpPd4t4eDp8KcGzkzw/0fUI\nTw5l94DPVbxOnCxM15gWkUgAogqIVjReYtOIGDIqkopmjlPmaZBxPWlWhjbvRREBMhCraJqS\nS/aI+htbXJV6+SKShKJgPKLBEFkG59B1cBZRsHAKjtErWhUoVMGiQgTiNAbQov9KSgxCJBjV\nKAoSJVjiZV4vCmZVoRBhnYrQS2vTEdoWMYRofZuarA62R1InuQLEL+zc0nBFd66g16MzZ7+V\nYHj142WOupflO93Ey4TXylpHxFfmO0HrRouYdHE41Zk5NV+fJmVnkCVbDNmffHmAtfHOF/sn\nzqX7dbO+4lR38NzZ8XY6myug3pMxAKQscfWyEPHmNrZP3hP9j21vXKibx8oyIa5F3zIofmj1\nxDzGgWUAzlATJT/au308un98BJF/2DhxOFzNXZLcsLgUxgysZl13ZF0j6ggpUVSMk2RLYvBh\nDrLAPEYmBNVWlRTOciexjWqIlJRADgxogDqQJw3QKOqYomIe48KHXY4njhLCROS5uulZ87fj\nydCaqY93ZFnKfKnpLjXtD2+s3pG9rIJPr+HE7qGHHlqE3f7+/qVLlyCBvBciVgIpLVS9SKFg\nIlVRosXtbaHNQQhCnskySDgKeDKJyPJNb4MIzTtNKc/SRfGDQRlIjGlZTd1VhtrUcUhMcNHV\nJiQKlzRVcKmm/TKKC+F5lW3SwwvjznSdXuoZa1R5fJjP8jkPJrLXi5O93vk5n6hpUIcqVaxF\nrFWY95SPayWdhTdYK1F4lCni1xftYCCt82tiIotx0QrUBrswPhCkVpvFyOFyByx7rEsFkwCQ\nwNnYweQg87yyiQTAwBgoQ7pjbuzS5OCbXp5XaExIVb9+WOT68vSiLf/6r//6Yx/72IkTJ37/\n93+/3++LyMc//vF+v7/4hDvvvPN973vf5z//+YVL9wIviroeRmf8E06nm3IhoWqedCeqtaEZ\nOWpM9BXWp3S2xtAwMUggURVEhkhEE4kxScchemOeWV1fryt1TgBHGLbNX8fB+OAoYRLg1Ly8\ndT5Dr8jJCHRtZbixsgpVLUsYpizXNF1Wv0RhHbraG6lFJEbDRIBXZZBdDiRQNLwsYy+oPqKg\nJaKiU40sVtUAcG7ZZo2RjNHOq3P09VdXZEGhUG+FHVrvemSa3To53c5MO0Jyo9QJEa2t0XSi\nGxuUfYMS4GsKL3PUfQ+/yU28dvDyR92upyARJCKY2q5c2Z+6fLUzZ2a35axO1XDxXP9qdW5z\nuL5+8mgHqkRkhhtlv5+PM718SfZ2KUnRtTqZmFvP4dxt1B+gbfWf//ENP/Bv71hbfXDQr0V6\nzJuJc0T/bTZfmMOuGRsPDr7vwlPz1Y0L65vTGNcmowck/N3GiewGCzBLVBB1hOAjp2gVrJox\nD0l2Q+w7WzB3IikzQI6ikohSSsiNmVKsgzpiSzCEWRQDJtIgsKQpswJMqkAjAqAV8gRRBNUy\nihrMO8mYDdGp1F1rPREYeKaq/mY8/anTJ75/0HvZqLKv4cRuQSMC8OlPf/qRRx6h+28j1cWN\nihaNQAXRkgzISpEUBFZSQEhZGQBEACfUptGcneVxPpj0M8Cz6ua8brw0OaIBAy5ITzXvok9s\nJ9KKDoMlOC7BRoidxNSxD45yw7ud7xuTEdezeRPy+bTkNd8brANIxyN05ahgonaz26rygct6\nK5gnfm8ItjZZE2MsvEirCogjMkRKmOVYrzCsMO0hvujPmSxFzau+sAIgJY6WhEFKBFULCFEE\nlrnmjXvTookmnbh0cdLYQiPIIpaAWQ7VMcNmEH9sAstAfKm63fVXXqF+0fr6uve+qqqiKADE\nGMuy3Nx8saXzZDL53d/93d3d3Z/8yZ985zvfuVjdmHkweJ7K1O/3T5w48aIn4BdF3X1xEsmc\njU+lcc4UKxuv9q+dKLeQlIGdC81qvLrr7i1xVkCQkCIaCDTkUS/3+61DolTFWOb5rdPRVPJZ\nCBtN/eV+/2mb+LZbdTZhHifpPf7oYucjvKi2Krdn2d1Z1j86gmHNBzKZoOeMWYiVmDbL5nXF\nbJIYvLFeYY6HIZdQMACRyGyAyAwQQUiELBsijRIMqygxiwg1tTrHqkRg0ZdQtBFFYlUhHuLZ\n0Ux0oABTtKkJ5ddtb1083Df9Pm459+1e6lcRXuao+95+mZt4jeBljjpVPcJAeXs1XhCwlSYS\nHeQHh7l5aiB3DnuD/l0XMBQ7Z0pkNn3Hen5u3vEgJTIhszh5C52+1a6sIESZz3Q6put/+1mG\nlRU9PCiGK4V5QddSgAX/wBGdmI2v5X01RlWPfMh7vdNHh28arNSD512qg6IWCb3eevQzlQlz\njzlj2pxWj62sVwrvAxH12Uxi6FREkRhe6n+BHYmqClBGaVX6TIuJ54S404UXvHpFJ2qZFvWi\nVhRAUAlBU6ZGpIyxZ3jPe1FtRU4mbh7jI3uHfzsa/8ftzTf1ey/DvfE1nNi9CJFglwocyzF+\nfT69W/606CFGRANWKCsrpLUewq5DZXsXeyJUG1ZnuHSu3+XQGLlRoE540kur1AzqcOveLHZC\nVUYgdT5Sa4xVa9Nq6pOh75mMuYwhNLUfz6WXuTyDWEFVTUbJ4UE0GIoSxUSmGja2m2w6vDYW\n15dr1O1xOAcumOCAoGhFCsOOKDCOChQdhDEtXlgvo0WeBWVZzMmz2MV3XyjO5XGv5q0bG7mq\nLxiDo9D4mMPcUI0TqMIwRCERZMALlRMBAI3foCZ3/cVXZmYUt956a5Zljz322MMPPwzg8ccf\nZ+Y77rjjxm1U9Td/8zfX19c/+tGPLtbEBR599NGPf/zjv/Ebv9Hv9wHUdb2/v3+dnvaSMNqd\n9f89j0esssicxunUiF1rVig5aJELzG3tF8bmamU2kzgmsiAWxYkGF3PdKL9UJXfvSXHVrBWD\nlfWq7IleyvKvFYOEGCwgFEyapgeDwXqMrTGt6jjGL0xmXyP3ZpOerLvKDzOBG9VEWZpQjLIP\nHVhnYxQlr3pdlW45Bqfq2bCqgy7Su2CtA6BQZhBBQaqdIhjqRJPYOUBEVMFRTIxMsMT2erVA\nIrIUzIAuWbBsWMLCM1j1eALvhQHDwxWdzyl42Nf8pN3LHHU3cRN4JaKu1NRw1knmZMaIrADQ\nwbWuesrIqivupPXhZMzN7OSILq2kecAtnW91nCcDun2NztxCi2zyufPEL1wObALvv/43pkRe\nBcBO05yxJu31xkwX60bEJ8blaboJXK9gK3Dk/TxKLy/+6213/Q/PPVOlqbNu4Nt/XN14enX9\n0PsN6xxjJiEhaoiOFffVi4rIQkyDiRyjjhREGiAjakWDarcQ3FjI8KtWISxEXReJWsKkQFAV\nhWMeLhwgYTrVRuSrVbU+7H/64GgW4tuGg5Rf8vb5XcPrJ7FTIlYw+PmMZaFFsXBbWnRmEQNL\n4JiICaxQIajXWskJ5Y31g2CSGEdFJkBNHNwsj2emvXHkZm8tr1ILJSUmxbBjClbTDmCJNXNG\nZNQwN0KJdU7L6ggTk5tQJ32QatT68Gh1/6pTDdQpYERJyfnRynxYpsNh4doklgnYH4oQkC5i\niIiqKLnhhChYkqCDBt6iesGDDSmFJiuzusfRkLCS4HqdXgExy+l2BQmMEmg5KkcASFnath0g\nR2yx8MhYJHPRY8Evvk6VIAMwyEAj9HrR7vkDWb77Sik2FEXxwz/8w3/2Z3+2sbHBzH/8x3/8\nQz/0Q6urqwA++9nPdl33rne960tf+tIzzzzznve852tf+9r1Hc+cOXP//ffv7u5++MMf/rEf\n+zFr7Sc/+cnTp09f14V/SfRlr5Dh4ssKFkmRjrKxAdbqtVlSRpCAN+SZXI5Kux3hPPUywfmB\n2evljtTG0Xq8OMbmoe29aV5/eWX9uf5QjSXAEdVRcmYmnqT5idHBTlEEkfWQuJZXG+6a5hm7\nqfnYO3OymiHRbKYMb/MUZLiaklChxrMoL21BCGBox8bE6KAQ9cZEIAGgUEJUJRHP5A13bC0o\ngbJhJgKBwV6kjhRV+pZTYkOEEJBliAIQZ+A5JBrECJHQsk3hei/1GGCd7uzoyVO0tv5dDoKX\nHS9z1N3ETeCViDov7Wb3NCMosUhggsAYdHPaGMXs1Gh0x26UlS2frO+vXB4qTct2t6+bK2f6\nq/fAJagrZClcAuvwwpax+vYQ/avzsowxZ7Ph7C1ZykBOsfLThub77fysHvXaI2ND4+t1uRj4\ntk3vL4dR0pyMbiuatTLqgQ+r1gDYX1v7E/N953x3wvDY2v9mXBU1sxygGdiAiOBV+sYY4irG\nJkZm9iIZ8YInYYG5CBPVogtR14Uk/6KIEpY3vqUIBAOdCIiiKkOfKmvuYd1aJnJEa9aMJf7z\nvPzB4eBvx5NO5R2rK+57eY98/SR2LmrgkIilZd1Al7NkCwonQSGRVAgENMZH0sBg5Wg1CRoY\nedCtJpBSZFenQhSFqJO282BRCr1B3TNiAOwPsnPVkWqrC1MGYkUkGCECReqI/byrPEdqsxXv\nCgptrI/6zTUiLhMXY0iCZi2guZqEIaverMxta083tphlDWNCThXZogi0uMGT4c7QSkTrNPXU\nOMgLWqoIzqOBiZaUhY7f1OPkLiQLTgkAIVh6vjlntazcndEUrBCPWMNkS9U6Edh0aVDBx1W6\n5cnl41MsywN4XtDulWvFAnj/+9/PzL/zO78jIg8//PDP/MzPLF7/3Oc+V5blu971rvPnz6vq\nhz/84Rv3+sAHPvDud7/7wx/+8J/8yZ986EMfYuYHH3zwgx/84NcLft4IQiREgrAu0yYFAsW9\n7Mgjnqq2Sts2NogYw8FoTHSUkB92vfODRIEIblGUSAddc67a//vtM3dOyc/zqbWzJE4T71Xb\nKJmla0WRNEXSNInkG2XS2s5i8szgrPPriVzbDLPZYHs42x8hLVofYRCdT/N+PWFiK9xBPCsD\nTqVlEwmWoRGBORIBCIARCcQShYjEWrXOEAUR6rwe60strrtjckAdZa6yRnBpCusQmuihADlo\nJKgQw/VNVCTHOgaq8FOEFhBwgsSlKEu89hM7vLxRdxM3scDLGXVXyp20gdPKaqtEDFUFw4O4\nH/bKbiObxvNWT/TvyfNbtBnGMBVp1kZ+0ww5l3bnWTk/k+2teiOpteztfZXiZn/tdmcKadsr\nBwd/0V9LxtPMcBvlqKvfko3usrNpN96/+F9GOtlrjmzd297buNbvix3UfGajaXd6mLlR0Vzi\nclplD4xxtkfbi6N1ICp616i/CxLS0HbWkAWqGAtmBirR7SS1QCfaYwbgiDLmVgRALaqqstDC\nPTbI1GPqIaBxOaO+hAKdglQdE0SnMX5hOl+zpm/MduISYsc8i+F83ZzL0r8dTwtjHh4OvneZ\nHX2r8r+vYnz6059+z3ve8ys/9NYP/sBbspgsxsywdC9VJdJFrUohJnYcAa1NBwDgzsqSZxFP\nqAws/MLo9KDXnye1ks+61YPenF1h9Q7v2mgjABv4lt32xPxKmSdknMIzF8yp82VNa15sx4fO\nE1G/s2smq4rq8pRtX8djmxBR0ZZrja94UDpDBAOYkLXpKItNxUilEB5dGl6buxWiZV1uUeMd\nGF6vKevUKh0VqK7PjEoLMyCznlW9E1fP2eiWFbtlIhZTORzxA2CnBgAEsIRj7UTN49VD95DH\nGhiuBxVAEGq4DL4BGbADO8Qai9O4FCumb1iWUwEZvO1/+15c7VcLFlH3H/7jyf/5f1ph9Vhy\nX5eeuUqgiFzzYdtfb1Ya03kTSurVZmutTZ7u87VekRslib3WDuv86by3n2/cOrsv1wt3zP3c\nFYr84vDqUbqv4NRmpaR9n3z/QRl0ezV2vTA73++TbCYhyYOVZO9WbRicl1NhyZswcWwhW+2c\nNQYwKVdWEgms0rIDNI8hEicQIV5MK6QxeGYl6lzSWadERLAirm1dstCzBscYkkSu3wBExHu3\nstJzSZxUPtmixEIQq06R6NoJPzw7OEfp6pJ20+yj2ofNAIJ0cK7q3b1m7v1WtSpvYhF1v/3b\nv/0rv/Irr/Sx3MS/Fiyi7u3vf8Od7+mf9v8kMALmpRQqGLGlXh5OvnX/7qdXVk6c+A+39k+n\nRMb74ZNP9Iv+ypq5lu8fls8kti+z8R4uFfc+uI4zfDDaHOWrvXM7m2f/C9vt06dCe0WkDeEg\n+Mml6RO39k7EWD82udy2OzUNC3ZnJs0bdzAxfaXeM5ubB2tZnS2eOTX4cdVeafMH58l9gQcC\njEO8NUtbiU/X7SSEnJmARmRgTY9NB2xZC9IqyrXOQ2EIEahjbGWp4i6Le+XxE/tSnnVhx6nL\nLOOF1RUQYAiOaGhtFWPGvJW4hNirzII8MOxtGHsqSwj4X09s3f2N1KO+Y7x+KnY3TPEQoAyO\nCl6U7JadMmUxiXJt60jqxHiORri2IQuFS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8ruBLdfSAFjE50CGim3WkdA0UvC0MW+F/FuryvOXxmssPRTXydagmNL6rmrnGf9Su5vW2se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\nW9g4RG9Tp04F0NnZWShFuGPHjiMbS3+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c2M7tfQ+iyyPTDDSHfATYI9KH95UBQt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\nTK69qLX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UTUQ1SrlxhIJc/jsNeX5GCD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\nsccA4I477qA1Bord1LZ69WrlLYZhAKBjx46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i/PjxAFBYWFhrzfHx8SzL1qwqNTUVAHr06FG1sPIyLS3twoULANC7d++q7/bs2dPb/l0l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\naURyYlUo94oAAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figS1_combined <- ggarrange(ind_cts_mse_fnr, ind_cts_stab_fnr, ind_bin_ROC_fnr, ind_bin_stab_fnr, \n",
+    "                            toe_cts_mse_fnr, toe_cts_stab_fnr, toe_bin_ROC_fnr, toe_bin_stab_fnr, \n",
+    "                            block_cts_mse_fnr, block_cts_stab_fnr, block_bin_ROC_fnr, block_bin_stab_fnr,       \n",
+    "                            nrow=3, ncol = 4, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "figS1_combined\n",
+    "\n",
+    "ggsave(\"../figures_combined/figS1_combined.png\", figS1_combined, dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 2: best method chosen by Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe5_cts_stab <- ggplot(toe_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe9_cts_stab <- ggplot(toe_cts_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.9\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0,1) \n",
+    "\n",
+    "block5_cts_stab <- ggplot(block_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block1_cts_stab <- ggplot(block_cts_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.1\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe5_cts_stab, toe9_cts_stab, block5_cts_stab, block1_cts_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe5_bin_stab <- ggplot(toe_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe9_bin_stab <- ggplot(toe_bin_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.9 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0,1) \n",
+    "\n",
+    "block5_bin_stab <- ggplot(block_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block1_bin_stab <- ggplot(block_bin_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.1 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe5_bin_stab, toe9_bin_stab, block5_bin_stab, block1_bin_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 11 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig2_combined <- ggarrange(toe5_cts_stab + rremove(\"x.title\"), toe9_cts_stab + rremove(\"x.title\"),  \n",
+    "          block5_cts_stab + rremove(\"x.title\"), block1_cts_stab + rremove(\"x.title\"),       \n",
+    "          toe5_bin_stab + rremove(\"x.title\"), toe9_bin_stab + rremove(\"x.title\"), \n",
+    "          block5_bin_stab, block1_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "\n",
+    "ggsave(\"../figures_combined/fig2_combined.png\", fig2_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S2: best method chosen by Stability in easy Toeplitz correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe1_cts_stab <- ggplot(toe_cts_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.1\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe3_cts_stab <- ggplot(toe_cts_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.3\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.3, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe5_cts_stab <- ggplot(toe_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe7_cts_stab <- ggplot(toe_cts_sub7, aes(fill=method, y=Stab, x=N)) + ylim(0, 1) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.7\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe1_cts_stab, toe3_cts_stab, toe5_cts_stab, toe7_cts_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe1_bin_stab <- ggplot(toe_bin_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.1 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe3_bin_stab <- ggplot(toe_bin_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.3 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.3, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe5_bin_stab <- ggplot(toe_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe7_bin_stab <- ggplot(toe_bin_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.7 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe1_bin_stab, toe3_bin_stab, toe5_bin_stab, toe7_bin_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS2_combined <- ggarrange(toe1_cts_stab + rremove(\"x.title\"), toe3_cts_stab + rremove(\"x.title\"),  \n",
+    "          toe5_cts_stab + rremove(\"x.title\"), toe7_cts_stab + rremove(\"x.title\"),       \n",
+    "          toe1_bin_stab + rremove(\"x.title\"), toe3_bin_stab + rremove(\"x.title\"), \n",
+    "          toe5_bin_stab, toe7_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/figS2_combined.png\", figS2_combined, dpi = 300, width = 6.5, height = 8)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S3: best method chosen by Stability in easy Block correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block3_cts_stab <- ggplot(block_cts_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.3\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block5_cts_stab <- ggplot(block_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block7_cts_stab <- ggplot(block_cts_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.7\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block9_cts_stab <- ggplot(block_cts_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.9\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "# ggarrange(block9_cts_stab, block7_cts_stab, block5_cts_stab, block3_cts_stab,    \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block3_bin_stab <- ggplot(block_bin_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.3 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block5_bin_stab <- ggplot(block_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block7_bin_stab <- ggplot(block_bin_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.7 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block9_bin_stab <- ggplot(block_bin_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.9 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "# ggarrange(block9_bin_stab, block7_bin_stab, block5_bin_stab, block3_bin_stab,    \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS3_combined <- ggarrange(block9_cts_stab + rremove(\"x.title\"), block7_cts_stab + rremove(\"x.title\"),  \n",
+    "          block5_cts_stab + rremove(\"x.title\"), block3_cts_stab + rremove(\"x.title\"),       \n",
+    "          block9_bin_stab + rremove(\"x.title\"), block7_bin_stab + rremove(\"x.title\"), \n",
+    "          block5_bin_stab, block3_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/figS3_combined.png\", figS3_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 3: best method based on MSE/AUC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe9_cts_mse <- ggplot(toe_cts_sub9, aes(fill=method, y=MSE_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz 0.9\") + xlab('') + ylab(\"MSE\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block1_cts_mse <- ggplot(block_cts_sub1, aes(fill=method, y=MSE_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ggtitle(\"Block 0.1\") + ylab(\"MSE\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "toe9_cts_FNR <- ggplot(toe_cts_sub9, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_cts_FNR <- ggplot(block_cts_sub1, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "toe9_cts_FPR <- ggplot(toe_cts_sub9, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_cts_FPR <- ggplot(block_cts_sub1, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe9_cts_mse, block1_cts_mse, toe9_cts_FNR, block1_cts_FNR, toe9_cts_FPR, block1_cts_FPR,         \n",
+    "#   nrow=3, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe9_bin_ROC <- ggplot(toe_bin_sub9, aes(fill=method, y=ROC_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz 0.9 (Binary)\") + xlab('') + ylab(\"AUC\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block1_bin_ROC <- ggplot(block_bin_sub1, aes(fill=method, y=ROC_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ggtitle(\"Block 0.1 (Binary)\") + ylab(\"AUC\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "toe9_bin_FNR <- ggplot(toe_bin_sub9, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_bin_FNR <- ggplot(block_bin_sub1, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "toe9_bin_FPR <- ggplot(toe_bin_sub9, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_bin_FPR <- ggplot(block_bin_sub1, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe9_bin_ROC, block1_bin_ROC, toe9_bin_FNR, block1_bin_FNR, toe9_bin_FPR, block1_bin_FPR,         \n",
+    "#   nrow=3, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 1 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 1 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 11 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig3_combined <- ggarrange(toe9_cts_mse, block1_cts_mse, toe9_bin_ROC, block1_bin_ROC,\n",
+    "                           toe9_cts_FNR, block1_cts_FNR, toe9_bin_FNR, block1_bin_FNR, \n",
+    "                           toe9_cts_FPR, block1_cts_FPR, toe9_bin_FPR, block1_bin_FPR,\n",
+    "                           nrow=3, ncol = 4, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/fig3_combined.png\", fig3_combined, dpi = 300, width = 10, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S4: check feature correlation impact (continuous, selected case)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"lasso: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"elnet: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"rf: n: 100 p: 1000 rou: 0\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>6</li><li>7</li><li>8</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 7\n",
+       "\\item 8\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 1\n",
+       "2. 2\n",
+       "3. 3\n",
+       "4. 6\n",
+       "5. 7\n",
+       "6. 8\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 1 2 3 6 7 8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load results for 4 methods\n",
+    "load('../sim_data/independent_Lasso.RData')\n",
+    "load('../sim_data/independent_Elnet.RData')\n",
+    "load('../sim_data/independent_compLasso.RData')\n",
+    "load('../sim_data/independent_RF.RData')\n",
+    "\n",
+    "# check index\n",
+    "b = 14\n",
+    "print(paste0('lasso: ', 'n: ', results_ind_lasso[[b]]$n, ' p: ', results_ind_lasso[[b]]$p, \n",
+    "            ' rou: ', results_ind_lasso[[b]]$rou))\n",
+    "print(paste0('elnet: ', 'n: ', results_ind_elnet[[b]]$n, ' p: ', results_ind_elnet[[b]]$p, \n",
+    "            ' rou: ', results_ind_elnet[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_ind_compLasso[[b]]$n, ' p: ', results_ind_compLasso[[b]]$p, \n",
+    "            ' rou: ', results_ind_compLasso[[b]]$rou))\n",
+    "print(paste0('rf: ', 'n: ', results_ind_rf[[b]]$n, ' p: ', results_ind_rf[[b]]$p, \n",
+    "            ' rou: ', results_ind_rf[[b]]$rou))\n",
+    "\n",
+    "# load simulated data\n",
+    "load('../sim_data/sim_independent_P_1000_N_100.RData', dat <- new.env())\n",
+    "i = 1\n",
+    "sub = dat$sim_array[[i]]\n",
+    "coef = sub$beta\n",
+    "coef.true = which(coef != 0)\n",
+    "coef.true\n",
+    "\n",
+    "# summarize results\n",
+    "table_ind = data.frame(matrix(0, nrow=1000, ncol=6))\n",
+    "colnames(table_ind) = c('FeatureIndex', 'FeatureTrue', 'Lasso', 'Elnet', 'compLasso', 'RF')\n",
+    "table_ind$FeatureIndex = seq(1, 1000, 1)\n",
+    "table_ind$FeatureTrue[coef.true] =  1\n",
+    "\n",
+    "table_ind$Lasso = colMeans(results_ind_lasso[[b]]$Stab.table)\n",
+    "table_ind$Elnet = colMeans(results_ind_elnet[[b]]$Stab.table)\n",
+    "table_ind$compLasso = colMeans(results_ind_compLasso[[b]]$Stab.table)\n",
+    "table_ind$RF = colMeans(results_ind_rf[[b]]$Stab.table)\n",
+    "\n",
+    "# zoom in first 15 features\n",
+    "ind_cts_Lasso <- ggplot(table_ind, aes(x=FeatureIndex, y=Lasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") + \n",
+    "             labs(title='Lasso (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_Elnet <- ggplot(table_ind, aes(x=FeatureIndex, y=Elnet, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Elastic Net (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_compLasso <- ggplot(table_ind, aes(x=FeatureIndex, y=compLasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Compositional Lasso (Ind)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_RF <- ggplot(table_ind, aes(x=FeatureIndex, y=RF, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Random Forests (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"lasso: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"elnet: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0.9\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>6</li><li>7</li><li>8</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 7\n",
+       "\\item 8\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 1\n",
+       "2. 2\n",
+       "3. 3\n",
+       "4. 6\n",
+       "5. 7\n",
+       "6. 8\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 1 2 3 6 7 8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load results for 4 methods\n",
+    "load('../sim_data/toe_Lasso.RData')\n",
+    "load('../sim_data/toe_Elnet.RData')\n",
+    "load('../sim_data/toe_compLasso.RData')\n",
+    "load('../sim_data/toe_RF.RData')\n",
+    "\n",
+    "# check index\n",
+    "b = 78\n",
+    "print(paste0('lasso: ', 'n: ', results_toe_lasso[[b]]$n, ' p: ', results_toe_lasso[[b]]$p, \n",
+    "            ' rou: ', results_toe_lasso[[b]]$rou))\n",
+    "print(paste0('elnet: ', 'n: ', results_toe_elnet[[b]]$n, ' p: ', results_toe_elnet[[b]]$p, \n",
+    "            ' rou: ', results_toe_elnet[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_toe_compLasso[[b]]$n, ' p: ', results_toe_compLasso[[b]]$p, \n",
+    "            ' rou: ', results_toe_compLasso[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_toe_rf[[b]]$n, ' p: ', results_toe_rf[[b]]$p, \n",
+    "            ' rou: ', results_toe_rf[[b]]$rou))\n",
+    "\n",
+    "# load simulated data\n",
+    "load('../sim_data/sim_toeplitz_corr0.9P_1000_N_100.RData', dat <- new.env())\n",
+    "i = 1\n",
+    "sub = dat$sim_array[[i]]\n",
+    "coef = sub$beta\n",
+    "coef.true = which(coef != 0)\n",
+    "coef.true\n",
+    "\n",
+    "# summarize results\n",
+    "table_toe = data.frame(matrix(0, nrow=1000, ncol=6))\n",
+    "colnames(table_toe) = c('FeatureIndex', 'FeatureTrue', 'Lasso', 'Elnet', 'compLasso', 'RF')\n",
+    "table_toe$FeatureIndex = seq(1, 1000, 1)\n",
+    "table_toe$FeatureTrue[coef.true] =  1\n",
+    "\n",
+    "table_toe$Lasso = colMeans(results_toe_lasso[[b]]$Stab.table)\n",
+    "table_toe$Elnet = colMeans(results_toe_elnet[[b]]$Stab.table)\n",
+    "table_toe$compLasso = colMeans(results_toe_compLasso[[b]]$Stab.table)\n",
+    "table_toe$RF = colMeans(results_toe_rf[[b]]$Stab.table)\n",
+    "\n",
+    "# zoom in first 15 features\n",
+    "toe_cts_Lasso <- ggplot(table_toe, aes(x=FeatureIndex, y=Lasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") + \n",
+    "             labs(title='Lasso (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_Elnet <- ggplot(table_toe, aes(x=FeatureIndex, y=Elnet, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Elastic Net (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_compLasso <- ggplot(table_toe, aes(x=FeatureIndex, y=compLasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Compositional Lasso (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_RF <- ggplot(table_toe, aes(x=FeatureIndex, y=RF, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Random Forests (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS4_combined <- ggarrange(ind_cts_Lasso, toe_cts_Lasso, \n",
+    "                       ind_cts_Elnet, toe_cts_Elnet,\n",
+    "                       ind_cts_compLasso, toe_cts_compLasso,\n",
+    "                       ind_cts_RF, toe_cts_RF,      \n",
+    "                       nrow=4, ncol = 2, align = \"hv\", labels = \"AUTO\", \n",
+    "                       common.legend = T, legend = \"bottom\")\n",
+    "\n",
+    "\n",
+    "ggsave(\"../figures_combined/figS4_combined.png\", figS4_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Supplementary Figure 5: number of features (continuous, selected case)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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axZM1//cjgcZrOZIGswGDiOq66u9tfZqdVqo9Fot9stFotfggghvV7P8/z169f9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dWFhoNpudSshxXFhYWEVFRUlJichuIhVCCLl793EC4t4AACAASURBVK7IszqdTqvV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\n19NK/+qtmpcnKtNmQgqn5o5xpUKIO944tn4Mc5ldf7Hia94/hBUil8uZjyB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w4cM/+OAD608gP4vFMnv27Pnz52u1Wuvt6enps2fPrvWC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\n",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind_num_select <- ggplot(ind_cts, aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Independent\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe1_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.1,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.1\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe5_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.5,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.5\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe9_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.9,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.9\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "block9_num_select <- ggplot(block_cts[block_cts$Corr %in% 0.9,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Block 0.9\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "block1_num_select <- ggplot(block_cts[block_cts$Corr %in% 0.1,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Block 0.1\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "fig_cts_num_select <- ggarrange(ind_num_select, toe1_num_select, \n",
+    "                       toe5_num_select, toe9_num_select, \n",
+    "                       block9_num_select, block1_num_select,    \n",
+    "                       nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", \n",
+    "                       common.legend = T, legend = \"bottom\") \n",
+    "fig_cts_num_select\n",
+    "ggsave(\"../figures_combined/fig_cts_num_select.png\", fig_cts_num_select, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/0.1_sim_ind_lasso_binary_update.ipynb b/simulations/notebooks_sim_bin/0.1_sim_ind_lasso_binary_update.ipynb
new file mode 100644
index 0000000..7532ddd
--- /dev/null
+++ b/simulations/notebooks_sim_bin/0.1_sim_ind_lasso_binary_update.ipynb
@@ -0,0 +1,683 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>3.69 ( 0.25 )</td><td>2.5 ( 0.12 )</td><td>0.74 ( 0.01 )</td><td>0.33</td><td> 6.19</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>7.16 ( 0.37 )</td><td>0.64 ( 0.08 )</td><td>0.85 ( 0.01 )</td><td>0.35</td><td>11.52</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>5.21 ( 0.21 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.53</td><td>10.21</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>2.25 ( 0.11 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.8</td><td> 7.25</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>5.13 ( 0.34 )</td><td>2.93 ( 0.1 )</td><td>0.73 ( 0.01 )</td><td>0.25</td><td> 7.20</td><td>0.50</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>8.22 ( 0.44 )</td><td>1.03 ( 0.09 )</td><td>0.83 ( 0.01 )</td><td>0.32</td><td>12.19</td><td>0.54</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 3.69 ( 0.25 ) & 2.5 ( 0.12 ) & 0.74 ( 0.01 ) & 0.33 &  6.19 & 0.37\\\\\n",
+       "\t2 & 100 & 50 & 0 & 7.16 ( 0.37 ) & 0.64 ( 0.08 ) & 0.85 ( 0.01 ) & 0.35 & 11.52 & 0.48\\\\\n",
+       "\t3 & 500 & 50 & 0 & 5.21 ( 0.21 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.53 & 10.21 & 0.39\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 2.25 ( 0.11 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.8 &  7.25 & 0.15\\\\\n",
+       "\t5 & 50 & 100 & 0 & 5.13 ( 0.34 ) & 2.93 ( 0.1 ) & 0.73 ( 0.01 ) & 0.25 &  7.20 & 0.50\\\\\n",
+       "\t6 & 100 & 100 & 0 & 8.22 ( 0.44 ) & 1.03 ( 0.09 ) & 0.83 ( 0.01 ) & 0.32 & 12.19 & 0.54\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 3.69 ( 0.25 ) | 2.5 ( 0.12 ) | 0.74 ( 0.01 ) | 0.33 |  6.19 | 0.37 |\n",
+       "| 2 | 100 | 50 | 0 | 7.16 ( 0.37 ) | 0.64 ( 0.08 ) | 0.85 ( 0.01 ) | 0.35 | 11.52 | 0.48 |\n",
+       "| 3 | 500 | 50 | 0 | 5.21 ( 0.21 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.53 | 10.21 | 0.39 |\n",
+       "| 4 | 1000 | 50 | 0 | 2.25 ( 0.11 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.8 |  7.25 | 0.15 |\n",
+       "| 5 | 50 | 100 | 0 | 5.13 ( 0.34 ) | 2.93 ( 0.1 ) | 0.73 ( 0.01 ) | 0.25 |  7.20 | 0.50 |\n",
+       "| 6 | 100 | 100 | 0 | 8.22 ( 0.44 ) | 1.03 ( 0.09 ) | 0.83 ( 0.01 ) | 0.32 | 12.19 | 0.54 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0   3.69 ( 0.25 ) 2.5 ( 0.12 )  0.74 ( 0.01 ) 0.33  6.19      0.37\n",
+       "2 100  50  0   7.16 ( 0.37 ) 0.64 ( 0.08 ) 0.85 ( 0.01 ) 0.35 11.52      0.48\n",
+       "3 500  50  0   5.21 ( 0.21 ) 0 ( 0 )       0.93 ( 0 )    0.53 10.21      0.39\n",
+       "4 1000 50  0   2.25 ( 0.11 ) 0 ( 0 )       0.93 ( 0 )    0.8   7.25      0.15\n",
+       "5 50   100 0   5.13 ( 0.34 ) 2.93 ( 0.1 )  0.73 ( 0.01 ) 0.25  7.20      0.50\n",
+       "6 100  100 0   8.22 ( 0.44 ) 1.03 ( 0.09 ) 0.83 ( 0.01 ) 0.32 12.19      0.54"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>23.83 ( 0.69 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.2</td><td>28.83</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>11.38 ( 0.41 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.36</td><td>16.38</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>9.77 ( 0.24 )</td><td>4.03 ( 0.08 )</td><td>0.67 ( 0.01 )</td><td>0.09</td><td>10.74</td><td>0.81</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>11.91 ( 0.8 )</td><td>2.32 ( 0.1 )</td><td>0.78 ( 0.01 )</td><td>0.2</td><td>14.59</td><td>0.66</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>30.84 ( 0.92 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.16</td><td>35.84</td><td>0.82</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>19.96 ( 0.61 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.24</td><td>24.96</td><td>0.74</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 23.83 ( 0.69 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.2 & 28.83 & 0.78\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 11.38 ( 0.41 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.36 & 16.38 & 0.60\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 9.77 ( 0.24 ) & 4.03 ( 0.08 ) & 0.67 ( 0.01 ) & 0.09 & 10.74 & 0.81\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 11.91 ( 0.8 ) & 2.32 ( 0.1 ) & 0.78 ( 0.01 ) & 0.2 & 14.59 & 0.66\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 30.84 ( 0.92 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.16 & 35.84 & 0.82\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 19.96 ( 0.61 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.24 & 24.96 & 0.74\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 23.83 ( 0.69 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.2 | 28.83 | 0.78 |\n",
+       "| 12 | 1000 | 500 | 0 | 11.38 ( 0.41 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.36 | 16.38 | 0.60 |\n",
+       "| 13 | 50 | 1000 | 0 | 9.77 ( 0.24 ) | 4.03 ( 0.08 ) | 0.67 ( 0.01 ) | 0.09 | 10.74 | 0.81 |\n",
+       "| 14 | 100 | 1000 | 0 | 11.91 ( 0.8 ) | 2.32 ( 0.1 ) | 0.78 ( 0.01 ) | 0.2 | 14.59 | 0.66 |\n",
+       "| 15 | 500 | 1000 | 0 | 30.84 ( 0.92 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.16 | 35.84 | 0.82 |\n",
+       "| 16 | 1000 | 1000 | 0 | 19.96 ( 0.61 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.24 | 24.96 | 0.74 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "11 500  500  0   23.83 ( 0.69 ) 0 ( 0 )       0.92 ( 0 )    0.2  28.83     \n",
+       "12 1000 500  0   11.38 ( 0.41 ) 0 ( 0 )       0.93 ( 0 )    0.36 16.38     \n",
+       "13 50   1000 0   9.77 ( 0.24 )  4.03 ( 0.08 ) 0.67 ( 0.01 ) 0.09 10.74     \n",
+       "14 100  1000 0   11.91 ( 0.8 )  2.32 ( 0.1 )  0.78 ( 0.01 ) 0.2  14.59     \n",
+       "15 500  1000 0   30.84 ( 0.92 ) 0 ( 0 )       0.91 ( 0 )    0.16 35.84     \n",
+       "16 1000 1000 0   19.96 ( 0.61 ) 0 ( 0 )       0.93 ( 0 )    0.24 24.96     \n",
+       "   FDR \n",
+       "11 0.78\n",
+       "12 0.60\n",
+       "13 0.81\n",
+       "14 0.66\n",
+       "15 0.82\n",
+       "16 0.74"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.33</td><td>0.74 ( 0.01 )</td><td>3.69 ( 0.25 )</td><td>2.5 ( 0.12 ) </td><td> 6.19</td><td>0.37</td><td>0.74</td><td>3.69</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.35</td><td>0.85 ( 0.01 )</td><td>7.16 ( 0.37 )</td><td>0.64 ( 0.08 )</td><td>11.52</td><td>0.48</td><td>0.85</td><td>7.16</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.53</td><td>0.93 ( 0 )   </td><td>5.21 ( 0.21 )</td><td>0 ( 0 )      </td><td>10.21</td><td>0.39</td><td>0.93</td><td>5.21</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.80</td><td>0.93 ( 0 )   </td><td>2.25 ( 0.11 )</td><td>0 ( 0 )      </td><td> 7.25</td><td>0.15</td><td>0.93</td><td>2.25</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.25</td><td>0.73 ( 0.01 )</td><td>5.13 ( 0.34 )</td><td>2.93 ( 0.1 ) </td><td> 7.20</td><td>0.5 </td><td>0.73</td><td>5.13</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.32</td><td>0.83 ( 0.01 )</td><td>8.22 ( 0.44 )</td><td>1.03 ( 0.09 )</td><td>12.19</td><td>0.54</td><td>0.83</td><td>8.22</td><td>1.03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.33 & 0.74 ( 0.01 ) & 3.69 ( 0.25 ) & 2.5 ( 0.12 )  &  6.19 & 0.37 & 0.74 & 3.69 & 2.50\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.35 & 0.85 ( 0.01 ) & 7.16 ( 0.37 ) & 0.64 ( 0.08 ) & 11.52 & 0.48 & 0.85 & 7.16 & 0.64\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.53 & 0.93 ( 0 )    & 5.21 ( 0.21 ) & 0 ( 0 )       & 10.21 & 0.39 & 0.93 & 5.21 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.80 & 0.93 ( 0 )    & 2.25 ( 0.11 ) & 0 ( 0 )       &  7.25 & 0.15 & 0.93 & 2.25 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.25 & 0.73 ( 0.01 ) & 5.13 ( 0.34 ) & 2.93 ( 0.1 )  &  7.20 & 0.5  & 0.73 & 5.13 & 2.93\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.32 & 0.83 ( 0.01 ) & 8.22 ( 0.44 ) & 1.03 ( 0.09 ) & 12.19 & 0.54 & 0.83 & 8.22 & 1.03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.33 | 0.74 ( 0.01 ) | 3.69 ( 0.25 ) | 2.5 ( 0.12 )  |  6.19 | 0.37 | 0.74 | 3.69 | 2.50 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.35 | 0.85 ( 0.01 ) | 7.16 ( 0.37 ) | 0.64 ( 0.08 ) | 11.52 | 0.48 | 0.85 | 7.16 | 0.64 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.53 | 0.93 ( 0 )    | 5.21 ( 0.21 ) | 0 ( 0 )       | 10.21 | 0.39 | 0.93 | 5.21 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.80 | 0.93 ( 0 )    | 2.25 ( 0.11 ) | 0 ( 0 )       |  7.25 | 0.15 | 0.93 | 2.25 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.25 | 0.73 ( 0.01 ) | 5.13 ( 0.34 ) | 2.93 ( 0.1 )  |  7.20 | 0.5  | 0.73 | 5.13 | 2.93 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.32 | 0.83 ( 0.01 ) | 8.22 ( 0.44 ) | 1.03 ( 0.09 ) | 12.19 | 0.54 | 0.83 | 8.22 | 1.03 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0    1.00  0.33 0.74 ( 0.01 ) 3.69 ( 0.25 ) 2.5 ( 0.12 )   6.19     \n",
+       "2  100  50 0    0.50  0.35 0.85 ( 0.01 ) 7.16 ( 0.37 ) 0.64 ( 0.08 ) 11.52     \n",
+       "3  500  50 0    0.10  0.53 0.93 ( 0 )    5.21 ( 0.21 ) 0 ( 0 )       10.21     \n",
+       "4 1000  50 0    0.05  0.80 0.93 ( 0 )    2.25 ( 0.11 ) 0 ( 0 )        7.25     \n",
+       "5   50 100 0    2.00  0.25 0.73 ( 0.01 ) 5.13 ( 0.34 ) 2.93 ( 0.1 )   7.20     \n",
+       "6  100 100 0    1.00  0.32 0.83 ( 0.01 ) 8.22 ( 0.44 ) 1.03 ( 0.09 ) 12.19     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.37 0.74     3.69    2.50   \n",
+       "2 0.48 0.85     7.16    0.64   \n",
+       "3 0.39 0.93     5.21    0.00   \n",
+       "4 0.15 0.93     2.25    0.00   \n",
+       "5 0.5  0.73     5.13    2.93   \n",
+       "6 0.54 0.83     8.22    1.03   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.20</td><td>0.92 ( 0 )   </td><td>23.83 ( 0.69 )</td><td>0 ( 0 )      </td><td>28.83</td><td>0.78</td><td>0.92</td><td>23.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.38 ( 0.41 )</td><td>0 ( 0 )      </td><td>16.38</td><td>0.6 </td><td>0.93</td><td>11.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.09</td><td>0.67 ( 0.01 )</td><td>9.77 ( 0.24 ) </td><td>4.03 ( 0.08 )</td><td>10.74</td><td>0.81</td><td>0.67</td><td> 9.77</td><td>4.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>11.91 ( 0.8 ) </td><td>2.32 ( 0.1 ) </td><td>14.59</td><td>0.66</td><td>0.78</td><td>11.91</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.16</td><td>0.91 ( 0 )   </td><td>30.84 ( 0.92 )</td><td>0 ( 0 )      </td><td>35.84</td><td>0.82</td><td>0.91</td><td>30.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.24</td><td>0.93 ( 0 )   </td><td>19.96 ( 0.61 )</td><td>0 ( 0 )      </td><td>24.96</td><td>0.74</td><td>0.93</td><td>19.96</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.20 & 0.92 ( 0 )    & 23.83 ( 0.69 ) & 0 ( 0 )       & 28.83 & 0.78 & 0.92 & 23.83 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.36 & 0.93 ( 0 )    & 11.38 ( 0.41 ) & 0 ( 0 )       & 16.38 & 0.6  & 0.93 & 11.38 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.09 & 0.67 ( 0.01 ) & 9.77 ( 0.24 )  & 4.03 ( 0.08 ) & 10.74 & 0.81 & 0.67 &  9.77 & 4.03\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.20 & 0.78 ( 0.01 ) & 11.91 ( 0.8 )  & 2.32 ( 0.1 )  & 14.59 & 0.66 & 0.78 & 11.91 & 2.32\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.16 & 0.91 ( 0 )    & 30.84 ( 0.92 ) & 0 ( 0 )       & 35.84 & 0.82 & 0.91 & 30.84 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.24 & 0.93 ( 0 )    & 19.96 ( 0.61 ) & 0 ( 0 )       & 24.96 & 0.74 & 0.93 & 19.96 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.20 | 0.92 ( 0 )    | 23.83 ( 0.69 ) | 0 ( 0 )       | 28.83 | 0.78 | 0.92 | 23.83 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.36 | 0.93 ( 0 )    | 11.38 ( 0.41 ) | 0 ( 0 )       | 16.38 | 0.6  | 0.93 | 11.38 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.09 | 0.67 ( 0.01 ) | 9.77 ( 0.24 )  | 4.03 ( 0.08 ) | 10.74 | 0.81 | 0.67 |  9.77 | 4.03 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.20 | 0.78 ( 0.01 ) | 11.91 ( 0.8 )  | 2.32 ( 0.1 )  | 14.59 | 0.66 | 0.78 | 11.91 | 2.32 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.16 | 0.91 ( 0 )    | 30.84 ( 0.92 ) | 0 ( 0 )       | 35.84 | 0.82 | 0.91 | 30.84 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.24 | 0.93 ( 0 )    | 19.96 ( 0.61 ) | 0 ( 0 )       | 24.96 | 0.74 | 0.93 | 19.96 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "11  500  500 0     1.0  0.20 0.92 ( 0 )    23.83 ( 0.69 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.5  0.36 0.93 ( 0 )    11.38 ( 0.41 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.09 0.67 ( 0.01 ) 9.77 ( 0.24 )  4.03 ( 0.08 )\n",
+       "14  100 1000 0    10.0  0.20 0.78 ( 0.01 ) 11.91 ( 0.8 )  2.32 ( 0.1 ) \n",
+       "15  500 1000 0     2.0  0.16 0.91 ( 0 )    30.84 ( 0.92 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.0  0.24 0.93 ( 0 )    19.96 ( 0.61 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11 28.83      0.78 0.92     23.83   0.00   \n",
+       "12 16.38      0.6  0.93     11.38   0.00   \n",
+       "13 10.74      0.81 0.67      9.77   4.03   \n",
+       "14 14.59      0.66 0.78     11.91   2.32   \n",
+       "15 35.84      0.82 0.91     30.84   0.00   \n",
+       "16 24.96      0.74 0.93     19.96   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.33</td><td>0.74 ( 0.01 )</td><td>3.69 ( 0.25 ) </td><td>2.5 ( 0.12 ) </td><td> 6.19</td><td>0.37</td><td>0.74</td><td> 3.69</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.35</td><td>0.85 ( 0.01 )</td><td>7.16 ( 0.37 ) </td><td>0.64 ( 0.08 )</td><td>11.52</td><td>0.48</td><td>0.85</td><td> 7.16</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.53</td><td>0.93 ( 0 )   </td><td>5.21 ( 0.21 ) </td><td>0 ( 0 )      </td><td>10.21</td><td>0.39</td><td>0.93</td><td> 5.21</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.80</td><td>0.93 ( 0 )   </td><td>2.25 ( 0.11 ) </td><td>0 ( 0 )      </td><td> 7.25</td><td>0.15</td><td>0.93</td><td> 2.25</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.25</td><td>0.73 ( 0.01 )</td><td>5.13 ( 0.34 ) </td><td>2.93 ( 0.1 ) </td><td> 7.20</td><td>0.5 </td><td>0.73</td><td> 5.13</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.32</td><td>0.83 ( 0.01 )</td><td>8.22 ( 0.44 ) </td><td>1.03 ( 0.09 )</td><td>12.19</td><td>0.54</td><td>0.83</td><td> 8.22</td><td>1.03</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.40</td><td>0.92 ( 0 )   </td><td>8.89 ( 0.29 ) </td><td>0 ( 0 )      </td><td>13.89</td><td>0.55</td><td>0.92</td><td> 8.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.69</td><td>0.93 ( 0 )   </td><td>3.53 ( 0.16 ) </td><td>0 ( 0 )      </td><td> 8.53</td><td>0.27</td><td>0.93</td><td> 3.53</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.13</td><td>0.7 ( 0.01 ) </td><td>8.05 ( 0.28 ) </td><td>3.74 ( 0.09 )</td><td> 9.31</td><td>0.74</td><td>0.70</td><td> 8.05</td><td>3.74</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.24</td><td>0.79 ( 0.01 )</td><td>11.14 ( 0.64 )</td><td>1.71 ( 0.1 ) </td><td>14.43</td><td>0.63</td><td>0.79</td><td>11.14</td><td>1.71</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.20</td><td>0.92 ( 0 )   </td><td>23.83 ( 0.69 )</td><td>0 ( 0 )      </td><td>28.83</td><td>0.78</td><td>0.92</td><td>23.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.38 ( 0.41 )</td><td>0 ( 0 )      </td><td>16.38</td><td>0.6 </td><td>0.93</td><td>11.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.09</td><td>0.67 ( 0.01 )</td><td>9.77 ( 0.24 ) </td><td>4.03 ( 0.08 )</td><td>10.74</td><td>0.81</td><td>0.67</td><td> 9.77</td><td>4.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>11.91 ( 0.8 ) </td><td>2.32 ( 0.1 ) </td><td>14.59</td><td>0.66</td><td>0.78</td><td>11.91</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.16</td><td>0.91 ( 0 )   </td><td>30.84 ( 0.92 )</td><td>0 ( 0 )      </td><td>35.84</td><td>0.82</td><td>0.91</td><td>30.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.24</td><td>0.93 ( 0 )   </td><td>19.96 ( 0.61 )</td><td>0 ( 0 )      </td><td>24.96</td><td>0.74</td><td>0.93</td><td>19.96</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.33 & 0.74 ( 0.01 ) & 3.69 ( 0.25 )  & 2.5 ( 0.12 )  &  6.19 & 0.37 & 0.74 &  3.69 & 2.50\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.35 & 0.85 ( 0.01 ) & 7.16 ( 0.37 )  & 0.64 ( 0.08 ) & 11.52 & 0.48 & 0.85 &  7.16 & 0.64\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.53 & 0.93 ( 0 )    & 5.21 ( 0.21 )  & 0 ( 0 )       & 10.21 & 0.39 & 0.93 &  5.21 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.80 & 0.93 ( 0 )    & 2.25 ( 0.11 )  & 0 ( 0 )       &  7.25 & 0.15 & 0.93 &  2.25 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.25 & 0.73 ( 0.01 ) & 5.13 ( 0.34 )  & 2.93 ( 0.1 )  &  7.20 & 0.5  & 0.73 &  5.13 & 2.93\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.32 & 0.83 ( 0.01 ) & 8.22 ( 0.44 )  & 1.03 ( 0.09 ) & 12.19 & 0.54 & 0.83 &  8.22 & 1.03\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.40 & 0.92 ( 0 )    & 8.89 ( 0.29 )  & 0 ( 0 )       & 13.89 & 0.55 & 0.92 &  8.89 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.69 & 0.93 ( 0 )    & 3.53 ( 0.16 )  & 0 ( 0 )       &  8.53 & 0.27 & 0.93 &  3.53 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.13 & 0.7 ( 0.01 )  & 8.05 ( 0.28 )  & 3.74 ( 0.09 ) &  9.31 & 0.74 & 0.70 &  8.05 & 3.74\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.24 & 0.79 ( 0.01 ) & 11.14 ( 0.64 ) & 1.71 ( 0.1 )  & 14.43 & 0.63 & 0.79 & 11.14 & 1.71\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.20 & 0.92 ( 0 )    & 23.83 ( 0.69 ) & 0 ( 0 )       & 28.83 & 0.78 & 0.92 & 23.83 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.36 & 0.93 ( 0 )    & 11.38 ( 0.41 ) & 0 ( 0 )       & 16.38 & 0.6  & 0.93 & 11.38 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.09 & 0.67 ( 0.01 ) & 9.77 ( 0.24 )  & 4.03 ( 0.08 ) & 10.74 & 0.81 & 0.67 &  9.77 & 4.03\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.20 & 0.78 ( 0.01 ) & 11.91 ( 0.8 )  & 2.32 ( 0.1 )  & 14.59 & 0.66 & 0.78 & 11.91 & 2.32\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.16 & 0.91 ( 0 )    & 30.84 ( 0.92 ) & 0 ( 0 )       & 35.84 & 0.82 & 0.91 & 30.84 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.24 & 0.93 ( 0 )    & 19.96 ( 0.61 ) & 0 ( 0 )       & 24.96 & 0.74 & 0.93 & 19.96 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.33 | 0.74 ( 0.01 ) | 3.69 ( 0.25 )  | 2.5 ( 0.12 )  |  6.19 | 0.37 | 0.74 |  3.69 | 2.50 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.35 | 0.85 ( 0.01 ) | 7.16 ( 0.37 )  | 0.64 ( 0.08 ) | 11.52 | 0.48 | 0.85 |  7.16 | 0.64 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.53 | 0.93 ( 0 )    | 5.21 ( 0.21 )  | 0 ( 0 )       | 10.21 | 0.39 | 0.93 |  5.21 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.80 | 0.93 ( 0 )    | 2.25 ( 0.11 )  | 0 ( 0 )       |  7.25 | 0.15 | 0.93 |  2.25 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.25 | 0.73 ( 0.01 ) | 5.13 ( 0.34 )  | 2.93 ( 0.1 )  |  7.20 | 0.5  | 0.73 |  5.13 | 2.93 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.32 | 0.83 ( 0.01 ) | 8.22 ( 0.44 )  | 1.03 ( 0.09 ) | 12.19 | 0.54 | 0.83 |  8.22 | 1.03 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.40 | 0.92 ( 0 )    | 8.89 ( 0.29 )  | 0 ( 0 )       | 13.89 | 0.55 | 0.92 |  8.89 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.69 | 0.93 ( 0 )    | 3.53 ( 0.16 )  | 0 ( 0 )       |  8.53 | 0.27 | 0.93 |  3.53 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.13 | 0.7 ( 0.01 )  | 8.05 ( 0.28 )  | 3.74 ( 0.09 ) |  9.31 | 0.74 | 0.70 |  8.05 | 3.74 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.24 | 0.79 ( 0.01 ) | 11.14 ( 0.64 ) | 1.71 ( 0.1 )  | 14.43 | 0.63 | 0.79 | 11.14 | 1.71 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.20 | 0.92 ( 0 )    | 23.83 ( 0.69 ) | 0 ( 0 )       | 28.83 | 0.78 | 0.92 | 23.83 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.36 | 0.93 ( 0 )    | 11.38 ( 0.41 ) | 0 ( 0 )       | 16.38 | 0.6  | 0.93 | 11.38 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.09 | 0.67 ( 0.01 ) | 9.77 ( 0.24 )  | 4.03 ( 0.08 ) | 10.74 | 0.81 | 0.67 |  9.77 | 4.03 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.20 | 0.78 ( 0.01 ) | 11.91 ( 0.8 )  | 2.32 ( 0.1 )  | 14.59 | 0.66 | 0.78 | 11.91 | 2.32 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.16 | 0.91 ( 0 )    | 30.84 ( 0.92 ) | 0 ( 0 )       | 35.84 | 0.82 | 0.91 | 30.84 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.24 | 0.93 ( 0 )    | 19.96 ( 0.61 ) | 0 ( 0 )       | 24.96 | 0.74 | 0.93 | 19.96 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0     1.00 0.33 0.74 ( 0.01 ) 3.69 ( 0.25 )  2.5 ( 0.12 ) \n",
+       "2   100   50 0     0.50 0.35 0.85 ( 0.01 ) 7.16 ( 0.37 )  0.64 ( 0.08 )\n",
+       "3   500   50 0     0.10 0.53 0.93 ( 0 )    5.21 ( 0.21 )  0 ( 0 )      \n",
+       "4  1000   50 0     0.05 0.80 0.93 ( 0 )    2.25 ( 0.11 )  0 ( 0 )      \n",
+       "5    50  100 0     2.00 0.25 0.73 ( 0.01 ) 5.13 ( 0.34 )  2.93 ( 0.1 ) \n",
+       "6   100  100 0     1.00 0.32 0.83 ( 0.01 ) 8.22 ( 0.44 )  1.03 ( 0.09 )\n",
+       "7   500  100 0     0.20 0.40 0.92 ( 0 )    8.89 ( 0.29 )  0 ( 0 )      \n",
+       "8  1000  100 0     0.10 0.69 0.93 ( 0 )    3.53 ( 0.16 )  0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.13 0.7 ( 0.01 )  8.05 ( 0.28 )  3.74 ( 0.09 )\n",
+       "10  100  500 0     5.00 0.24 0.79 ( 0.01 ) 11.14 ( 0.64 ) 1.71 ( 0.1 ) \n",
+       "11  500  500 0     1.00 0.20 0.92 ( 0 )    23.83 ( 0.69 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.50 0.36 0.93 ( 0 )    11.38 ( 0.41 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.09 0.67 ( 0.01 ) 9.77 ( 0.24 )  4.03 ( 0.08 )\n",
+       "14  100 1000 0    10.00 0.20 0.78 ( 0.01 ) 11.91 ( 0.8 )  2.32 ( 0.1 ) \n",
+       "15  500 1000 0     2.00 0.16 0.91 ( 0 )    30.84 ( 0.92 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.00 0.24 0.93 ( 0 )    19.96 ( 0.61 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   6.19      0.37 0.74      3.69   2.50   \n",
+       "2  11.52      0.48 0.85      7.16   0.64   \n",
+       "3  10.21      0.39 0.93      5.21   0.00   \n",
+       "4   7.25      0.15 0.93      2.25   0.00   \n",
+       "5   7.20      0.5  0.73      5.13   2.93   \n",
+       "6  12.19      0.54 0.83      8.22   1.03   \n",
+       "7  13.89      0.55 0.92      8.89   0.00   \n",
+       "8   8.53      0.27 0.93      3.53   0.00   \n",
+       "9   9.31      0.74 0.70      8.05   3.74   \n",
+       "10 14.43      0.63 0.79     11.14   1.71   \n",
+       "11 28.83      0.78 0.92     23.83   0.00   \n",
+       "12 16.38      0.6  0.93     11.38   0.00   \n",
+       "13 10.74      0.81 0.67      9.77   4.03   \n",
+       "14 14.59      0.66 0.78     11.91   2.32   \n",
+       "15 35.84      0.82 0.91     30.84   0.00   \n",
+       "16 24.96      0.74 0.93     19.96   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_lasso_binary.txt', \n",
+    "            sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/0.2_sim_ind_elnet_binary_update.ipynb b/simulations/notebooks_sim_bin/0.2_sim_ind_elnet_binary_update.ipynb
new file mode 100644
index 0000000..da8ff21
--- /dev/null
+++ b/simulations/notebooks_sim_bin/0.2_sim_ind_elnet_binary_update.ipynb
@@ -0,0 +1,680 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>0.76 ( 0.01 )</td><td>0.15</td><td>15.62</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>0.84 ( 0.01 )</td><td>0.23</td><td>15.98</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>8.74 ( 0.72 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.36</td><td>13.74</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>7.56 ( 0.72 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.41</td><td>12.56</td><td>0.40</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>18.92 ( 1.7 )</td><td>1.81 ( 0.12 )</td><td>0.72 ( 0.01 )</td><td>0.1</td><td>22.11</td><td>0.72</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>12.48 ( 0.9 )</td><td>0.96 ( 0.09 )</td><td>0.82 ( 0.01 )</td><td>0.22</td><td>16.52</td><td>0.58</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 0.76 ( 0.01 ) & 0.15 & 15.62 & 0.61\\\\\n",
+       "\t2 & 100 & 50 & 0 & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 0.84 ( 0.01 ) & 0.23 & 15.98 & 0.56\\\\\n",
+       "\t3 & 500 & 50 & 0 & 8.74 ( 0.72 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.36 & 13.74 & 0.45\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 7.56 ( 0.72 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.41 & 12.56 & 0.40\\\\\n",
+       "\t5 & 50 & 100 & 0 & 18.92 ( 1.7 ) & 1.81 ( 0.12 ) & 0.72 ( 0.01 ) & 0.1 & 22.11 & 0.72\\\\\n",
+       "\t6 & 100 & 100 & 0 & 12.48 ( 0.9 ) & 0.96 ( 0.09 ) & 0.82 ( 0.01 ) & 0.22 & 16.52 & 0.58\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 0.76 ( 0.01 ) | 0.15 | 15.62 | 0.61 |\n",
+       "| 2 | 100 | 50 | 0 | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 0.84 ( 0.01 ) | 0.23 | 15.98 | 0.56 |\n",
+       "| 3 | 500 | 50 | 0 | 8.74 ( 0.72 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.36 | 13.74 | 0.45 |\n",
+       "| 4 | 1000 | 50 | 0 | 7.56 ( 0.72 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.41 | 12.56 | 0.40 |\n",
+       "| 5 | 50 | 100 | 0 | 18.92 ( 1.7 ) | 1.81 ( 0.12 ) | 0.72 ( 0.01 ) | 0.1 | 22.11 | 0.72 |\n",
+       "| 6 | 100 | 100 | 0 | 12.48 ( 0.9 ) | 0.96 ( 0.09 ) | 0.82 ( 0.01 ) | 0.22 | 16.52 | 0.58 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0   12.04 ( 0.89 ) 1.42 ( 0.11 ) 0.76 ( 0.01 ) 0.15 15.62      0.61\n",
+       "2 100  50  0   11.44 ( 0.76 ) 0.46 ( 0.07 ) 0.84 ( 0.01 ) 0.23 15.98      0.56\n",
+       "3 500  50  0   8.74 ( 0.72 )  0 ( 0 )       0.92 ( 0 )    0.36 13.74      0.45\n",
+       "4 1000 50  0   7.56 ( 0.72 )  0 ( 0 )       0.93 ( 0 )    0.41 12.56      0.40\n",
+       "5 50   100 0   18.92 ( 1.7 )  1.81 ( 0.12 ) 0.72 ( 0.01 ) 0.1  22.11      0.72\n",
+       "6 100  100 0   12.48 ( 0.9 )  0.96 ( 0.09 ) 0.82 ( 0.01 ) 0.22 16.52      0.58"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>16.79 ( 1.47 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.27</td><td>21.79</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>11.47 ( 1.14 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.36</td><td>16.47</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>0.67 ( 0.01 )</td><td>0.02</td><td>74.99</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )</td><td>0.76 ( 0.01 )</td><td>0.09</td><td>32.95</td><td>0.77</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>19.47 ( 1.76 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.24</td><td>24.47</td><td>0.62</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>18.47 ( 1.76 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.25</td><td>23.47</td><td>0.58</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 16.79 ( 1.47 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.27 & 21.79 & 0.61\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 11.47 ( 1.14 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.36 & 16.47 & 0.48\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 0.67 ( 0.01 ) & 0.02 & 74.99 & 0.91\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 29.95 ( 4.05 ) & 2 ( 0.1 ) & 0.76 ( 0.01 ) & 0.09 & 32.95 & 0.77\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 19.47 ( 1.76 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.24 & 24.47 & 0.62\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 18.47 ( 1.76 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.25 & 23.47 & 0.58\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 16.79 ( 1.47 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.27 | 21.79 | 0.61 |\n",
+       "| 12 | 1000 | 500 | 0 | 11.47 ( 1.14 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.36 | 16.47 | 0.48 |\n",
+       "| 13 | 50 | 1000 | 0 | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 0.67 ( 0.01 ) | 0.02 | 74.99 | 0.91 |\n",
+       "| 14 | 100 | 1000 | 0 | 29.95 ( 4.05 ) | 2 ( 0.1 ) | 0.76 ( 0.01 ) | 0.09 | 32.95 | 0.77 |\n",
+       "| 15 | 500 | 1000 | 0 | 19.47 ( 1.76 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.24 | 24.47 | 0.62 |\n",
+       "| 16 | 1000 | 1000 | 0 | 18.47 ( 1.76 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.25 | 23.47 | 0.58 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "11 500  500  0   16.79 ( 1.47 ) 0 ( 0 )       0.92 ( 0 )    0.27 21.79     \n",
+       "12 1000 500  0   11.47 ( 1.14 ) 0 ( 0 )       0.93 ( 0 )    0.36 16.47     \n",
+       "13 50   1000 0   72.88 ( 8.55 ) 2.89 ( 0.13 ) 0.67 ( 0.01 ) 0.02 74.99     \n",
+       "14 100  1000 0   29.95 ( 4.05 ) 2 ( 0.1 )     0.76 ( 0.01 ) 0.09 32.95     \n",
+       "15 500  1000 0   19.47 ( 1.76 ) 0 ( 0 )       0.91 ( 0 )    0.24 24.47     \n",
+       "16 1000 1000 0   18.47 ( 1.76 ) 0 ( 0 )       0.93 ( 0 )    0.25 23.47     \n",
+       "   FDR \n",
+       "11 0.61\n",
+       "12 0.48\n",
+       "13 0.91\n",
+       "14 0.77\n",
+       "15 0.62\n",
+       "16 0.58"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.15</td><td>0.76 ( 0.01 )</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>15.62</td><td>0.61</td><td>0.76</td><td>12.04</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.23</td><td>0.84 ( 0.01 )</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>15.98</td><td>0.56</td><td>0.84</td><td>11.44</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.36</td><td>0.92 ( 0 )   </td><td>8.74 ( 0.72 ) </td><td>0 ( 0 )      </td><td>13.74</td><td>0.45</td><td>0.92</td><td> 8.74</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.41</td><td>0.93 ( 0 )   </td><td>7.56 ( 0.72 ) </td><td>0 ( 0 )      </td><td>12.56</td><td>0.4 </td><td>0.93</td><td> 7.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.10</td><td>0.72 ( 0.01 )</td><td>18.92 ( 1.7 ) </td><td>1.81 ( 0.12 )</td><td>22.11</td><td>0.72</td><td>0.72</td><td>18.92</td><td>1.81</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.22</td><td>0.82 ( 0.01 )</td><td>12.48 ( 0.9 ) </td><td>0.96 ( 0.09 )</td><td>16.52</td><td>0.58</td><td>0.82</td><td>12.48</td><td>0.96</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.15 & 0.76 ( 0.01 ) & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 15.62 & 0.61 & 0.76 & 12.04 & 1.42\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.23 & 0.84 ( 0.01 ) & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 15.98 & 0.56 & 0.84 & 11.44 & 0.46\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.36 & 0.92 ( 0 )    & 8.74 ( 0.72 )  & 0 ( 0 )       & 13.74 & 0.45 & 0.92 &  8.74 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.41 & 0.93 ( 0 )    & 7.56 ( 0.72 )  & 0 ( 0 )       & 12.56 & 0.4  & 0.93 &  7.56 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.10 & 0.72 ( 0.01 ) & 18.92 ( 1.7 )  & 1.81 ( 0.12 ) & 22.11 & 0.72 & 0.72 & 18.92 & 1.81\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.22 & 0.82 ( 0.01 ) & 12.48 ( 0.9 )  & 0.96 ( 0.09 ) & 16.52 & 0.58 & 0.82 & 12.48 & 0.96\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.15 | 0.76 ( 0.01 ) | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 15.62 | 0.61 | 0.76 | 12.04 | 1.42 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.23 | 0.84 ( 0.01 ) | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 15.98 | 0.56 | 0.84 | 11.44 | 0.46 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.36 | 0.92 ( 0 )    | 8.74 ( 0.72 )  | 0 ( 0 )       | 13.74 | 0.45 | 0.92 |  8.74 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.41 | 0.93 ( 0 )    | 7.56 ( 0.72 )  | 0 ( 0 )       | 12.56 | 0.4  | 0.93 |  7.56 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.10 | 0.72 ( 0.01 ) | 18.92 ( 1.7 )  | 1.81 ( 0.12 ) | 22.11 | 0.72 | 0.72 | 18.92 | 1.81 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.22 | 0.82 ( 0.01 ) | 12.48 ( 0.9 )  | 0.96 ( 0.09 ) | 16.52 | 0.58 | 0.82 | 12.48 | 0.96 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0    1.00  0.15 0.76 ( 0.01 ) 12.04 ( 0.89 ) 1.42 ( 0.11 )\n",
+       "2  100  50 0    0.50  0.23 0.84 ( 0.01 ) 11.44 ( 0.76 ) 0.46 ( 0.07 )\n",
+       "3  500  50 0    0.10  0.36 0.92 ( 0 )    8.74 ( 0.72 )  0 ( 0 )      \n",
+       "4 1000  50 0    0.05  0.41 0.93 ( 0 )    7.56 ( 0.72 )  0 ( 0 )      \n",
+       "5   50 100 0    2.00  0.10 0.72 ( 0.01 ) 18.92 ( 1.7 )  1.81 ( 0.12 )\n",
+       "6  100 100 0    1.00  0.22 0.82 ( 0.01 ) 12.48 ( 0.9 )  0.96 ( 0.09 )\n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 15.62      0.61 0.76     12.04   1.42   \n",
+       "2 15.98      0.56 0.84     11.44   0.46   \n",
+       "3 13.74      0.45 0.92      8.74   0.00   \n",
+       "4 12.56      0.4  0.93      7.56   0.00   \n",
+       "5 22.11      0.72 0.72     18.92   1.81   \n",
+       "6 16.52      0.58 0.82     12.48   0.96   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.27</td><td>0.92 ( 0 )   </td><td>16.79 ( 1.47 )</td><td>0 ( 0 )      </td><td>21.79</td><td>0.61</td><td>0.92</td><td>16.79</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.47 ( 1.14 )</td><td>0 ( 0 )      </td><td>16.47</td><td>0.48</td><td>0.93</td><td>11.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>74.99</td><td>0.91</td><td>0.67</td><td>72.88</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.09</td><td>0.76 ( 0.01 )</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )    </td><td>32.95</td><td>0.77</td><td>0.76</td><td>29.95</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.24</td><td>0.91 ( 0 )   </td><td>19.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>24.47</td><td>0.62</td><td>0.91</td><td>19.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.25</td><td>0.93 ( 0 )   </td><td>18.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>23.47</td><td>0.58</td><td>0.93</td><td>18.47</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.27 & 0.92 ( 0 )    & 16.79 ( 1.47 ) & 0 ( 0 )       & 21.79 & 0.61 & 0.92 & 16.79 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.36 & 0.93 ( 0 )    & 11.47 ( 1.14 ) & 0 ( 0 )       & 16.47 & 0.48 & 0.93 & 11.47 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.02 & 0.67 ( 0.01 ) & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 74.99 & 0.91 & 0.67 & 72.88 & 2.89\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.09 & 0.76 ( 0.01 ) & 29.95 ( 4.05 ) & 2 ( 0.1 )     & 32.95 & 0.77 & 0.76 & 29.95 & 2.00\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.24 & 0.91 ( 0 )    & 19.47 ( 1.76 ) & 0 ( 0 )       & 24.47 & 0.62 & 0.91 & 19.47 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.25 & 0.93 ( 0 )    & 18.47 ( 1.76 ) & 0 ( 0 )       & 23.47 & 0.58 & 0.93 & 18.47 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.27 | 0.92 ( 0 )    | 16.79 ( 1.47 ) | 0 ( 0 )       | 21.79 | 0.61 | 0.92 | 16.79 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.36 | 0.93 ( 0 )    | 11.47 ( 1.14 ) | 0 ( 0 )       | 16.47 | 0.48 | 0.93 | 11.47 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.02 | 0.67 ( 0.01 ) | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 74.99 | 0.91 | 0.67 | 72.88 | 2.89 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.09 | 0.76 ( 0.01 ) | 29.95 ( 4.05 ) | 2 ( 0.1 )     | 32.95 | 0.77 | 0.76 | 29.95 | 2.00 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.24 | 0.91 ( 0 )    | 19.47 ( 1.76 ) | 0 ( 0 )       | 24.47 | 0.62 | 0.91 | 19.47 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.25 | 0.93 ( 0 )    | 18.47 ( 1.76 ) | 0 ( 0 )       | 23.47 | 0.58 | 0.93 | 18.47 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "11  500  500 0     1.0  0.27 0.92 ( 0 )    16.79 ( 1.47 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.5  0.36 0.93 ( 0 )    11.47 ( 1.14 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.02 0.67 ( 0.01 ) 72.88 ( 8.55 ) 2.89 ( 0.13 )\n",
+       "14  100 1000 0    10.0  0.09 0.76 ( 0.01 ) 29.95 ( 4.05 ) 2 ( 0.1 )    \n",
+       "15  500 1000 0     2.0  0.24 0.91 ( 0 )    19.47 ( 1.76 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.0  0.25 0.93 ( 0 )    18.47 ( 1.76 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11 21.79      0.61 0.92     16.79   0.00   \n",
+       "12 16.47      0.48 0.93     11.47   0.00   \n",
+       "13 74.99      0.91 0.67     72.88   2.89   \n",
+       "14 32.95      0.77 0.76     29.95   2.00   \n",
+       "15 24.47      0.62 0.91     19.47   0.00   \n",
+       "16 23.47      0.58 0.93     18.47   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.15</td><td>0.76 ( 0.01 )</td><td>12.04 ( 0.89 )</td><td>1.42 ( 0.11 )</td><td>15.62</td><td>0.61</td><td>0.76</td><td>12.04</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.23</td><td>0.84 ( 0.01 )</td><td>11.44 ( 0.76 )</td><td>0.46 ( 0.07 )</td><td>15.98</td><td>0.56</td><td>0.84</td><td>11.44</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.36</td><td>0.92 ( 0 )   </td><td>8.74 ( 0.72 ) </td><td>0 ( 0 )      </td><td>13.74</td><td>0.45</td><td>0.92</td><td> 8.74</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.41</td><td>0.93 ( 0 )   </td><td>7.56 ( 0.72 ) </td><td>0 ( 0 )      </td><td>12.56</td><td>0.4 </td><td>0.93</td><td> 7.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.10</td><td>0.72 ( 0.01 )</td><td>18.92 ( 1.7 ) </td><td>1.81 ( 0.12 )</td><td>22.11</td><td>0.72</td><td>0.72</td><td>18.92</td><td>1.81</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.22</td><td>0.82 ( 0.01 )</td><td>12.48 ( 0.9 ) </td><td>0.96 ( 0.09 )</td><td>16.52</td><td>0.58</td><td>0.82</td><td>12.48</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.32</td><td>0.92 ( 0 )   </td><td>11.78 ( 0.99 )</td><td>0 ( 0 )      </td><td>16.78</td><td>0.52</td><td>0.92</td><td>11.78</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.34</td><td>0.93 ( 0 )   </td><td>10.71 ( 1.03 )</td><td>0 ( 0 )      </td><td>15.71</td><td>0.47</td><td>0.93</td><td>10.71</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.03</td><td>0.68 ( 0.01 )</td><td>57.17 ( 5.72 )</td><td>2.27 ( 0.13 )</td><td>59.90</td><td>0.88</td><td>0.68</td><td>57.17</td><td>2.27</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>20.84 ( 2.08 )</td><td>1.49 ( 0.1 ) </td><td>24.35</td><td>0.69</td><td>0.77</td><td>20.84</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.27</td><td>0.92 ( 0 )   </td><td>16.79 ( 1.47 )</td><td>0 ( 0 )      </td><td>21.79</td><td>0.61</td><td>0.92</td><td>16.79</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.36</td><td>0.93 ( 0 )   </td><td>11.47 ( 1.14 )</td><td>0 ( 0 )      </td><td>16.47</td><td>0.48</td><td>0.93</td><td>11.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>72.88 ( 8.55 )</td><td>2.89 ( 0.13 )</td><td>74.99</td><td>0.91</td><td>0.67</td><td>72.88</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.09</td><td>0.76 ( 0.01 )</td><td>29.95 ( 4.05 )</td><td>2 ( 0.1 )    </td><td>32.95</td><td>0.77</td><td>0.76</td><td>29.95</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.24</td><td>0.91 ( 0 )   </td><td>19.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>24.47</td><td>0.62</td><td>0.91</td><td>19.47</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.25</td><td>0.93 ( 0 )   </td><td>18.47 ( 1.76 )</td><td>0 ( 0 )      </td><td>23.47</td><td>0.58</td><td>0.93</td><td>18.47</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.15 & 0.76 ( 0.01 ) & 12.04 ( 0.89 ) & 1.42 ( 0.11 ) & 15.62 & 0.61 & 0.76 & 12.04 & 1.42\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.23 & 0.84 ( 0.01 ) & 11.44 ( 0.76 ) & 0.46 ( 0.07 ) & 15.98 & 0.56 & 0.84 & 11.44 & 0.46\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.36 & 0.92 ( 0 )    & 8.74 ( 0.72 )  & 0 ( 0 )       & 13.74 & 0.45 & 0.92 &  8.74 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.41 & 0.93 ( 0 )    & 7.56 ( 0.72 )  & 0 ( 0 )       & 12.56 & 0.4  & 0.93 &  7.56 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.10 & 0.72 ( 0.01 ) & 18.92 ( 1.7 )  & 1.81 ( 0.12 ) & 22.11 & 0.72 & 0.72 & 18.92 & 1.81\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.22 & 0.82 ( 0.01 ) & 12.48 ( 0.9 )  & 0.96 ( 0.09 ) & 16.52 & 0.58 & 0.82 & 12.48 & 0.96\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.32 & 0.92 ( 0 )    & 11.78 ( 0.99 ) & 0 ( 0 )       & 16.78 & 0.52 & 0.92 & 11.78 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.34 & 0.93 ( 0 )    & 10.71 ( 1.03 ) & 0 ( 0 )       & 15.71 & 0.47 & 0.93 & 10.71 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.03 & 0.68 ( 0.01 ) & 57.17 ( 5.72 ) & 2.27 ( 0.13 ) & 59.90 & 0.88 & 0.68 & 57.17 & 2.27\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.15 & 0.77 ( 0.01 ) & 20.84 ( 2.08 ) & 1.49 ( 0.1 )  & 24.35 & 0.69 & 0.77 & 20.84 & 1.49\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.27 & 0.92 ( 0 )    & 16.79 ( 1.47 ) & 0 ( 0 )       & 21.79 & 0.61 & 0.92 & 16.79 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.36 & 0.93 ( 0 )    & 11.47 ( 1.14 ) & 0 ( 0 )       & 16.47 & 0.48 & 0.93 & 11.47 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.02 & 0.67 ( 0.01 ) & 72.88 ( 8.55 ) & 2.89 ( 0.13 ) & 74.99 & 0.91 & 0.67 & 72.88 & 2.89\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.09 & 0.76 ( 0.01 ) & 29.95 ( 4.05 ) & 2 ( 0.1 )     & 32.95 & 0.77 & 0.76 & 29.95 & 2.00\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.24 & 0.91 ( 0 )    & 19.47 ( 1.76 ) & 0 ( 0 )       & 24.47 & 0.62 & 0.91 & 19.47 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.25 & 0.93 ( 0 )    & 18.47 ( 1.76 ) & 0 ( 0 )       & 23.47 & 0.58 & 0.93 & 18.47 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.15 | 0.76 ( 0.01 ) | 12.04 ( 0.89 ) | 1.42 ( 0.11 ) | 15.62 | 0.61 | 0.76 | 12.04 | 1.42 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.23 | 0.84 ( 0.01 ) | 11.44 ( 0.76 ) | 0.46 ( 0.07 ) | 15.98 | 0.56 | 0.84 | 11.44 | 0.46 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.36 | 0.92 ( 0 )    | 8.74 ( 0.72 )  | 0 ( 0 )       | 13.74 | 0.45 | 0.92 |  8.74 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.41 | 0.93 ( 0 )    | 7.56 ( 0.72 )  | 0 ( 0 )       | 12.56 | 0.4  | 0.93 |  7.56 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.10 | 0.72 ( 0.01 ) | 18.92 ( 1.7 )  | 1.81 ( 0.12 ) | 22.11 | 0.72 | 0.72 | 18.92 | 1.81 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.22 | 0.82 ( 0.01 ) | 12.48 ( 0.9 )  | 0.96 ( 0.09 ) | 16.52 | 0.58 | 0.82 | 12.48 | 0.96 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.32 | 0.92 ( 0 )    | 11.78 ( 0.99 ) | 0 ( 0 )       | 16.78 | 0.52 | 0.92 | 11.78 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.34 | 0.93 ( 0 )    | 10.71 ( 1.03 ) | 0 ( 0 )       | 15.71 | 0.47 | 0.93 | 10.71 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.03 | 0.68 ( 0.01 ) | 57.17 ( 5.72 ) | 2.27 ( 0.13 ) | 59.90 | 0.88 | 0.68 | 57.17 | 2.27 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.15 | 0.77 ( 0.01 ) | 20.84 ( 2.08 ) | 1.49 ( 0.1 )  | 24.35 | 0.69 | 0.77 | 20.84 | 1.49 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.27 | 0.92 ( 0 )    | 16.79 ( 1.47 ) | 0 ( 0 )       | 21.79 | 0.61 | 0.92 | 16.79 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.36 | 0.93 ( 0 )    | 11.47 ( 1.14 ) | 0 ( 0 )       | 16.47 | 0.48 | 0.93 | 11.47 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.02 | 0.67 ( 0.01 ) | 72.88 ( 8.55 ) | 2.89 ( 0.13 ) | 74.99 | 0.91 | 0.67 | 72.88 | 2.89 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.09 | 0.76 ( 0.01 ) | 29.95 ( 4.05 ) | 2 ( 0.1 )     | 32.95 | 0.77 | 0.76 | 29.95 | 2.00 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.24 | 0.91 ( 0 )    | 19.47 ( 1.76 ) | 0 ( 0 )       | 24.47 | 0.62 | 0.91 | 19.47 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.25 | 0.93 ( 0 )    | 18.47 ( 1.76 ) | 0 ( 0 )       | 23.47 | 0.58 | 0.93 | 18.47 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0     1.00 0.15 0.76 ( 0.01 ) 12.04 ( 0.89 ) 1.42 ( 0.11 )\n",
+       "2   100   50 0     0.50 0.23 0.84 ( 0.01 ) 11.44 ( 0.76 ) 0.46 ( 0.07 )\n",
+       "3   500   50 0     0.10 0.36 0.92 ( 0 )    8.74 ( 0.72 )  0 ( 0 )      \n",
+       "4  1000   50 0     0.05 0.41 0.93 ( 0 )    7.56 ( 0.72 )  0 ( 0 )      \n",
+       "5    50  100 0     2.00 0.10 0.72 ( 0.01 ) 18.92 ( 1.7 )  1.81 ( 0.12 )\n",
+       "6   100  100 0     1.00 0.22 0.82 ( 0.01 ) 12.48 ( 0.9 )  0.96 ( 0.09 )\n",
+       "7   500  100 0     0.20 0.32 0.92 ( 0 )    11.78 ( 0.99 ) 0 ( 0 )      \n",
+       "8  1000  100 0     0.10 0.34 0.93 ( 0 )    10.71 ( 1.03 ) 0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.03 0.68 ( 0.01 ) 57.17 ( 5.72 ) 2.27 ( 0.13 )\n",
+       "10  100  500 0     5.00 0.15 0.77 ( 0.01 ) 20.84 ( 2.08 ) 1.49 ( 0.1 ) \n",
+       "11  500  500 0     1.00 0.27 0.92 ( 0 )    16.79 ( 1.47 ) 0 ( 0 )      \n",
+       "12 1000  500 0     0.50 0.36 0.93 ( 0 )    11.47 ( 1.14 ) 0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.02 0.67 ( 0.01 ) 72.88 ( 8.55 ) 2.89 ( 0.13 )\n",
+       "14  100 1000 0    10.00 0.09 0.76 ( 0.01 ) 29.95 ( 4.05 ) 2 ( 0.1 )    \n",
+       "15  500 1000 0     2.00 0.24 0.91 ( 0 )    19.47 ( 1.76 ) 0 ( 0 )      \n",
+       "16 1000 1000 0     1.00 0.25 0.93 ( 0 )    18.47 ( 1.76 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1  15.62      0.61 0.76     12.04   1.42   \n",
+       "2  15.98      0.56 0.84     11.44   0.46   \n",
+       "3  13.74      0.45 0.92      8.74   0.00   \n",
+       "4  12.56      0.4  0.93      7.56   0.00   \n",
+       "5  22.11      0.72 0.72     18.92   1.81   \n",
+       "6  16.52      0.58 0.82     12.48   0.96   \n",
+       "7  16.78      0.52 0.92     11.78   0.00   \n",
+       "8  15.71      0.47 0.93     10.71   0.00   \n",
+       "9  59.90      0.88 0.68     57.17   2.27   \n",
+       "10 24.35      0.69 0.77     20.84   1.49   \n",
+       "11 21.79      0.61 0.92     16.79   0.00   \n",
+       "12 16.47      0.48 0.93     11.47   0.00   \n",
+       "13 74.99      0.91 0.67     72.88   2.89   \n",
+       "14 32.95      0.77 0.76     29.95   2.00   \n",
+       "15 24.47      0.62 0.91     19.47   0.00   \n",
+       "16 23.47      0.58 0.93     18.47   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/0.3_sim_ind_rf_binary_update.ipynb b/simulations/notebooks_sim_bin/0.3_sim_ind_rf_binary_update.ipynb
new file mode 100644
index 0000000..6dfa2c6
--- /dev/null
+++ b/simulations/notebooks_sim_bin/0.3_sim_ind_rf_binary_update.ipynb
@@ -0,0 +1,694 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>1.97 ( 0.13 )</td><td>4.62 ( 0.08 )</td><td>1 ( 0 )</td><td>0.1</td><td>3.34</td><td>0.55</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>1.26 ( 0.12 )</td><td>2.19 ( 0.08 )</td><td>1 ( 0 )</td><td>0.54</td><td>5.07</td><td>0.22</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>0.86 ( 0.09 )</td><td>1.12 ( 0.08 )</td><td>1 ( 0 )</td><td>0.73</td><td>5.74</td><td>0.13</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>4.29 ( 0.17 )</td><td>4.79 ( 0.09 )</td><td>1 ( 0 )</td><td>0.05</td><td>5.50</td><td>0.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0 & 1.97 ( 0.13 ) & 4.62 ( 0.08 ) & 1 ( 0 ) & 0.1 & 3.34 & 0.55\\\\\n",
+       "\t3 & 500 & 50 & 0 & 1.26 ( 0.12 ) & 2.19 ( 0.08 ) & 1 ( 0 ) & 0.54 & 5.07 & 0.22\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 0.86 ( 0.09 ) & 1.12 ( 0.08 ) & 1 ( 0 ) & 0.73 & 5.74 & 0.13\\\\\n",
+       "\t5 & 50 & 100 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0 & 4.29 ( 0.17 ) & 4.79 ( 0.09 ) & 1 ( 0 ) & 0.05 & 5.50 & 0.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0 | 1.97 ( 0.13 ) | 4.62 ( 0.08 ) | 1 ( 0 ) | 0.1 | 3.34 | 0.55 |\n",
+       "| 3 | 500 | 50 | 0 | 1.26 ( 0.12 ) | 2.19 ( 0.08 ) | 1 ( 0 ) | 0.54 | 5.07 | 0.22 |\n",
+       "| 4 | 1000 | 50 | 0 | 0.86 ( 0.09 ) | 1.12 ( 0.08 ) | 1 ( 0 ) | 0.73 | 5.74 | 0.13 |\n",
+       "| 5 | 50 | 100 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0 | 4.29 ( 0.17 ) | 4.79 ( 0.09 ) | 1 ( 0 ) | 0.05 | 5.50 | 0.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0   1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0   1.97 ( 0.13 ) 4.62 ( 0.08 ) 1 ( 0 ) 0.1  3.34       0.55\n",
+       "3 500  50  0   1.26 ( 0.12 ) 2.19 ( 0.08 ) 1 ( 0 ) 0.54 5.07       0.22\n",
+       "4 1000 50  0   0.86 ( 0.09 ) 1.12 ( 0.08 ) 1 ( 0 ) 0.73 5.74       0.13\n",
+       "5 50   100 0   1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0   4.29 ( 0.17 ) 4.79 ( 0.09 ) 1 ( 0 ) 0.05 5.50       0.77"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 )</td><td>1 ( 0 )</td><td>0.09</td><td>26.74</td><td>0.86</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>1 ( 0 )</td><td>0.14</td><td>27.38</td><td>0.83</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>1 ( 0 )</td><td>0.01</td><td>47.90</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )</td><td>1 ( 0 )</td><td>0.04</td><td>51.32</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>1 ( 0 )</td><td>0.07</td><td>51.68</td><td>0.91</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 23.08 ( 0.51 ) & 2.34 ( 0.1 ) & 1 ( 0 ) & 0.09 & 26.74 & 0.86\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 1 ( 0 ) & 0.14 & 27.38 & 0.83\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 1 ( 0 ) & 0.01 & 47.90 & 0.98\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 47.82 ( 0.66 ) & 2.5 ( 0.1 ) & 1 ( 0 ) & 0.04 & 51.32 & 0.93\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 1 ( 0 ) & 0.07 & 51.68 & 0.91\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 23.08 ( 0.51 ) | 2.34 ( 0.1 ) | 1 ( 0 ) | 0.09 | 26.74 | 0.86 |\n",
+       "| 12 | 1000 | 500 | 0 | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 1 ( 0 ) | 0.14 | 27.38 | 0.83 |\n",
+       "| 13 | 50 | 1000 | 0 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 14 | 100 | 1000 | 0 | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 1 ( 0 ) | 0.01 | 47.90 | 0.98 |\n",
+       "| 15 | 500 | 1000 | 0 | 47.82 ( 0.66 ) | 2.5 ( 0.1 ) | 1 ( 0 ) | 0.04 | 51.32 | 0.93 |\n",
+       "| 16 | 1000 | 1000 | 0 | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 1 ( 0 ) | 0.07 | 51.68 | 0.91 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "11 500  500  0   23.08 ( 0.51 ) 2.34 ( 0.1 )  1 ( 0 ) 0.09 26.74      0.86\n",
+       "12 1000 500  0   22.72 ( 0.46 ) 1.34 ( 0.08 ) 1 ( 0 ) 0.14 27.38      0.83\n",
+       "13 50   1000 0   1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "14 100  1000 0   46.76 ( 0.67 ) 4.86 ( 0.09 ) 1 ( 0 ) 0.01 47.90      0.98\n",
+       "15 500  1000 0   47.82 ( 0.66 ) 2.5 ( 0.1 )   1 ( 0 ) 0.04 51.32      0.93\n",
+       "16 1000 1000 0   47.14 ( 0.77 ) 1.46 ( 0.08 ) 1 ( 0 ) 0.07 51.68      0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0     1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0     2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0    10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0    20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.97 ( 0.13 )</td><td>4.62 ( 0.08 )</td><td>3.34</td><td>0.55</td><td>1</td><td>1.97</td><td>4.62</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.54</td><td>1 ( 0 )</td><td>1.26 ( 0.12 )</td><td>2.19 ( 0.08 )</td><td>5.07</td><td>0.22</td><td>1</td><td>1.26</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.73</td><td>1 ( 0 )</td><td>0.86 ( 0.09 )</td><td>1.12 ( 0.08 )</td><td>5.74</td><td>0.13</td><td>1</td><td>0.86</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.29 ( 0.17 )</td><td>4.79 ( 0.09 )</td><td>5.50</td><td>0.77</td><td>1</td><td>4.29</td><td>4.79</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.10 & 1 ( 0 ) & 1.97 ( 0.13 ) & 4.62 ( 0.08 ) & 3.34 & 0.55 & 1 & 1.97 & 4.62\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.54 & 1 ( 0 ) & 1.26 ( 0.12 ) & 2.19 ( 0.08 ) & 5.07 & 0.22 & 1 & 1.26 & 2.19\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.73 & 1 ( 0 ) & 0.86 ( 0.09 ) & 1.12 ( 0.08 ) & 5.74 & 0.13 & 1 & 0.86 & 1.12\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.05 & 1 ( 0 ) & 4.29 ( 0.17 ) & 4.79 ( 0.09 ) & 5.50 & 0.77 & 1 & 4.29 & 4.79\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.10 | 1 ( 0 ) | 1.97 ( 0.13 ) | 4.62 ( 0.08 ) | 3.34 | 0.55 | 1 | 1.97 | 4.62 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.54 | 1 ( 0 ) | 1.26 ( 0.12 ) | 2.19 ( 0.08 ) | 5.07 | 0.22 | 1 | 1.26 | 2.19 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.73 | 1 ( 0 ) | 0.86 ( 0.09 ) | 1.12 ( 0.08 ) | 5.74 | 0.13 | 1 | 0.86 | 1.12 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.05 | 1 ( 0 ) | 4.29 ( 0.17 ) | 4.79 ( 0.09 ) | 5.50 | 0.77 | 1 | 4.29 | 4.79 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0    1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0    0.50  0.10 1 ( 0 ) 1.97 ( 0.13 ) 4.62 ( 0.08 ) 3.34       0.55\n",
+       "3  500  50 0    0.10  0.54 1 ( 0 ) 1.26 ( 0.12 ) 2.19 ( 0.08 ) 5.07       0.22\n",
+       "4 1000  50 0    0.05  0.73 1 ( 0 ) 0.86 ( 0.09 ) 1.12 ( 0.08 ) 5.74       0.13\n",
+       "5   50 100 0    2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0    1.00  0.05 1 ( 0 ) 4.29 ( 0.17 ) 4.79 ( 0.09 ) 5.50       0.77\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        1.97    4.62   \n",
+       "3 1        1.26    2.19   \n",
+       "4 1        0.86    1.12   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        4.29    4.79   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.09</td><td>1 ( 0 )</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 ) </td><td>26.74</td><td>0.86</td><td>1</td><td>23.08</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.14</td><td>1 ( 0 )</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>27.38</td><td>0.83</td><td>1</td><td>22.72</td><td>1.34</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.01</td><td>1 ( 0 )</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>47.90</td><td>0.98</td><td>1</td><td>46.76</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )  </td><td>51.32</td><td>0.93</td><td>1</td><td>47.82</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>51.68</td><td>0.91</td><td>1</td><td>47.14</td><td>1.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.09 & 1 ( 0 ) & 23.08 ( 0.51 ) & 2.34 ( 0.1 )  & 26.74 & 0.86 & 1 & 23.08 & 2.34\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.14 & 1 ( 0 ) & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 27.38 & 0.83 & 1 & 22.72 & 1.34\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.01 & 1 ( 0 ) & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 47.90 & 0.98 & 1 & 46.76 & 4.86\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.04 & 1 ( 0 ) & 47.82 ( 0.66 ) & 2.5 ( 0.1 )   & 51.32 & 0.93 & 1 & 47.82 & 2.50\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.07 & 1 ( 0 ) & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 51.68 & 0.91 & 1 & 47.14 & 1.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.09 | 1 ( 0 ) | 23.08 ( 0.51 ) | 2.34 ( 0.1 )  | 26.74 | 0.86 | 1 | 23.08 | 2.34 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.14 | 1 ( 0 ) | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 27.38 | 0.83 | 1 | 22.72 | 1.34 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.01 | 1 ( 0 ) | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 47.90 | 0.98 | 1 | 46.76 | 4.86 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.04 | 1 ( 0 ) | 47.82 ( 0.66 ) | 2.5 ( 0.1 )   | 51.32 | 0.93 | 1 | 47.82 | 2.50 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.07 | 1 ( 0 ) | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 51.68 | 0.91 | 1 | 47.14 | 1.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "11  500  500 0     1.0  0.09 1 ( 0 ) 23.08 ( 0.51 ) 2.34 ( 0.1 )  26.74     \n",
+       "12 1000  500 0     0.5  0.14 1 ( 0 ) 22.72 ( 0.46 ) 1.34 ( 0.08 ) 27.38     \n",
+       "13   50 1000 0    20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0    10.0  0.01 1 ( 0 ) 46.76 ( 0.67 ) 4.86 ( 0.09 ) 47.90     \n",
+       "15  500 1000 0     2.0  0.04 1 ( 0 ) 47.82 ( 0.66 ) 2.5 ( 0.1 )   51.32     \n",
+       "16 1000 1000 0     1.0  0.07 1 ( 0 ) 47.14 ( 0.77 ) 1.46 ( 0.08 ) 51.68     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "11 0.86 1        23.08   2.34   \n",
+       "12 0.83 1        22.72   1.34   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        46.76   4.86   \n",
+       "15 0.93 1        47.82   2.50   \n",
+       "16 0.91 1        47.14   1.46   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.97 ( 0.13 ) </td><td>4.62 ( 0.08 )</td><td> 3.34</td><td>0.55</td><td>1</td><td> 1.97</td><td>4.62</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.54</td><td>1 ( 0 )</td><td>1.26 ( 0.12 ) </td><td>2.19 ( 0.08 )</td><td> 5.07</td><td>0.22</td><td>1</td><td> 1.26</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.73</td><td>1 ( 0 )</td><td>0.86 ( 0.09 ) </td><td>1.12 ( 0.08 )</td><td> 5.74</td><td>0.13</td><td>1</td><td> 0.86</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.29 ( 0.17 ) </td><td>4.79 ( 0.09 )</td><td> 5.50</td><td>0.77</td><td>1</td><td> 4.29</td><td>4.79</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.40</td><td>1 ( 0 )</td><td>3.42 ( 0.16 ) </td><td>2.01 ( 0.09 )</td><td> 7.41</td><td>0.44</td><td>1</td><td> 3.42</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.49</td><td>1 ( 0 )</td><td>3.39 ( 0.19 ) </td><td>1.14 ( 0.07 )</td><td> 8.25</td><td>0.38</td><td>1</td><td> 3.39</td><td>1.14</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.68 ( 0.48 )</td><td>4.5 ( 0.09 ) </td><td>26.18</td><td>0.94</td><td>1</td><td>24.68</td><td>4.50</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.09</td><td>1 ( 0 )</td><td>23.08 ( 0.51 )</td><td>2.34 ( 0.1 ) </td><td>26.74</td><td>0.86</td><td>1</td><td>23.08</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>22.72 ( 0.46 )</td><td>1.34 ( 0.08 )</td><td>27.38</td><td>0.83</td><td>1</td><td>22.72</td><td>1.34</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>46.76 ( 0.67 )</td><td>4.86 ( 0.09 )</td><td>47.90</td><td>0.98</td><td>1</td><td>46.76</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>47.82 ( 0.66 )</td><td>2.5 ( 0.1 )  </td><td>51.32</td><td>0.93</td><td>1</td><td>47.82</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.77 )</td><td>1.46 ( 0.08 )</td><td>51.68</td><td>0.91</td><td>1</td><td>47.14</td><td>1.46</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.10 & 1 ( 0 ) & 1.97 ( 0.13 )  & 4.62 ( 0.08 ) &  3.34 & 0.55 & 1 &  1.97 & 4.62\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.54 & 1 ( 0 ) & 1.26 ( 0.12 )  & 2.19 ( 0.08 ) &  5.07 & 0.22 & 1 &  1.26 & 2.19\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.73 & 1 ( 0 ) & 0.86 ( 0.09 )  & 1.12 ( 0.08 ) &  5.74 & 0.13 & 1 &  0.86 & 1.12\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.05 & 1 ( 0 ) & 4.29 ( 0.17 )  & 4.79 ( 0.09 ) &  5.50 & 0.77 & 1 &  4.29 & 4.79\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.40 & 1 ( 0 ) & 3.42 ( 0.16 )  & 2.01 ( 0.09 ) &  7.41 & 0.44 & 1 &  3.42 & 2.01\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.49 & 1 ( 0 ) & 3.39 ( 0.19 )  & 1.14 ( 0.07 ) &  8.25 & 0.38 & 1 &  3.39 & 1.14\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.01 & 1 ( 0 ) & 24.68 ( 0.48 ) & 4.5 ( 0.09 )  & 26.18 & 0.94 & 1 & 24.68 & 4.50\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.09 & 1 ( 0 ) & 23.08 ( 0.51 ) & 2.34 ( 0.1 )  & 26.74 & 0.86 & 1 & 23.08 & 2.34\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.14 & 1 ( 0 ) & 22.72 ( 0.46 ) & 1.34 ( 0.08 ) & 27.38 & 0.83 & 1 & 22.72 & 1.34\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.01 & 1 ( 0 ) & 46.76 ( 0.67 ) & 4.86 ( 0.09 ) & 47.90 & 0.98 & 1 & 46.76 & 4.86\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.04 & 1 ( 0 ) & 47.82 ( 0.66 ) & 2.5 ( 0.1 )   & 51.32 & 0.93 & 1 & 47.82 & 2.50\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.07 & 1 ( 0 ) & 47.14 ( 0.77 ) & 1.46 ( 0.08 ) & 51.68 & 0.91 & 1 & 47.14 & 1.46\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.10 | 1 ( 0 ) | 1.97 ( 0.13 )  | 4.62 ( 0.08 ) |  3.34 | 0.55 | 1 |  1.97 | 4.62 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.54 | 1 ( 0 ) | 1.26 ( 0.12 )  | 2.19 ( 0.08 ) |  5.07 | 0.22 | 1 |  1.26 | 2.19 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.73 | 1 ( 0 ) | 0.86 ( 0.09 )  | 1.12 ( 0.08 ) |  5.74 | 0.13 | 1 |  0.86 | 1.12 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.05 | 1 ( 0 ) | 4.29 ( 0.17 )  | 4.79 ( 0.09 ) |  5.50 | 0.77 | 1 |  4.29 | 4.79 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.40 | 1 ( 0 ) | 3.42 ( 0.16 )  | 2.01 ( 0.09 ) |  7.41 | 0.44 | 1 |  3.42 | 2.01 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.49 | 1 ( 0 ) | 3.39 ( 0.19 )  | 1.14 ( 0.07 ) |  8.25 | 0.38 | 1 |  3.39 | 1.14 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.01 | 1 ( 0 ) | 24.68 ( 0.48 ) | 4.5 ( 0.09 )  | 26.18 | 0.94 | 1 | 24.68 | 4.50 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.09 | 1 ( 0 ) | 23.08 ( 0.51 ) | 2.34 ( 0.1 )  | 26.74 | 0.86 | 1 | 23.08 | 2.34 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.14 | 1 ( 0 ) | 22.72 ( 0.46 ) | 1.34 ( 0.08 ) | 27.38 | 0.83 | 1 | 22.72 | 1.34 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.01 | 1 ( 0 ) | 46.76 ( 0.67 ) | 4.86 ( 0.09 ) | 47.90 | 0.98 | 1 | 46.76 | 4.86 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.04 | 1 ( 0 ) | 47.82 ( 0.66 ) | 2.5 ( 0.1 )   | 51.32 | 0.93 | 1 | 47.82 | 2.50 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.07 | 1 ( 0 ) | 47.14 ( 0.77 ) | 1.46 ( 0.08 ) | 51.68 | 0.91 | 1 | 47.14 | 1.46 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0     1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0     0.50 0.10 1 ( 0 ) 1.97 ( 0.13 )  4.62 ( 0.08 )  3.34     \n",
+       "3   500   50 0     0.10 0.54 1 ( 0 ) 1.26 ( 0.12 )  2.19 ( 0.08 )  5.07     \n",
+       "4  1000   50 0     0.05 0.73 1 ( 0 ) 0.86 ( 0.09 )  1.12 ( 0.08 )  5.74     \n",
+       "5    50  100 0     2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0     1.00 0.05 1 ( 0 ) 4.29 ( 0.17 )  4.79 ( 0.09 )  5.50     \n",
+       "7   500  100 0     0.20 0.40 1 ( 0 ) 3.42 ( 0.16 )  2.01 ( 0.09 )  7.41     \n",
+       "8  1000  100 0     0.10 0.49 1 ( 0 ) 3.39 ( 0.19 )  1.14 ( 0.07 )  8.25     \n",
+       "9    50  500 0    10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0     5.00 0.01 1 ( 0 ) 24.68 ( 0.48 ) 4.5 ( 0.09 )  26.18     \n",
+       "11  500  500 0     1.00 0.09 1 ( 0 ) 23.08 ( 0.51 ) 2.34 ( 0.1 )  26.74     \n",
+       "12 1000  500 0     0.50 0.14 1 ( 0 ) 22.72 ( 0.46 ) 1.34 ( 0.08 ) 27.38     \n",
+       "13   50 1000 0    20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0    10.00 0.01 1 ( 0 ) 46.76 ( 0.67 ) 4.86 ( 0.09 ) 47.90     \n",
+       "15  500 1000 0     2.00 0.04 1 ( 0 ) 47.82 ( 0.66 ) 2.5 ( 0.1 )   51.32     \n",
+       "16 1000 1000 0     1.00 0.07 1 ( 0 ) 47.14 ( 0.77 ) 1.46 ( 0.08 ) 51.68     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.55 1         1.97   4.62   \n",
+       "3  0.22 1         1.26   2.19   \n",
+       "4  0.13 1         0.86   1.12   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.77 1         4.29   4.79   \n",
+       "7  0.44 1         3.42   2.01   \n",
+       "8  0.38 1         3.39   1.14   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.94 1        24.68   4.50   \n",
+       "11 0.86 1        23.08   2.34   \n",
+       "12 0.83 1        22.72   1.34   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        46.76   4.86   \n",
+       "15 0.93 1        47.82   2.50   \n",
+       "16 0.91 1        47.14   1.46   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_ind_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/0.4_sim_ind_compLasso_binary_update.ipynb b/simulations/notebooks_sim_bin/0.4_sim_ind_compLasso_binary_update.ipynb
new file mode 100644
index 0000000..e765396
--- /dev/null
+++ b/simulations/notebooks_sim_bin/0.4_sim_ind_compLasso_binary_update.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Independent Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16"
+      ],
+      "text/latex": [
+       "16"
+      ],
+      "text/markdown": [
+       "16"
+      ],
+      "text/plain": [
+       "[1] 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
+      "[1] \"indx: 8\"\n",
+      "[1] \"indx: 9\"\n",
+      "[1] \"indx: 10\"\n",
+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
+      "[1] \"indx: 13\"\n",
+      "[1] \"indx: 14\"\n",
+      "[1] \"indx: 15\"\n",
+      "[1] \"indx: 16\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/ind_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_ind_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0</td><td>16.38 ( 0.96 )</td><td>0.61 ( 0.06 )</td><td>0.98 ( 0 )</td><td>0.13</td><td>21.77</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0</td><td>7.94 ( 0.54 )</td><td>0.34 ( 0.06 )</td><td>0.93 ( 0 )</td><td>0.32</td><td>13.60</td><td>0.54</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0</td><td>2.67 ( 0.6 )</td><td>0.1 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.63</td><td> 8.57</td><td>0.19</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0</td><td>1.61 ( 0.48 )</td><td>0.04 ( 0.02 )</td><td>0.91 ( 0 )</td><td>0.75</td><td> 7.57</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0</td><td>39.18 ( 2.14 )</td><td>0.63 ( 0.07 )</td><td>0.99 ( 0 )</td><td>0.06</td><td>44.55</td><td>0.85</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0</td><td>16.29 ( 1.16 )</td><td>0.47 ( 0.06 )</td><td>0.94 ( 0 )</td><td>0.2</td><td>21.82</td><td>0.70</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0 & 16.38 ( 0.96 ) & 0.61 ( 0.06 ) & 0.98 ( 0 ) & 0.13 & 21.77 & 0.71\\\\\n",
+       "\t2 & 100 & 50 & 0 & 7.94 ( 0.54 ) & 0.34 ( 0.06 ) & 0.93 ( 0 ) & 0.32 & 13.60 & 0.54\\\\\n",
+       "\t3 & 500 & 50 & 0 & 2.67 ( 0.6 ) & 0.1 ( 0.03 ) & 0.91 ( 0 ) & 0.63 &  8.57 & 0.19\\\\\n",
+       "\t4 & 1000 & 50 & 0 & 1.61 ( 0.48 ) & 0.04 ( 0.02 ) & 0.91 ( 0 ) & 0.75 &  7.57 & 0.09\\\\\n",
+       "\t5 & 50 & 100 & 0 & 39.18 ( 2.14 ) & 0.63 ( 0.07 ) & 0.99 ( 0 ) & 0.06 & 44.55 & 0.85\\\\\n",
+       "\t6 & 100 & 100 & 0 & 16.29 ( 1.16 ) & 0.47 ( 0.06 ) & 0.94 ( 0 ) & 0.2 & 21.82 & 0.70\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0 | 16.38 ( 0.96 ) | 0.61 ( 0.06 ) | 0.98 ( 0 ) | 0.13 | 21.77 | 0.71 |\n",
+       "| 2 | 100 | 50 | 0 | 7.94 ( 0.54 ) | 0.34 ( 0.06 ) | 0.93 ( 0 ) | 0.32 | 13.60 | 0.54 |\n",
+       "| 3 | 500 | 50 | 0 | 2.67 ( 0.6 ) | 0.1 ( 0.03 ) | 0.91 ( 0 ) | 0.63 |  8.57 | 0.19 |\n",
+       "| 4 | 1000 | 50 | 0 | 1.61 ( 0.48 ) | 0.04 ( 0.02 ) | 0.91 ( 0 ) | 0.75 |  7.57 | 0.09 |\n",
+       "| 5 | 50 | 100 | 0 | 39.18 ( 2.14 ) | 0.63 ( 0.07 ) | 0.99 ( 0 ) | 0.06 | 44.55 | 0.85 |\n",
+       "| 6 | 100 | 100 | 0 | 16.29 ( 1.16 ) | 0.47 ( 0.06 ) | 0.94 ( 0 ) | 0.2 | 21.82 | 0.70 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC        Stab num_select FDR \n",
+       "1 50   50  0   16.38 ( 0.96 ) 0.61 ( 0.06 ) 0.98 ( 0 ) 0.13 21.77      0.71\n",
+       "2 100  50  0   7.94 ( 0.54 )  0.34 ( 0.06 ) 0.93 ( 0 ) 0.32 13.60      0.54\n",
+       "3 500  50  0   2.67 ( 0.6 )   0.1 ( 0.03 )  0.91 ( 0 ) 0.63  8.57      0.19\n",
+       "4 1000 50  0   1.61 ( 0.48 )  0.04 ( 0.02 ) 0.91 ( 0 ) 0.75  7.57      0.09\n",
+       "5 50   100 0   39.18 ( 2.14 ) 0.63 ( 0.07 ) 0.99 ( 0 ) 0.06 44.55      0.85\n",
+       "6 100  100 0   16.29 ( 1.16 ) 0.47 ( 0.06 ) 0.94 ( 0 ) 0.2  21.82      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td>500</td><td>500</td><td>0</td><td>8.32 ( 1.15 )</td><td>0.1 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.43</td><td> 14.22</td><td>0.41</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td>500</td><td>0</td><td>4.63 ( 1.57 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.6</td><td> 10.63</td><td>0.21</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0</td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>1 ( 0 )</td><td>0.01</td><td>411.59</td><td>0.99</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100</td><td>1000</td><td>0</td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>1 ( 0 )</td><td>0.02</td><td>288.75</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td>500</td><td>1000</td><td>0</td><td>9.4 ( 1.07 )</td><td>0.07 ( 0.03 )</td><td>0.91 ( 0 )</td><td>0.4</td><td> 15.33</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td>1.91 ( 0.29 )</td><td>0.01 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.82</td><td>  7.90</td><td>0.18</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 & 500 & 500 & 0 & 8.32 ( 1.15 ) & 0.1 ( 0.03 ) & 0.91 ( 0 ) & 0.43 &  14.22 & 0.41\\\\\n",
+       "\t12 & 1000 & 500 & 0 & 4.63 ( 1.57 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.6 &  10.63 & 0.21\\\\\n",
+       "\t13 & 50 & 1000 & 0 & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 1 ( 0 ) & 0.01 & 411.59 & 0.99\\\\\n",
+       "\t14 & 100 & 1000 & 0 & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 1 ( 0 ) & 0.02 & 288.75 & 0.98\\\\\n",
+       "\t15 & 500 & 1000 & 0 & 9.4 ( 1.07 ) & 0.07 ( 0.03 ) & 0.91 ( 0 ) & 0.4 &  15.33 & 0.47\\\\\n",
+       "\t16 & 1000 & 1000 & 0 & 1.91 ( 0.29 ) & 0.01 ( 0.01 ) & 0.91 ( 0 ) & 0.82 &   7.90 & 0.18\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 | 500 | 500 | 0 | 8.32 ( 1.15 ) | 0.1 ( 0.03 ) | 0.91 ( 0 ) | 0.43 |  14.22 | 0.41 |\n",
+       "| 12 | 1000 | 500 | 0 | 4.63 ( 1.57 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.6 |  10.63 | 0.21 |\n",
+       "| 13 | 50 | 1000 | 0 | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 1 ( 0 ) | 0.01 | 411.59 | 0.99 |\n",
+       "| 14 | 100 | 1000 | 0 | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 1 ( 0 ) | 0.02 | 288.75 | 0.98 |\n",
+       "| 15 | 500 | 1000 | 0 | 9.4 ( 1.07 ) | 0.07 ( 0.03 ) | 0.91 ( 0 ) | 0.4 |  15.33 | 0.47 |\n",
+       "| 16 | 1000 | 1000 | 0 | 1.91 ( 0.29 ) | 0.01 ( 0.01 ) | 0.91 ( 0 ) | 0.82 |   7.90 | 0.18 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP              FN            ROC        Stab num_select FDR \n",
+       "11 500  500  0   8.32 ( 1.15 )   0.1 ( 0.03 )  0.91 ( 0 ) 0.43  14.22     0.41\n",
+       "12 1000 500  0   4.63 ( 1.57 )   0 ( 0 )       0.91 ( 0 ) 0.6   10.63     0.21\n",
+       "13 50   1000 0   406.32 ( 7.33 ) 0.73 ( 0.07 ) 1 ( 0 )    0.01 411.59     0.99\n",
+       "14 100  1000 0   283.24 ( 7.31 ) 0.49 ( 0.06 ) 1 ( 0 )    0.02 288.75     0.98\n",
+       "15 500  1000 0   9.4 ( 1.07 )    0.07 ( 0.03 ) 0.91 ( 0 ) 0.4   15.33     0.47\n",
+       "16 1000 1000 0   1.91 ( 0.29 )   0.01 ( 0.01 ) 0.91 ( 0 ) 0.82   7.90     0.18"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "# result.table_toe$ROC_mean = as.numeric(substr(result.table_toe$ROC, start=1, stop=4))\n",
+    "# result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "# result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "# result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0</td><td>1.00</td><td>0.13</td><td>0.98 ( 0 )</td><td>16.38 ( 0.96 )</td><td>0.61 ( 0.06 )</td><td>21.77</td><td>0.71</td><td>0.98</td><td>16.38</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0</td><td>0.50</td><td>0.32</td><td>0.93 ( 0 )</td><td>7.94 ( 0.54 ) </td><td>0.34 ( 0.06 )</td><td>13.60</td><td>0.54</td><td>0.93</td><td> 7.94</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0</td><td>0.10</td><td>0.63</td><td>0.91 ( 0 )</td><td>2.67 ( 0.6 )  </td><td>0.1 ( 0.03 ) </td><td> 8.57</td><td>0.19</td><td>0.91</td><td> 2.67</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0</td><td>0.05</td><td>0.75</td><td>0.91 ( 0 )</td><td>1.61 ( 0.48 ) </td><td>0.04 ( 0.02 )</td><td> 7.57</td><td>0.09</td><td>0.91</td><td> 1.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0</td><td>2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>39.18 ( 2.14 )</td><td>0.63 ( 0.07 )</td><td>44.55</td><td>0.85</td><td>0.99</td><td>39.18</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0</td><td>1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.29 ( 1.16 )</td><td>0.47 ( 0.06 )</td><td>21.82</td><td>0.7 </td><td>0.94</td><td>16.29</td><td>0.47</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0 & 1.00 & 0.13 & 0.98 ( 0 ) & 16.38 ( 0.96 ) & 0.61 ( 0.06 ) & 21.77 & 0.71 & 0.98 & 16.38 & 0.61\\\\\n",
+       "\t2 &  100 &  50 & 0 & 0.50 & 0.32 & 0.93 ( 0 ) & 7.94 ( 0.54 )  & 0.34 ( 0.06 ) & 13.60 & 0.54 & 0.93 &  7.94 & 0.34\\\\\n",
+       "\t3 &  500 &  50 & 0 & 0.10 & 0.63 & 0.91 ( 0 ) & 2.67 ( 0.6 )   & 0.1 ( 0.03 )  &  8.57 & 0.19 & 0.91 &  2.67 & 0.10\\\\\n",
+       "\t4 & 1000 &  50 & 0 & 0.05 & 0.75 & 0.91 ( 0 ) & 1.61 ( 0.48 )  & 0.04 ( 0.02 ) &  7.57 & 0.09 & 0.91 &  1.61 & 0.04\\\\\n",
+       "\t5 &   50 & 100 & 0 & 2.00 & 0.06 & 0.99 ( 0 ) & 39.18 ( 2.14 ) & 0.63 ( 0.07 ) & 44.55 & 0.85 & 0.99 & 39.18 & 0.63\\\\\n",
+       "\t6 &  100 & 100 & 0 & 1.00 & 0.20 & 0.94 ( 0 ) & 16.29 ( 1.16 ) & 0.47 ( 0.06 ) & 21.82 & 0.7  & 0.94 & 16.29 & 0.47\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0 | 1.00 | 0.13 | 0.98 ( 0 ) | 16.38 ( 0.96 ) | 0.61 ( 0.06 ) | 21.77 | 0.71 | 0.98 | 16.38 | 0.61 |\n",
+       "| 2 |  100 |  50 | 0 | 0.50 | 0.32 | 0.93 ( 0 ) | 7.94 ( 0.54 )  | 0.34 ( 0.06 ) | 13.60 | 0.54 | 0.93 |  7.94 | 0.34 |\n",
+       "| 3 |  500 |  50 | 0 | 0.10 | 0.63 | 0.91 ( 0 ) | 2.67 ( 0.6 )   | 0.1 ( 0.03 )  |  8.57 | 0.19 | 0.91 |  2.67 | 0.10 |\n",
+       "| 4 | 1000 |  50 | 0 | 0.05 | 0.75 | 0.91 ( 0 ) | 1.61 ( 0.48 )  | 0.04 ( 0.02 ) |  7.57 | 0.09 | 0.91 |  1.61 | 0.04 |\n",
+       "| 5 |   50 | 100 | 0 | 2.00 | 0.06 | 0.99 ( 0 ) | 39.18 ( 2.14 ) | 0.63 ( 0.07 ) | 44.55 | 0.85 | 0.99 | 39.18 | 0.63 |\n",
+       "| 6 |  100 | 100 | 0 | 1.00 | 0.20 | 0.94 ( 0 ) | 16.29 ( 1.16 ) | 0.47 ( 0.06 ) | 21.82 | 0.7  | 0.94 | 16.29 | 0.47 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC        FP             FN            num_select\n",
+       "1   50  50 0    1.00  0.13 0.98 ( 0 ) 16.38 ( 0.96 ) 0.61 ( 0.06 ) 21.77     \n",
+       "2  100  50 0    0.50  0.32 0.93 ( 0 ) 7.94 ( 0.54 )  0.34 ( 0.06 ) 13.60     \n",
+       "3  500  50 0    0.10  0.63 0.91 ( 0 ) 2.67 ( 0.6 )   0.1 ( 0.03 )   8.57     \n",
+       "4 1000  50 0    0.05  0.75 0.91 ( 0 ) 1.61 ( 0.48 )  0.04 ( 0.02 )  7.57     \n",
+       "5   50 100 0    2.00  0.06 0.99 ( 0 ) 39.18 ( 2.14 ) 0.63 ( 0.07 ) 44.55     \n",
+       "6  100 100 0    1.00  0.20 0.94 ( 0 ) 16.29 ( 1.16 ) 0.47 ( 0.06 ) 21.82     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.71 0.98     16.38   0.61   \n",
+       "2 0.54 0.93      7.94   0.34   \n",
+       "3 0.19 0.91      2.67   0.10   \n",
+       "4 0.09 0.91      1.61   0.04   \n",
+       "5 0.85 0.99     39.18   0.63   \n",
+       "6 0.7  0.94     16.29   0.47   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.0</td><td>0.43</td><td>0.91 ( 0 )</td><td>8.32 ( 1.15 )  </td><td>0.1 ( 0.03 ) </td><td> 14.22</td><td>0.41</td><td>0.91</td><td>  8.32</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.5</td><td>0.60</td><td>0.91 ( 0 )</td><td>4.63 ( 1.57 )  </td><td>0 ( 0 )      </td><td> 10.63</td><td>0.21</td><td>0.91</td><td>  4.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>411.59</td><td>0.99</td><td>1.00</td><td>406.32</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.0</td><td>0.02</td><td>1 ( 0 )   </td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>288.75</td><td>0.98</td><td>1.00</td><td>283.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.0</td><td>0.40</td><td>0.91 ( 0 )</td><td>9.4 ( 1.07 )   </td><td>0.07 ( 0.03 )</td><td> 15.33</td><td>0.47</td><td>0.91</td><td>  9.40</td><td>0.07</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.0</td><td>0.82</td><td>0.91 ( 0 )</td><td>1.91 ( 0.29 )  </td><td>0.01 ( 0.01 )</td><td>  7.90</td><td>0.18</td><td>0.91</td><td>  1.91</td><td>0.01</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t11 &  500 &  500 & 0 &  1.0 & 0.43 & 0.91 ( 0 ) & 8.32 ( 1.15 )   & 0.1 ( 0.03 )  &  14.22 & 0.41 & 0.91 &   8.32 & 0.10\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.5 & 0.60 & 0.91 ( 0 ) & 4.63 ( 1.57 )   & 0 ( 0 )       &  10.63 & 0.21 & 0.91 &   4.63 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.0 & 0.01 & 1 ( 0 )    & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 411.59 & 0.99 & 1.00 & 406.32 & 0.73\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.0 & 0.02 & 1 ( 0 )    & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 288.75 & 0.98 & 1.00 & 283.24 & 0.49\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.0 & 0.40 & 0.91 ( 0 ) & 9.4 ( 1.07 )    & 0.07 ( 0.03 ) &  15.33 & 0.47 & 0.91 &   9.40 & 0.07\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.0 & 0.82 & 0.91 ( 0 ) & 1.91 ( 0.29 )   & 0.01 ( 0.01 ) &   7.90 & 0.18 & 0.91 &   1.91 & 0.01\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 11 |  500 |  500 | 0 |  1.0 | 0.43 | 0.91 ( 0 ) | 8.32 ( 1.15 )   | 0.1 ( 0.03 )  |  14.22 | 0.41 | 0.91 |   8.32 | 0.10 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.5 | 0.60 | 0.91 ( 0 ) | 4.63 ( 1.57 )   | 0 ( 0 )       |  10.63 | 0.21 | 0.91 |   4.63 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.0 | 0.01 | 1 ( 0 )    | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 411.59 | 0.99 | 1.00 | 406.32 | 0.73 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.0 | 0.02 | 1 ( 0 )    | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 288.75 | 0.98 | 1.00 | 283.24 | 0.49 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.0 | 0.40 | 0.91 ( 0 ) | 9.4 ( 1.07 )    | 0.07 ( 0.03 ) |  15.33 | 0.47 | 0.91 |   9.40 | 0.07 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.0 | 0.82 | 0.91 ( 0 ) | 1.91 ( 0.29 )   | 0.01 ( 0.01 ) |   7.90 | 0.18 | 0.91 |   1.91 | 0.01 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "11  500  500 0     1.0  0.43 0.91 ( 0 ) 8.32 ( 1.15 )   0.1 ( 0.03 ) \n",
+       "12 1000  500 0     0.5  0.60 0.91 ( 0 ) 4.63 ( 1.57 )   0 ( 0 )      \n",
+       "13   50 1000 0    20.0  0.01 1 ( 0 )    406.32 ( 7.33 ) 0.73 ( 0.07 )\n",
+       "14  100 1000 0    10.0  0.02 1 ( 0 )    283.24 ( 7.31 ) 0.49 ( 0.06 )\n",
+       "15  500 1000 0     2.0  0.40 0.91 ( 0 ) 9.4 ( 1.07 )    0.07 ( 0.03 )\n",
+       "16 1000 1000 0     1.0  0.82 0.91 ( 0 ) 1.91 ( 0.29 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "11  14.22     0.41 0.91       8.32  0.10   \n",
+       "12  10.63     0.21 0.91       4.63  0.00   \n",
+       "13 411.59     0.99 1.00     406.32  0.73   \n",
+       "14 288.75     0.98 1.00     283.24  0.49   \n",
+       "15  15.33     0.47 0.91       9.40  0.07   \n",
+       "16   7.90     0.18 0.91       1.91  0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 16 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0</td><td> 1.00</td><td>0.13</td><td>0.98 ( 0 )</td><td>16.38 ( 0.96 ) </td><td>0.61 ( 0.06 )</td><td> 21.77</td><td>0.71</td><td>0.98</td><td> 16.38</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0</td><td> 0.50</td><td>0.32</td><td>0.93 ( 0 )</td><td>7.94 ( 0.54 )  </td><td>0.34 ( 0.06 )</td><td> 13.60</td><td>0.54</td><td>0.93</td><td>  7.94</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0</td><td> 0.10</td><td>0.63</td><td>0.91 ( 0 )</td><td>2.67 ( 0.6 )   </td><td>0.1 ( 0.03 ) </td><td>  8.57</td><td>0.19</td><td>0.91</td><td>  2.67</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0</td><td> 0.05</td><td>0.75</td><td>0.91 ( 0 )</td><td>1.61 ( 0.48 )  </td><td>0.04 ( 0.02 )</td><td>  7.57</td><td>0.09</td><td>0.91</td><td>  1.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>39.18 ( 2.14 ) </td><td>0.63 ( 0.07 )</td><td> 44.55</td><td>0.85</td><td>0.99</td><td> 39.18</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0</td><td> 1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.29 ( 1.16 ) </td><td>0.47 ( 0.06 )</td><td> 21.82</td><td>0.7 </td><td>0.94</td><td> 16.29</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0</td><td> 0.20</td><td>0.47</td><td>0.91 ( 0 )</td><td>5.63 ( 0.84 )  </td><td>0.13 ( 0.03 )</td><td> 11.50</td><td>0.3 </td><td>0.91</td><td>  5.63</td><td>0.13</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0</td><td> 0.10</td><td>0.43</td><td>0.91 ( 0 )</td><td>6.87 ( 1.8 )   </td><td>0 ( 0 )      </td><td> 12.87</td><td>0.17</td><td>0.91</td><td>  6.87</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>251.02 ( 6.8 ) </td><td>0.36 ( 0.06 )</td><td>256.66</td><td>0.98</td><td>1.00</td><td>251.02</td><td>0.36</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0</td><td> 5.00</td><td>0.03</td><td>1 ( 0 )   </td><td>134.32 ( 6.63 )</td><td>0.33 ( 0.05 )</td><td>139.99</td><td>0.95</td><td>1.00</td><td>134.32</td><td>0.33</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0</td><td> 1.00</td><td>0.43</td><td>0.91 ( 0 )</td><td>8.32 ( 1.15 )  </td><td>0.1 ( 0.03 ) </td><td> 14.22</td><td>0.41</td><td>0.91</td><td>  8.32</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0</td><td> 0.50</td><td>0.60</td><td>0.91 ( 0 )</td><td>4.63 ( 1.57 )  </td><td>0 ( 0 )      </td><td> 10.63</td><td>0.21</td><td>0.91</td><td>  4.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>406.32 ( 7.33 )</td><td>0.73 ( 0.07 )</td><td>411.59</td><td>0.99</td><td>1.00</td><td>406.32</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>283.24 ( 7.31 )</td><td>0.49 ( 0.06 )</td><td>288.75</td><td>0.98</td><td>1.00</td><td>283.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0</td><td> 2.00</td><td>0.40</td><td>0.91 ( 0 )</td><td>9.4 ( 1.07 )   </td><td>0.07 ( 0.03 )</td><td> 15.33</td><td>0.47</td><td>0.91</td><td>  9.40</td><td>0.07</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0</td><td> 1.00</td><td>0.82</td><td>0.91 ( 0 )</td><td>1.91 ( 0.29 )  </td><td>0.01 ( 0.01 )</td><td>  7.90</td><td>0.18</td><td>0.91</td><td>  1.91</td><td>0.01</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 16 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0 &  1.00 & 0.13 & 0.98 ( 0 ) & 16.38 ( 0.96 )  & 0.61 ( 0.06 ) &  21.77 & 0.71 & 0.98 &  16.38 & 0.61\\\\\n",
+       "\t2 &  100 &   50 & 0 &  0.50 & 0.32 & 0.93 ( 0 ) & 7.94 ( 0.54 )   & 0.34 ( 0.06 ) &  13.60 & 0.54 & 0.93 &   7.94 & 0.34\\\\\n",
+       "\t3 &  500 &   50 & 0 &  0.10 & 0.63 & 0.91 ( 0 ) & 2.67 ( 0.6 )    & 0.1 ( 0.03 )  &   8.57 & 0.19 & 0.91 &   2.67 & 0.10\\\\\n",
+       "\t4 & 1000 &   50 & 0 &  0.05 & 0.75 & 0.91 ( 0 ) & 1.61 ( 0.48 )   & 0.04 ( 0.02 ) &   7.57 & 0.09 & 0.91 &   1.61 & 0.04\\\\\n",
+       "\t5 &   50 &  100 & 0 &  2.00 & 0.06 & 0.99 ( 0 ) & 39.18 ( 2.14 )  & 0.63 ( 0.07 ) &  44.55 & 0.85 & 0.99 &  39.18 & 0.63\\\\\n",
+       "\t6 &  100 &  100 & 0 &  1.00 & 0.20 & 0.94 ( 0 ) & 16.29 ( 1.16 )  & 0.47 ( 0.06 ) &  21.82 & 0.7  & 0.94 &  16.29 & 0.47\\\\\n",
+       "\t7 &  500 &  100 & 0 &  0.20 & 0.47 & 0.91 ( 0 ) & 5.63 ( 0.84 )   & 0.13 ( 0.03 ) &  11.50 & 0.3  & 0.91 &   5.63 & 0.13\\\\\n",
+       "\t8 & 1000 &  100 & 0 &  0.10 & 0.43 & 0.91 ( 0 ) & 6.87 ( 1.8 )    & 0 ( 0 )       &  12.87 & 0.17 & 0.91 &   6.87 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0 & 10.00 & 0.01 & 1 ( 0 )    & 251.02 ( 6.8 )  & 0.36 ( 0.06 ) & 256.66 & 0.98 & 1.00 & 251.02 & 0.36\\\\\n",
+       "\t10 &  100 &  500 & 0 &  5.00 & 0.03 & 1 ( 0 )    & 134.32 ( 6.63 ) & 0.33 ( 0.05 ) & 139.99 & 0.95 & 1.00 & 134.32 & 0.33\\\\\n",
+       "\t11 &  500 &  500 & 0 &  1.00 & 0.43 & 0.91 ( 0 ) & 8.32 ( 1.15 )   & 0.1 ( 0.03 )  &  14.22 & 0.41 & 0.91 &   8.32 & 0.10\\\\\n",
+       "\t12 & 1000 &  500 & 0 &  0.50 & 0.60 & 0.91 ( 0 ) & 4.63 ( 1.57 )   & 0 ( 0 )       &  10.63 & 0.21 & 0.91 &   4.63 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0 & 20.00 & 0.01 & 1 ( 0 )    & 406.32 ( 7.33 ) & 0.73 ( 0.07 ) & 411.59 & 0.99 & 1.00 & 406.32 & 0.73\\\\\n",
+       "\t14 &  100 & 1000 & 0 & 10.00 & 0.02 & 1 ( 0 )    & 283.24 ( 7.31 ) & 0.49 ( 0.06 ) & 288.75 & 0.98 & 1.00 & 283.24 & 0.49\\\\\n",
+       "\t15 &  500 & 1000 & 0 &  2.00 & 0.40 & 0.91 ( 0 ) & 9.4 ( 1.07 )    & 0.07 ( 0.03 ) &  15.33 & 0.47 & 0.91 &   9.40 & 0.07\\\\\n",
+       "\t16 & 1000 & 1000 & 0 &  1.00 & 0.82 & 0.91 ( 0 ) & 1.91 ( 0.29 )   & 0.01 ( 0.01 ) &   7.90 & 0.18 & 0.91 &   1.91 & 0.01\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 16 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0 |  1.00 | 0.13 | 0.98 ( 0 ) | 16.38 ( 0.96 )  | 0.61 ( 0.06 ) |  21.77 | 0.71 | 0.98 |  16.38 | 0.61 |\n",
+       "| 2 |  100 |   50 | 0 |  0.50 | 0.32 | 0.93 ( 0 ) | 7.94 ( 0.54 )   | 0.34 ( 0.06 ) |  13.60 | 0.54 | 0.93 |   7.94 | 0.34 |\n",
+       "| 3 |  500 |   50 | 0 |  0.10 | 0.63 | 0.91 ( 0 ) | 2.67 ( 0.6 )    | 0.1 ( 0.03 )  |   8.57 | 0.19 | 0.91 |   2.67 | 0.10 |\n",
+       "| 4 | 1000 |   50 | 0 |  0.05 | 0.75 | 0.91 ( 0 ) | 1.61 ( 0.48 )   | 0.04 ( 0.02 ) |   7.57 | 0.09 | 0.91 |   1.61 | 0.04 |\n",
+       "| 5 |   50 |  100 | 0 |  2.00 | 0.06 | 0.99 ( 0 ) | 39.18 ( 2.14 )  | 0.63 ( 0.07 ) |  44.55 | 0.85 | 0.99 |  39.18 | 0.63 |\n",
+       "| 6 |  100 |  100 | 0 |  1.00 | 0.20 | 0.94 ( 0 ) | 16.29 ( 1.16 )  | 0.47 ( 0.06 ) |  21.82 | 0.7  | 0.94 |  16.29 | 0.47 |\n",
+       "| 7 |  500 |  100 | 0 |  0.20 | 0.47 | 0.91 ( 0 ) | 5.63 ( 0.84 )   | 0.13 ( 0.03 ) |  11.50 | 0.3  | 0.91 |   5.63 | 0.13 |\n",
+       "| 8 | 1000 |  100 | 0 |  0.10 | 0.43 | 0.91 ( 0 ) | 6.87 ( 1.8 )    | 0 ( 0 )       |  12.87 | 0.17 | 0.91 |   6.87 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0 | 10.00 | 0.01 | 1 ( 0 )    | 251.02 ( 6.8 )  | 0.36 ( 0.06 ) | 256.66 | 0.98 | 1.00 | 251.02 | 0.36 |\n",
+       "| 10 |  100 |  500 | 0 |  5.00 | 0.03 | 1 ( 0 )    | 134.32 ( 6.63 ) | 0.33 ( 0.05 ) | 139.99 | 0.95 | 1.00 | 134.32 | 0.33 |\n",
+       "| 11 |  500 |  500 | 0 |  1.00 | 0.43 | 0.91 ( 0 ) | 8.32 ( 1.15 )   | 0.1 ( 0.03 )  |  14.22 | 0.41 | 0.91 |   8.32 | 0.10 |\n",
+       "| 12 | 1000 |  500 | 0 |  0.50 | 0.60 | 0.91 ( 0 ) | 4.63 ( 1.57 )   | 0 ( 0 )       |  10.63 | 0.21 | 0.91 |   4.63 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0 | 20.00 | 0.01 | 1 ( 0 )    | 406.32 ( 7.33 ) | 0.73 ( 0.07 ) | 411.59 | 0.99 | 1.00 | 406.32 | 0.73 |\n",
+       "| 14 |  100 | 1000 | 0 | 10.00 | 0.02 | 1 ( 0 )    | 283.24 ( 7.31 ) | 0.49 ( 0.06 ) | 288.75 | 0.98 | 1.00 | 283.24 | 0.49 |\n",
+       "| 15 |  500 | 1000 | 0 |  2.00 | 0.40 | 0.91 ( 0 ) | 9.4 ( 1.07 )    | 0.07 ( 0.03 ) |  15.33 | 0.47 | 0.91 |   9.40 | 0.07 |\n",
+       "| 16 | 1000 | 1000 | 0 |  1.00 | 0.82 | 0.91 ( 0 ) | 1.91 ( 0.29 )   | 0.01 ( 0.01 ) |   7.90 | 0.18 | 0.91 |   1.91 | 0.01 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "1    50   50 0     1.00 0.13 0.98 ( 0 ) 16.38 ( 0.96 )  0.61 ( 0.06 )\n",
+       "2   100   50 0     0.50 0.32 0.93 ( 0 ) 7.94 ( 0.54 )   0.34 ( 0.06 )\n",
+       "3   500   50 0     0.10 0.63 0.91 ( 0 ) 2.67 ( 0.6 )    0.1 ( 0.03 ) \n",
+       "4  1000   50 0     0.05 0.75 0.91 ( 0 ) 1.61 ( 0.48 )   0.04 ( 0.02 )\n",
+       "5    50  100 0     2.00 0.06 0.99 ( 0 ) 39.18 ( 2.14 )  0.63 ( 0.07 )\n",
+       "6   100  100 0     1.00 0.20 0.94 ( 0 ) 16.29 ( 1.16 )  0.47 ( 0.06 )\n",
+       "7   500  100 0     0.20 0.47 0.91 ( 0 ) 5.63 ( 0.84 )   0.13 ( 0.03 )\n",
+       "8  1000  100 0     0.10 0.43 0.91 ( 0 ) 6.87 ( 1.8 )    0 ( 0 )      \n",
+       "9    50  500 0    10.00 0.01 1 ( 0 )    251.02 ( 6.8 )  0.36 ( 0.06 )\n",
+       "10  100  500 0     5.00 0.03 1 ( 0 )    134.32 ( 6.63 ) 0.33 ( 0.05 )\n",
+       "11  500  500 0     1.00 0.43 0.91 ( 0 ) 8.32 ( 1.15 )   0.1 ( 0.03 ) \n",
+       "12 1000  500 0     0.50 0.60 0.91 ( 0 ) 4.63 ( 1.57 )   0 ( 0 )      \n",
+       "13   50 1000 0    20.00 0.01 1 ( 0 )    406.32 ( 7.33 ) 0.73 ( 0.07 )\n",
+       "14  100 1000 0    10.00 0.02 1 ( 0 )    283.24 ( 7.31 ) 0.49 ( 0.06 )\n",
+       "15  500 1000 0     2.00 0.40 0.91 ( 0 ) 9.4 ( 1.07 )    0.07 ( 0.03 )\n",
+       "16 1000 1000 0     1.00 0.82 0.91 ( 0 ) 1.91 ( 0.29 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   21.77     0.71 0.98      16.38  0.61   \n",
+       "2   13.60     0.54 0.93       7.94  0.34   \n",
+       "3    8.57     0.19 0.91       2.67  0.10   \n",
+       "4    7.57     0.09 0.91       1.61  0.04   \n",
+       "5   44.55     0.85 0.99      39.18  0.63   \n",
+       "6   21.82     0.7  0.94      16.29  0.47   \n",
+       "7   11.50     0.3  0.91       5.63  0.13   \n",
+       "8   12.87     0.17 0.91       6.87  0.00   \n",
+       "9  256.66     0.98 1.00     251.02  0.36   \n",
+       "10 139.99     0.95 1.00     134.32  0.33   \n",
+       "11  14.22     0.41 0.91       8.32  0.10   \n",
+       "12  10.63     0.21 0.91       4.63  0.00   \n",
+       "13 411.59     0.99 1.00     406.32  0.73   \n",
+       "14 288.75     0.98 1.00     283.24  0.49   \n",
+       "15  15.33     0.47 0.91       9.40  0.07   \n",
+       "16   7.90     0.18 0.91       1.91  0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_updated/sim_ind_GencompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/1.1_sim_toe_lasso_binary_update.ipynb b/simulations/notebooks_sim_bin/1.1_sim_toe_lasso_binary_update.ipynb
new file mode 100644
index 0000000..339fd90
--- /dev/null
+++ b/simulations/notebooks_sim_bin/1.1_sim_toe_lasso_binary_update.ipynb
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
+      "[1] \"indx: 6\"\n",
+      "[1] \"indx: 7\"\n",
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+      "[1] \"indx: 11\"\n",
+      "[1] \"indx: 12\"\n",
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+      "[1] \"indx: 21\"\n",
+      "[1] \"indx: 22\"\n",
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+      "[1] \"indx: 29\"\n",
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+      "[1] \"indx: 50\"\n",
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+      "[1] \"indx: 57\"\n",
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+      "[1] \"indx: 59\"\n",
+      "[1] \"indx: 60\"\n",
+      "[1] \"indx: 61\"\n",
+      "[1] \"indx: 62\"\n",
+      "[1] \"indx: 63\"\n",
+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>3.65 ( 0.23 )</td><td>2.83 ( 0.11 )</td><td>0.78 ( 0.01 )</td><td>0.32</td><td> 5.82</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>7.35 ( 0.34 )</td><td>0.68 ( 0.09 )</td><td>0.85 ( 0.01 )</td><td>0.34</td><td>11.67</td><td>0.50</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>4.99 ( 0.22 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.55</td><td> 9.99</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>2.42 ( 0.12 )</td><td>0 ( 0 )</td><td>0.93 ( 0 )</td><td>0.78</td><td> 7.42</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>4.65 ( 0.29 )</td><td>2.93 ( 0.12 )</td><td>0.74 ( 0.01 )</td><td>0.26</td><td> 6.72</td><td>0.48</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>8.11 ( 0.41 )</td><td>0.86 ( 0.09 )</td><td>0.85 ( 0.01 )</td><td>0.33</td><td>12.25</td><td>0.52</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 3.65 ( 0.23 ) & 2.83 ( 0.11 ) & 0.78 ( 0.01 ) & 0.32 &  5.82 & 0.39\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 7.35 ( 0.34 ) & 0.68 ( 0.09 ) & 0.85 ( 0.01 ) & 0.34 & 11.67 & 0.50\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 4.99 ( 0.22 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.55 &  9.99 & 0.37\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 2.42 ( 0.12 ) & 0 ( 0 ) & 0.93 ( 0 ) & 0.78 &  7.42 & 0.17\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 4.65 ( 0.29 ) & 2.93 ( 0.12 ) & 0.74 ( 0.01 ) & 0.26 &  6.72 & 0.48\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 8.11 ( 0.41 ) & 0.86 ( 0.09 ) & 0.85 ( 0.01 ) & 0.33 & 12.25 & 0.52\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 3.65 ( 0.23 ) | 2.83 ( 0.11 ) | 0.78 ( 0.01 ) | 0.32 |  5.82 | 0.39 |\n",
+       "| 2 | 100 | 50 | 0.1 | 7.35 ( 0.34 ) | 0.68 ( 0.09 ) | 0.85 ( 0.01 ) | 0.34 | 11.67 | 0.50 |\n",
+       "| 3 | 500 | 50 | 0.1 | 4.99 ( 0.22 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.55 |  9.99 | 0.37 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 2.42 ( 0.12 ) | 0 ( 0 ) | 0.93 ( 0 ) | 0.78 |  7.42 | 0.17 |\n",
+       "| 5 | 50 | 100 | 0.1 | 4.65 ( 0.29 ) | 2.93 ( 0.12 ) | 0.74 ( 0.01 ) | 0.26 |  6.72 | 0.48 |\n",
+       "| 6 | 100 | 100 | 0.1 | 8.11 ( 0.41 ) | 0.86 ( 0.09 ) | 0.85 ( 0.01 ) | 0.33 | 12.25 | 0.52 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 3.65 ( 0.23 ) 2.83 ( 0.11 ) 0.78 ( 0.01 ) 0.32  5.82      0.39\n",
+       "2 100  50  0.1 7.35 ( 0.34 ) 0.68 ( 0.09 ) 0.85 ( 0.01 ) 0.34 11.67      0.50\n",
+       "3 500  50  0.1 4.99 ( 0.22 ) 0 ( 0 )       0.92 ( 0 )    0.55  9.99      0.37\n",
+       "4 1000 50  0.1 2.42 ( 0.12 ) 0 ( 0 )       0.93 ( 0 )    0.78  7.42      0.17\n",
+       "5 50   100 0.1 4.65 ( 0.29 ) 2.93 ( 0.12 ) 0.74 ( 0.01 ) 0.26  6.72      0.48\n",
+       "6 100  100 0.1 8.11 ( 0.41 ) 0.86 ( 0.09 ) 0.85 ( 0.01 ) 0.33 12.25      0.52"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 )</td><td>0.81 ( 0 )</td><td>0.19</td><td>19.51</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>0.83 ( 0 )</td><td>0.31</td><td>16.54</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>8.02 ( 0.28 )</td><td>4.9 ( 0.07 )</td><td>0.66 ( 0.01 )</td><td>0.08</td><td> 8.12</td><td>0.85</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>5.85 ( 0.58 )</td><td>4.37 ( 0.07 )</td><td>0.69 ( 0.01 )</td><td>0.18</td><td> 6.48</td><td>0.61</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>18.7 ( 0.93 )</td><td>1.88 ( 0.1 )</td><td>0.8 ( 0 )</td><td>0.16</td><td>21.82</td><td>0.77</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>0.83 ( 0 )</td><td>0.21</td><td>24.66</td><td>0.75</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 15.93 ( 0.73 ) & 1.42 ( 0.1 ) & 0.81 ( 0 ) & 0.19 & 19.51 & 0.73\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 0.83 ( 0 ) & 0.31 & 16.54 & 0.63\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 8.02 ( 0.28 ) & 4.9 ( 0.07 ) & 0.66 ( 0.01 ) & 0.08 &  8.12 & 0.85\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 5.85 ( 0.58 ) & 4.37 ( 0.07 ) & 0.69 ( 0.01 ) & 0.18 &  6.48 & 0.61\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 18.7 ( 0.93 ) & 1.88 ( 0.1 ) & 0.8 ( 0 ) & 0.16 & 21.82 & 0.77\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 0.83 ( 0 ) & 0.21 & 24.66 & 0.75\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 15.93 ( 0.73 ) | 1.42 ( 0.1 ) | 0.81 ( 0 ) | 0.19 | 19.51 | 0.73 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 0.83 ( 0 ) | 0.31 | 16.54 | 0.63 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 8.02 ( 0.28 ) | 4.9 ( 0.07 ) | 0.66 ( 0.01 ) | 0.08 |  8.12 | 0.85 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 5.85 ( 0.58 ) | 4.37 ( 0.07 ) | 0.69 ( 0.01 ) | 0.18 |  6.48 | 0.61 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 18.7 ( 0.93 ) | 1.88 ( 0.1 ) | 0.8 ( 0 ) | 0.16 | 21.82 | 0.77 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 0.83 ( 0 ) | 0.21 | 24.66 | 0.75 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 15.93 ( 0.73 ) 1.42 ( 0.1 )  0.81 ( 0 )    0.19 19.51     \n",
+       "76 1000 500  0.9 11.96 ( 0.46 ) 0.42 ( 0.06 ) 0.83 ( 0 )    0.31 16.54     \n",
+       "77 50   1000 0.9 8.02 ( 0.28 )  4.9 ( 0.07 )  0.66 ( 0.01 ) 0.08  8.12     \n",
+       "78 100  1000 0.9 5.85 ( 0.58 )  4.37 ( 0.07 ) 0.69 ( 0.01 ) 0.18  6.48     \n",
+       "79 500  1000 0.9 18.7 ( 0.93 )  1.88 ( 0.1 )  0.8 ( 0 )     0.16 21.82     \n",
+       "80 1000 1000 0.9 20.07 ( 0.72 ) 0.41 ( 0.05 ) 0.83 ( 0 )    0.21 24.66     \n",
+       "   FDR \n",
+       "75 0.73\n",
+       "76 0.63\n",
+       "77 0.85\n",
+       "78 0.61\n",
+       "79 0.77\n",
+       "80 0.75"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>3.65 ( 0.23 )</td><td>2.83 ( 0.11 )</td><td> 5.82</td><td>0.39</td><td>0.78</td><td>3.65</td><td>2.83</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.34</td><td>0.85 ( 0.01 )</td><td>7.35 ( 0.34 )</td><td>0.68 ( 0.09 )</td><td>11.67</td><td>0.5 </td><td>0.85</td><td>7.35</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.55</td><td>0.92 ( 0 )   </td><td>4.99 ( 0.22 )</td><td>0 ( 0 )      </td><td> 9.99</td><td>0.37</td><td>0.92</td><td>4.99</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.78</td><td>0.93 ( 0 )   </td><td>2.42 ( 0.12 )</td><td>0 ( 0 )      </td><td> 7.42</td><td>0.17</td><td>0.93</td><td>2.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>4.65 ( 0.29 )</td><td>2.93 ( 0.12 )</td><td> 6.72</td><td>0.48</td><td>0.74</td><td>4.65</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.11 ( 0.41 )</td><td>0.86 ( 0.09 )</td><td>12.25</td><td>0.52</td><td>0.85</td><td>8.11</td><td>0.86</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.32 & 0.78 ( 0.01 ) & 3.65 ( 0.23 ) & 2.83 ( 0.11 ) &  5.82 & 0.39 & 0.78 & 3.65 & 2.83\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.34 & 0.85 ( 0.01 ) & 7.35 ( 0.34 ) & 0.68 ( 0.09 ) & 11.67 & 0.5  & 0.85 & 7.35 & 0.68\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.55 & 0.92 ( 0 )    & 4.99 ( 0.22 ) & 0 ( 0 )       &  9.99 & 0.37 & 0.92 & 4.99 & 0.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.78 & 0.93 ( 0 )    & 2.42 ( 0.12 ) & 0 ( 0 )       &  7.42 & 0.17 & 0.93 & 2.42 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.26 & 0.74 ( 0.01 ) & 4.65 ( 0.29 ) & 2.93 ( 0.12 ) &  6.72 & 0.48 & 0.74 & 4.65 & 2.93\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.33 & 0.85 ( 0.01 ) & 8.11 ( 0.41 ) & 0.86 ( 0.09 ) & 12.25 & 0.52 & 0.85 & 8.11 & 0.86\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.32 | 0.78 ( 0.01 ) | 3.65 ( 0.23 ) | 2.83 ( 0.11 ) |  5.82 | 0.39 | 0.78 | 3.65 | 2.83 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.34 | 0.85 ( 0.01 ) | 7.35 ( 0.34 ) | 0.68 ( 0.09 ) | 11.67 | 0.5  | 0.85 | 7.35 | 0.68 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.55 | 0.92 ( 0 )    | 4.99 ( 0.22 ) | 0 ( 0 )       |  9.99 | 0.37 | 0.92 | 4.99 | 0.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.78 | 0.93 ( 0 )    | 2.42 ( 0.12 ) | 0 ( 0 )       |  7.42 | 0.17 | 0.93 | 2.42 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.26 | 0.74 ( 0.01 ) | 4.65 ( 0.29 ) | 2.93 ( 0.12 ) |  6.72 | 0.48 | 0.74 | 4.65 | 2.93 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.33 | 0.85 ( 0.01 ) | 8.11 ( 0.41 ) | 0.86 ( 0.09 ) | 12.25 | 0.52 | 0.85 | 8.11 | 0.86 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.32 0.78 ( 0.01 ) 3.65 ( 0.23 ) 2.83 ( 0.11 )  5.82     \n",
+       "2  100  50 0.1  0.50  0.34 0.85 ( 0.01 ) 7.35 ( 0.34 ) 0.68 ( 0.09 ) 11.67     \n",
+       "3  500  50 0.1  0.10  0.55 0.92 ( 0 )    4.99 ( 0.22 ) 0 ( 0 )        9.99     \n",
+       "4 1000  50 0.1  0.05  0.78 0.93 ( 0 )    2.42 ( 0.12 ) 0 ( 0 )        7.42     \n",
+       "5   50 100 0.1  2.00  0.26 0.74 ( 0.01 ) 4.65 ( 0.29 ) 2.93 ( 0.12 )  6.72     \n",
+       "6  100 100 0.1  1.00  0.33 0.85 ( 0.01 ) 8.11 ( 0.41 ) 0.86 ( 0.09 ) 12.25     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.39 0.78     3.65    2.83   \n",
+       "2 0.5  0.85     7.35    0.68   \n",
+       "3 0.37 0.92     4.99    0.00   \n",
+       "4 0.17 0.93     2.42    0.00   \n",
+       "5 0.48 0.74     4.65    2.93   \n",
+       "6 0.52 0.85     8.11    0.86   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.19</td><td>0.81 ( 0 )   </td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 ) </td><td>19.51</td><td>0.73</td><td>0.81</td><td>15.93</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.31</td><td>0.83 ( 0 )   </td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>16.54</td><td>0.63</td><td>0.83</td><td>11.96</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.08</td><td>0.66 ( 0.01 )</td><td>8.02 ( 0.28 ) </td><td>4.9 ( 0.07 ) </td><td> 8.12</td><td>0.85</td><td>0.66</td><td> 8.02</td><td>4.90</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.18</td><td>0.69 ( 0.01 )</td><td>5.85 ( 0.58 ) </td><td>4.37 ( 0.07 )</td><td> 6.48</td><td>0.61</td><td>0.69</td><td> 5.85</td><td>4.37</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.16</td><td>0.8 ( 0 )    </td><td>18.7 ( 0.93 ) </td><td>1.88 ( 0.1 ) </td><td>21.82</td><td>0.77</td><td>0.80</td><td>18.70</td><td>1.88</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.21</td><td>0.83 ( 0 )   </td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>24.66</td><td>0.75</td><td>0.83</td><td>20.07</td><td>0.41</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.19 & 0.81 ( 0 )    & 15.93 ( 0.73 ) & 1.42 ( 0.1 )  & 19.51 & 0.73 & 0.81 & 15.93 & 1.42\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.31 & 0.83 ( 0 )    & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 16.54 & 0.63 & 0.83 & 11.96 & 0.42\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.08 & 0.66 ( 0.01 ) & 8.02 ( 0.28 )  & 4.9 ( 0.07 )  &  8.12 & 0.85 & 0.66 &  8.02 & 4.90\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.18 & 0.69 ( 0.01 ) & 5.85 ( 0.58 )  & 4.37 ( 0.07 ) &  6.48 & 0.61 & 0.69 &  5.85 & 4.37\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.16 & 0.8 ( 0 )     & 18.7 ( 0.93 )  & 1.88 ( 0.1 )  & 21.82 & 0.77 & 0.80 & 18.70 & 1.88\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.21 & 0.83 ( 0 )    & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 24.66 & 0.75 & 0.83 & 20.07 & 0.41\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.19 | 0.81 ( 0 )    | 15.93 ( 0.73 ) | 1.42 ( 0.1 )  | 19.51 | 0.73 | 0.81 | 15.93 | 1.42 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.31 | 0.83 ( 0 )    | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 16.54 | 0.63 | 0.83 | 11.96 | 0.42 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.08 | 0.66 ( 0.01 ) | 8.02 ( 0.28 )  | 4.9 ( 0.07 )  |  8.12 | 0.85 | 0.66 |  8.02 | 4.90 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.18 | 0.69 ( 0.01 ) | 5.85 ( 0.58 )  | 4.37 ( 0.07 ) |  6.48 | 0.61 | 0.69 |  5.85 | 4.37 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.16 | 0.8 ( 0 )     | 18.7 ( 0.93 )  | 1.88 ( 0.1 )  | 21.82 | 0.77 | 0.80 | 18.70 | 1.88 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.21 | 0.83 ( 0 )    | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 24.66 | 0.75 | 0.83 | 20.07 | 0.41 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.19 0.81 ( 0 )    15.93 ( 0.73 ) 1.42 ( 0.1 ) \n",
+       "76 1000  500 0.9   0.5  0.31 0.83 ( 0 )    11.96 ( 0.46 ) 0.42 ( 0.06 )\n",
+       "77   50 1000 0.9  20.0  0.08 0.66 ( 0.01 ) 8.02 ( 0.28 )  4.9 ( 0.07 ) \n",
+       "78  100 1000 0.9  10.0  0.18 0.69 ( 0.01 ) 5.85 ( 0.58 )  4.37 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.16 0.8 ( 0 )     18.7 ( 0.93 )  1.88 ( 0.1 ) \n",
+       "80 1000 1000 0.9   1.0  0.21 0.83 ( 0 )    20.07 ( 0.72 ) 0.41 ( 0.05 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 19.51      0.73 0.81     15.93   1.42   \n",
+       "76 16.54      0.63 0.83     11.96   0.42   \n",
+       "77  8.12      0.85 0.66      8.02   4.90   \n",
+       "78  6.48      0.61 0.69      5.85   4.37   \n",
+       "79 21.82      0.77 0.80     18.70   1.88   \n",
+       "80 24.66      0.75 0.83     20.07   0.41   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>3.65 ( 0.23 ) </td><td>2.83 ( 0.11 )</td><td> 5.82</td><td>0.39</td><td>0.78</td><td> 3.65</td><td>2.83</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.34</td><td>0.85 ( 0.01 )</td><td>7.35 ( 0.34 ) </td><td>0.68 ( 0.09 )</td><td>11.67</td><td>0.5 </td><td>0.85</td><td> 7.35</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.55</td><td>0.92 ( 0 )   </td><td>4.99 ( 0.22 ) </td><td>0 ( 0 )      </td><td> 9.99</td><td>0.37</td><td>0.92</td><td> 4.99</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.78</td><td>0.93 ( 0 )   </td><td>2.42 ( 0.12 ) </td><td>0 ( 0 )      </td><td> 7.42</td><td>0.17</td><td>0.93</td><td> 2.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>4.65 ( 0.29 ) </td><td>2.93 ( 0.12 )</td><td> 6.72</td><td>0.48</td><td>0.74</td><td> 4.65</td><td>2.93</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.11 ( 0.41 ) </td><td>0.86 ( 0.09 )</td><td>12.25</td><td>0.52</td><td>0.85</td><td> 8.11</td><td>0.86</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.40</td><td>0.92 ( 0 )   </td><td>8.63 ( 0.29 ) </td><td>0 ( 0 )      </td><td>13.63</td><td>0.54</td><td>0.92</td><td> 8.63</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.64</td><td>0.92 ( 0 )   </td><td>4.02 ( 0.18 ) </td><td>0 ( 0 )      </td><td> 9.02</td><td>0.31</td><td>0.92</td><td> 4.02</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.12</td><td>0.71 ( 0.01 )</td><td>8.29 ( 0.25 ) </td><td>3.89 ( 0.1 ) </td><td> 9.40</td><td>0.77</td><td>0.71</td><td> 8.29</td><td>3.89</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.24</td><td>0.8 ( 0.01 ) </td><td>8.84 ( 0.61 ) </td><td>2.19 ( 0.12 )</td><td>11.65</td><td>0.57</td><td>0.80</td><td> 8.84</td><td>2.19</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.21</td><td>0.92 ( 0 )   </td><td>22.86 ( 0.75 )</td><td>0 ( 0 )      </td><td>27.86</td><td>0.77</td><td>0.92</td><td>22.86</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.33</td><td>0.93 ( 0 )   </td><td>12.73 ( 0.35 )</td><td>0 ( 0 )      </td><td>17.73</td><td>0.65</td><td>0.93</td><td>12.73</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.09</td><td>0.68 ( 0.01 )</td><td>10.35 ( 0.33 )</td><td>4.14 ( 0.09 )</td><td>11.21</td><td>0.82</td><td>0.68</td><td>10.35</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.19</td><td>0.77 ( 0.01 )</td><td>10.84 ( 0.75 )</td><td>2.46 ( 0.1 ) </td><td>13.38</td><td>0.63</td><td>0.77</td><td>10.84</td><td>2.46</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.16</td><td>0.92 ( 0 )   </td><td>30.83 ( 1.05 )</td><td>0 ( 0 )      </td><td>35.83</td><td>0.81</td><td>0.92</td><td>30.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.23</td><td>0.93 ( 0 )   </td><td>20.39 ( 0.59 )</td><td>0 ( 0 )      </td><td>25.39</td><td>0.75</td><td>0.93</td><td>20.39</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.28</td><td>0.75 ( 0.01 )</td><td>3.69 ( 0.22 ) </td><td>3.04 ( 0.1 ) </td><td> 5.65</td><td>0.4 </td><td>0.75</td><td> 3.69</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.32</td><td>0.84 ( 0.01 )</td><td>7.11 ( 0.33 ) </td><td>0.93 ( 0.1 ) </td><td>11.18</td><td>0.5 </td><td>0.84</td><td> 7.11</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.51</td><td>0.92 ( 0 )   </td><td>5.67 ( 0.22 ) </td><td>0 ( 0 )      </td><td>10.67</td><td>0.41</td><td>0.92</td><td> 5.67</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.78</td><td>0.92 ( 0 )   </td><td>2.48 ( 0.13 ) </td><td>0 ( 0 )      </td><td> 7.48</td><td>0.18</td><td>0.92</td><td> 2.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.26</td><td>0.75 ( 0.01 )</td><td>4.41 ( 0.27 ) </td><td>3.47 ( 0.09 )</td><td> 5.94</td><td>0.49</td><td>0.75</td><td> 4.41</td><td>3.47</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.28</td><td>0.82 ( 0.01 )</td><td>8.16 ( 0.53 ) </td><td>1.46 ( 0.11 )</td><td>11.70</td><td>0.52</td><td>0.82</td><td> 8.16</td><td>1.46</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.41</td><td>0.92 ( 0 )   </td><td>8.48 ( 0.34 ) </td><td>0 ( 0 )      </td><td>13.48</td><td>0.53</td><td>0.92</td><td> 8.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.67</td><td>0.92 ( 0 )   </td><td>3.71 ( 0.15 ) </td><td>0 ( 0 )      </td><td> 8.71</td><td>0.29</td><td>0.92</td><td> 3.71</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.13</td><td>0.69 ( 0.01 )</td><td>7.94 ( 0.3 )  </td><td>4.11 ( 0.08 )</td><td> 8.83</td><td>0.77</td><td>0.69</td><td> 7.94</td><td>4.11</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.20</td><td>0.78 ( 0.01 )</td><td>10.16 ( 0.68 )</td><td>2.49 ( 0.11 )</td><td>12.67</td><td>0.63</td><td>0.78</td><td>10.16</td><td>2.49</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.20</td><td>0.91 ( 0 )   </td><td>23.65 ( 0.7 ) </td><td>0 ( 0 )      </td><td>28.65</td><td>0.78</td><td>0.91</td><td>23.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.34</td><td>0.92 ( 0 )   </td><td>12.28 ( 0.34 )</td><td>0 ( 0 )      </td><td>17.28</td><td>0.64</td><td>0.92</td><td>12.28</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.10</td><td>0.68 ( 0.01 )</td><td>9.77 ( 0.31 ) </td><td>4.3 ( 0.06 ) </td><td>10.47</td><td>0.83</td><td>0.68</td><td> 9.77</td><td>4.30</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.17</td><td>0.77 ( 0.01 )</td><td>10.68 ( 0.8 ) </td><td>3.02 ( 0.11 )</td><td>12.66</td><td>0.67</td><td>0.77</td><td>10.68</td><td>3.02</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.57</td><td>0.89 ( 0 )   </td><td>4.61 ( 0.17 ) </td><td>0.04 ( 0.02 )</td><td> 9.57</td><td>0.36</td><td>0.89</td><td> 4.61</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.74</td><td>0.89 ( 0 )   </td><td>2.8 ( 0.15 )  </td><td>0 ( 0 )      </td><td> 7.80</td><td>0.21</td><td>0.89</td><td> 2.80</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.24</td><td>0.71 ( 0.01 )</td><td>4.31 ( 0.28 ) </td><td>4.05 ( 0.09 )</td><td> 5.26</td><td>0.55</td><td>0.71</td><td> 4.31</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.27</td><td>0.79 ( 0.01 )</td><td>6.34 ( 0.4 )  </td><td>2.67 ( 0.12 )</td><td> 8.67</td><td>0.53</td><td>0.79</td><td> 6.34</td><td>2.67</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.43</td><td>0.89 ( 0 )   </td><td>7.92 ( 0.24 ) </td><td>0.04 ( 0.02 )</td><td>12.88</td><td>0.52</td><td>0.89</td><td> 7.92</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.66</td><td>0.9 ( 0 )    </td><td>3.85 ( 0.16 ) </td><td>0 ( 0 )      </td><td> 8.85</td><td>0.3 </td><td>0.90</td><td> 3.85</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.14</td><td>0.7 ( 0.01 ) </td><td>6.5 ( 0.24 )  </td><td>4.63 ( 0.06 )</td><td> 6.87</td><td>0.77</td><td>0.70</td><td> 6.50</td><td>4.63</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.21</td><td>0.76 ( 0.01 )</td><td>6.98 ( 0.58 ) </td><td>3.65 ( 0.08 )</td><td> 8.33</td><td>0.56</td><td>0.76</td><td> 6.98</td><td>3.65</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.20</td><td>0.88 ( 0 )   </td><td>22.04 ( 0.74 )</td><td>0.14 ( 0.04 )</td><td>26.90</td><td>0.76</td><td>0.88</td><td>22.04</td><td>0.14</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.33</td><td>0.89 ( 0 )   </td><td>13.15 ( 0.38 )</td><td>0 ( 0 )      </td><td>18.15</td><td>0.65</td><td>0.89</td><td>13.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.10</td><td>0.66 ( 0.01 )</td><td>8.82 ( 0.28 ) </td><td>4.72 ( 0.05 )</td><td> 9.10</td><td>0.85</td><td>0.66</td><td> 8.82</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.18</td><td>0.76 ( 0.01 )</td><td>6.93 ( 0.59 ) </td><td>4 ( 0.08 )   </td><td> 7.93</td><td>0.62</td><td>0.76</td><td> 6.93</td><td>4.00</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.16</td><td>0.87 ( 0 )   </td><td>29.07 ( 1 )   </td><td>0.16 ( 0.04 )</td><td>33.91</td><td>0.81</td><td>0.87</td><td>29.07</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.22</td><td>0.89 ( 0 )   </td><td>21.8 ( 0.63 ) </td><td>0 ( 0 )      </td><td>26.80</td><td>0.76</td><td>0.89</td><td>21.80</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.30</td><td>0.69 ( 0.01 )</td><td>2.9 ( 0.2 )   </td><td>4.22 ( 0.09 )</td><td> 3.68</td><td>0.41</td><td>0.69</td><td> 2.90</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.37</td><td>0.76 ( 0.01 )</td><td>3.93 ( 0.21 ) </td><td>2.74 ( 0.11 )</td><td> 6.19</td><td>0.41</td><td>0.76</td><td> 3.93</td><td>2.74</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.57</td><td>0.83 ( 0 )   </td><td>3.65 ( 0.16 ) </td><td>0.56 ( 0.08 )</td><td> 8.09</td><td>0.31</td><td>0.83</td><td> 3.65</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.76</td><td>0.83 ( 0 )   </td><td>2.41 ( 0.12 ) </td><td>0.23 ( 0.04 )</td><td> 7.18</td><td>0.18</td><td>0.83</td><td> 2.41</td><td>0.23</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.26</td><td>0.7 ( 0.01 ) </td><td>3.29 ( 0.23 ) </td><td>4.29 ( 0.09 )</td><td> 4.00</td><td>0.45</td><td>0.70</td><td> 3.29</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.32</td><td>0.76 ( 0.01 )</td><td>4.77 ( 0.33 ) </td><td>3.26 ( 0.09 )</td><td> 6.51</td><td>0.46</td><td>0.76</td><td> 4.77</td><td>3.26</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.45</td><td>0.82 ( 0 )   </td><td>5.65 ( 0.21 ) </td><td>0.92 ( 0.09 )</td><td> 9.73</td><td>0.45</td><td>0.82</td><td> 5.65</td><td>0.92</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.63</td><td>0.83 ( 0 )   </td><td>3.87 ( 0.18 ) </td><td>0.24 ( 0.05 )</td><td> 8.63</td><td>0.3 </td><td>0.83</td><td> 3.87</td><td>0.24</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.15</td><td>0.66 ( 0.01 )</td><td>5.8 ( 0.24 )  </td><td>4.65 ( 0.07 )</td><td> 6.15</td><td>0.75</td><td>0.66</td><td> 5.80</td><td>4.65</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.21</td><td>0.7 ( 0.01 ) </td><td>5.55 ( 0.55 ) </td><td>4.14 ( 0.09 )</td><td> 6.41</td><td>0.51</td><td>0.70</td><td> 5.55</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.19</td><td>0.81 ( 0 )   </td><td>15.93 ( 0.73 )</td><td>1.42 ( 0.1 ) </td><td>19.51</td><td>0.73</td><td>0.81</td><td>15.93</td><td>1.42</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.31</td><td>0.83 ( 0 )   </td><td>11.96 ( 0.46 )</td><td>0.42 ( 0.06 )</td><td>16.54</td><td>0.63</td><td>0.83</td><td>11.96</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.08</td><td>0.66 ( 0.01 )</td><td>8.02 ( 0.28 ) </td><td>4.9 ( 0.07 ) </td><td> 8.12</td><td>0.85</td><td>0.66</td><td> 8.02</td><td>4.90</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.18</td><td>0.69 ( 0.01 )</td><td>5.85 ( 0.58 ) </td><td>4.37 ( 0.07 )</td><td> 6.48</td><td>0.61</td><td>0.69</td><td> 5.85</td><td>4.37</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.16</td><td>0.8 ( 0 )    </td><td>18.7 ( 0.93 ) </td><td>1.88 ( 0.1 ) </td><td>21.82</td><td>0.77</td><td>0.80</td><td>18.70</td><td>1.88</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.21</td><td>0.83 ( 0 )   </td><td>20.07 ( 0.72 )</td><td>0.41 ( 0.05 )</td><td>24.66</td><td>0.75</td><td>0.83</td><td>20.07</td><td>0.41</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.32 & 0.78 ( 0.01 ) & 3.65 ( 0.23 )  & 2.83 ( 0.11 ) &  5.82 & 0.39 & 0.78 &  3.65 & 2.83\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.34 & 0.85 ( 0.01 ) & 7.35 ( 0.34 )  & 0.68 ( 0.09 ) & 11.67 & 0.5  & 0.85 &  7.35 & 0.68\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.55 & 0.92 ( 0 )    & 4.99 ( 0.22 )  & 0 ( 0 )       &  9.99 & 0.37 & 0.92 &  4.99 & 0.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.78 & 0.93 ( 0 )    & 2.42 ( 0.12 )  & 0 ( 0 )       &  7.42 & 0.17 & 0.93 &  2.42 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.26 & 0.74 ( 0.01 ) & 4.65 ( 0.29 )  & 2.93 ( 0.12 ) &  6.72 & 0.48 & 0.74 &  4.65 & 2.93\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.33 & 0.85 ( 0.01 ) & 8.11 ( 0.41 )  & 0.86 ( 0.09 ) & 12.25 & 0.52 & 0.85 &  8.11 & 0.86\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.40 & 0.92 ( 0 )    & 8.63 ( 0.29 )  & 0 ( 0 )       & 13.63 & 0.54 & 0.92 &  8.63 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.64 & 0.92 ( 0 )    & 4.02 ( 0.18 )  & 0 ( 0 )       &  9.02 & 0.31 & 0.92 &  4.02 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.12 & 0.71 ( 0.01 ) & 8.29 ( 0.25 )  & 3.89 ( 0.1 )  &  9.40 & 0.77 & 0.71 &  8.29 & 3.89\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.24 & 0.8 ( 0.01 )  & 8.84 ( 0.61 )  & 2.19 ( 0.12 ) & 11.65 & 0.57 & 0.80 &  8.84 & 2.19\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.21 & 0.92 ( 0 )    & 22.86 ( 0.75 ) & 0 ( 0 )       & 27.86 & 0.77 & 0.92 & 22.86 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.33 & 0.93 ( 0 )    & 12.73 ( 0.35 ) & 0 ( 0 )       & 17.73 & 0.65 & 0.93 & 12.73 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.09 & 0.68 ( 0.01 ) & 10.35 ( 0.33 ) & 4.14 ( 0.09 ) & 11.21 & 0.82 & 0.68 & 10.35 & 4.14\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.19 & 0.77 ( 0.01 ) & 10.84 ( 0.75 ) & 2.46 ( 0.1 )  & 13.38 & 0.63 & 0.77 & 10.84 & 2.46\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.16 & 0.92 ( 0 )    & 30.83 ( 1.05 ) & 0 ( 0 )       & 35.83 & 0.81 & 0.92 & 30.83 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.23 & 0.93 ( 0 )    & 20.39 ( 0.59 ) & 0 ( 0 )       & 25.39 & 0.75 & 0.93 & 20.39 & 0.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.28 & 0.75 ( 0.01 ) & 3.69 ( 0.22 )  & 3.04 ( 0.1 )  &  5.65 & 0.4  & 0.75 &  3.69 & 3.04\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.32 & 0.84 ( 0.01 ) & 7.11 ( 0.33 )  & 0.93 ( 0.1 )  & 11.18 & 0.5  & 0.84 &  7.11 & 0.93\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.51 & 0.92 ( 0 )    & 5.67 ( 0.22 )  & 0 ( 0 )       & 10.67 & 0.41 & 0.92 &  5.67 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.78 & 0.92 ( 0 )    & 2.48 ( 0.13 )  & 0 ( 0 )       &  7.48 & 0.18 & 0.92 &  2.48 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.26 & 0.75 ( 0.01 ) & 4.41 ( 0.27 )  & 3.47 ( 0.09 ) &  5.94 & 0.49 & 0.75 &  4.41 & 3.47\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.28 & 0.82 ( 0.01 ) & 8.16 ( 0.53 )  & 1.46 ( 0.11 ) & 11.70 & 0.52 & 0.82 &  8.16 & 1.46\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.41 & 0.92 ( 0 )    & 8.48 ( 0.34 )  & 0 ( 0 )       & 13.48 & 0.53 & 0.92 &  8.48 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.67 & 0.92 ( 0 )    & 3.71 ( 0.15 )  & 0 ( 0 )       &  8.71 & 0.29 & 0.92 &  3.71 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.13 & 0.69 ( 0.01 ) & 7.94 ( 0.3 )   & 4.11 ( 0.08 ) &  8.83 & 0.77 & 0.69 &  7.94 & 4.11\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.20 & 0.78 ( 0.01 ) & 10.16 ( 0.68 ) & 2.49 ( 0.11 ) & 12.67 & 0.63 & 0.78 & 10.16 & 2.49\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.20 & 0.91 ( 0 )    & 23.65 ( 0.7 )  & 0 ( 0 )       & 28.65 & 0.78 & 0.91 & 23.65 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.34 & 0.92 ( 0 )    & 12.28 ( 0.34 ) & 0 ( 0 )       & 17.28 & 0.64 & 0.92 & 12.28 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.10 & 0.68 ( 0.01 ) & 9.77 ( 0.31 )  & 4.3 ( 0.06 )  & 10.47 & 0.83 & 0.68 &  9.77 & 4.30\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.17 & 0.77 ( 0.01 ) & 10.68 ( 0.8 )  & 3.02 ( 0.11 ) & 12.66 & 0.67 & 0.77 & 10.68 & 3.02\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.57 & 0.89 ( 0 )    & 4.61 ( 0.17 )  & 0.04 ( 0.02 ) &  9.57 & 0.36 & 0.89 &  4.61 & 0.04\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.74 & 0.89 ( 0 )    & 2.8 ( 0.15 )   & 0 ( 0 )       &  7.80 & 0.21 & 0.89 &  2.80 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.24 & 0.71 ( 0.01 ) & 4.31 ( 0.28 )  & 4.05 ( 0.09 ) &  5.26 & 0.55 & 0.71 &  4.31 & 4.05\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.27 & 0.79 ( 0.01 ) & 6.34 ( 0.4 )   & 2.67 ( 0.12 ) &  8.67 & 0.53 & 0.79 &  6.34 & 2.67\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.43 & 0.89 ( 0 )    & 7.92 ( 0.24 )  & 0.04 ( 0.02 ) & 12.88 & 0.52 & 0.89 &  7.92 & 0.04\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.66 & 0.9 ( 0 )     & 3.85 ( 0.16 )  & 0 ( 0 )       &  8.85 & 0.3  & 0.90 &  3.85 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.14 & 0.7 ( 0.01 )  & 6.5 ( 0.24 )   & 4.63 ( 0.06 ) &  6.87 & 0.77 & 0.70 &  6.50 & 4.63\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.21 & 0.76 ( 0.01 ) & 6.98 ( 0.58 )  & 3.65 ( 0.08 ) &  8.33 & 0.56 & 0.76 &  6.98 & 3.65\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.20 & 0.88 ( 0 )    & 22.04 ( 0.74 ) & 0.14 ( 0.04 ) & 26.90 & 0.76 & 0.88 & 22.04 & 0.14\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.33 & 0.89 ( 0 )    & 13.15 ( 0.38 ) & 0 ( 0 )       & 18.15 & 0.65 & 0.89 & 13.15 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.10 & 0.66 ( 0.01 ) & 8.82 ( 0.28 )  & 4.72 ( 0.05 ) &  9.10 & 0.85 & 0.66 &  8.82 & 4.72\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.18 & 0.76 ( 0.01 ) & 6.93 ( 0.59 )  & 4 ( 0.08 )    &  7.93 & 0.62 & 0.76 &  6.93 & 4.00\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.16 & 0.87 ( 0 )    & 29.07 ( 1 )    & 0.16 ( 0.04 ) & 33.91 & 0.81 & 0.87 & 29.07 & 0.16\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.22 & 0.89 ( 0 )    & 21.8 ( 0.63 )  & 0 ( 0 )       & 26.80 & 0.76 & 0.89 & 21.80 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.30 & 0.69 ( 0.01 ) & 2.9 ( 0.2 )    & 4.22 ( 0.09 ) &  3.68 & 0.41 & 0.69 &  2.90 & 4.22\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.37 & 0.76 ( 0.01 ) & 3.93 ( 0.21 )  & 2.74 ( 0.11 ) &  6.19 & 0.41 & 0.76 &  3.93 & 2.74\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.57 & 0.83 ( 0 )    & 3.65 ( 0.16 )  & 0.56 ( 0.08 ) &  8.09 & 0.31 & 0.83 &  3.65 & 0.56\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.76 & 0.83 ( 0 )    & 2.41 ( 0.12 )  & 0.23 ( 0.04 ) &  7.18 & 0.18 & 0.83 &  2.41 & 0.23\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.26 & 0.7 ( 0.01 )  & 3.29 ( 0.23 )  & 4.29 ( 0.09 ) &  4.00 & 0.45 & 0.70 &  3.29 & 4.29\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.32 & 0.76 ( 0.01 ) & 4.77 ( 0.33 )  & 3.26 ( 0.09 ) &  6.51 & 0.46 & 0.76 &  4.77 & 3.26\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.45 & 0.82 ( 0 )    & 5.65 ( 0.21 )  & 0.92 ( 0.09 ) &  9.73 & 0.45 & 0.82 &  5.65 & 0.92\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.63 & 0.83 ( 0 )    & 3.87 ( 0.18 )  & 0.24 ( 0.05 ) &  8.63 & 0.3  & 0.83 &  3.87 & 0.24\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.15 & 0.66 ( 0.01 ) & 5.8 ( 0.24 )   & 4.65 ( 0.07 ) &  6.15 & 0.75 & 0.66 &  5.80 & 4.65\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.21 & 0.7 ( 0.01 )  & 5.55 ( 0.55 )  & 4.14 ( 0.09 ) &  6.41 & 0.51 & 0.70 &  5.55 & 4.14\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.19 & 0.81 ( 0 )    & 15.93 ( 0.73 ) & 1.42 ( 0.1 )  & 19.51 & 0.73 & 0.81 & 15.93 & 1.42\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.31 & 0.83 ( 0 )    & 11.96 ( 0.46 ) & 0.42 ( 0.06 ) & 16.54 & 0.63 & 0.83 & 11.96 & 0.42\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.08 & 0.66 ( 0.01 ) & 8.02 ( 0.28 )  & 4.9 ( 0.07 )  &  8.12 & 0.85 & 0.66 &  8.02 & 4.90\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.18 & 0.69 ( 0.01 ) & 5.85 ( 0.58 )  & 4.37 ( 0.07 ) &  6.48 & 0.61 & 0.69 &  5.85 & 4.37\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.16 & 0.8 ( 0 )     & 18.7 ( 0.93 )  & 1.88 ( 0.1 )  & 21.82 & 0.77 & 0.80 & 18.70 & 1.88\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.21 & 0.83 ( 0 )    & 20.07 ( 0.72 ) & 0.41 ( 0.05 ) & 24.66 & 0.75 & 0.83 & 20.07 & 0.41\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.32 | 0.78 ( 0.01 ) | 3.65 ( 0.23 )  | 2.83 ( 0.11 ) |  5.82 | 0.39 | 0.78 |  3.65 | 2.83 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.34 | 0.85 ( 0.01 ) | 7.35 ( 0.34 )  | 0.68 ( 0.09 ) | 11.67 | 0.5  | 0.85 |  7.35 | 0.68 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.55 | 0.92 ( 0 )    | 4.99 ( 0.22 )  | 0 ( 0 )       |  9.99 | 0.37 | 0.92 |  4.99 | 0.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.78 | 0.93 ( 0 )    | 2.42 ( 0.12 )  | 0 ( 0 )       |  7.42 | 0.17 | 0.93 |  2.42 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.26 | 0.74 ( 0.01 ) | 4.65 ( 0.29 )  | 2.93 ( 0.12 ) |  6.72 | 0.48 | 0.74 |  4.65 | 2.93 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.33 | 0.85 ( 0.01 ) | 8.11 ( 0.41 )  | 0.86 ( 0.09 ) | 12.25 | 0.52 | 0.85 |  8.11 | 0.86 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.40 | 0.92 ( 0 )    | 8.63 ( 0.29 )  | 0 ( 0 )       | 13.63 | 0.54 | 0.92 |  8.63 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.64 | 0.92 ( 0 )    | 4.02 ( 0.18 )  | 0 ( 0 )       |  9.02 | 0.31 | 0.92 |  4.02 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.12 | 0.71 ( 0.01 ) | 8.29 ( 0.25 )  | 3.89 ( 0.1 )  |  9.40 | 0.77 | 0.71 |  8.29 | 3.89 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.24 | 0.8 ( 0.01 )  | 8.84 ( 0.61 )  | 2.19 ( 0.12 ) | 11.65 | 0.57 | 0.80 |  8.84 | 2.19 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.21 | 0.92 ( 0 )    | 22.86 ( 0.75 ) | 0 ( 0 )       | 27.86 | 0.77 | 0.92 | 22.86 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.33 | 0.93 ( 0 )    | 12.73 ( 0.35 ) | 0 ( 0 )       | 17.73 | 0.65 | 0.93 | 12.73 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.09 | 0.68 ( 0.01 ) | 10.35 ( 0.33 ) | 4.14 ( 0.09 ) | 11.21 | 0.82 | 0.68 | 10.35 | 4.14 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.19 | 0.77 ( 0.01 ) | 10.84 ( 0.75 ) | 2.46 ( 0.1 )  | 13.38 | 0.63 | 0.77 | 10.84 | 2.46 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.16 | 0.92 ( 0 )    | 30.83 ( 1.05 ) | 0 ( 0 )       | 35.83 | 0.81 | 0.92 | 30.83 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.23 | 0.93 ( 0 )    | 20.39 ( 0.59 ) | 0 ( 0 )       | 25.39 | 0.75 | 0.93 | 20.39 | 0.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.28 | 0.75 ( 0.01 ) | 3.69 ( 0.22 )  | 3.04 ( 0.1 )  |  5.65 | 0.4  | 0.75 |  3.69 | 3.04 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.32 | 0.84 ( 0.01 ) | 7.11 ( 0.33 )  | 0.93 ( 0.1 )  | 11.18 | 0.5  | 0.84 |  7.11 | 0.93 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.51 | 0.92 ( 0 )    | 5.67 ( 0.22 )  | 0 ( 0 )       | 10.67 | 0.41 | 0.92 |  5.67 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.78 | 0.92 ( 0 )    | 2.48 ( 0.13 )  | 0 ( 0 )       |  7.48 | 0.18 | 0.92 |  2.48 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.26 | 0.75 ( 0.01 ) | 4.41 ( 0.27 )  | 3.47 ( 0.09 ) |  5.94 | 0.49 | 0.75 |  4.41 | 3.47 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.28 | 0.82 ( 0.01 ) | 8.16 ( 0.53 )  | 1.46 ( 0.11 ) | 11.70 | 0.52 | 0.82 |  8.16 | 1.46 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.41 | 0.92 ( 0 )    | 8.48 ( 0.34 )  | 0 ( 0 )       | 13.48 | 0.53 | 0.92 |  8.48 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.67 | 0.92 ( 0 )    | 3.71 ( 0.15 )  | 0 ( 0 )       |  8.71 | 0.29 | 0.92 |  3.71 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.13 | 0.69 ( 0.01 ) | 7.94 ( 0.3 )   | 4.11 ( 0.08 ) |  8.83 | 0.77 | 0.69 |  7.94 | 4.11 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.20 | 0.78 ( 0.01 ) | 10.16 ( 0.68 ) | 2.49 ( 0.11 ) | 12.67 | 0.63 | 0.78 | 10.16 | 2.49 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.20 | 0.91 ( 0 )    | 23.65 ( 0.7 )  | 0 ( 0 )       | 28.65 | 0.78 | 0.91 | 23.65 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.34 | 0.92 ( 0 )    | 12.28 ( 0.34 ) | 0 ( 0 )       | 17.28 | 0.64 | 0.92 | 12.28 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.10 | 0.68 ( 0.01 ) | 9.77 ( 0.31 )  | 4.3 ( 0.06 )  | 10.47 | 0.83 | 0.68 |  9.77 | 4.30 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.17 | 0.77 ( 0.01 ) | 10.68 ( 0.8 )  | 3.02 ( 0.11 ) | 12.66 | 0.67 | 0.77 | 10.68 | 3.02 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.57 | 0.89 ( 0 )    | 4.61 ( 0.17 )  | 0.04 ( 0.02 ) |  9.57 | 0.36 | 0.89 |  4.61 | 0.04 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.74 | 0.89 ( 0 )    | 2.8 ( 0.15 )   | 0 ( 0 )       |  7.80 | 0.21 | 0.89 |  2.80 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.24 | 0.71 ( 0.01 ) | 4.31 ( 0.28 )  | 4.05 ( 0.09 ) |  5.26 | 0.55 | 0.71 |  4.31 | 4.05 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.27 | 0.79 ( 0.01 ) | 6.34 ( 0.4 )   | 2.67 ( 0.12 ) |  8.67 | 0.53 | 0.79 |  6.34 | 2.67 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.43 | 0.89 ( 0 )    | 7.92 ( 0.24 )  | 0.04 ( 0.02 ) | 12.88 | 0.52 | 0.89 |  7.92 | 0.04 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.66 | 0.9 ( 0 )     | 3.85 ( 0.16 )  | 0 ( 0 )       |  8.85 | 0.3  | 0.90 |  3.85 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.14 | 0.7 ( 0.01 )  | 6.5 ( 0.24 )   | 4.63 ( 0.06 ) |  6.87 | 0.77 | 0.70 |  6.50 | 4.63 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.21 | 0.76 ( 0.01 ) | 6.98 ( 0.58 )  | 3.65 ( 0.08 ) |  8.33 | 0.56 | 0.76 |  6.98 | 3.65 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.20 | 0.88 ( 0 )    | 22.04 ( 0.74 ) | 0.14 ( 0.04 ) | 26.90 | 0.76 | 0.88 | 22.04 | 0.14 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.33 | 0.89 ( 0 )    | 13.15 ( 0.38 ) | 0 ( 0 )       | 18.15 | 0.65 | 0.89 | 13.15 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.10 | 0.66 ( 0.01 ) | 8.82 ( 0.28 )  | 4.72 ( 0.05 ) |  9.10 | 0.85 | 0.66 |  8.82 | 4.72 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.18 | 0.76 ( 0.01 ) | 6.93 ( 0.59 )  | 4 ( 0.08 )    |  7.93 | 0.62 | 0.76 |  6.93 | 4.00 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.16 | 0.87 ( 0 )    | 29.07 ( 1 )    | 0.16 ( 0.04 ) | 33.91 | 0.81 | 0.87 | 29.07 | 0.16 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.22 | 0.89 ( 0 )    | 21.8 ( 0.63 )  | 0 ( 0 )       | 26.80 | 0.76 | 0.89 | 21.80 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.30 | 0.69 ( 0.01 ) | 2.9 ( 0.2 )    | 4.22 ( 0.09 ) |  3.68 | 0.41 | 0.69 |  2.90 | 4.22 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.37 | 0.76 ( 0.01 ) | 3.93 ( 0.21 )  | 2.74 ( 0.11 ) |  6.19 | 0.41 | 0.76 |  3.93 | 2.74 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.57 | 0.83 ( 0 )    | 3.65 ( 0.16 )  | 0.56 ( 0.08 ) |  8.09 | 0.31 | 0.83 |  3.65 | 0.56 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.76 | 0.83 ( 0 )    | 2.41 ( 0.12 )  | 0.23 ( 0.04 ) |  7.18 | 0.18 | 0.83 |  2.41 | 0.23 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.26 | 0.7 ( 0.01 )  | 3.29 ( 0.23 )  | 4.29 ( 0.09 ) |  4.00 | 0.45 | 0.70 |  3.29 | 4.29 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.32 | 0.76 ( 0.01 ) | 4.77 ( 0.33 )  | 3.26 ( 0.09 ) |  6.51 | 0.46 | 0.76 |  4.77 | 3.26 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.45 | 0.82 ( 0 )    | 5.65 ( 0.21 )  | 0.92 ( 0.09 ) |  9.73 | 0.45 | 0.82 |  5.65 | 0.92 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.63 | 0.83 ( 0 )    | 3.87 ( 0.18 )  | 0.24 ( 0.05 ) |  8.63 | 0.3  | 0.83 |  3.87 | 0.24 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.15 | 0.66 ( 0.01 ) | 5.8 ( 0.24 )   | 4.65 ( 0.07 ) |  6.15 | 0.75 | 0.66 |  5.80 | 4.65 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.21 | 0.7 ( 0.01 )  | 5.55 ( 0.55 )  | 4.14 ( 0.09 ) |  6.41 | 0.51 | 0.70 |  5.55 | 4.14 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.19 | 0.81 ( 0 )    | 15.93 ( 0.73 ) | 1.42 ( 0.1 )  | 19.51 | 0.73 | 0.81 | 15.93 | 1.42 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.31 | 0.83 ( 0 )    | 11.96 ( 0.46 ) | 0.42 ( 0.06 ) | 16.54 | 0.63 | 0.83 | 11.96 | 0.42 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.08 | 0.66 ( 0.01 ) | 8.02 ( 0.28 )  | 4.9 ( 0.07 )  |  8.12 | 0.85 | 0.66 |  8.02 | 4.90 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.18 | 0.69 ( 0.01 ) | 5.85 ( 0.58 )  | 4.37 ( 0.07 ) |  6.48 | 0.61 | 0.69 |  5.85 | 4.37 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.16 | 0.8 ( 0 )     | 18.7 ( 0.93 )  | 1.88 ( 0.1 )  | 21.82 | 0.77 | 0.80 | 18.70 | 1.88 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.21 | 0.83 ( 0 )    | 20.07 ( 0.72 ) | 0.41 ( 0.05 ) | 24.66 | 0.75 | 0.83 | 20.07 | 0.41 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.32 0.78 ( 0.01 ) 3.65 ( 0.23 )  2.83 ( 0.11 )\n",
+       "2   100   50 0.1   0.50 0.34 0.85 ( 0.01 ) 7.35 ( 0.34 )  0.68 ( 0.09 )\n",
+       "3   500   50 0.1   0.10 0.55 0.92 ( 0 )    4.99 ( 0.22 )  0 ( 0 )      \n",
+       "4  1000   50 0.1   0.05 0.78 0.93 ( 0 )    2.42 ( 0.12 )  0 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.26 0.74 ( 0.01 ) 4.65 ( 0.29 )  2.93 ( 0.12 )\n",
+       "6   100  100 0.1   1.00 0.33 0.85 ( 0.01 ) 8.11 ( 0.41 )  0.86 ( 0.09 )\n",
+       "7   500  100 0.1   0.20 0.40 0.92 ( 0 )    8.63 ( 0.29 )  0 ( 0 )      \n",
+       "8  1000  100 0.1   0.10 0.64 0.92 ( 0 )    4.02 ( 0.18 )  0 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.12 0.71 ( 0.01 ) 8.29 ( 0.25 )  3.89 ( 0.1 ) \n",
+       "10  100  500 0.1   5.00 0.24 0.8 ( 0.01 )  8.84 ( 0.61 )  2.19 ( 0.12 )\n",
+       "11  500  500 0.1   1.00 0.21 0.92 ( 0 )    22.86 ( 0.75 ) 0 ( 0 )      \n",
+       "12 1000  500 0.1   0.50 0.33 0.93 ( 0 )    12.73 ( 0.35 ) 0 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.09 0.68 ( 0.01 ) 10.35 ( 0.33 ) 4.14 ( 0.09 )\n",
+       "14  100 1000 0.1  10.00 0.19 0.77 ( 0.01 ) 10.84 ( 0.75 ) 2.46 ( 0.1 ) \n",
+       "15  500 1000 0.1   2.00 0.16 0.92 ( 0 )    30.83 ( 1.05 ) 0 ( 0 )      \n",
+       "16 1000 1000 0.1   1.00 0.23 0.93 ( 0 )    20.39 ( 0.59 ) 0 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.28 0.75 ( 0.01 ) 3.69 ( 0.22 )  3.04 ( 0.1 ) \n",
+       "18  100   50 0.3   0.50 0.32 0.84 ( 0.01 ) 7.11 ( 0.33 )  0.93 ( 0.1 ) \n",
+       "19  500   50 0.3   0.10 0.51 0.92 ( 0 )    5.67 ( 0.22 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.78 0.92 ( 0 )    2.48 ( 0.13 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.26 0.75 ( 0.01 ) 4.41 ( 0.27 )  3.47 ( 0.09 )\n",
+       "22  100  100 0.3   1.00 0.28 0.82 ( 0.01 ) 8.16 ( 0.53 )  1.46 ( 0.11 )\n",
+       "23  500  100 0.3   0.20 0.41 0.92 ( 0 )    8.48 ( 0.34 )  0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.67 0.92 ( 0 )    3.71 ( 0.15 )  0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.13 0.69 ( 0.01 ) 7.94 ( 0.3 )   4.11 ( 0.08 )\n",
+       "26  100  500 0.3   5.00 0.20 0.78 ( 0.01 ) 10.16 ( 0.68 ) 2.49 ( 0.11 )\n",
+       "27  500  500 0.3   1.00 0.20 0.91 ( 0 )    23.65 ( 0.7 )  0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.34 0.92 ( 0 )    12.28 ( 0.34 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.10 0.68 ( 0.01 ) 9.77 ( 0.31 )  4.3 ( 0.06 ) \n",
+       "30  100 1000 0.3  10.00 0.17 0.77 ( 0.01 ) 10.68 ( 0.8 )  3.02 ( 0.11 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.57 0.89 ( 0 )    4.61 ( 0.17 )  0.04 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.74 0.89 ( 0 )    2.8 ( 0.15 )   0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.24 0.71 ( 0.01 ) 4.31 ( 0.28 )  4.05 ( 0.09 )\n",
+       "54  100  100 0.7   1.00 0.27 0.79 ( 0.01 ) 6.34 ( 0.4 )   2.67 ( 0.12 )\n",
+       "55  500  100 0.7   0.20 0.43 0.89 ( 0 )    7.92 ( 0.24 )  0.04 ( 0.02 )\n",
+       "56 1000  100 0.7   0.10 0.66 0.9 ( 0 )     3.85 ( 0.16 )  0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.14 0.7 ( 0.01 )  6.5 ( 0.24 )   4.63 ( 0.06 )\n",
+       "58  100  500 0.7   5.00 0.21 0.76 ( 0.01 ) 6.98 ( 0.58 )  3.65 ( 0.08 )\n",
+       "59  500  500 0.7   1.00 0.20 0.88 ( 0 )    22.04 ( 0.74 ) 0.14 ( 0.04 )\n",
+       "60 1000  500 0.7   0.50 0.33 0.89 ( 0 )    13.15 ( 0.38 ) 0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.10 0.66 ( 0.01 ) 8.82 ( 0.28 )  4.72 ( 0.05 )\n",
+       "62  100 1000 0.7  10.00 0.18 0.76 ( 0.01 ) 6.93 ( 0.59 )  4 ( 0.08 )   \n",
+       "63  500 1000 0.7   2.00 0.16 0.87 ( 0 )    29.07 ( 1 )    0.16 ( 0.04 )\n",
+       "64 1000 1000 0.7   1.00 0.22 0.89 ( 0 )    21.8 ( 0.63 )  0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.30 0.69 ( 0.01 ) 2.9 ( 0.2 )    4.22 ( 0.09 )\n",
+       "66  100   50 0.9   0.50 0.37 0.76 ( 0.01 ) 3.93 ( 0.21 )  2.74 ( 0.11 )\n",
+       "67  500   50 0.9   0.10 0.57 0.83 ( 0 )    3.65 ( 0.16 )  0.56 ( 0.08 )\n",
+       "68 1000   50 0.9   0.05 0.76 0.83 ( 0 )    2.41 ( 0.12 )  0.23 ( 0.04 )\n",
+       "69   50  100 0.9   2.00 0.26 0.7 ( 0.01 )  3.29 ( 0.23 )  4.29 ( 0.09 )\n",
+       "70  100  100 0.9   1.00 0.32 0.76 ( 0.01 ) 4.77 ( 0.33 )  3.26 ( 0.09 )\n",
+       "71  500  100 0.9   0.20 0.45 0.82 ( 0 )    5.65 ( 0.21 )  0.92 ( 0.09 )\n",
+       "72 1000  100 0.9   0.10 0.63 0.83 ( 0 )    3.87 ( 0.18 )  0.24 ( 0.05 )\n",
+       "73   50  500 0.9  10.00 0.15 0.66 ( 0.01 ) 5.8 ( 0.24 )   4.65 ( 0.07 )\n",
+       "74  100  500 0.9   5.00 0.21 0.7 ( 0.01 )  5.55 ( 0.55 )  4.14 ( 0.09 )\n",
+       "75  500  500 0.9   1.00 0.19 0.81 ( 0 )    15.93 ( 0.73 ) 1.42 ( 0.1 ) \n",
+       "76 1000  500 0.9   0.50 0.31 0.83 ( 0 )    11.96 ( 0.46 ) 0.42 ( 0.06 )\n",
+       "77   50 1000 0.9  20.00 0.08 0.66 ( 0.01 ) 8.02 ( 0.28 )  4.9 ( 0.07 ) \n",
+       "78  100 1000 0.9  10.00 0.18 0.69 ( 0.01 ) 5.85 ( 0.58 )  4.37 ( 0.07 )\n",
+       "79  500 1000 0.9   2.00 0.16 0.8 ( 0 )     18.7 ( 0.93 )  1.88 ( 0.1 ) \n",
+       "80 1000 1000 0.9   1.00 0.21 0.83 ( 0 )    20.07 ( 0.72 ) 0.41 ( 0.05 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   5.82      0.39 0.78      3.65   2.83   \n",
+       "2  11.67      0.5  0.85      7.35   0.68   \n",
+       "3   9.99      0.37 0.92      4.99   0.00   \n",
+       "4   7.42      0.17 0.93      2.42   0.00   \n",
+       "5   6.72      0.48 0.74      4.65   2.93   \n",
+       "6  12.25      0.52 0.85      8.11   0.86   \n",
+       "7  13.63      0.54 0.92      8.63   0.00   \n",
+       "8   9.02      0.31 0.92      4.02   0.00   \n",
+       "9   9.40      0.77 0.71      8.29   3.89   \n",
+       "10 11.65      0.57 0.80      8.84   2.19   \n",
+       "11 27.86      0.77 0.92     22.86   0.00   \n",
+       "12 17.73      0.65 0.93     12.73   0.00   \n",
+       "13 11.21      0.82 0.68     10.35   4.14   \n",
+       "14 13.38      0.63 0.77     10.84   2.46   \n",
+       "15 35.83      0.81 0.92     30.83   0.00   \n",
+       "16 25.39      0.75 0.93     20.39   0.00   \n",
+       "17  5.65      0.4  0.75      3.69   3.04   \n",
+       "18 11.18      0.5  0.84      7.11   0.93   \n",
+       "19 10.67      0.41 0.92      5.67   0.00   \n",
+       "20  7.48      0.18 0.92      2.48   0.00   \n",
+       "21  5.94      0.49 0.75      4.41   3.47   \n",
+       "22 11.70      0.52 0.82      8.16   1.46   \n",
+       "23 13.48      0.53 0.92      8.48   0.00   \n",
+       "24  8.71      0.29 0.92      3.71   0.00   \n",
+       "25  8.83      0.77 0.69      7.94   4.11   \n",
+       "26 12.67      0.63 0.78     10.16   2.49   \n",
+       "27 28.65      0.78 0.91     23.65   0.00   \n",
+       "28 17.28      0.64 0.92     12.28   0.00   \n",
+       "29 10.47      0.83 0.68      9.77   4.30   \n",
+       "30 12.66      0.67 0.77     10.68   3.02   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51  9.57      0.36 0.89      4.61   0.04   \n",
+       "52  7.80      0.21 0.89      2.80   0.00   \n",
+       "53  5.26      0.55 0.71      4.31   4.05   \n",
+       "54  8.67      0.53 0.79      6.34   2.67   \n",
+       "55 12.88      0.52 0.89      7.92   0.04   \n",
+       "56  8.85      0.3  0.90      3.85   0.00   \n",
+       "57  6.87      0.77 0.70      6.50   4.63   \n",
+       "58  8.33      0.56 0.76      6.98   3.65   \n",
+       "59 26.90      0.76 0.88     22.04   0.14   \n",
+       "60 18.15      0.65 0.89     13.15   0.00   \n",
+       "61  9.10      0.85 0.66      8.82   4.72   \n",
+       "62  7.93      0.62 0.76      6.93   4.00   \n",
+       "63 33.91      0.81 0.87     29.07   0.16   \n",
+       "64 26.80      0.76 0.89     21.80   0.00   \n",
+       "65  3.68      0.41 0.69      2.90   4.22   \n",
+       "66  6.19      0.41 0.76      3.93   2.74   \n",
+       "67  8.09      0.31 0.83      3.65   0.56   \n",
+       "68  7.18      0.18 0.83      2.41   0.23   \n",
+       "69  4.00      0.45 0.70      3.29   4.29   \n",
+       "70  6.51      0.46 0.76      4.77   3.26   \n",
+       "71  9.73      0.45 0.82      5.65   0.92   \n",
+       "72  8.63      0.3  0.83      3.87   0.24   \n",
+       "73  6.15      0.75 0.66      5.80   4.65   \n",
+       "74  6.41      0.51 0.70      5.55   4.14   \n",
+       "75 19.51      0.73 0.81     15.93   1.42   \n",
+       "76 16.54      0.63 0.83     11.96   0.42   \n",
+       "77  8.12      0.85 0.66      8.02   4.90   \n",
+       "78  6.48      0.61 0.69      5.85   4.37   \n",
+       "79 21.82      0.77 0.80     18.70   1.88   \n",
+       "80 24.66      0.75 0.83     20.07   0.41   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_lasso_binary.txt', \n",
+    "            sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/1.2_sim_toe_elnet_binary_update.ipynb b/simulations/notebooks_sim_bin/1.2_sim_toe_elnet_binary_update.ipynb
new file mode 100644
index 0000000..b011e9c
--- /dev/null
+++ b/simulations/notebooks_sim_bin/1.2_sim_toe_elnet_binary_update.ipynb
@@ -0,0 +1,973 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>0.77 ( 0.01 )</td><td>0.13</td><td>16.51</td><td>0.63</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>0.85 ( 0.01 )</td><td>0.24</td><td>15.15</td><td>0.57</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>7.43 ( 0.6 )</td><td>0.02 ( 0.01 )</td><td>0.92 ( 0 )</td><td>0.41</td><td>12.41</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>7.82 ( 0.76 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.39</td><td>12.82</td><td>0.40</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>0.73 ( 0.01 )</td><td>0.09</td><td>24.91</td><td>0.73</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 )</td><td>0.83 ( 0.01 )</td><td>0.18</td><td>19.75</td><td>0.62</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 0.77 ( 0.01 ) & 0.13 & 16.51 & 0.63\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 0.85 ( 0.01 ) & 0.24 & 15.15 & 0.57\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 7.43 ( 0.6 ) & 0.02 ( 0.01 ) & 0.92 ( 0 ) & 0.41 & 12.41 & 0.42\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 7.82 ( 0.76 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.39 & 12.82 & 0.40\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 0.73 ( 0.01 ) & 0.09 & 24.91 & 0.73\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 15.59 ( 1.32 ) & 0.84 ( 0.1 ) & 0.83 ( 0.01 ) & 0.18 & 19.75 & 0.62\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 0.77 ( 0.01 ) | 0.13 | 16.51 | 0.63 |\n",
+       "| 2 | 100 | 50 | 0.1 | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 0.85 ( 0.01 ) | 0.24 | 15.15 | 0.57 |\n",
+       "| 3 | 500 | 50 | 0.1 | 7.43 ( 0.6 ) | 0.02 ( 0.01 ) | 0.92 ( 0 ) | 0.41 | 12.41 | 0.42 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 7.82 ( 0.76 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.39 | 12.82 | 0.40 |\n",
+       "| 5 | 50 | 100 | 0.1 | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 0.73 ( 0.01 ) | 0.09 | 24.91 | 0.73 |\n",
+       "| 6 | 100 | 100 | 0.1 | 15.59 ( 1.32 ) | 0.84 ( 0.1 ) | 0.83 ( 0.01 ) | 0.18 | 19.75 | 0.62 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 13.02 ( 1.02 ) 1.51 ( 0.11 ) 0.77 ( 0.01 ) 0.13 16.51      0.63\n",
+       "2 100  50  0.1 10.71 ( 0.62 ) 0.56 ( 0.07 ) 0.85 ( 0.01 ) 0.24 15.15      0.57\n",
+       "3 500  50  0.1 7.43 ( 0.6 )   0.02 ( 0.01 ) 0.92 ( 0 )    0.41 12.41      0.42\n",
+       "4 1000 50  0.1 7.82 ( 0.76 )  0 ( 0 )       0.92 ( 0 )    0.39 12.82      0.40\n",
+       "5 50   100 0.1 21.55 ( 1.94 ) 1.64 ( 0.11 ) 0.73 ( 0.01 ) 0.09 24.91      0.73\n",
+       "6 100  100 0.1 15.59 ( 1.32 ) 0.84 ( 0.1 )  0.83 ( 0.01 ) 0.18 19.75      0.62"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>0.81 ( 0 )</td><td>0.14</td><td>29.61</td><td>0.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>0.83 ( 0 )</td><td>0.21</td><td>28.34</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>0.65 ( 0.01 )</td><td>0.02</td><td>49.79</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>0.66 ( 0.01 )</td><td>0.05</td><td>34.18</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>0.8 ( 0 )</td><td>0.11</td><td>34.39</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>0.83 ( 0 )</td><td>0.14</td><td>40.43</td><td>0.82</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 0.81 ( 0 ) & 0.14 & 29.61 & 0.76\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 0.83 ( 0 ) & 0.21 & 28.34 & 0.75\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 0.65 ( 0.01 ) & 0.02 & 49.79 & 0.93\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 0.66 ( 0.01 ) & 0.05 & 34.18 & 0.80\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 0.8 ( 0 ) & 0.11 & 34.39 & 0.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 0.83 ( 0 ) & 0.14 & 40.43 & 0.82\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 0.81 ( 0 ) | 0.14 | 29.61 | 0.76 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 0.83 ( 0 ) | 0.21 | 28.34 | 0.75 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 0.65 ( 0.01 ) | 0.02 | 49.79 | 0.93 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 0.66 ( 0.01 ) | 0.05 | 34.18 | 0.80 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 0.8 ( 0 ) | 0.11 | 34.39 | 0.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 0.83 ( 0 ) | 0.14 | 40.43 | 0.82 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 25.68 ( 1.61 ) 1.07 ( 0.11 ) 0.81 ( 0 )    0.14 29.61     \n",
+       "76 1000 500  0.9 23.49 ( 1.26 ) 0.15 ( 0.05 ) 0.83 ( 0 )    0.21 28.34     \n",
+       "77 50   1000 0.9 49.08 ( 6.86 ) 4.29 ( 0.09 ) 0.65 ( 0.01 ) 0.02 49.79     \n",
+       "78 100  1000 0.9 32.99 ( 5.22 ) 3.81 ( 0.09 ) 0.66 ( 0.01 ) 0.05 34.18     \n",
+       "79 500  1000 0.9 30.91 ( 2.22 ) 1.52 ( 0.12 ) 0.8 ( 0 )     0.11 34.39     \n",
+       "80 1000 1000 0.9 35.66 ( 1.98 ) 0.23 ( 0.06 ) 0.83 ( 0 )    0.14 40.43     \n",
+       "   FDR \n",
+       "75 0.76\n",
+       "76 0.75\n",
+       "77 0.93\n",
+       "78 0.80\n",
+       "79 0.78\n",
+       "80 0.82"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.13</td><td>0.77 ( 0.01 )</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>16.51</td><td>0.63</td><td>0.77</td><td>13.02</td><td>1.51</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.24</td><td>0.85 ( 0.01 )</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>15.15</td><td>0.57</td><td>0.85</td><td>10.71</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.43 ( 0.6 )  </td><td>0.02 ( 0.01 )</td><td>12.41</td><td>0.42</td><td>0.92</td><td> 7.43</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.82 ( 0.76 ) </td><td>0 ( 0 )      </td><td>12.82</td><td>0.4 </td><td>0.92</td><td> 7.82</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.09</td><td>0.73 ( 0.01 )</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>24.91</td><td>0.73</td><td>0.73</td><td>21.55</td><td>1.64</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 ) </td><td>19.75</td><td>0.62</td><td>0.83</td><td>15.59</td><td>0.84</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.13 & 0.77 ( 0.01 ) & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 16.51 & 0.63 & 0.77 & 13.02 & 1.51\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.24 & 0.85 ( 0.01 ) & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 15.15 & 0.57 & 0.85 & 10.71 & 0.56\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.41 & 0.92 ( 0 )    & 7.43 ( 0.6 )   & 0.02 ( 0.01 ) & 12.41 & 0.42 & 0.92 &  7.43 & 0.02\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.39 & 0.92 ( 0 )    & 7.82 ( 0.76 )  & 0 ( 0 )       & 12.82 & 0.4  & 0.92 &  7.82 & 0.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.09 & 0.73 ( 0.01 ) & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 24.91 & 0.73 & 0.73 & 21.55 & 1.64\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.18 & 0.83 ( 0.01 ) & 15.59 ( 1.32 ) & 0.84 ( 0.1 )  & 19.75 & 0.62 & 0.83 & 15.59 & 0.84\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.13 | 0.77 ( 0.01 ) | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 16.51 | 0.63 | 0.77 | 13.02 | 1.51 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.24 | 0.85 ( 0.01 ) | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 15.15 | 0.57 | 0.85 | 10.71 | 0.56 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.41 | 0.92 ( 0 )    | 7.43 ( 0.6 )   | 0.02 ( 0.01 ) | 12.41 | 0.42 | 0.92 |  7.43 | 0.02 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.39 | 0.92 ( 0 )    | 7.82 ( 0.76 )  | 0 ( 0 )       | 12.82 | 0.4  | 0.92 |  7.82 | 0.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.09 | 0.73 ( 0.01 ) | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 24.91 | 0.73 | 0.73 | 21.55 | 1.64 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.18 | 0.83 ( 0.01 ) | 15.59 ( 1.32 ) | 0.84 ( 0.1 )  | 19.75 | 0.62 | 0.83 | 15.59 | 0.84 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.13 0.77 ( 0.01 ) 13.02 ( 1.02 ) 1.51 ( 0.11 )\n",
+       "2  100  50 0.1  0.50  0.24 0.85 ( 0.01 ) 10.71 ( 0.62 ) 0.56 ( 0.07 )\n",
+       "3  500  50 0.1  0.10  0.41 0.92 ( 0 )    7.43 ( 0.6 )   0.02 ( 0.01 )\n",
+       "4 1000  50 0.1  0.05  0.39 0.92 ( 0 )    7.82 ( 0.76 )  0 ( 0 )      \n",
+       "5   50 100 0.1  2.00  0.09 0.73 ( 0.01 ) 21.55 ( 1.94 ) 1.64 ( 0.11 )\n",
+       "6  100 100 0.1  1.00  0.18 0.83 ( 0.01 ) 15.59 ( 1.32 ) 0.84 ( 0.1 ) \n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 16.51      0.63 0.77     13.02   1.51   \n",
+       "2 15.15      0.57 0.85     10.71   0.56   \n",
+       "3 12.41      0.42 0.92      7.43   0.02   \n",
+       "4 12.82      0.4  0.92      7.82   0.00   \n",
+       "5 24.91      0.73 0.73     21.55   1.64   \n",
+       "6 19.75      0.62 0.83     15.59   0.84   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.14</td><td>0.81 ( 0 )   </td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>29.61</td><td>0.76</td><td>0.81</td><td>25.68</td><td>1.07</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.21</td><td>0.83 ( 0 )   </td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>28.34</td><td>0.75</td><td>0.83</td><td>23.49</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>49.79</td><td>0.93</td><td>0.65</td><td>49.08</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.05</td><td>0.66 ( 0.01 )</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>34.18</td><td>0.8 </td><td>0.66</td><td>32.99</td><td>3.81</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.11</td><td>0.8 ( 0 )    </td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>34.39</td><td>0.78</td><td>0.80</td><td>30.91</td><td>1.52</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.14</td><td>0.83 ( 0 )   </td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>40.43</td><td>0.82</td><td>0.83</td><td>35.66</td><td>0.23</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.14 & 0.81 ( 0 )    & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 29.61 & 0.76 & 0.81 & 25.68 & 1.07\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.21 & 0.83 ( 0 )    & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 28.34 & 0.75 & 0.83 & 23.49 & 0.15\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.02 & 0.65 ( 0.01 ) & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 49.79 & 0.93 & 0.65 & 49.08 & 4.29\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.05 & 0.66 ( 0.01 ) & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 34.18 & 0.8  & 0.66 & 32.99 & 3.81\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.11 & 0.8 ( 0 )     & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 34.39 & 0.78 & 0.80 & 30.91 & 1.52\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.14 & 0.83 ( 0 )    & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 40.43 & 0.82 & 0.83 & 35.66 & 0.23\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.14 | 0.81 ( 0 )    | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 29.61 | 0.76 | 0.81 | 25.68 | 1.07 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.21 | 0.83 ( 0 )    | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 28.34 | 0.75 | 0.83 | 23.49 | 0.15 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.02 | 0.65 ( 0.01 ) | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 49.79 | 0.93 | 0.65 | 49.08 | 4.29 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.05 | 0.66 ( 0.01 ) | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 34.18 | 0.8  | 0.66 | 32.99 | 3.81 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.11 | 0.8 ( 0 )     | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 34.39 | 0.78 | 0.80 | 30.91 | 1.52 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.14 | 0.83 ( 0 )    | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 40.43 | 0.82 | 0.83 | 35.66 | 0.23 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.14 0.81 ( 0 )    25.68 ( 1.61 ) 1.07 ( 0.11 )\n",
+       "76 1000  500 0.9   0.5  0.21 0.83 ( 0 )    23.49 ( 1.26 ) 0.15 ( 0.05 )\n",
+       "77   50 1000 0.9  20.0  0.02 0.65 ( 0.01 ) 49.08 ( 6.86 ) 4.29 ( 0.09 )\n",
+       "78  100 1000 0.9  10.0  0.05 0.66 ( 0.01 ) 32.99 ( 5.22 ) 3.81 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.11 0.8 ( 0 )     30.91 ( 2.22 ) 1.52 ( 0.12 )\n",
+       "80 1000 1000 0.9   1.0  0.14 0.83 ( 0 )    35.66 ( 1.98 ) 0.23 ( 0.06 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 29.61      0.76 0.81     25.68   1.07   \n",
+       "76 28.34      0.75 0.83     23.49   0.15   \n",
+       "77 49.79      0.93 0.65     49.08   4.29   \n",
+       "78 34.18      0.8  0.66     32.99   3.81   \n",
+       "79 34.39      0.78 0.80     30.91   1.52   \n",
+       "80 40.43      0.82 0.83     35.66   0.23   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.13</td><td>0.77 ( 0.01 )</td><td>13.02 ( 1.02 )</td><td>1.51 ( 0.11 )</td><td>16.51</td><td>0.63</td><td>0.77</td><td>13.02</td><td>1.51</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.24</td><td>0.85 ( 0.01 )</td><td>10.71 ( 0.62 )</td><td>0.56 ( 0.07 )</td><td>15.15</td><td>0.57</td><td>0.85</td><td>10.71</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.43 ( 0.6 )  </td><td>0.02 ( 0.01 )</td><td>12.41</td><td>0.42</td><td>0.92</td><td> 7.43</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.82 ( 0.76 ) </td><td>0 ( 0 )      </td><td>12.82</td><td>0.4 </td><td>0.92</td><td> 7.82</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.09</td><td>0.73 ( 0.01 )</td><td>21.55 ( 1.94 )</td><td>1.64 ( 0.11 )</td><td>24.91</td><td>0.73</td><td>0.73</td><td>21.55</td><td>1.64</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>15.59 ( 1.32 )</td><td>0.84 ( 0.1 ) </td><td>19.75</td><td>0.62</td><td>0.83</td><td>15.59</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.32</td><td>0.92 ( 0 )   </td><td>11.5 ( 0.9 )  </td><td>0 ( 0 )      </td><td>16.50</td><td>0.54</td><td>0.92</td><td>11.50</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.34</td><td>0.92 ( 0 )   </td><td>11.02 ( 1.05 )</td><td>0 ( 0 )      </td><td>16.02</td><td>0.49</td><td>0.92</td><td>11.02</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.03</td><td>0.66 ( 0.01 )</td><td>58.7 ( 5.86 ) </td><td>2.64 ( 0.12 )</td><td>61.06</td><td>0.89</td><td>0.66</td><td>58.70</td><td>2.64</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.14</td><td>0.78 ( 0.01 )</td><td>19.29 ( 1.92 )</td><td>1.76 ( 0.1 ) </td><td>22.53</td><td>0.68</td><td>0.78</td><td>19.29</td><td>1.76</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.26</td><td>0.91 ( 0 )   </td><td>17.05 ( 1.43 )</td><td>0 ( 0 )      </td><td>22.05</td><td>0.62</td><td>0.91</td><td>17.05</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.29</td><td>0.93 ( 0 )   </td><td>15.14 ( 1.39 )</td><td>0 ( 0 )      </td><td>20.14</td><td>0.54</td><td>0.93</td><td>15.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>85.97 ( 9 )   </td><td>3.03 ( 0.13 )</td><td>87.94</td><td>0.92</td><td>0.65</td><td>85.97</td><td>3.03</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.12</td><td>0.76 ( 0.01 )</td><td>22.02 ( 1.97 )</td><td>2.07 ( 0.1 ) </td><td>24.95</td><td>0.75</td><td>0.76</td><td>22.02</td><td>2.07</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.23</td><td>0.91 ( 0 )   </td><td>21.07 ( 1.75 )</td><td>0 ( 0 )      </td><td>26.07</td><td>0.66</td><td>0.91</td><td>21.07</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.24</td><td>0.92 ( 0 )   </td><td>19.56 ( 2.29 )</td><td>0 ( 0 )      </td><td>24.56</td><td>0.57</td><td>0.92</td><td>19.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.13</td><td>0.76 ( 0.01 )</td><td>11.32 ( 0.99 )</td><td>1.87 ( 0.14 )</td><td>14.45</td><td>0.59</td><td>0.76</td><td>11.32</td><td>1.87</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.18</td><td>0.83 ( 0.01 )</td><td>12.96 ( 0.86 )</td><td>0.75 ( 0.09 )</td><td>17.21</td><td>0.61</td><td>0.83</td><td>12.96</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.33</td><td>0.91 ( 0 )   </td><td>9.89 ( 0.75 ) </td><td>0 ( 0 )      </td><td>14.89</td><td>0.51</td><td>0.91</td><td> 9.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.38</td><td>0.92 ( 0 )   </td><td>8.56 ( 0.69 ) </td><td>0 ( 0 )      </td><td>13.56</td><td>0.44</td><td>0.92</td><td> 8.56</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.08</td><td>0.74 ( 0.01 )</td><td>17.99 ( 1.78 )</td><td>2.32 ( 0.14 )</td><td>20.67</td><td>0.72</td><td>0.74</td><td>17.99</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.17</td><td>0.82 ( 0.01 )</td><td>14.36 ( 1.25 )</td><td>1.36 ( 0.12 )</td><td>18.00</td><td>0.6 </td><td>0.82</td><td>14.36</td><td>1.36</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.29</td><td>0.91 ( 0 )   </td><td>13.08 ( 1 )   </td><td>0 ( 0 )      </td><td>18.08</td><td>0.56</td><td>0.91</td><td>13.08</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.38</td><td>0.92 ( 0 )   </td><td>9.67 ( 0.76 ) </td><td>0 ( 0 )      </td><td>14.67</td><td>0.49</td><td>0.92</td><td> 9.67</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.03</td><td>0.66 ( 0.01 )</td><td>49.2 ( 5.65 ) </td><td>3.15 ( 0.13 )</td><td>51.05</td><td>0.89</td><td>0.66</td><td>49.20</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.11</td><td>0.77 ( 0.01 )</td><td>23.84 ( 2.37 )</td><td>2.05 ( 0.11 )</td><td>26.79</td><td>0.73</td><td>0.77</td><td>23.84</td><td>2.05</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.91 ( 0 )   </td><td>25.04 ( 1.69 )</td><td>0 ( 0 )      </td><td>30.04</td><td>0.72</td><td>0.91</td><td>25.04</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.92 ( 0 )   </td><td>18.51 ( 1.25 )</td><td>0 ( 0 )      </td><td>23.51</td><td>0.66</td><td>0.92</td><td>18.51</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>0.67 ( 0.01 )</td><td>54.74 ( 6.8 ) </td><td>3.54 ( 0.12 )</td><td>56.20</td><td>0.91</td><td>0.67</td><td>54.74</td><td>3.54</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.08</td><td>0.75 ( 0.01 )</td><td>28.55 ( 5.11 )</td><td>2.58 ( 0.11 )</td><td>30.97</td><td>0.75</td><td>0.75</td><td>28.55</td><td>2.58</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.31</td><td>0.89 ( 0 )   </td><td>11.14 ( 0.74 )</td><td>0.02 ( 0.02 )</td><td>16.12</td><td>0.56</td><td>0.89</td><td>11.14</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.37</td><td>0.89 ( 0 )   </td><td>9.65 ( 0.77 ) </td><td>0 ( 0 )      </td><td>14.65</td><td>0.49</td><td>0.89</td><td> 9.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.08</td><td>0.71 ( 0.01 )</td><td>17.76 ( 1.65 )</td><td>2.81 ( 0.14 )</td><td>19.95</td><td>0.74</td><td>0.71</td><td>17.76</td><td>2.81</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.13</td><td>0.79 ( 0.01 )</td><td>16.43 ( 1.31 )</td><td>1.79 ( 0.13 )</td><td>19.64</td><td>0.68</td><td>0.79</td><td>16.43</td><td>1.79</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.25</td><td>0.89 ( 0 )   </td><td>16.01 ( 0.82 )</td><td>0.03 ( 0.03 )</td><td>20.98</td><td>0.67</td><td>0.89</td><td>16.01</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.33</td><td>0.89 ( 0 )   </td><td>12.77 ( 0.94 )</td><td>0 ( 0 )      </td><td>17.77</td><td>0.58</td><td>0.89</td><td>12.77</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.03</td><td>0.67 ( 0.01 )</td><td>43.83 ( 5.17 )</td><td>3.75 ( 0.12 )</td><td>45.08</td><td>0.9 </td><td>0.67</td><td>43.83</td><td>3.75</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.10</td><td>0.74 ( 0.01 )</td><td>19.1 ( 1.88 ) </td><td>3.08 ( 0.1 ) </td><td>21.02</td><td>0.73</td><td>0.74</td><td>19.10</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.15</td><td>0.87 ( 0 )   </td><td>31.23 ( 1.88 )</td><td>0.17 ( 0.04 )</td><td>36.06</td><td>0.78</td><td>0.87</td><td>31.23</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.22</td><td>0.89 ( 0 )   </td><td>22.46 ( 1.45 )</td><td>0 ( 0 )      </td><td>27.46</td><td>0.71</td><td>0.89</td><td>22.46</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.02</td><td>0.64 ( 0.01 )</td><td>47.17 ( 5.85 )</td><td>4.12 ( 0.09 )</td><td>48.05</td><td>0.92</td><td>0.64</td><td>47.17</td><td>4.12</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.08</td><td>0.75 ( 0.01 )</td><td>20.86 ( 2.56 )</td><td>3.59 ( 0.09 )</td><td>22.27</td><td>0.77</td><td>0.75</td><td>20.86</td><td>3.59</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.13</td><td>0.87 ( 0 )   </td><td>36.32 ( 2.18 )</td><td>0.23 ( 0.05 )</td><td>41.09</td><td>0.81</td><td>0.87</td><td>36.32</td><td>0.23</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.16</td><td>0.89 ( 0 )   </td><td>32.22 ( 2.11 )</td><td>0.01 ( 0.01 )</td><td>37.21</td><td>0.8 </td><td>0.89</td><td>32.22</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.09</td><td>0.71 ( 0.01 )</td><td>14.15 ( 1 )   </td><td>2.3 ( 0.14 ) </td><td>16.85</td><td>0.68</td><td>0.71</td><td>14.15</td><td>2.30</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>11.74 ( 0.88 )</td><td>1.74 ( 0.13 )</td><td>15.00</td><td>0.64</td><td>0.77</td><td>11.74</td><td>1.74</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.27</td><td>0.83 ( 0 )   </td><td>12.28 ( 0.9 ) </td><td>0.18 ( 0.06 )</td><td>17.10</td><td>0.57</td><td>0.83</td><td>12.28</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.39</td><td>0.84 ( 0 )   </td><td>9.58 ( 0.68 ) </td><td>0.01 ( 0.01 )</td><td>14.57</td><td>0.51</td><td>0.84</td><td> 9.58</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.08</td><td>0.7 ( 0.01 ) </td><td>15.1 ( 1.65 ) </td><td>3.16 ( 0.13 )</td><td>16.94</td><td>0.7 </td><td>0.70</td><td>15.10</td><td>3.16</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.15</td><td>0.75 ( 0.01 )</td><td>14.19 ( 1.38 )</td><td>2.23 ( 0.13 )</td><td>16.96</td><td>0.64</td><td>0.75</td><td>14.19</td><td>2.23</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.25</td><td>0.83 ( 0 )   </td><td>16.33 ( 1.01 )</td><td>0.2 ( 0.06 ) </td><td>21.13</td><td>0.67</td><td>0.83</td><td>16.33</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.32</td><td>0.84 ( 0 )   </td><td>13.82 ( 1.17 )</td><td>0.03 ( 0.02 )</td><td>18.79</td><td>0.6 </td><td>0.84</td><td>13.82</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.05</td><td>0.66 ( 0.02 )</td><td>28.64 ( 3.14 )</td><td>4.13 ( 0.07 )</td><td>29.51</td><td>0.88</td><td>0.66</td><td>28.64</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.07</td><td>0.7 ( 0.01 ) </td><td>25.71 ( 4.03 )</td><td>3.42 ( 0.1 ) </td><td>27.29</td><td>0.78</td><td>0.70</td><td>25.71</td><td>3.42</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.81 ( 0 )   </td><td>25.68 ( 1.61 )</td><td>1.07 ( 0.11 )</td><td>29.61</td><td>0.76</td><td>0.81</td><td>25.68</td><td>1.07</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.21</td><td>0.83 ( 0 )   </td><td>23.49 ( 1.26 )</td><td>0.15 ( 0.05 )</td><td>28.34</td><td>0.75</td><td>0.83</td><td>23.49</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.02</td><td>0.65 ( 0.01 )</td><td>49.08 ( 6.86 )</td><td>4.29 ( 0.09 )</td><td>49.79</td><td>0.93</td><td>0.65</td><td>49.08</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.05</td><td>0.66 ( 0.01 )</td><td>32.99 ( 5.22 )</td><td>3.81 ( 0.09 )</td><td>34.18</td><td>0.8 </td><td>0.66</td><td>32.99</td><td>3.81</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.11</td><td>0.8 ( 0 )    </td><td>30.91 ( 2.22 )</td><td>1.52 ( 0.12 )</td><td>34.39</td><td>0.78</td><td>0.80</td><td>30.91</td><td>1.52</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.83 ( 0 )   </td><td>35.66 ( 1.98 )</td><td>0.23 ( 0.06 )</td><td>40.43</td><td>0.82</td><td>0.83</td><td>35.66</td><td>0.23</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.13 & 0.77 ( 0.01 ) & 13.02 ( 1.02 ) & 1.51 ( 0.11 ) & 16.51 & 0.63 & 0.77 & 13.02 & 1.51\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.24 & 0.85 ( 0.01 ) & 10.71 ( 0.62 ) & 0.56 ( 0.07 ) & 15.15 & 0.57 & 0.85 & 10.71 & 0.56\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.41 & 0.92 ( 0 )    & 7.43 ( 0.6 )   & 0.02 ( 0.01 ) & 12.41 & 0.42 & 0.92 &  7.43 & 0.02\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.39 & 0.92 ( 0 )    & 7.82 ( 0.76 )  & 0 ( 0 )       & 12.82 & 0.4  & 0.92 &  7.82 & 0.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.09 & 0.73 ( 0.01 ) & 21.55 ( 1.94 ) & 1.64 ( 0.11 ) & 24.91 & 0.73 & 0.73 & 21.55 & 1.64\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.18 & 0.83 ( 0.01 ) & 15.59 ( 1.32 ) & 0.84 ( 0.1 )  & 19.75 & 0.62 & 0.83 & 15.59 & 0.84\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.32 & 0.92 ( 0 )    & 11.5 ( 0.9 )   & 0 ( 0 )       & 16.50 & 0.54 & 0.92 & 11.50 & 0.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.34 & 0.92 ( 0 )    & 11.02 ( 1.05 ) & 0 ( 0 )       & 16.02 & 0.49 & 0.92 & 11.02 & 0.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.03 & 0.66 ( 0.01 ) & 58.7 ( 5.86 )  & 2.64 ( 0.12 ) & 61.06 & 0.89 & 0.66 & 58.70 & 2.64\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.14 & 0.78 ( 0.01 ) & 19.29 ( 1.92 ) & 1.76 ( 0.1 )  & 22.53 & 0.68 & 0.78 & 19.29 & 1.76\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.26 & 0.91 ( 0 )    & 17.05 ( 1.43 ) & 0 ( 0 )       & 22.05 & 0.62 & 0.91 & 17.05 & 0.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.29 & 0.93 ( 0 )    & 15.14 ( 1.39 ) & 0 ( 0 )       & 20.14 & 0.54 & 0.93 & 15.14 & 0.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.02 & 0.65 ( 0.01 ) & 85.97 ( 9 )    & 3.03 ( 0.13 ) & 87.94 & 0.92 & 0.65 & 85.97 & 3.03\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.12 & 0.76 ( 0.01 ) & 22.02 ( 1.97 ) & 2.07 ( 0.1 )  & 24.95 & 0.75 & 0.76 & 22.02 & 2.07\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.23 & 0.91 ( 0 )    & 21.07 ( 1.75 ) & 0 ( 0 )       & 26.07 & 0.66 & 0.91 & 21.07 & 0.00\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.24 & 0.92 ( 0 )    & 19.56 ( 2.29 ) & 0 ( 0 )       & 24.56 & 0.57 & 0.92 & 19.56 & 0.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.13 & 0.76 ( 0.01 ) & 11.32 ( 0.99 ) & 1.87 ( 0.14 ) & 14.45 & 0.59 & 0.76 & 11.32 & 1.87\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.18 & 0.83 ( 0.01 ) & 12.96 ( 0.86 ) & 0.75 ( 0.09 ) & 17.21 & 0.61 & 0.83 & 12.96 & 0.75\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.33 & 0.91 ( 0 )    & 9.89 ( 0.75 )  & 0 ( 0 )       & 14.89 & 0.51 & 0.91 &  9.89 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.38 & 0.92 ( 0 )    & 8.56 ( 0.69 )  & 0 ( 0 )       & 13.56 & 0.44 & 0.92 &  8.56 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.08 & 0.74 ( 0.01 ) & 17.99 ( 1.78 ) & 2.32 ( 0.14 ) & 20.67 & 0.72 & 0.74 & 17.99 & 2.32\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.17 & 0.82 ( 0.01 ) & 14.36 ( 1.25 ) & 1.36 ( 0.12 ) & 18.00 & 0.6  & 0.82 & 14.36 & 1.36\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.29 & 0.91 ( 0 )    & 13.08 ( 1 )    & 0 ( 0 )       & 18.08 & 0.56 & 0.91 & 13.08 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.38 & 0.92 ( 0 )    & 9.67 ( 0.76 )  & 0 ( 0 )       & 14.67 & 0.49 & 0.92 &  9.67 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.03 & 0.66 ( 0.01 ) & 49.2 ( 5.65 )  & 3.15 ( 0.13 ) & 51.05 & 0.89 & 0.66 & 49.20 & 3.15\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.11 & 0.77 ( 0.01 ) & 23.84 ( 2.37 ) & 2.05 ( 0.11 ) & 26.79 & 0.73 & 0.77 & 23.84 & 2.05\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.19 & 0.91 ( 0 )    & 25.04 ( 1.69 ) & 0 ( 0 )       & 30.04 & 0.72 & 0.91 & 25.04 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.25 & 0.92 ( 0 )    & 18.51 ( 1.25 ) & 0 ( 0 )       & 23.51 & 0.66 & 0.92 & 18.51 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 0.67 ( 0.01 ) & 54.74 ( 6.8 )  & 3.54 ( 0.12 ) & 56.20 & 0.91 & 0.67 & 54.74 & 3.54\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.08 & 0.75 ( 0.01 ) & 28.55 ( 5.11 ) & 2.58 ( 0.11 ) & 30.97 & 0.75 & 0.75 & 28.55 & 2.58\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.31 & 0.89 ( 0 )    & 11.14 ( 0.74 ) & 0.02 ( 0.02 ) & 16.12 & 0.56 & 0.89 & 11.14 & 0.02\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.37 & 0.89 ( 0 )    & 9.65 ( 0.77 )  & 0 ( 0 )       & 14.65 & 0.49 & 0.89 &  9.65 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.08 & 0.71 ( 0.01 ) & 17.76 ( 1.65 ) & 2.81 ( 0.14 ) & 19.95 & 0.74 & 0.71 & 17.76 & 2.81\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.13 & 0.79 ( 0.01 ) & 16.43 ( 1.31 ) & 1.79 ( 0.13 ) & 19.64 & 0.68 & 0.79 & 16.43 & 1.79\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.25 & 0.89 ( 0 )    & 16.01 ( 0.82 ) & 0.03 ( 0.03 ) & 20.98 & 0.67 & 0.89 & 16.01 & 0.03\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.33 & 0.89 ( 0 )    & 12.77 ( 0.94 ) & 0 ( 0 )       & 17.77 & 0.58 & 0.89 & 12.77 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.03 & 0.67 ( 0.01 ) & 43.83 ( 5.17 ) & 3.75 ( 0.12 ) & 45.08 & 0.9  & 0.67 & 43.83 & 3.75\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.10 & 0.74 ( 0.01 ) & 19.1 ( 1.88 )  & 3.08 ( 0.1 )  & 21.02 & 0.73 & 0.74 & 19.10 & 3.08\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.15 & 0.87 ( 0 )    & 31.23 ( 1.88 ) & 0.17 ( 0.04 ) & 36.06 & 0.78 & 0.87 & 31.23 & 0.17\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.22 & 0.89 ( 0 )    & 22.46 ( 1.45 ) & 0 ( 0 )       & 27.46 & 0.71 & 0.89 & 22.46 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.02 & 0.64 ( 0.01 ) & 47.17 ( 5.85 ) & 4.12 ( 0.09 ) & 48.05 & 0.92 & 0.64 & 47.17 & 4.12\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.08 & 0.75 ( 0.01 ) & 20.86 ( 2.56 ) & 3.59 ( 0.09 ) & 22.27 & 0.77 & 0.75 & 20.86 & 3.59\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.13 & 0.87 ( 0 )    & 36.32 ( 2.18 ) & 0.23 ( 0.05 ) & 41.09 & 0.81 & 0.87 & 36.32 & 0.23\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.16 & 0.89 ( 0 )    & 32.22 ( 2.11 ) & 0.01 ( 0.01 ) & 37.21 & 0.8  & 0.89 & 32.22 & 0.01\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.09 & 0.71 ( 0.01 ) & 14.15 ( 1 )    & 2.3 ( 0.14 )  & 16.85 & 0.68 & 0.71 & 14.15 & 2.30\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.15 & 0.77 ( 0.01 ) & 11.74 ( 0.88 ) & 1.74 ( 0.13 ) & 15.00 & 0.64 & 0.77 & 11.74 & 1.74\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.27 & 0.83 ( 0 )    & 12.28 ( 0.9 )  & 0.18 ( 0.06 ) & 17.10 & 0.57 & 0.83 & 12.28 & 0.18\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.39 & 0.84 ( 0 )    & 9.58 ( 0.68 )  & 0.01 ( 0.01 ) & 14.57 & 0.51 & 0.84 &  9.58 & 0.01\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.08 & 0.7 ( 0.01 )  & 15.1 ( 1.65 )  & 3.16 ( 0.13 ) & 16.94 & 0.7  & 0.70 & 15.10 & 3.16\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.15 & 0.75 ( 0.01 ) & 14.19 ( 1.38 ) & 2.23 ( 0.13 ) & 16.96 & 0.64 & 0.75 & 14.19 & 2.23\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.25 & 0.83 ( 0 )    & 16.33 ( 1.01 ) & 0.2 ( 0.06 )  & 21.13 & 0.67 & 0.83 & 16.33 & 0.20\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.32 & 0.84 ( 0 )    & 13.82 ( 1.17 ) & 0.03 ( 0.02 ) & 18.79 & 0.6  & 0.84 & 13.82 & 0.03\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.05 & 0.66 ( 0.02 ) & 28.64 ( 3.14 ) & 4.13 ( 0.07 ) & 29.51 & 0.88 & 0.66 & 28.64 & 4.13\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.07 & 0.7 ( 0.01 )  & 25.71 ( 4.03 ) & 3.42 ( 0.1 )  & 27.29 & 0.78 & 0.70 & 25.71 & 3.42\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.14 & 0.81 ( 0 )    & 25.68 ( 1.61 ) & 1.07 ( 0.11 ) & 29.61 & 0.76 & 0.81 & 25.68 & 1.07\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.21 & 0.83 ( 0 )    & 23.49 ( 1.26 ) & 0.15 ( 0.05 ) & 28.34 & 0.75 & 0.83 & 23.49 & 0.15\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.02 & 0.65 ( 0.01 ) & 49.08 ( 6.86 ) & 4.29 ( 0.09 ) & 49.79 & 0.93 & 0.65 & 49.08 & 4.29\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.05 & 0.66 ( 0.01 ) & 32.99 ( 5.22 ) & 3.81 ( 0.09 ) & 34.18 & 0.8  & 0.66 & 32.99 & 3.81\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.11 & 0.8 ( 0 )     & 30.91 ( 2.22 ) & 1.52 ( 0.12 ) & 34.39 & 0.78 & 0.80 & 30.91 & 1.52\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.14 & 0.83 ( 0 )    & 35.66 ( 1.98 ) & 0.23 ( 0.06 ) & 40.43 & 0.82 & 0.83 & 35.66 & 0.23\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.13 | 0.77 ( 0.01 ) | 13.02 ( 1.02 ) | 1.51 ( 0.11 ) | 16.51 | 0.63 | 0.77 | 13.02 | 1.51 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.24 | 0.85 ( 0.01 ) | 10.71 ( 0.62 ) | 0.56 ( 0.07 ) | 15.15 | 0.57 | 0.85 | 10.71 | 0.56 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.41 | 0.92 ( 0 )    | 7.43 ( 0.6 )   | 0.02 ( 0.01 ) | 12.41 | 0.42 | 0.92 |  7.43 | 0.02 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.39 | 0.92 ( 0 )    | 7.82 ( 0.76 )  | 0 ( 0 )       | 12.82 | 0.4  | 0.92 |  7.82 | 0.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.09 | 0.73 ( 0.01 ) | 21.55 ( 1.94 ) | 1.64 ( 0.11 ) | 24.91 | 0.73 | 0.73 | 21.55 | 1.64 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.18 | 0.83 ( 0.01 ) | 15.59 ( 1.32 ) | 0.84 ( 0.1 )  | 19.75 | 0.62 | 0.83 | 15.59 | 0.84 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.32 | 0.92 ( 0 )    | 11.5 ( 0.9 )   | 0 ( 0 )       | 16.50 | 0.54 | 0.92 | 11.50 | 0.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.34 | 0.92 ( 0 )    | 11.02 ( 1.05 ) | 0 ( 0 )       | 16.02 | 0.49 | 0.92 | 11.02 | 0.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.03 | 0.66 ( 0.01 ) | 58.7 ( 5.86 )  | 2.64 ( 0.12 ) | 61.06 | 0.89 | 0.66 | 58.70 | 2.64 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.14 | 0.78 ( 0.01 ) | 19.29 ( 1.92 ) | 1.76 ( 0.1 )  | 22.53 | 0.68 | 0.78 | 19.29 | 1.76 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.26 | 0.91 ( 0 )    | 17.05 ( 1.43 ) | 0 ( 0 )       | 22.05 | 0.62 | 0.91 | 17.05 | 0.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.29 | 0.93 ( 0 )    | 15.14 ( 1.39 ) | 0 ( 0 )       | 20.14 | 0.54 | 0.93 | 15.14 | 0.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.02 | 0.65 ( 0.01 ) | 85.97 ( 9 )    | 3.03 ( 0.13 ) | 87.94 | 0.92 | 0.65 | 85.97 | 3.03 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.12 | 0.76 ( 0.01 ) | 22.02 ( 1.97 ) | 2.07 ( 0.1 )  | 24.95 | 0.75 | 0.76 | 22.02 | 2.07 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.23 | 0.91 ( 0 )    | 21.07 ( 1.75 ) | 0 ( 0 )       | 26.07 | 0.66 | 0.91 | 21.07 | 0.00 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.24 | 0.92 ( 0 )    | 19.56 ( 2.29 ) | 0 ( 0 )       | 24.56 | 0.57 | 0.92 | 19.56 | 0.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.13 | 0.76 ( 0.01 ) | 11.32 ( 0.99 ) | 1.87 ( 0.14 ) | 14.45 | 0.59 | 0.76 | 11.32 | 1.87 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.18 | 0.83 ( 0.01 ) | 12.96 ( 0.86 ) | 0.75 ( 0.09 ) | 17.21 | 0.61 | 0.83 | 12.96 | 0.75 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.33 | 0.91 ( 0 )    | 9.89 ( 0.75 )  | 0 ( 0 )       | 14.89 | 0.51 | 0.91 |  9.89 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.38 | 0.92 ( 0 )    | 8.56 ( 0.69 )  | 0 ( 0 )       | 13.56 | 0.44 | 0.92 |  8.56 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.08 | 0.74 ( 0.01 ) | 17.99 ( 1.78 ) | 2.32 ( 0.14 ) | 20.67 | 0.72 | 0.74 | 17.99 | 2.32 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.17 | 0.82 ( 0.01 ) | 14.36 ( 1.25 ) | 1.36 ( 0.12 ) | 18.00 | 0.6  | 0.82 | 14.36 | 1.36 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.29 | 0.91 ( 0 )    | 13.08 ( 1 )    | 0 ( 0 )       | 18.08 | 0.56 | 0.91 | 13.08 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.38 | 0.92 ( 0 )    | 9.67 ( 0.76 )  | 0 ( 0 )       | 14.67 | 0.49 | 0.92 |  9.67 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.03 | 0.66 ( 0.01 ) | 49.2 ( 5.65 )  | 3.15 ( 0.13 ) | 51.05 | 0.89 | 0.66 | 49.20 | 3.15 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.11 | 0.77 ( 0.01 ) | 23.84 ( 2.37 ) | 2.05 ( 0.11 ) | 26.79 | 0.73 | 0.77 | 23.84 | 2.05 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.19 | 0.91 ( 0 )    | 25.04 ( 1.69 ) | 0 ( 0 )       | 30.04 | 0.72 | 0.91 | 25.04 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.25 | 0.92 ( 0 )    | 18.51 ( 1.25 ) | 0 ( 0 )       | 23.51 | 0.66 | 0.92 | 18.51 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 0.67 ( 0.01 ) | 54.74 ( 6.8 )  | 3.54 ( 0.12 ) | 56.20 | 0.91 | 0.67 | 54.74 | 3.54 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.08 | 0.75 ( 0.01 ) | 28.55 ( 5.11 ) | 2.58 ( 0.11 ) | 30.97 | 0.75 | 0.75 | 28.55 | 2.58 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.31 | 0.89 ( 0 )    | 11.14 ( 0.74 ) | 0.02 ( 0.02 ) | 16.12 | 0.56 | 0.89 | 11.14 | 0.02 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.37 | 0.89 ( 0 )    | 9.65 ( 0.77 )  | 0 ( 0 )       | 14.65 | 0.49 | 0.89 |  9.65 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.08 | 0.71 ( 0.01 ) | 17.76 ( 1.65 ) | 2.81 ( 0.14 ) | 19.95 | 0.74 | 0.71 | 17.76 | 2.81 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.13 | 0.79 ( 0.01 ) | 16.43 ( 1.31 ) | 1.79 ( 0.13 ) | 19.64 | 0.68 | 0.79 | 16.43 | 1.79 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.25 | 0.89 ( 0 )    | 16.01 ( 0.82 ) | 0.03 ( 0.03 ) | 20.98 | 0.67 | 0.89 | 16.01 | 0.03 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.33 | 0.89 ( 0 )    | 12.77 ( 0.94 ) | 0 ( 0 )       | 17.77 | 0.58 | 0.89 | 12.77 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.03 | 0.67 ( 0.01 ) | 43.83 ( 5.17 ) | 3.75 ( 0.12 ) | 45.08 | 0.9  | 0.67 | 43.83 | 3.75 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.10 | 0.74 ( 0.01 ) | 19.1 ( 1.88 )  | 3.08 ( 0.1 )  | 21.02 | 0.73 | 0.74 | 19.10 | 3.08 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.15 | 0.87 ( 0 )    | 31.23 ( 1.88 ) | 0.17 ( 0.04 ) | 36.06 | 0.78 | 0.87 | 31.23 | 0.17 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.22 | 0.89 ( 0 )    | 22.46 ( 1.45 ) | 0 ( 0 )       | 27.46 | 0.71 | 0.89 | 22.46 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.02 | 0.64 ( 0.01 ) | 47.17 ( 5.85 ) | 4.12 ( 0.09 ) | 48.05 | 0.92 | 0.64 | 47.17 | 4.12 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.08 | 0.75 ( 0.01 ) | 20.86 ( 2.56 ) | 3.59 ( 0.09 ) | 22.27 | 0.77 | 0.75 | 20.86 | 3.59 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.13 | 0.87 ( 0 )    | 36.32 ( 2.18 ) | 0.23 ( 0.05 ) | 41.09 | 0.81 | 0.87 | 36.32 | 0.23 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.16 | 0.89 ( 0 )    | 32.22 ( 2.11 ) | 0.01 ( 0.01 ) | 37.21 | 0.8  | 0.89 | 32.22 | 0.01 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.09 | 0.71 ( 0.01 ) | 14.15 ( 1 )    | 2.3 ( 0.14 )  | 16.85 | 0.68 | 0.71 | 14.15 | 2.30 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.15 | 0.77 ( 0.01 ) | 11.74 ( 0.88 ) | 1.74 ( 0.13 ) | 15.00 | 0.64 | 0.77 | 11.74 | 1.74 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.27 | 0.83 ( 0 )    | 12.28 ( 0.9 )  | 0.18 ( 0.06 ) | 17.10 | 0.57 | 0.83 | 12.28 | 0.18 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.39 | 0.84 ( 0 )    | 9.58 ( 0.68 )  | 0.01 ( 0.01 ) | 14.57 | 0.51 | 0.84 |  9.58 | 0.01 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.08 | 0.7 ( 0.01 )  | 15.1 ( 1.65 )  | 3.16 ( 0.13 ) | 16.94 | 0.7  | 0.70 | 15.10 | 3.16 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.15 | 0.75 ( 0.01 ) | 14.19 ( 1.38 ) | 2.23 ( 0.13 ) | 16.96 | 0.64 | 0.75 | 14.19 | 2.23 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.25 | 0.83 ( 0 )    | 16.33 ( 1.01 ) | 0.2 ( 0.06 )  | 21.13 | 0.67 | 0.83 | 16.33 | 0.20 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.32 | 0.84 ( 0 )    | 13.82 ( 1.17 ) | 0.03 ( 0.02 ) | 18.79 | 0.6  | 0.84 | 13.82 | 0.03 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.05 | 0.66 ( 0.02 ) | 28.64 ( 3.14 ) | 4.13 ( 0.07 ) | 29.51 | 0.88 | 0.66 | 28.64 | 4.13 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.07 | 0.7 ( 0.01 )  | 25.71 ( 4.03 ) | 3.42 ( 0.1 )  | 27.29 | 0.78 | 0.70 | 25.71 | 3.42 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.14 | 0.81 ( 0 )    | 25.68 ( 1.61 ) | 1.07 ( 0.11 ) | 29.61 | 0.76 | 0.81 | 25.68 | 1.07 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.21 | 0.83 ( 0 )    | 23.49 ( 1.26 ) | 0.15 ( 0.05 ) | 28.34 | 0.75 | 0.83 | 23.49 | 0.15 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.02 | 0.65 ( 0.01 ) | 49.08 ( 6.86 ) | 4.29 ( 0.09 ) | 49.79 | 0.93 | 0.65 | 49.08 | 4.29 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.05 | 0.66 ( 0.01 ) | 32.99 ( 5.22 ) | 3.81 ( 0.09 ) | 34.18 | 0.8  | 0.66 | 32.99 | 3.81 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.11 | 0.8 ( 0 )     | 30.91 ( 2.22 ) | 1.52 ( 0.12 ) | 34.39 | 0.78 | 0.80 | 30.91 | 1.52 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.14 | 0.83 ( 0 )    | 35.66 ( 1.98 ) | 0.23 ( 0.06 ) | 40.43 | 0.82 | 0.83 | 35.66 | 0.23 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.13 0.77 ( 0.01 ) 13.02 ( 1.02 ) 1.51 ( 0.11 )\n",
+       "2   100   50 0.1   0.50 0.24 0.85 ( 0.01 ) 10.71 ( 0.62 ) 0.56 ( 0.07 )\n",
+       "3   500   50 0.1   0.10 0.41 0.92 ( 0 )    7.43 ( 0.6 )   0.02 ( 0.01 )\n",
+       "4  1000   50 0.1   0.05 0.39 0.92 ( 0 )    7.82 ( 0.76 )  0 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.09 0.73 ( 0.01 ) 21.55 ( 1.94 ) 1.64 ( 0.11 )\n",
+       "6   100  100 0.1   1.00 0.18 0.83 ( 0.01 ) 15.59 ( 1.32 ) 0.84 ( 0.1 ) \n",
+       "7   500  100 0.1   0.20 0.32 0.92 ( 0 )    11.5 ( 0.9 )   0 ( 0 )      \n",
+       "8  1000  100 0.1   0.10 0.34 0.92 ( 0 )    11.02 ( 1.05 ) 0 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.03 0.66 ( 0.01 ) 58.7 ( 5.86 )  2.64 ( 0.12 )\n",
+       "10  100  500 0.1   5.00 0.14 0.78 ( 0.01 ) 19.29 ( 1.92 ) 1.76 ( 0.1 ) \n",
+       "11  500  500 0.1   1.00 0.26 0.91 ( 0 )    17.05 ( 1.43 ) 0 ( 0 )      \n",
+       "12 1000  500 0.1   0.50 0.29 0.93 ( 0 )    15.14 ( 1.39 ) 0 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.02 0.65 ( 0.01 ) 85.97 ( 9 )    3.03 ( 0.13 )\n",
+       "14  100 1000 0.1  10.00 0.12 0.76 ( 0.01 ) 22.02 ( 1.97 ) 2.07 ( 0.1 ) \n",
+       "15  500 1000 0.1   2.00 0.23 0.91 ( 0 )    21.07 ( 1.75 ) 0 ( 0 )      \n",
+       "16 1000 1000 0.1   1.00 0.24 0.92 ( 0 )    19.56 ( 2.29 ) 0 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.13 0.76 ( 0.01 ) 11.32 ( 0.99 ) 1.87 ( 0.14 )\n",
+       "18  100   50 0.3   0.50 0.18 0.83 ( 0.01 ) 12.96 ( 0.86 ) 0.75 ( 0.09 )\n",
+       "19  500   50 0.3   0.10 0.33 0.91 ( 0 )    9.89 ( 0.75 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.38 0.92 ( 0 )    8.56 ( 0.69 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.08 0.74 ( 0.01 ) 17.99 ( 1.78 ) 2.32 ( 0.14 )\n",
+       "22  100  100 0.3   1.00 0.17 0.82 ( 0.01 ) 14.36 ( 1.25 ) 1.36 ( 0.12 )\n",
+       "23  500  100 0.3   0.20 0.29 0.91 ( 0 )    13.08 ( 1 )    0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.38 0.92 ( 0 )    9.67 ( 0.76 )  0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.03 0.66 ( 0.01 ) 49.2 ( 5.65 )  3.15 ( 0.13 )\n",
+       "26  100  500 0.3   5.00 0.11 0.77 ( 0.01 ) 23.84 ( 2.37 ) 2.05 ( 0.11 )\n",
+       "27  500  500 0.3   1.00 0.19 0.91 ( 0 )    25.04 ( 1.69 ) 0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.25 0.92 ( 0 )    18.51 ( 1.25 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.02 0.67 ( 0.01 ) 54.74 ( 6.8 )  3.54 ( 0.12 )\n",
+       "30  100 1000 0.3  10.00 0.08 0.75 ( 0.01 ) 28.55 ( 5.11 ) 2.58 ( 0.11 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.31 0.89 ( 0 )    11.14 ( 0.74 ) 0.02 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.37 0.89 ( 0 )    9.65 ( 0.77 )  0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.08 0.71 ( 0.01 ) 17.76 ( 1.65 ) 2.81 ( 0.14 )\n",
+       "54  100  100 0.7   1.00 0.13 0.79 ( 0.01 ) 16.43 ( 1.31 ) 1.79 ( 0.13 )\n",
+       "55  500  100 0.7   0.20 0.25 0.89 ( 0 )    16.01 ( 0.82 ) 0.03 ( 0.03 )\n",
+       "56 1000  100 0.7   0.10 0.33 0.89 ( 0 )    12.77 ( 0.94 ) 0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.03 0.67 ( 0.01 ) 43.83 ( 5.17 ) 3.75 ( 0.12 )\n",
+       "58  100  500 0.7   5.00 0.10 0.74 ( 0.01 ) 19.1 ( 1.88 )  3.08 ( 0.1 ) \n",
+       "59  500  500 0.7   1.00 0.15 0.87 ( 0 )    31.23 ( 1.88 ) 0.17 ( 0.04 )\n",
+       "60 1000  500 0.7   0.50 0.22 0.89 ( 0 )    22.46 ( 1.45 ) 0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.02 0.64 ( 0.01 ) 47.17 ( 5.85 ) 4.12 ( 0.09 )\n",
+       "62  100 1000 0.7  10.00 0.08 0.75 ( 0.01 ) 20.86 ( 2.56 ) 3.59 ( 0.09 )\n",
+       "63  500 1000 0.7   2.00 0.13 0.87 ( 0 )    36.32 ( 2.18 ) 0.23 ( 0.05 )\n",
+       "64 1000 1000 0.7   1.00 0.16 0.89 ( 0 )    32.22 ( 2.11 ) 0.01 ( 0.01 )\n",
+       "65   50   50 0.9   1.00 0.09 0.71 ( 0.01 ) 14.15 ( 1 )    2.3 ( 0.14 ) \n",
+       "66  100   50 0.9   0.50 0.15 0.77 ( 0.01 ) 11.74 ( 0.88 ) 1.74 ( 0.13 )\n",
+       "67  500   50 0.9   0.10 0.27 0.83 ( 0 )    12.28 ( 0.9 )  0.18 ( 0.06 )\n",
+       "68 1000   50 0.9   0.05 0.39 0.84 ( 0 )    9.58 ( 0.68 )  0.01 ( 0.01 )\n",
+       "69   50  100 0.9   2.00 0.08 0.7 ( 0.01 )  15.1 ( 1.65 )  3.16 ( 0.13 )\n",
+       "70  100  100 0.9   1.00 0.15 0.75 ( 0.01 ) 14.19 ( 1.38 ) 2.23 ( 0.13 )\n",
+       "71  500  100 0.9   0.20 0.25 0.83 ( 0 )    16.33 ( 1.01 ) 0.2 ( 0.06 ) \n",
+       "72 1000  100 0.9   0.10 0.32 0.84 ( 0 )    13.82 ( 1.17 ) 0.03 ( 0.02 )\n",
+       "73   50  500 0.9  10.00 0.05 0.66 ( 0.02 ) 28.64 ( 3.14 ) 4.13 ( 0.07 )\n",
+       "74  100  500 0.9   5.00 0.07 0.7 ( 0.01 )  25.71 ( 4.03 ) 3.42 ( 0.1 ) \n",
+       "75  500  500 0.9   1.00 0.14 0.81 ( 0 )    25.68 ( 1.61 ) 1.07 ( 0.11 )\n",
+       "76 1000  500 0.9   0.50 0.21 0.83 ( 0 )    23.49 ( 1.26 ) 0.15 ( 0.05 )\n",
+       "77   50 1000 0.9  20.00 0.02 0.65 ( 0.01 ) 49.08 ( 6.86 ) 4.29 ( 0.09 )\n",
+       "78  100 1000 0.9  10.00 0.05 0.66 ( 0.01 ) 32.99 ( 5.22 ) 3.81 ( 0.09 )\n",
+       "79  500 1000 0.9   2.00 0.11 0.8 ( 0 )     30.91 ( 2.22 ) 1.52 ( 0.12 )\n",
+       "80 1000 1000 0.9   1.00 0.14 0.83 ( 0 )    35.66 ( 1.98 ) 0.23 ( 0.06 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1  16.51      0.63 0.77     13.02   1.51   \n",
+       "2  15.15      0.57 0.85     10.71   0.56   \n",
+       "3  12.41      0.42 0.92      7.43   0.02   \n",
+       "4  12.82      0.4  0.92      7.82   0.00   \n",
+       "5  24.91      0.73 0.73     21.55   1.64   \n",
+       "6  19.75      0.62 0.83     15.59   0.84   \n",
+       "7  16.50      0.54 0.92     11.50   0.00   \n",
+       "8  16.02      0.49 0.92     11.02   0.00   \n",
+       "9  61.06      0.89 0.66     58.70   2.64   \n",
+       "10 22.53      0.68 0.78     19.29   1.76   \n",
+       "11 22.05      0.62 0.91     17.05   0.00   \n",
+       "12 20.14      0.54 0.93     15.14   0.00   \n",
+       "13 87.94      0.92 0.65     85.97   3.03   \n",
+       "14 24.95      0.75 0.76     22.02   2.07   \n",
+       "15 26.07      0.66 0.91     21.07   0.00   \n",
+       "16 24.56      0.57 0.92     19.56   0.00   \n",
+       "17 14.45      0.59 0.76     11.32   1.87   \n",
+       "18 17.21      0.61 0.83     12.96   0.75   \n",
+       "19 14.89      0.51 0.91      9.89   0.00   \n",
+       "20 13.56      0.44 0.92      8.56   0.00   \n",
+       "21 20.67      0.72 0.74     17.99   2.32   \n",
+       "22 18.00      0.6  0.82     14.36   1.36   \n",
+       "23 18.08      0.56 0.91     13.08   0.00   \n",
+       "24 14.67      0.49 0.92      9.67   0.00   \n",
+       "25 51.05      0.89 0.66     49.20   3.15   \n",
+       "26 26.79      0.73 0.77     23.84   2.05   \n",
+       "27 30.04      0.72 0.91     25.04   0.00   \n",
+       "28 23.51      0.66 0.92     18.51   0.00   \n",
+       "29 56.20      0.91 0.67     54.74   3.54   \n",
+       "30 30.97      0.75 0.75     28.55   2.58   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 16.12      0.56 0.89     11.14   0.02   \n",
+       "52 14.65      0.49 0.89      9.65   0.00   \n",
+       "53 19.95      0.74 0.71     17.76   2.81   \n",
+       "54 19.64      0.68 0.79     16.43   1.79   \n",
+       "55 20.98      0.67 0.89     16.01   0.03   \n",
+       "56 17.77      0.58 0.89     12.77   0.00   \n",
+       "57 45.08      0.9  0.67     43.83   3.75   \n",
+       "58 21.02      0.73 0.74     19.10   3.08   \n",
+       "59 36.06      0.78 0.87     31.23   0.17   \n",
+       "60 27.46      0.71 0.89     22.46   0.00   \n",
+       "61 48.05      0.92 0.64     47.17   4.12   \n",
+       "62 22.27      0.77 0.75     20.86   3.59   \n",
+       "63 41.09      0.81 0.87     36.32   0.23   \n",
+       "64 37.21      0.8  0.89     32.22   0.01   \n",
+       "65 16.85      0.68 0.71     14.15   2.30   \n",
+       "66 15.00      0.64 0.77     11.74   1.74   \n",
+       "67 17.10      0.57 0.83     12.28   0.18   \n",
+       "68 14.57      0.51 0.84      9.58   0.01   \n",
+       "69 16.94      0.7  0.70     15.10   3.16   \n",
+       "70 16.96      0.64 0.75     14.19   2.23   \n",
+       "71 21.13      0.67 0.83     16.33   0.20   \n",
+       "72 18.79      0.6  0.84     13.82   0.03   \n",
+       "73 29.51      0.88 0.66     28.64   4.13   \n",
+       "74 27.29      0.78 0.70     25.71   3.42   \n",
+       "75 29.61      0.76 0.81     25.68   1.07   \n",
+       "76 28.34      0.75 0.83     23.49   0.15   \n",
+       "77 49.79      0.93 0.65     49.08   4.29   \n",
+       "78 34.18      0.8  0.66     32.99   3.81   \n",
+       "79 34.39      0.78 0.80     30.91   1.52   \n",
+       "80 40.43      0.82 0.83     35.66   0.23   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/1.3_sim_toe_rf_binary_update.ipynb b/simulations/notebooks_sim_bin/1.3_sim_toe_rf_binary_update.ipynb
new file mode 100644
index 0000000..a03a60e
--- /dev/null
+++ b/simulations/notebooks_sim_bin/1.3_sim_toe_rf_binary_update.ipynb
@@ -0,0 +1,1067 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 62\"\n",
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+      "[1] \"indx: 66\"\n",
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+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.05 ( 0.14 )</td><td>4.71 ( 0.08 )</td><td>1 ( 0 )</td><td>0.1</td><td>3.31</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>1.4 ( 0.12 )</td><td>2.4 ( 0.08 )</td><td>1 ( 0 )</td><td>0.5</td><td>5.00</td><td>0.25</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>0.98 ( 0.09 )</td><td>1.2 ( 0.08 )</td><td>1 ( 0 )</td><td>0.69</td><td>5.78</td><td>0.15</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>3.95 ( 0.21 )</td><td>4.66 ( 0.08 )</td><td>1 ( 0 )</td><td>0.06</td><td>5.29</td><td>0.72</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.05 ( 0.14 ) & 4.71 ( 0.08 ) & 1 ( 0 ) & 0.1 & 3.31 & 0.56\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 1.4 ( 0.12 ) & 2.4 ( 0.08 ) & 1 ( 0 ) & 0.5 & 5.00 & 0.25\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 0.98 ( 0.09 ) & 1.2 ( 0.08 ) & 1 ( 0 ) & 0.69 & 5.78 & 0.15\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 3.95 ( 0.21 ) & 4.66 ( 0.08 ) & 1 ( 0 ) & 0.06 & 5.29 & 0.72\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.05 ( 0.14 ) | 4.71 ( 0.08 ) | 1 ( 0 ) | 0.1 | 3.31 | 0.56 |\n",
+       "| 3 | 500 | 50 | 0.1 | 1.4 ( 0.12 ) | 2.4 ( 0.08 ) | 1 ( 0 ) | 0.5 | 5.00 | 0.25 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 0.98 ( 0.09 ) | 1.2 ( 0.08 ) | 1 ( 0 ) | 0.69 | 5.78 | 0.15 |\n",
+       "| 5 | 50 | 100 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0.1 | 3.95 ( 0.21 ) | 4.66 ( 0.08 ) | 1 ( 0 ) | 0.06 | 5.29 | 0.72 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0.1 2.05 ( 0.14 ) 4.71 ( 0.08 ) 1 ( 0 ) 0.1  3.31       0.56\n",
+       "3 500  50  0.1 1.4 ( 0.12 )  2.4 ( 0.08 )  1 ( 0 ) 0.5  5.00       0.25\n",
+       "4 1000 50  0.1 0.98 ( 0.09 ) 1.2 ( 0.08 )  1 ( 0 ) 0.69 5.78       0.15\n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0.1 3.95 ( 0.21 ) 4.66 ( 0.08 ) 1 ( 0 ) 0.06 5.29       0.72"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>1 ( 0 )</td><td>0.08</td><td>27.38</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>1 ( 0 )</td><td>0.13</td><td>27.69</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 )</td><td>1 ( 0 )</td><td>0</td><td>47.99</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>1 ( 0 )</td><td>0.04</td><td>50.33</td><td>0.95</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>1 ( 0 )</td><td>0.07</td><td>52.50</td><td>0.94</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 1 ( 0 ) & 0.08 & 27.38 & 0.91\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 1 ( 0 ) & 0.13 & 27.69 & 0.88\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 47.19 ( 0.96 ) & 5.2 ( 0.08 ) & 1 ( 0 ) & 0 & 47.99 & 0.98\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 1 ( 0 ) & 0.04 & 50.33 & 0.95\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 1 ( 0 ) & 0.07 & 52.50 & 0.94\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 1 ( 0 ) | 0.08 | 27.38 | 0.91 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 1 ( 0 ) | 0.13 | 27.69 | 0.88 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 78 | 100 | 1000 | 0.9 | 47.19 ( 0.96 ) | 5.2 ( 0.08 ) | 1 ( 0 ) | 0 | 47.99 | 0.98 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 1 ( 0 ) | 0.04 | 50.33 | 0.95 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 1 ( 0 ) | 0.07 | 52.50 | 0.94 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "75 500  500  0.9 24.91 ( 0.57 ) 3.53 ( 0.09 ) 1 ( 0 ) 0.08 27.38      0.91\n",
+       "76 1000 500  0.9 24.53 ( 0.53 ) 2.84 ( 0.08 ) 1 ( 0 ) 0.13 27.69      0.88\n",
+       "77 50   1000 0.9 1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "78 100  1000 0.9 47.19 ( 0.96 ) 5.2 ( 0.08 )  1 ( 0 ) 0    47.99      0.98\n",
+       "79 500  1000 0.9 48.11 ( 0.92 ) 3.78 ( 0.08 ) 1 ( 0 ) 0.04 50.33      0.95\n",
+       "80 1000 1000 0.9 49.47 ( 0.78 ) 2.97 ( 0.07 ) 1 ( 0 ) 0.07 52.50      0.94"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0.1 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0.1 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0.1 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0.1 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t17 & 50 &   50 & 0.3 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t21 & 50 &  100 & 0.3 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t25 & 50 &  500 & 0.3 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t29 & 50 & 1000 & 0.3 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t33 & 50 &   50 & 0.5 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t37 & 50 &  100 & 0.5 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t41 & 50 &  500 & 0.5 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t45 & 50 & 1000 & 0.5 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t49 & 50 &   50 & 0.7 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t53 & 50 &  100 & 0.7 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t57 & 50 &  500 & 0.7 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t61 & 50 & 1000 & 0.7 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t65 & 50 &   50 & 0.9 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t69 & 50 &  100 & 0.9 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t73 & 50 &  500 & 0.9 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0.1 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0.1 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0.1 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0.1 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 17 | 50 |   50 | 0.3 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 21 | 50 |  100 | 0.3 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 25 | 50 |  500 | 0.3 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 29 | 50 | 1000 | 0.3 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 33 | 50 |   50 | 0.5 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 37 | 50 |  100 | 0.5 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 41 | 50 |  500 | 0.5 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 45 | 50 | 1000 | 0.5 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 49 | 50 |   50 | 0.7 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 53 | 50 |  100 | 0.7 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 57 | 50 |  500 | 0.7 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 61 | 50 | 1000 | 0.7 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 65 | 50 |   50 | 0.9 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 69 | 50 |  100 | 0.9 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 73 | 50 |  500 | 0.9 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0.1   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0.1   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0.1  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0.1  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "17 50   50 0.3   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "21 50  100 0.3   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "25 50  500 0.3  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "29 50 1000 0.3  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "33 50   50 0.5   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0.5   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0.5  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0.5  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "49 50   50 0.7   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "53 50  100 0.7   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "57 50  500 0.7  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "61 50 1000 0.7  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "65 50   50 0.9   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "69 50  100 0.9   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "73 50  500 0.9  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "77 50 1000 0.9  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      \n",
+       "17 1       6      \n",
+       "21 1       6      \n",
+       "25 1       6      \n",
+       "29 1       6      \n",
+       "33 1       6      \n",
+       "37 1       6      \n",
+       "41 1       6      \n",
+       "45 1       6      \n",
+       "49 1       6      \n",
+       "53 1       6      \n",
+       "57 1       6      \n",
+       "61 1       6      \n",
+       "65 1       6      \n",
+       "69 1       6      \n",
+       "73 1       6      \n",
+       "77 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.10</td><td>1 ( 0 )</td><td>2.05 ( 0.14 )</td><td>4.71 ( 0.08 )</td><td>3.31</td><td>0.56</td><td>1</td><td>2.05</td><td>4.71</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.50</td><td>1 ( 0 )</td><td>1.4 ( 0.12 ) </td><td>2.4 ( 0.08 ) </td><td>5.00</td><td>0.25</td><td>1</td><td>1.40</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.69</td><td>1 ( 0 )</td><td>0.98 ( 0.09 )</td><td>1.2 ( 0.08 ) </td><td>5.78</td><td>0.15</td><td>1</td><td>0.98</td><td>1.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.06</td><td>1 ( 0 )</td><td>3.95 ( 0.21 )</td><td>4.66 ( 0.08 )</td><td>5.29</td><td>0.72</td><td>1</td><td>3.95</td><td>4.66</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.10 & 1 ( 0 ) & 2.05 ( 0.14 ) & 4.71 ( 0.08 ) & 3.31 & 0.56 & 1 & 2.05 & 4.71\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.50 & 1 ( 0 ) & 1.4 ( 0.12 )  & 2.4 ( 0.08 )  & 5.00 & 0.25 & 1 & 1.40 & 2.40\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.69 & 1 ( 0 ) & 0.98 ( 0.09 ) & 1.2 ( 0.08 )  & 5.78 & 0.15 & 1 & 0.98 & 1.20\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.06 & 1 ( 0 ) & 3.95 ( 0.21 ) & 4.66 ( 0.08 ) & 5.29 & 0.72 & 1 & 3.95 & 4.66\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.10 | 1 ( 0 ) | 2.05 ( 0.14 ) | 4.71 ( 0.08 ) | 3.31 | 0.56 | 1 | 2.05 | 4.71 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.50 | 1 ( 0 ) | 1.4 ( 0.12 )  | 2.4 ( 0.08 )  | 5.00 | 0.25 | 1 | 1.40 | 2.40 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.69 | 1 ( 0 ) | 0.98 ( 0.09 ) | 1.2 ( 0.08 )  | 5.78 | 0.15 | 1 | 0.98 | 1.20 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.06 | 1 ( 0 ) | 3.95 ( 0.21 ) | 4.66 ( 0.08 ) | 5.29 | 0.72 | 1 | 3.95 | 4.66 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0.1  1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0.1  0.50  0.10 1 ( 0 ) 2.05 ( 0.14 ) 4.71 ( 0.08 ) 3.31       0.56\n",
+       "3  500  50 0.1  0.10  0.50 1 ( 0 ) 1.4 ( 0.12 )  2.4 ( 0.08 )  5.00       0.25\n",
+       "4 1000  50 0.1  0.05  0.69 1 ( 0 ) 0.98 ( 0.09 ) 1.2 ( 0.08 )  5.78       0.15\n",
+       "5   50 100 0.1  2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0.1  1.00  0.06 1 ( 0 ) 3.95 ( 0.21 ) 4.66 ( 0.08 ) 5.29       0.72\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        2.05    4.71   \n",
+       "3 1        1.40    2.40   \n",
+       "4 1        0.98    1.20   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        3.95    4.66   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.08</td><td>1 ( 0 )</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>27.38</td><td>0.91</td><td>1</td><td>24.91</td><td>3.53</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.13</td><td>1 ( 0 )</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>27.69</td><td>0.88</td><td>1</td><td>24.53</td><td>2.84</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.00</td><td>1 ( 0 )</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 ) </td><td>47.99</td><td>0.98</td><td>1</td><td>47.19</td><td>5.20</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>50.33</td><td>0.95</td><td>1</td><td>48.11</td><td>3.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>52.50</td><td>0.94</td><td>1</td><td>49.47</td><td>2.97</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.08 & 1 ( 0 ) & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 27.38 & 0.91 & 1 & 24.91 & 3.53\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.13 & 1 ( 0 ) & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 27.69 & 0.88 & 1 & 24.53 & 2.84\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.00 & 1 ( 0 ) & 47.19 ( 0.96 ) & 5.2 ( 0.08 )  & 47.99 & 0.98 & 1 & 47.19 & 5.20\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.04 & 1 ( 0 ) & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 50.33 & 0.95 & 1 & 48.11 & 3.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 52.50 & 0.94 & 1 & 49.47 & 2.97\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.08 | 1 ( 0 ) | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 27.38 | 0.91 | 1 | 24.91 | 3.53 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.13 | 1 ( 0 ) | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 27.69 | 0.88 | 1 | 24.53 | 2.84 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.00 | 1 ( 0 ) | 47.19 ( 0.96 ) | 5.2 ( 0.08 )  | 47.99 | 0.98 | 1 | 47.19 | 5.20 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.04 | 1 ( 0 ) | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 50.33 | 0.95 | 1 | 48.11 | 3.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 52.50 | 0.94 | 1 | 49.47 | 2.97 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "75  500  500 0.9   1.0  0.08 1 ( 0 ) 24.91 ( 0.57 ) 3.53 ( 0.09 ) 27.38     \n",
+       "76 1000  500 0.9   0.5  0.13 1 ( 0 ) 24.53 ( 0.53 ) 2.84 ( 0.08 ) 27.69     \n",
+       "77   50 1000 0.9  20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.0  0.00 1 ( 0 ) 47.19 ( 0.96 ) 5.2 ( 0.08 )  47.99     \n",
+       "79  500 1000 0.9   2.0  0.04 1 ( 0 ) 48.11 ( 0.92 ) 3.78 ( 0.08 ) 50.33     \n",
+       "80 1000 1000 0.9   1.0  0.07 1 ( 0 ) 49.47 ( 0.78 ) 2.97 ( 0.07 ) 52.50     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "75 0.91 1        24.91   3.53   \n",
+       "76 0.88 1        24.53   2.84   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.19   5.20   \n",
+       "79 0.95 1        48.11   3.78   \n",
+       "80 0.94 1        49.47   2.97   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>2.05 ( 0.14 ) </td><td>4.71 ( 0.08 )</td><td> 3.31</td><td>0.56</td><td>1</td><td> 2.05</td><td>4.71</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.50</td><td>1 ( 0 )</td><td>1.4 ( 0.12 )  </td><td>2.4 ( 0.08 ) </td><td> 5.00</td><td>0.25</td><td>1</td><td> 1.40</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.69</td><td>1 ( 0 )</td><td>0.98 ( 0.09 ) </td><td>1.2 ( 0.08 ) </td><td> 5.78</td><td>0.15</td><td>1</td><td> 0.98</td><td>1.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>3.95 ( 0.21 ) </td><td>4.66 ( 0.08 )</td><td> 5.29</td><td>0.72</td><td>1</td><td> 3.95</td><td>4.66</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.35</td><td>1 ( 0 )</td><td>3.91 ( 0.21 ) </td><td>2.17 ( 0.08 )</td><td> 7.74</td><td>0.48</td><td>1</td><td> 3.91</td><td>2.17</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.51</td><td>1 ( 0 )</td><td>2.97 ( 0.15 ) </td><td>1.29 ( 0.07 )</td><td> 7.68</td><td>0.36</td><td>1</td><td> 2.97</td><td>1.29</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.57 ( 0.55 )</td><td>4.72 ( 0.08 )</td><td>25.85</td><td>0.95</td><td>1</td><td>24.57</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.09</td><td>1 ( 0 )</td><td>23.57 ( 0.51 )</td><td>2.43 ( 0.1 ) </td><td>27.14</td><td>0.86</td><td>1</td><td>23.57</td><td>2.43</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>22.61 ( 0.5 ) </td><td>1.31 ( 0.08 )</td><td>27.30</td><td>0.82</td><td>1</td><td>22.61</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>48.35 ( 0.66 )</td><td>4.88 ( 0.08 )</td><td>49.47</td><td>0.98</td><td>1</td><td>48.35</td><td>4.88</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.59 ( 0.73 )</td><td>2.71 ( 0.1 ) </td><td>51.88</td><td>0.94</td><td>1</td><td>48.59</td><td>2.71</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>46.74 ( 0.64 )</td><td>1.75 ( 0.09 )</td><td>50.99</td><td>0.92</td><td>1</td><td>46.74</td><td>1.75</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.10</td><td>1 ( 0 )</td><td>1.68 ( 0.12 ) </td><td>4.86 ( 0.08 )</td><td> 2.81</td><td>0.54</td><td>1</td><td> 1.68</td><td>4.86</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>1 ( 0 )</td><td>1.57 ( 0.12 ) </td><td>2.74 ( 0.08 )</td><td> 4.83</td><td>0.3 </td><td>1</td><td> 1.57</td><td>2.74</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.62</td><td>1 ( 0 )</td><td>1.33 ( 0.1 )  </td><td>1.44 ( 0.08 )</td><td> 5.89</td><td>0.21</td><td>1</td><td> 1.33</td><td>1.44</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.05</td><td>1 ( 0 )</td><td>4.45 ( 0.2 )  </td><td>4.85 ( 0.08 )</td><td> 5.60</td><td>0.78</td><td>1</td><td> 4.45</td><td>4.85</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.33</td><td>1 ( 0 )</td><td>3.36 ( 0.19 ) </td><td>2.57 ( 0.09 )</td><td> 6.79</td><td>0.46</td><td>1</td><td> 3.36</td><td>2.57</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.47</td><td>1 ( 0 )</td><td>3.43 ( 0.17 ) </td><td>1.61 ( 0.09 )</td><td> 7.82</td><td>0.41</td><td>1</td><td> 3.43</td><td>1.61</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.69 ( 0.53 )</td><td>4.87 ( 0.08 )</td><td>24.82</td><td>0.95</td><td>1</td><td>23.69</td><td>4.87</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>22.84 ( 0.44 )</td><td>3.11 ( 0.09 )</td><td>25.73</td><td>0.89</td><td>1</td><td>22.84</td><td>3.11</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>22.24 ( 0.43 )</td><td>1.77 ( 0.09 )</td><td>26.47</td><td>0.84</td><td>1</td><td>22.24</td><td>1.77</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>48.06 ( 0.76 )</td><td>5.02 ( 0.08 )</td><td>49.04</td><td>0.98</td><td>1</td><td>48.06</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.45</td><td>1 ( 0 )</td><td>2.14 ( 0.13 ) </td><td>3.36 ( 0.08 )</td><td> 4.78</td><td>0.43</td><td>1</td><td> 2.14</td><td>3.36</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.60</td><td>1 ( 0 )</td><td>2.66 ( 0.14 ) </td><td>2.27 ( 0.08 )</td><td> 6.39</td><td>0.39</td><td>1</td><td> 2.66</td><td>2.27</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>4.36 ( 0.22 ) </td><td>4.8 ( 0.09 ) </td><td> 5.56</td><td>0.77</td><td>1</td><td> 4.36</td><td>4.80</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.38</td><td>1 ( 0 )</td><td>4.03 ( 0.18 ) </td><td>3.1 ( 0.09 ) </td><td> 6.93</td><td>0.56</td><td>1</td><td> 4.03</td><td>3.10</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.50</td><td>1 ( 0 )</td><td>4.65 ( 0.22 ) </td><td>2 ( 0.07 )   </td><td> 8.65</td><td>0.51</td><td>1</td><td> 4.65</td><td>2.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.02</td><td>1 ( 0 )</td><td>23.58 ( 0.53 )</td><td>4.72 ( 0.09 )</td><td>24.86</td><td>0.95</td><td>1</td><td>23.58</td><td>4.72</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.10</td><td>1 ( 0 )</td><td>23.09 ( 0.56 )</td><td>3.38 ( 0.09 )</td><td>25.71</td><td>0.89</td><td>1</td><td>23.09</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.14</td><td>1 ( 0 )</td><td>23.33 ( 0.53 )</td><td>2.58 ( 0.07 )</td><td>26.75</td><td>0.87</td><td>1</td><td>23.33</td><td>2.58</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.01</td><td>1 ( 0 )</td><td>46.8 ( 0.69 ) </td><td>5.13 ( 0.07 )</td><td>47.67</td><td>0.98</td><td>1</td><td>46.80</td><td>5.13</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.05</td><td>1 ( 0 )</td><td>48.68 ( 0.77 )</td><td>3.54 ( 0.08 )</td><td>51.14</td><td>0.95</td><td>1</td><td>48.68</td><td>3.54</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>47.14 ( 0.7 ) </td><td>2.73 ( 0.07 )</td><td>50.41</td><td>0.93</td><td>1</td><td>47.14</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.12</td><td>1 ( 0 )</td><td>2.09 ( 0.16 ) </td><td>4.98 ( 0.09 )</td><td> 3.06</td><td>0.62</td><td>1</td><td> 2.09</td><td>4.98</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.45</td><td>1 ( 0 )</td><td>2.6 ( 0.13 )  </td><td>3.33 ( 0.08 )</td><td> 5.27</td><td>0.47</td><td>1</td><td> 2.60</td><td>3.33</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.51</td><td>1 ( 0 )</td><td>3.48 ( 0.17 ) </td><td>2.35 ( 0.08 )</td><td> 7.13</td><td>0.47</td><td>1</td><td> 3.48</td><td>2.35</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>4.4 ( 0.23 )  </td><td>4.96 ( 0.09 )</td><td> 5.43</td><td>0.81</td><td>1</td><td> 4.40</td><td>4.96</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.35</td><td>1 ( 0 )</td><td>4.57 ( 0.24 ) </td><td>3.5 ( 0.08 ) </td><td> 7.07</td><td>0.62</td><td>1</td><td> 4.57</td><td>3.50</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.37</td><td>1 ( 0 )</td><td>6.43 ( 0.28 ) </td><td>2.57 ( 0.08 )</td><td> 9.86</td><td>0.63</td><td>1</td><td> 6.43</td><td>2.57</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>24.58 ( 0.66 )</td><td>5.1 ( 0.09 ) </td><td>25.48</td><td>0.96</td><td>1</td><td>24.58</td><td>5.10</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>24.91 ( 0.57 )</td><td>3.53 ( 0.09 )</td><td>27.38</td><td>0.91</td><td>1</td><td>24.91</td><td>3.53</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>24.53 ( 0.53 )</td><td>2.84 ( 0.08 )</td><td>27.69</td><td>0.88</td><td>1</td><td>24.53</td><td>2.84</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.19 ( 0.96 )</td><td>5.2 ( 0.08 ) </td><td>47.99</td><td>0.98</td><td>1</td><td>47.19</td><td>5.20</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.11 ( 0.92 )</td><td>3.78 ( 0.08 )</td><td>50.33</td><td>0.95</td><td>1</td><td>48.11</td><td>3.78</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>49.47 ( 0.78 )</td><td>2.97 ( 0.07 )</td><td>52.50</td><td>0.94</td><td>1</td><td>49.47</td><td>2.97</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.10 & 1 ( 0 ) & 2.05 ( 0.14 )  & 4.71 ( 0.08 ) &  3.31 & 0.56 & 1 &  2.05 & 4.71\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.50 & 1 ( 0 ) & 1.4 ( 0.12 )   & 2.4 ( 0.08 )  &  5.00 & 0.25 & 1 &  1.40 & 2.40\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.69 & 1 ( 0 ) & 0.98 ( 0.09 )  & 1.2 ( 0.08 )  &  5.78 & 0.15 & 1 &  0.98 & 1.20\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.06 & 1 ( 0 ) & 3.95 ( 0.21 )  & 4.66 ( 0.08 ) &  5.29 & 0.72 & 1 &  3.95 & 4.66\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.35 & 1 ( 0 ) & 3.91 ( 0.21 )  & 2.17 ( 0.08 ) &  7.74 & 0.48 & 1 &  3.91 & 2.17\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.51 & 1 ( 0 ) & 2.97 ( 0.15 )  & 1.29 ( 0.07 ) &  7.68 & 0.36 & 1 &  2.97 & 1.29\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.01 & 1 ( 0 ) & 24.57 ( 0.55 ) & 4.72 ( 0.08 ) & 25.85 & 0.95 & 1 & 24.57 & 4.72\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.09 & 1 ( 0 ) & 23.57 ( 0.51 ) & 2.43 ( 0.1 )  & 27.14 & 0.86 & 1 & 23.57 & 2.43\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.14 & 1 ( 0 ) & 22.61 ( 0.5 )  & 1.31 ( 0.08 ) & 27.30 & 0.82 & 1 & 22.61 & 1.31\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.01 & 1 ( 0 ) & 48.35 ( 0.66 ) & 4.88 ( 0.08 ) & 49.47 & 0.98 & 1 & 48.35 & 4.88\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.04 & 1 ( 0 ) & 48.59 ( 0.73 ) & 2.71 ( 0.1 )  & 51.88 & 0.94 & 1 & 48.59 & 2.71\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.06 & 1 ( 0 ) & 46.74 ( 0.64 ) & 1.75 ( 0.09 ) & 50.99 & 0.92 & 1 & 46.74 & 1.75\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.10 & 1 ( 0 ) & 1.68 ( 0.12 )  & 4.86 ( 0.08 ) &  2.81 & 0.54 & 1 &  1.68 & 4.86\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 1 ( 0 ) & 1.57 ( 0.12 )  & 2.74 ( 0.08 ) &  4.83 & 0.3  & 1 &  1.57 & 2.74\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.62 & 1 ( 0 ) & 1.33 ( 0.1 )   & 1.44 ( 0.08 ) &  5.89 & 0.21 & 1 &  1.33 & 1.44\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.05 & 1 ( 0 ) & 4.45 ( 0.2 )   & 4.85 ( 0.08 ) &  5.60 & 0.78 & 1 &  4.45 & 4.85\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.33 & 1 ( 0 ) & 3.36 ( 0.19 )  & 2.57 ( 0.09 ) &  6.79 & 0.46 & 1 &  3.36 & 2.57\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.47 & 1 ( 0 ) & 3.43 ( 0.17 )  & 1.61 ( 0.09 ) &  7.82 & 0.41 & 1 &  3.43 & 1.61\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.01 & 1 ( 0 ) & 23.69 ( 0.53 ) & 4.87 ( 0.08 ) & 24.82 & 0.95 & 1 & 23.69 & 4.87\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.07 & 1 ( 0 ) & 22.84 ( 0.44 ) & 3.11 ( 0.09 ) & 25.73 & 0.89 & 1 & 22.84 & 3.11\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.13 & 1 ( 0 ) & 22.24 ( 0.43 ) & 1.77 ( 0.09 ) & 26.47 & 0.84 & 1 & 22.24 & 1.77\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.01 & 1 ( 0 ) & 48.06 ( 0.76 ) & 5.02 ( 0.08 ) & 49.04 & 0.98 & 1 & 48.06 & 5.02\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.45 & 1 ( 0 ) & 2.14 ( 0.13 )  & 3.36 ( 0.08 ) &  4.78 & 0.43 & 1 &  2.14 & 3.36\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.60 & 1 ( 0 ) & 2.66 ( 0.14 )  & 2.27 ( 0.08 ) &  6.39 & 0.39 & 1 &  2.66 & 2.27\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.08 & 1 ( 0 ) & 4.36 ( 0.22 )  & 4.8 ( 0.09 )  &  5.56 & 0.77 & 1 &  4.36 & 4.80\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.38 & 1 ( 0 ) & 4.03 ( 0.18 )  & 3.1 ( 0.09 )  &  6.93 & 0.56 & 1 &  4.03 & 3.10\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.50 & 1 ( 0 ) & 4.65 ( 0.22 )  & 2 ( 0.07 )    &  8.65 & 0.51 & 1 &  4.65 & 2.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.02 & 1 ( 0 ) & 23.58 ( 0.53 ) & 4.72 ( 0.09 ) & 24.86 & 0.95 & 1 & 23.58 & 4.72\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.10 & 1 ( 0 ) & 23.09 ( 0.56 ) & 3.38 ( 0.09 ) & 25.71 & 0.89 & 1 & 23.09 & 3.38\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.14 & 1 ( 0 ) & 23.33 ( 0.53 ) & 2.58 ( 0.07 ) & 26.75 & 0.87 & 1 & 23.33 & 2.58\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.01 & 1 ( 0 ) & 46.8 ( 0.69 )  & 5.13 ( 0.07 ) & 47.67 & 0.98 & 1 & 46.80 & 5.13\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.05 & 1 ( 0 ) & 48.68 ( 0.77 ) & 3.54 ( 0.08 ) & 51.14 & 0.95 & 1 & 48.68 & 3.54\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.07 & 1 ( 0 ) & 47.14 ( 0.7 )  & 2.73 ( 0.07 ) & 50.41 & 0.93 & 1 & 47.14 & 2.73\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.12 & 1 ( 0 ) & 2.09 ( 0.16 )  & 4.98 ( 0.09 ) &  3.06 & 0.62 & 1 &  2.09 & 4.98\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.45 & 1 ( 0 ) & 2.6 ( 0.13 )   & 3.33 ( 0.08 ) &  5.27 & 0.47 & 1 &  2.60 & 3.33\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.51 & 1 ( 0 ) & 3.48 ( 0.17 )  & 2.35 ( 0.08 ) &  7.13 & 0.47 & 1 &  3.48 & 2.35\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 4.4 ( 0.23 )   & 4.96 ( 0.09 ) &  5.43 & 0.81 & 1 &  4.40 & 4.96\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.35 & 1 ( 0 ) & 4.57 ( 0.24 )  & 3.5 ( 0.08 )  &  7.07 & 0.62 & 1 &  4.57 & 3.50\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.37 & 1 ( 0 ) & 6.43 ( 0.28 )  & 2.57 ( 0.08 ) &  9.86 & 0.63 & 1 &  6.43 & 2.57\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.01 & 1 ( 0 ) & 24.58 ( 0.66 ) & 5.1 ( 0.09 )  & 25.48 & 0.96 & 1 & 24.58 & 5.10\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.08 & 1 ( 0 ) & 24.91 ( 0.57 ) & 3.53 ( 0.09 ) & 27.38 & 0.91 & 1 & 24.91 & 3.53\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.13 & 1 ( 0 ) & 24.53 ( 0.53 ) & 2.84 ( 0.08 ) & 27.69 & 0.88 & 1 & 24.53 & 2.84\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.00 & 1 ( 0 ) & 47.19 ( 0.96 ) & 5.2 ( 0.08 )  & 47.99 & 0.98 & 1 & 47.19 & 5.20\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.04 & 1 ( 0 ) & 48.11 ( 0.92 ) & 3.78 ( 0.08 ) & 50.33 & 0.95 & 1 & 48.11 & 3.78\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 49.47 ( 0.78 ) & 2.97 ( 0.07 ) & 52.50 & 0.94 & 1 & 49.47 & 2.97\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.10 | 1 ( 0 ) | 2.05 ( 0.14 )  | 4.71 ( 0.08 ) |  3.31 | 0.56 | 1 |  2.05 | 4.71 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.50 | 1 ( 0 ) | 1.4 ( 0.12 )   | 2.4 ( 0.08 )  |  5.00 | 0.25 | 1 |  1.40 | 2.40 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.69 | 1 ( 0 ) | 0.98 ( 0.09 )  | 1.2 ( 0.08 )  |  5.78 | 0.15 | 1 |  0.98 | 1.20 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.06 | 1 ( 0 ) | 3.95 ( 0.21 )  | 4.66 ( 0.08 ) |  5.29 | 0.72 | 1 |  3.95 | 4.66 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.35 | 1 ( 0 ) | 3.91 ( 0.21 )  | 2.17 ( 0.08 ) |  7.74 | 0.48 | 1 |  3.91 | 2.17 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.51 | 1 ( 0 ) | 2.97 ( 0.15 )  | 1.29 ( 0.07 ) |  7.68 | 0.36 | 1 |  2.97 | 1.29 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.01 | 1 ( 0 ) | 24.57 ( 0.55 ) | 4.72 ( 0.08 ) | 25.85 | 0.95 | 1 | 24.57 | 4.72 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.09 | 1 ( 0 ) | 23.57 ( 0.51 ) | 2.43 ( 0.1 )  | 27.14 | 0.86 | 1 | 23.57 | 2.43 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.14 | 1 ( 0 ) | 22.61 ( 0.5 )  | 1.31 ( 0.08 ) | 27.30 | 0.82 | 1 | 22.61 | 1.31 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.01 | 1 ( 0 ) | 48.35 ( 0.66 ) | 4.88 ( 0.08 ) | 49.47 | 0.98 | 1 | 48.35 | 4.88 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.04 | 1 ( 0 ) | 48.59 ( 0.73 ) | 2.71 ( 0.1 )  | 51.88 | 0.94 | 1 | 48.59 | 2.71 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.06 | 1 ( 0 ) | 46.74 ( 0.64 ) | 1.75 ( 0.09 ) | 50.99 | 0.92 | 1 | 46.74 | 1.75 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.10 | 1 ( 0 ) | 1.68 ( 0.12 )  | 4.86 ( 0.08 ) |  2.81 | 0.54 | 1 |  1.68 | 4.86 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 1 ( 0 ) | 1.57 ( 0.12 )  | 2.74 ( 0.08 ) |  4.83 | 0.3  | 1 |  1.57 | 2.74 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.62 | 1 ( 0 ) | 1.33 ( 0.1 )   | 1.44 ( 0.08 ) |  5.89 | 0.21 | 1 |  1.33 | 1.44 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.05 | 1 ( 0 ) | 4.45 ( 0.2 )   | 4.85 ( 0.08 ) |  5.60 | 0.78 | 1 |  4.45 | 4.85 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.33 | 1 ( 0 ) | 3.36 ( 0.19 )  | 2.57 ( 0.09 ) |  6.79 | 0.46 | 1 |  3.36 | 2.57 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.47 | 1 ( 0 ) | 3.43 ( 0.17 )  | 1.61 ( 0.09 ) |  7.82 | 0.41 | 1 |  3.43 | 1.61 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.01 | 1 ( 0 ) | 23.69 ( 0.53 ) | 4.87 ( 0.08 ) | 24.82 | 0.95 | 1 | 23.69 | 4.87 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.07 | 1 ( 0 ) | 22.84 ( 0.44 ) | 3.11 ( 0.09 ) | 25.73 | 0.89 | 1 | 22.84 | 3.11 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.13 | 1 ( 0 ) | 22.24 ( 0.43 ) | 1.77 ( 0.09 ) | 26.47 | 0.84 | 1 | 22.24 | 1.77 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.01 | 1 ( 0 ) | 48.06 ( 0.76 ) | 5.02 ( 0.08 ) | 49.04 | 0.98 | 1 | 48.06 | 5.02 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.45 | 1 ( 0 ) | 2.14 ( 0.13 )  | 3.36 ( 0.08 ) |  4.78 | 0.43 | 1 |  2.14 | 3.36 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.60 | 1 ( 0 ) | 2.66 ( 0.14 )  | 2.27 ( 0.08 ) |  6.39 | 0.39 | 1 |  2.66 | 2.27 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.08 | 1 ( 0 ) | 4.36 ( 0.22 )  | 4.8 ( 0.09 )  |  5.56 | 0.77 | 1 |  4.36 | 4.80 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.38 | 1 ( 0 ) | 4.03 ( 0.18 )  | 3.1 ( 0.09 )  |  6.93 | 0.56 | 1 |  4.03 | 3.10 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.50 | 1 ( 0 ) | 4.65 ( 0.22 )  | 2 ( 0.07 )    |  8.65 | 0.51 | 1 |  4.65 | 2.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.02 | 1 ( 0 ) | 23.58 ( 0.53 ) | 4.72 ( 0.09 ) | 24.86 | 0.95 | 1 | 23.58 | 4.72 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.10 | 1 ( 0 ) | 23.09 ( 0.56 ) | 3.38 ( 0.09 ) | 25.71 | 0.89 | 1 | 23.09 | 3.38 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.14 | 1 ( 0 ) | 23.33 ( 0.53 ) | 2.58 ( 0.07 ) | 26.75 | 0.87 | 1 | 23.33 | 2.58 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.01 | 1 ( 0 ) | 46.8 ( 0.69 )  | 5.13 ( 0.07 ) | 47.67 | 0.98 | 1 | 46.80 | 5.13 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.05 | 1 ( 0 ) | 48.68 ( 0.77 ) | 3.54 ( 0.08 ) | 51.14 | 0.95 | 1 | 48.68 | 3.54 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.07 | 1 ( 0 ) | 47.14 ( 0.7 )  | 2.73 ( 0.07 ) | 50.41 | 0.93 | 1 | 47.14 | 2.73 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.12 | 1 ( 0 ) | 2.09 ( 0.16 )  | 4.98 ( 0.09 ) |  3.06 | 0.62 | 1 |  2.09 | 4.98 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.45 | 1 ( 0 ) | 2.6 ( 0.13 )   | 3.33 ( 0.08 ) |  5.27 | 0.47 | 1 |  2.60 | 3.33 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.51 | 1 ( 0 ) | 3.48 ( 0.17 )  | 2.35 ( 0.08 ) |  7.13 | 0.47 | 1 |  3.48 | 2.35 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 4.4 ( 0.23 )   | 4.96 ( 0.09 ) |  5.43 | 0.81 | 1 |  4.40 | 4.96 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.35 | 1 ( 0 ) | 4.57 ( 0.24 )  | 3.5 ( 0.08 )  |  7.07 | 0.62 | 1 |  4.57 | 3.50 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.37 | 1 ( 0 ) | 6.43 ( 0.28 )  | 2.57 ( 0.08 ) |  9.86 | 0.63 | 1 |  6.43 | 2.57 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.01 | 1 ( 0 ) | 24.58 ( 0.66 ) | 5.1 ( 0.09 )  | 25.48 | 0.96 | 1 | 24.58 | 5.10 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.08 | 1 ( 0 ) | 24.91 ( 0.57 ) | 3.53 ( 0.09 ) | 27.38 | 0.91 | 1 | 24.91 | 3.53 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.13 | 1 ( 0 ) | 24.53 ( 0.53 ) | 2.84 ( 0.08 ) | 27.69 | 0.88 | 1 | 24.53 | 2.84 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.00 | 1 ( 0 ) | 47.19 ( 0.96 ) | 5.2 ( 0.08 )  | 47.99 | 0.98 | 1 | 47.19 | 5.20 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.04 | 1 ( 0 ) | 48.11 ( 0.92 ) | 3.78 ( 0.08 ) | 50.33 | 0.95 | 1 | 48.11 | 3.78 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 49.47 ( 0.78 ) | 2.97 ( 0.07 ) | 52.50 | 0.94 | 1 | 49.47 | 2.97 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0.1   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0.1   0.50 0.10 1 ( 0 ) 2.05 ( 0.14 )  4.71 ( 0.08 )  3.31     \n",
+       "3   500   50 0.1   0.10 0.50 1 ( 0 ) 1.4 ( 0.12 )   2.4 ( 0.08 )   5.00     \n",
+       "4  1000   50 0.1   0.05 0.69 1 ( 0 ) 0.98 ( 0.09 )  1.2 ( 0.08 )   5.78     \n",
+       "5    50  100 0.1   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0.1   1.00 0.06 1 ( 0 ) 3.95 ( 0.21 )  4.66 ( 0.08 )  5.29     \n",
+       "7   500  100 0.1   0.20 0.35 1 ( 0 ) 3.91 ( 0.21 )  2.17 ( 0.08 )  7.74     \n",
+       "8  1000  100 0.1   0.10 0.51 1 ( 0 ) 2.97 ( 0.15 )  1.29 ( 0.07 )  7.68     \n",
+       "9    50  500 0.1  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0.1   5.00 0.01 1 ( 0 ) 24.57 ( 0.55 ) 4.72 ( 0.08 ) 25.85     \n",
+       "11  500  500 0.1   1.00 0.09 1 ( 0 ) 23.57 ( 0.51 ) 2.43 ( 0.1 )  27.14     \n",
+       "12 1000  500 0.1   0.50 0.14 1 ( 0 ) 22.61 ( 0.5 )  1.31 ( 0.08 ) 27.30     \n",
+       "13   50 1000 0.1  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0.1  10.00 0.01 1 ( 0 ) 48.35 ( 0.66 ) 4.88 ( 0.08 ) 49.47     \n",
+       "15  500 1000 0.1   2.00 0.04 1 ( 0 ) 48.59 ( 0.73 ) 2.71 ( 0.1 )  51.88     \n",
+       "16 1000 1000 0.1   1.00 0.06 1 ( 0 ) 46.74 ( 0.64 ) 1.75 ( 0.09 ) 50.99     \n",
+       "17   50   50 0.3   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "18  100   50 0.3   0.50 0.10 1 ( 0 ) 1.68 ( 0.12 )  4.86 ( 0.08 )  2.81     \n",
+       "19  500   50 0.3   0.10 0.44 1 ( 0 ) 1.57 ( 0.12 )  2.74 ( 0.08 )  4.83     \n",
+       "20 1000   50 0.3   0.05 0.62 1 ( 0 ) 1.33 ( 0.1 )   1.44 ( 0.08 )  5.89     \n",
+       "21   50  100 0.3   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "22  100  100 0.3   1.00 0.05 1 ( 0 ) 4.45 ( 0.2 )   4.85 ( 0.08 )  5.60     \n",
+       "23  500  100 0.3   0.20 0.33 1 ( 0 ) 3.36 ( 0.19 )  2.57 ( 0.09 )  6.79     \n",
+       "24 1000  100 0.3   0.10 0.47 1 ( 0 ) 3.43 ( 0.17 )  1.61 ( 0.09 )  7.82     \n",
+       "25   50  500 0.3  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "26  100  500 0.3   5.00 0.01 1 ( 0 ) 23.69 ( 0.53 ) 4.87 ( 0.08 ) 24.82     \n",
+       "27  500  500 0.3   1.00 0.07 1 ( 0 ) 22.84 ( 0.44 ) 3.11 ( 0.09 ) 25.73     \n",
+       "28 1000  500 0.3   0.50 0.13 1 ( 0 ) 22.24 ( 0.43 ) 1.77 ( 0.09 ) 26.47     \n",
+       "29   50 1000 0.3  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "30  100 1000 0.3  10.00 0.01 1 ( 0 ) 48.06 ( 0.76 ) 5.02 ( 0.08 ) 49.04     \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮       ⋮              ⋮             ⋮         \n",
+       "51  500   50 0.7   0.10 0.45 1 ( 0 ) 2.14 ( 0.13 )  3.36 ( 0.08 )  4.78     \n",
+       "52 1000   50 0.7   0.05 0.60 1 ( 0 ) 2.66 ( 0.14 )  2.27 ( 0.08 )  6.39     \n",
+       "53   50  100 0.7   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "54  100  100 0.7   1.00 0.08 1 ( 0 ) 4.36 ( 0.22 )  4.8 ( 0.09 )   5.56     \n",
+       "55  500  100 0.7   0.20 0.38 1 ( 0 ) 4.03 ( 0.18 )  3.1 ( 0.09 )   6.93     \n",
+       "56 1000  100 0.7   0.10 0.50 1 ( 0 ) 4.65 ( 0.22 )  2 ( 0.07 )     8.65     \n",
+       "57   50  500 0.7  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "58  100  500 0.7   5.00 0.02 1 ( 0 ) 23.58 ( 0.53 ) 4.72 ( 0.09 ) 24.86     \n",
+       "59  500  500 0.7   1.00 0.10 1 ( 0 ) 23.09 ( 0.56 ) 3.38 ( 0.09 ) 25.71     \n",
+       "60 1000  500 0.7   0.50 0.14 1 ( 0 ) 23.33 ( 0.53 ) 2.58 ( 0.07 ) 26.75     \n",
+       "61   50 1000 0.7  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "62  100 1000 0.7  10.00 0.01 1 ( 0 ) 46.8 ( 0.69 )  5.13 ( 0.07 ) 47.67     \n",
+       "63  500 1000 0.7   2.00 0.05 1 ( 0 ) 48.68 ( 0.77 ) 3.54 ( 0.08 ) 51.14     \n",
+       "64 1000 1000 0.7   1.00 0.07 1 ( 0 ) 47.14 ( 0.7 )  2.73 ( 0.07 ) 50.41     \n",
+       "65   50   50 0.9   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "66  100   50 0.9   0.50 0.12 1 ( 0 ) 2.09 ( 0.16 )  4.98 ( 0.09 )  3.06     \n",
+       "67  500   50 0.9   0.10 0.45 1 ( 0 ) 2.6 ( 0.13 )   3.33 ( 0.08 )  5.27     \n",
+       "68 1000   50 0.9   0.05 0.51 1 ( 0 ) 3.48 ( 0.17 )  2.35 ( 0.08 )  7.13     \n",
+       "69   50  100 0.9   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "70  100  100 0.9   1.00 0.07 1 ( 0 ) 4.4 ( 0.23 )   4.96 ( 0.09 )  5.43     \n",
+       "71  500  100 0.9   0.20 0.35 1 ( 0 ) 4.57 ( 0.24 )  3.5 ( 0.08 )   7.07     \n",
+       "72 1000  100 0.9   0.10 0.37 1 ( 0 ) 6.43 ( 0.28 )  2.57 ( 0.08 )  9.86     \n",
+       "73   50  500 0.9  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "74  100  500 0.9   5.00 0.01 1 ( 0 ) 24.58 ( 0.66 ) 5.1 ( 0.09 )  25.48     \n",
+       "75  500  500 0.9   1.00 0.08 1 ( 0 ) 24.91 ( 0.57 ) 3.53 ( 0.09 ) 27.38     \n",
+       "76 1000  500 0.9   0.50 0.13 1 ( 0 ) 24.53 ( 0.53 ) 2.84 ( 0.08 ) 27.69     \n",
+       "77   50 1000 0.9  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.00 0.00 1 ( 0 ) 47.19 ( 0.96 ) 5.2 ( 0.08 )  47.99     \n",
+       "79  500 1000 0.9   2.00 0.04 1 ( 0 ) 48.11 ( 0.92 ) 3.78 ( 0.08 ) 50.33     \n",
+       "80 1000 1000 0.9   1.00 0.07 1 ( 0 ) 49.47 ( 0.78 ) 2.97 ( 0.07 ) 52.50     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.56 1         2.05   4.71   \n",
+       "3  0.25 1         1.40   2.40   \n",
+       "4  0.15 1         0.98   1.20   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.72 1         3.95   4.66   \n",
+       "7  0.48 1         3.91   2.17   \n",
+       "8  0.36 1         2.97   1.29   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.95 1        24.57   4.72   \n",
+       "11 0.86 1        23.57   2.43   \n",
+       "12 0.82 1        22.61   1.31   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.98 1        48.35   4.88   \n",
+       "15 0.94 1        48.59   2.71   \n",
+       "16 0.92 1        46.74   1.75   \n",
+       "17 NaN  1         1.00   6.00   \n",
+       "18 0.54 1         1.68   4.86   \n",
+       "19 0.3  1         1.57   2.74   \n",
+       "20 0.21 1         1.33   1.44   \n",
+       "21 NaN  1         1.00   6.00   \n",
+       "22 0.78 1         4.45   4.85   \n",
+       "23 0.46 1         3.36   2.57   \n",
+       "24 0.41 1         3.43   1.61   \n",
+       "25 NaN  1         1.00   6.00   \n",
+       "26 0.95 1        23.69   4.87   \n",
+       "27 0.89 1        22.84   3.11   \n",
+       "28 0.84 1        22.24   1.77   \n",
+       "29 NaN  1         1.00   6.00   \n",
+       "30 0.98 1        48.06   5.02   \n",
+       "⋮  ⋮    ⋮        ⋮       ⋮      \n",
+       "51 0.43 1         2.14   3.36   \n",
+       "52 0.39 1         2.66   2.27   \n",
+       "53 NaN  1         1.00   6.00   \n",
+       "54 0.77 1         4.36   4.80   \n",
+       "55 0.56 1         4.03   3.10   \n",
+       "56 0.51 1         4.65   2.00   \n",
+       "57 NaN  1         1.00   6.00   \n",
+       "58 0.95 1        23.58   4.72   \n",
+       "59 0.89 1        23.09   3.38   \n",
+       "60 0.87 1        23.33   2.58   \n",
+       "61 NaN  1         1.00   6.00   \n",
+       "62 0.98 1        46.80   5.13   \n",
+       "63 0.95 1        48.68   3.54   \n",
+       "64 0.93 1        47.14   2.73   \n",
+       "65 NaN  1         1.00   6.00   \n",
+       "66 0.62 1         2.09   4.98   \n",
+       "67 0.47 1         2.60   3.33   \n",
+       "68 0.47 1         3.48   2.35   \n",
+       "69 NaN  1         1.00   6.00   \n",
+       "70 0.81 1         4.40   4.96   \n",
+       "71 0.62 1         4.57   3.50   \n",
+       "72 0.63 1         6.43   2.57   \n",
+       "73 NaN  1         1.00   6.00   \n",
+       "74 0.96 1        24.58   5.10   \n",
+       "75 0.91 1        24.91   3.53   \n",
+       "76 0.88 1        24.53   2.84   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.19   5.20   \n",
+       "79 0.95 1        48.11   3.78   \n",
+       "80 0.94 1        49.47   2.97   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/1.4_sim_toe_compLasso_binary_update.ipynb b/simulations/notebooks_sim_bin/1.4_sim_toe_compLasso_binary_update.ipynb
new file mode 100644
index 0000000..1e829f4
--- /dev/null
+++ b/simulations/notebooks_sim_bin/1.4_sim_toe_compLasso_binary_update.ipynb
@@ -0,0 +1,966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Toeplitz Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/toe_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_toe = rbind(table_toe, results_toe_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>14.84 ( 0.9 )</td><td>0.93 ( 0.09 )</td><td>0.97 ( 0 )</td><td>0.13</td><td>19.91</td><td>0.70</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>7.19 ( 0.43 )</td><td>0.49 ( 0.07 )</td><td>0.93 ( 0 )</td><td>0.33</td><td>12.70</td><td>0.53</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>1.93 ( 0.34 )</td><td>0.16 ( 0.04 )</td><td>0.9 ( 0 )</td><td>0.7</td><td> 7.77</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>1.37 ( 0.42 )</td><td>0.01 ( 0.01 )</td><td>0.9 ( 0 )</td><td>0.79</td><td> 7.36</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>36.64 ( 2.16 )</td><td>0.65 ( 0.07 )</td><td>0.99 ( 0 )</td><td>0.07</td><td>41.99</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>16.16 ( 1.22 )</td><td>0.47 ( 0.06 )</td><td>0.94 ( 0 )</td><td>0.2</td><td>21.69</td><td>0.70</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 14.84 ( 0.9 ) & 0.93 ( 0.09 ) & 0.97 ( 0 ) & 0.13 & 19.91 & 0.70\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 7.19 ( 0.43 ) & 0.49 ( 0.07 ) & 0.93 ( 0 ) & 0.33 & 12.70 & 0.53\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 1.93 ( 0.34 ) & 0.16 ( 0.04 ) & 0.9 ( 0 ) & 0.7 &  7.77 & 0.16\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 1.37 ( 0.42 ) & 0.01 ( 0.01 ) & 0.9 ( 0 ) & 0.79 &  7.36 & 0.10\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 36.64 ( 2.16 ) & 0.65 ( 0.07 ) & 0.99 ( 0 ) & 0.07 & 41.99 & 0.84\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 16.16 ( 1.22 ) & 0.47 ( 0.06 ) & 0.94 ( 0 ) & 0.2 & 21.69 & 0.70\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 14.84 ( 0.9 ) | 0.93 ( 0.09 ) | 0.97 ( 0 ) | 0.13 | 19.91 | 0.70 |\n",
+       "| 2 | 100 | 50 | 0.1 | 7.19 ( 0.43 ) | 0.49 ( 0.07 ) | 0.93 ( 0 ) | 0.33 | 12.70 | 0.53 |\n",
+       "| 3 | 500 | 50 | 0.1 | 1.93 ( 0.34 ) | 0.16 ( 0.04 ) | 0.9 ( 0 ) | 0.7 |  7.77 | 0.16 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 1.37 ( 0.42 ) | 0.01 ( 0.01 ) | 0.9 ( 0 ) | 0.79 |  7.36 | 0.10 |\n",
+       "| 5 | 50 | 100 | 0.1 | 36.64 ( 2.16 ) | 0.65 ( 0.07 ) | 0.99 ( 0 ) | 0.07 | 41.99 | 0.84 |\n",
+       "| 6 | 100 | 100 | 0.1 | 16.16 ( 1.22 ) | 0.47 ( 0.06 ) | 0.94 ( 0 ) | 0.2 | 21.69 | 0.70 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC        Stab num_select FDR \n",
+       "1 50   50  0.1 14.84 ( 0.9 )  0.93 ( 0.09 ) 0.97 ( 0 ) 0.13 19.91      0.70\n",
+       "2 100  50  0.1 7.19 ( 0.43 )  0.49 ( 0.07 ) 0.93 ( 0 ) 0.33 12.70      0.53\n",
+       "3 500  50  0.1 1.93 ( 0.34 )  0.16 ( 0.04 ) 0.9 ( 0 )  0.7   7.77      0.16\n",
+       "4 1000 50  0.1 1.37 ( 0.42 )  0.01 ( 0.01 ) 0.9 ( 0 )  0.79  7.36      0.10\n",
+       "5 50   100 0.1 36.64 ( 2.16 ) 0.65 ( 0.07 ) 0.99 ( 0 ) 0.07 41.99      0.84\n",
+       "6 100  100 0.1 16.16 ( 1.22 ) 0.47 ( 0.06 ) 0.94 ( 0 ) 0.2  21.69      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>6.75 ( 0.73 )</td><td>2.76 ( 0.07 )</td><td>0.79 ( 0 )</td><td>0.4</td><td>  9.99</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>2.48 ( 0.28 )</td><td>2.79 ( 0.05 )</td><td>0.8 ( 0 )</td><td>0.73</td><td>  5.69</td><td>0.37</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>1 ( 0 )</td><td>0.01</td><td>205.64</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>70.81 ( 4.4 )</td><td>3.05 ( 0.12 )</td><td>0.95 ( 0 )</td><td>0.03</td><td> 73.76</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>7.71 ( 1.09 )</td><td>2.85 ( 0.06 )</td><td>0.79 ( 0 )</td><td>0.38</td><td> 10.86</td><td>0.59</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>2.41 ( 0.17 )</td><td>2.77 ( 0.07 )</td><td>0.8 ( 0 )</td><td>0.76</td><td>  5.64</td><td>0.39</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 6.75 ( 0.73 ) & 2.76 ( 0.07 ) & 0.79 ( 0 ) & 0.4 &   9.99 & 0.56\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 2.48 ( 0.28 ) & 2.79 ( 0.05 ) & 0.8 ( 0 ) & 0.73 &   5.69 & 0.37\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 1 ( 0 ) & 0.01 & 205.64 & 0.98\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 70.81 ( 4.4 ) & 3.05 ( 0.12 ) & 0.95 ( 0 ) & 0.03 &  73.76 & 0.96\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 7.71 ( 1.09 ) & 2.85 ( 0.06 ) & 0.79 ( 0 ) & 0.38 &  10.86 & 0.59\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 2.41 ( 0.17 ) & 2.77 ( 0.07 ) & 0.8 ( 0 ) & 0.76 &   5.64 & 0.39\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 6.75 ( 0.73 ) | 2.76 ( 0.07 ) | 0.79 ( 0 ) | 0.4 |   9.99 | 0.56 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 2.48 ( 0.28 ) | 2.79 ( 0.05 ) | 0.8 ( 0 ) | 0.73 |   5.69 | 0.37 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 1 ( 0 ) | 0.01 | 205.64 | 0.98 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 70.81 ( 4.4 ) | 3.05 ( 0.12 ) | 0.95 ( 0 ) | 0.03 |  73.76 | 0.96 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 7.71 ( 1.09 ) | 2.85 ( 0.06 ) | 0.79 ( 0 ) | 0.38 |  10.86 | 0.59 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 2.41 ( 0.17 ) | 2.77 ( 0.07 ) | 0.8 ( 0 ) | 0.76 |   5.64 | 0.39 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP               FN            ROC        Stab num_select FDR \n",
+       "75 500  500  0.9 6.75 ( 0.73 )    2.76 ( 0.07 ) 0.79 ( 0 ) 0.4    9.99     0.56\n",
+       "76 1000 500  0.9 2.48 ( 0.28 )    2.79 ( 0.05 ) 0.8 ( 0 )  0.73   5.69     0.37\n",
+       "77 50   1000 0.9 201.98 ( 10.15 ) 2.34 ( 0.16 ) 1 ( 0 )    0.01 205.64     0.98\n",
+       "78 100  1000 0.9 70.81 ( 4.4 )    3.05 ( 0.12 ) 0.95 ( 0 ) 0.03  73.76     0.96\n",
+       "79 500  1000 0.9 7.71 ( 1.09 )    2.85 ( 0.06 ) 0.79 ( 0 ) 0.38  10.86     0.59\n",
+       "80 1000 1000 0.9 2.41 ( 0.17 )    2.77 ( 0.07 ) 0.8 ( 0 )  0.76   5.64     0.39"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))\n",
+    "\n",
+    "result.table_toe$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$ROC))\n",
+    "result.table_toe$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FP))\n",
+    "result.table_toe$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_toe$FN))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>14.84 ( 0.9 ) </td><td>0.93 ( 0.09 )</td><td>19.91</td><td>0.7 </td><td>0.97</td><td>14.84</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.33</td><td>0.93 ( 0 )</td><td>7.19 ( 0.43 ) </td><td>0.49 ( 0.07 )</td><td>12.70</td><td>0.53</td><td>0.93</td><td> 7.19</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.70</td><td>0.9 ( 0 ) </td><td>1.93 ( 0.34 ) </td><td>0.16 ( 0.04 )</td><td> 7.77</td><td>0.16</td><td>0.90</td><td> 1.93</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.79</td><td>0.9 ( 0 ) </td><td>1.37 ( 0.42 ) </td><td>0.01 ( 0.01 )</td><td> 7.36</td><td>0.1 </td><td>0.90</td><td> 1.37</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.07</td><td>0.99 ( 0 )</td><td>36.64 ( 2.16 )</td><td>0.65 ( 0.07 )</td><td>41.99</td><td>0.84</td><td>0.99</td><td>36.64</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.16 ( 1.22 )</td><td>0.47 ( 0.06 )</td><td>21.69</td><td>0.7 </td><td>0.94</td><td>16.16</td><td>0.47</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.13 & 0.97 ( 0 ) & 14.84 ( 0.9 )  & 0.93 ( 0.09 ) & 19.91 & 0.7  & 0.97 & 14.84 & 0.93\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.33 & 0.93 ( 0 ) & 7.19 ( 0.43 )  & 0.49 ( 0.07 ) & 12.70 & 0.53 & 0.93 &  7.19 & 0.49\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.70 & 0.9 ( 0 )  & 1.93 ( 0.34 )  & 0.16 ( 0.04 ) &  7.77 & 0.16 & 0.90 &  1.93 & 0.16\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.79 & 0.9 ( 0 )  & 1.37 ( 0.42 )  & 0.01 ( 0.01 ) &  7.36 & 0.1  & 0.90 &  1.37 & 0.01\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.07 & 0.99 ( 0 ) & 36.64 ( 2.16 ) & 0.65 ( 0.07 ) & 41.99 & 0.84 & 0.99 & 36.64 & 0.65\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.20 & 0.94 ( 0 ) & 16.16 ( 1.22 ) & 0.47 ( 0.06 ) & 21.69 & 0.7  & 0.94 & 16.16 & 0.47\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.13 | 0.97 ( 0 ) | 14.84 ( 0.9 )  | 0.93 ( 0.09 ) | 19.91 | 0.7  | 0.97 | 14.84 | 0.93 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.33 | 0.93 ( 0 ) | 7.19 ( 0.43 )  | 0.49 ( 0.07 ) | 12.70 | 0.53 | 0.93 |  7.19 | 0.49 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.70 | 0.9 ( 0 )  | 1.93 ( 0.34 )  | 0.16 ( 0.04 ) |  7.77 | 0.16 | 0.90 |  1.93 | 0.16 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.79 | 0.9 ( 0 )  | 1.37 ( 0.42 )  | 0.01 ( 0.01 ) |  7.36 | 0.1  | 0.90 |  1.37 | 0.01 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.07 | 0.99 ( 0 ) | 36.64 ( 2.16 ) | 0.65 ( 0.07 ) | 41.99 | 0.84 | 0.99 | 36.64 | 0.65 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.20 | 0.94 ( 0 ) | 16.16 ( 1.22 ) | 0.47 ( 0.06 ) | 21.69 | 0.7  | 0.94 | 16.16 | 0.47 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC        FP             FN            num_select\n",
+       "1   50  50 0.1  1.00  0.13 0.97 ( 0 ) 14.84 ( 0.9 )  0.93 ( 0.09 ) 19.91     \n",
+       "2  100  50 0.1  0.50  0.33 0.93 ( 0 ) 7.19 ( 0.43 )  0.49 ( 0.07 ) 12.70     \n",
+       "3  500  50 0.1  0.10  0.70 0.9 ( 0 )  1.93 ( 0.34 )  0.16 ( 0.04 )  7.77     \n",
+       "4 1000  50 0.1  0.05  0.79 0.9 ( 0 )  1.37 ( 0.42 )  0.01 ( 0.01 )  7.36     \n",
+       "5   50 100 0.1  2.00  0.07 0.99 ( 0 ) 36.64 ( 2.16 ) 0.65 ( 0.07 ) 41.99     \n",
+       "6  100 100 0.1  1.00  0.20 0.94 ( 0 ) 16.16 ( 1.22 ) 0.47 ( 0.06 ) 21.69     \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.7  0.97     14.84   0.93   \n",
+       "2 0.53 0.93      7.19   0.49   \n",
+       "3 0.16 0.90      1.93   0.16   \n",
+       "4 0.1  0.90      1.37   0.01   \n",
+       "5 0.84 0.99     36.64   0.65   \n",
+       "6 0.7  0.94     16.16   0.47   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.40</td><td>0.79 ( 0 )</td><td>6.75 ( 0.73 )   </td><td>2.76 ( 0.07 )</td><td>  9.99</td><td>0.56</td><td>0.79</td><td>  6.75</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.73</td><td>0.8 ( 0 ) </td><td>2.48 ( 0.28 )   </td><td>2.79 ( 0.05 )</td><td>  5.69</td><td>0.37</td><td>0.80</td><td>  2.48</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>205.64</td><td>0.98</td><td>1.00</td><td>201.98</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.03</td><td>0.95 ( 0 )</td><td>70.81 ( 4.4 )   </td><td>3.05 ( 0.12 )</td><td> 73.76</td><td>0.96</td><td>0.95</td><td> 70.81</td><td>3.05</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.38</td><td>0.79 ( 0 )</td><td>7.71 ( 1.09 )   </td><td>2.85 ( 0.06 )</td><td> 10.86</td><td>0.59</td><td>0.79</td><td>  7.71</td><td>2.85</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.76</td><td>0.8 ( 0 ) </td><td>2.41 ( 0.17 )   </td><td>2.77 ( 0.07 )</td><td>  5.64</td><td>0.39</td><td>0.80</td><td>  2.41</td><td>2.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.40 & 0.79 ( 0 ) & 6.75 ( 0.73 )    & 2.76 ( 0.07 ) &   9.99 & 0.56 & 0.79 &   6.75 & 2.76\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.73 & 0.8 ( 0 )  & 2.48 ( 0.28 )    & 2.79 ( 0.05 ) &   5.69 & 0.37 & 0.80 &   2.48 & 2.79\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.01 & 1 ( 0 )    & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 205.64 & 0.98 & 1.00 & 201.98 & 2.34\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.03 & 0.95 ( 0 ) & 70.81 ( 4.4 )    & 3.05 ( 0.12 ) &  73.76 & 0.96 & 0.95 &  70.81 & 3.05\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.38 & 0.79 ( 0 ) & 7.71 ( 1.09 )    & 2.85 ( 0.06 ) &  10.86 & 0.59 & 0.79 &   7.71 & 2.85\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.76 & 0.8 ( 0 )  & 2.41 ( 0.17 )    & 2.77 ( 0.07 ) &   5.64 & 0.39 & 0.80 &   2.41 & 2.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.40 | 0.79 ( 0 ) | 6.75 ( 0.73 )    | 2.76 ( 0.07 ) |   9.99 | 0.56 | 0.79 |   6.75 | 2.76 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.73 | 0.8 ( 0 )  | 2.48 ( 0.28 )    | 2.79 ( 0.05 ) |   5.69 | 0.37 | 0.80 |   2.48 | 2.79 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.01 | 1 ( 0 )    | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 205.64 | 0.98 | 1.00 | 201.98 | 2.34 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.03 | 0.95 ( 0 ) | 70.81 ( 4.4 )    | 3.05 ( 0.12 ) |  73.76 | 0.96 | 0.95 |  70.81 | 3.05 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.38 | 0.79 ( 0 ) | 7.71 ( 1.09 )    | 2.85 ( 0.06 ) |  10.86 | 0.59 | 0.79 |   7.71 | 2.85 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.76 | 0.8 ( 0 )  | 2.41 ( 0.17 )    | 2.77 ( 0.07 ) |   5.64 | 0.39 | 0.80 |   2.41 | 2.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP               FN           \n",
+       "75  500  500 0.9   1.0  0.40 0.79 ( 0 ) 6.75 ( 0.73 )    2.76 ( 0.07 )\n",
+       "76 1000  500 0.9   0.5  0.73 0.8 ( 0 )  2.48 ( 0.28 )    2.79 ( 0.05 )\n",
+       "77   50 1000 0.9  20.0  0.01 1 ( 0 )    201.98 ( 10.15 ) 2.34 ( 0.16 )\n",
+       "78  100 1000 0.9  10.0  0.03 0.95 ( 0 ) 70.81 ( 4.4 )    3.05 ( 0.12 )\n",
+       "79  500 1000 0.9   2.0  0.38 0.79 ( 0 ) 7.71 ( 1.09 )    2.85 ( 0.06 )\n",
+       "80 1000 1000 0.9   1.0  0.76 0.8 ( 0 )  2.41 ( 0.17 )    2.77 ( 0.07 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75   9.99     0.56 0.79       6.75  2.76   \n",
+       "76   5.69     0.37 0.80       2.48  2.79   \n",
+       "77 205.64     0.98 1.00     201.98  2.34   \n",
+       "78  73.76     0.96 0.95      70.81  3.05   \n",
+       "79  10.86     0.59 0.79       7.71  2.85   \n",
+       "80   5.64     0.39 0.80       2.41  2.77   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>14.84 ( 0.9 )  </td><td>0.93 ( 0.09 )</td><td> 19.91</td><td>0.7 </td><td>0.97</td><td> 14.84</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.33</td><td>0.93 ( 0 )</td><td>7.19 ( 0.43 )  </td><td>0.49 ( 0.07 )</td><td> 12.70</td><td>0.53</td><td>0.93</td><td>  7.19</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.70</td><td>0.9 ( 0 ) </td><td>1.93 ( 0.34 )  </td><td>0.16 ( 0.04 )</td><td>  7.77</td><td>0.16</td><td>0.90</td><td>  1.93</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.79</td><td>0.9 ( 0 ) </td><td>1.37 ( 0.42 )  </td><td>0.01 ( 0.01 )</td><td>  7.36</td><td>0.1 </td><td>0.90</td><td>  1.37</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.07</td><td>0.99 ( 0 )</td><td>36.64 ( 2.16 ) </td><td>0.65 ( 0.07 )</td><td> 41.99</td><td>0.84</td><td>0.99</td><td> 36.64</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.20</td><td>0.94 ( 0 )</td><td>16.16 ( 1.22 ) </td><td>0.47 ( 0.06 )</td><td> 21.69</td><td>0.7 </td><td>0.94</td><td> 16.16</td><td>0.47</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.36</td><td>0.9 ( 0 ) </td><td>8.64 ( 1.35 )  </td><td>0.08 ( 0.03 )</td><td> 14.56</td><td>0.37</td><td>0.90</td><td>  8.64</td><td>0.08</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.47</td><td>0.9 ( 0 ) </td><td>5.83 ( 1.4 )   </td><td>0.02 ( 0.01 )</td><td> 11.81</td><td>0.22</td><td>0.90</td><td>  5.83</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>248.06 ( 7.56 )</td><td>0.56 ( 0.07 )</td><td>253.50</td><td>0.98</td><td>1.00</td><td>248.06</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.03</td><td>1 ( 0 )   </td><td>132.04 ( 7.47 )</td><td>0.33 ( 0.05 )</td><td>137.71</td><td>0.95</td><td>1.00</td><td>132.04</td><td>0.33</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.34</td><td>0.91 ( 0 )</td><td>12.55 ( 1.81 ) </td><td>0.04 ( 0.02 )</td><td> 18.51</td><td>0.52</td><td>0.91</td><td> 12.55</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.69</td><td>0.9 ( 0 ) </td><td>3.02 ( 0.6 )   </td><td>0.01 ( 0.01 )</td><td>  9.01</td><td>0.22</td><td>0.90</td><td>  3.02</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>398.98 ( 6.9 ) </td><td>0.8 ( 0.08 ) </td><td>404.18</td><td>0.99</td><td>1.00</td><td>398.98</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>296.19 ( 8.29 )</td><td>0.35 ( 0.05 )</td><td>301.84</td><td>0.98</td><td>1.00</td><td>296.19</td><td>0.35</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.39</td><td>0.9 ( 0 ) </td><td>10.2 ( 0.98 )  </td><td>0.1 ( 0.03 ) </td><td> 16.10</td><td>0.52</td><td>0.90</td><td> 10.20</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.83</td><td>0.9 ( 0 ) </td><td>1.76 ( 0.26 )  </td><td>0.02 ( 0.01 )</td><td>  7.74</td><td>0.18</td><td>0.90</td><td>  1.76</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.96 ( 0 )</td><td>13.96 ( 0.83 ) </td><td>1.01 ( 0.09 )</td><td> 18.95</td><td>0.7 </td><td>0.96</td><td> 13.96</td><td>1.01</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.92 ( 0 )</td><td>9.38 ( 0.67 )  </td><td>0.68 ( 0.07 )</td><td> 14.70</td><td>0.58</td><td>0.92</td><td>  9.38</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>0.89 ( 0 )</td><td>5.43 ( 0.9 )   </td><td>0.14 ( 0.03 )</td><td> 11.29</td><td>0.3 </td><td>0.89</td><td>  5.43</td><td>0.14</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.48</td><td>0.88 ( 0 )</td><td>5.03 ( 1 )     </td><td>0.09 ( 0.03 )</td><td> 10.94</td><td>0.23</td><td>0.88</td><td>  5.03</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>36.33 ( 2.24 ) </td><td>0.9 ( 0.09 ) </td><td> 41.43</td><td>0.85</td><td>0.99</td><td> 36.33</td><td>0.90</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.93 ( 0 )</td><td>16.08 ( 0.95 ) </td><td>0.75 ( 0.07 )</td><td> 21.33</td><td>0.72</td><td>0.93</td><td> 16.08</td><td>0.75</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.37</td><td>0.89 ( 0 )</td><td>8.66 ( 1.15 )  </td><td>0.11 ( 0.03 )</td><td> 14.55</td><td>0.42</td><td>0.89</td><td>  8.66</td><td>0.11</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.39</td><td>0.88 ( 0 )</td><td>8.45 ( 1.17 )  </td><td>0.12 ( 0.03 )</td><td> 14.33</td><td>0.39</td><td>0.88</td><td>  8.45</td><td>0.12</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>228.37 ( 7.07 )</td><td>0.6 ( 0.08 ) </td><td>233.77</td><td>0.97</td><td>1.00</td><td>228.37</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.04</td><td>0.99 ( 0 )</td><td>114.53 ( 5.78 )</td><td>0.43 ( 0.06 )</td><td>120.10</td><td>0.94</td><td>0.99</td><td>114.53</td><td>0.43</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.31</td><td>0.89 ( 0 )</td><td>13.64 ( 2.08 ) </td><td>0.2 ( 0.04 ) </td><td> 19.44</td><td>0.57</td><td>0.89</td><td> 13.64</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.54</td><td>0.88 ( 0 )</td><td>6.3 ( 1.15 )   </td><td>0.09 ( 0.04 )</td><td> 12.21</td><td>0.38</td><td>0.88</td><td>  6.30</td><td>0.09</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>412.73 ( 7.91 )</td><td>0.88 ( 0.1 ) </td><td>417.85</td><td>0.99</td><td>1.00</td><td>412.73</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>279.68 ( 7.94 )</td><td>0.45 ( 0.07 )</td><td>285.23</td><td>0.98</td><td>1.00</td><td>279.68</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.67</td><td>0.86 ( 0 )   </td><td>1.32 ( 0.25 )   </td><td>2.42 ( 0.06 )</td><td>  4.90</td><td>0.19</td><td>0.86</td><td>  1.32</td><td>2.42</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.74</td><td>0.85 ( 0 )   </td><td>0.84 ( 0.14 )   </td><td>2.48 ( 0.06 )</td><td>  4.36</td><td>0.13</td><td>0.85</td><td>  0.84</td><td>2.48</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.07</td><td>0.96 ( 0 )   </td><td>24.54 ( 1.62 )  </td><td>2.08 ( 0.11 )</td><td> 28.46</td><td>0.84</td><td>0.96</td><td> 24.54</td><td>2.08</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.16</td><td>0.89 ( 0 )   </td><td>14.69 ( 1.28 )  </td><td>1.85 ( 0.08 )</td><td> 18.84</td><td>0.71</td><td>0.89</td><td> 14.69</td><td>1.85</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.55</td><td>0.86 ( 0 )   </td><td>2.19 ( 0.64 )   </td><td>2.33 ( 0.07 )</td><td>  5.86</td><td>0.18</td><td>0.86</td><td>  2.19</td><td>2.33</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.81</td><td>0.85 ( 0 )   </td><td>0.51 ( 0.1 )    </td><td>2.56 ( 0.05 )</td><td>  3.95</td><td>0.09</td><td>0.85</td><td>  0.51</td><td>2.56</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>1 ( 0 )      </td><td>161.23 ( 7.83 ) </td><td>1.29 ( 0.1 ) </td><td>165.94</td><td>0.96</td><td>1.00</td><td>161.23</td><td>1.29</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.06</td><td>0.96 ( 0 )   </td><td>60.05 ( 4.29 )  </td><td>1.63 ( 0.12 )</td><td> 64.42</td><td>0.92</td><td>0.96</td><td> 60.05</td><td>1.63</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.50</td><td>0.85 ( 0 )   </td><td>3.12 ( 1.31 )   </td><td>2.68 ( 0.07 )</td><td>  6.44</td><td>0.19</td><td>0.85</td><td>  3.12</td><td>2.68</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.89</td><td>0.85 ( 0 )   </td><td>0.39 ( 0.06 )   </td><td>2.88 ( 0.04 )</td><td>  3.51</td><td>0.09</td><td>0.85</td><td>  0.39</td><td>2.88</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>1 ( 0 )      </td><td>363.91 ( 11.45 )</td><td>1.39 ( 0.1 ) </td><td>368.52</td><td>0.99</td><td>1.00</td><td>363.91</td><td>1.39</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.03</td><td>0.99 ( 0 )   </td><td>145.9 ( 7.62 )  </td><td>1.24 ( 0.11 )</td><td>150.66</td><td>0.96</td><td>0.99</td><td>145.90</td><td>1.24</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.61</td><td>0.85 ( 0 )   </td><td>2.38 ( 0.4 )    </td><td>2.7 ( 0.06 ) </td><td>  5.68</td><td>0.27</td><td>0.85</td><td>  2.38</td><td>2.70</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.87</td><td>0.85 ( 0 )   </td><td>0.65 ( 0.08 )   </td><td>2.87 ( 0.04 )</td><td>  3.78</td><td>0.14</td><td>0.85</td><td>  0.65</td><td>2.87</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.84 ( 0.01 )</td><td>9.98 ( 0.78 )   </td><td>2.68 ( 0.11 )</td><td> 13.30</td><td>0.7 </td><td>0.84</td><td>  9.98</td><td>2.68</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.25</td><td>0.8 ( 0 )    </td><td>6.57 ( 0.7 )    </td><td>2.73 ( 0.09 )</td><td>  9.84</td><td>0.58</td><td>0.80</td><td>  6.57</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.52</td><td>0.8 ( 0 )    </td><td>2.9 ( 0.5 )     </td><td>2.75 ( 0.07 )</td><td>  6.15</td><td>0.33</td><td>0.80</td><td>  2.90</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.70</td><td>0.8 ( 0 )    </td><td>1.88 ( 0.19 )   </td><td>2.79 ( 0.05 )</td><td>  5.09</td><td>0.3 </td><td>0.80</td><td>  1.88</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.10</td><td>0.87 ( 0.01 )</td><td>15.93 ( 1 )     </td><td>2.88 ( 0.1 ) </td><td> 19.05</td><td>0.81</td><td>0.87</td><td> 15.93</td><td>2.88</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.81 ( 0 )   </td><td>14.34 ( 1.4 )   </td><td>2.75 ( 0.1 ) </td><td> 17.59</td><td>0.73</td><td>0.81</td><td> 14.34</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.56</td><td>0.8 ( 0 )    </td><td>3.51 ( 0.44 )   </td><td>2.86 ( 0.04 )</td><td>  6.65</td><td>0.4 </td><td>0.80</td><td>  3.51</td><td>2.86</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.57</td><td>0.8 ( 0 )    </td><td>3.18 ( 0.59 )   </td><td>2.86 ( 0.04 )</td><td>  6.32</td><td>0.34</td><td>0.80</td><td>  3.18</td><td>2.86</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.02</td><td>0.99 ( 0 )   </td><td>95.52 ( 6.42 )  </td><td>2.37 ( 0.15 )</td><td> 99.15</td><td>0.95</td><td>0.99</td><td> 95.52</td><td>2.37</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.04</td><td>0.88 ( 0 )   </td><td>53.08 ( 3.93 )  </td><td>2.8 ( 0.12 ) </td><td> 56.28</td><td>0.93</td><td>0.88</td><td> 53.08</td><td>2.80</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.40</td><td>0.79 ( 0 )   </td><td>6.75 ( 0.73 )   </td><td>2.76 ( 0.07 )</td><td>  9.99</td><td>0.56</td><td>0.79</td><td>  6.75</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.73</td><td>0.8 ( 0 )    </td><td>2.48 ( 0.28 )   </td><td>2.79 ( 0.05 )</td><td>  5.69</td><td>0.37</td><td>0.80</td><td>  2.48</td><td>2.79</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.01</td><td>1 ( 0 )      </td><td>201.98 ( 10.15 )</td><td>2.34 ( 0.16 )</td><td>205.64</td><td>0.98</td><td>1.00</td><td>201.98</td><td>2.34</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.03</td><td>0.95 ( 0 )   </td><td>70.81 ( 4.4 )   </td><td>3.05 ( 0.12 )</td><td> 73.76</td><td>0.96</td><td>0.95</td><td> 70.81</td><td>3.05</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.38</td><td>0.79 ( 0 )   </td><td>7.71 ( 1.09 )   </td><td>2.85 ( 0.06 )</td><td> 10.86</td><td>0.59</td><td>0.79</td><td>  7.71</td><td>2.85</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.76</td><td>0.8 ( 0 )    </td><td>2.41 ( 0.17 )   </td><td>2.77 ( 0.07 )</td><td>  5.64</td><td>0.39</td><td>0.80</td><td>  2.41</td><td>2.77</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.13 & 0.97 ( 0 ) & 14.84 ( 0.9 )   & 0.93 ( 0.09 ) &  19.91 & 0.7  & 0.97 &  14.84 & 0.93\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.33 & 0.93 ( 0 ) & 7.19 ( 0.43 )   & 0.49 ( 0.07 ) &  12.70 & 0.53 & 0.93 &   7.19 & 0.49\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.70 & 0.9 ( 0 )  & 1.93 ( 0.34 )   & 0.16 ( 0.04 ) &   7.77 & 0.16 & 0.90 &   1.93 & 0.16\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.79 & 0.9 ( 0 )  & 1.37 ( 0.42 )   & 0.01 ( 0.01 ) &   7.36 & 0.1  & 0.90 &   1.37 & 0.01\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.07 & 0.99 ( 0 ) & 36.64 ( 2.16 )  & 0.65 ( 0.07 ) &  41.99 & 0.84 & 0.99 &  36.64 & 0.65\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.20 & 0.94 ( 0 ) & 16.16 ( 1.22 )  & 0.47 ( 0.06 ) &  21.69 & 0.7  & 0.94 &  16.16 & 0.47\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.36 & 0.9 ( 0 )  & 8.64 ( 1.35 )   & 0.08 ( 0.03 ) &  14.56 & 0.37 & 0.90 &   8.64 & 0.08\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.47 & 0.9 ( 0 )  & 5.83 ( 1.4 )    & 0.02 ( 0.01 ) &  11.81 & 0.22 & 0.90 &   5.83 & 0.02\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.01 & 1 ( 0 )    & 248.06 ( 7.56 ) & 0.56 ( 0.07 ) & 253.50 & 0.98 & 1.00 & 248.06 & 0.56\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.03 & 1 ( 0 )    & 132.04 ( 7.47 ) & 0.33 ( 0.05 ) & 137.71 & 0.95 & 1.00 & 132.04 & 0.33\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.34 & 0.91 ( 0 ) & 12.55 ( 1.81 )  & 0.04 ( 0.02 ) &  18.51 & 0.52 & 0.91 &  12.55 & 0.04\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.69 & 0.9 ( 0 )  & 3.02 ( 0.6 )    & 0.01 ( 0.01 ) &   9.01 & 0.22 & 0.90 &   3.02 & 0.01\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.01 & 1 ( 0 )    & 398.98 ( 6.9 )  & 0.8 ( 0.08 )  & 404.18 & 0.99 & 1.00 & 398.98 & 0.80\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.02 & 1 ( 0 )    & 296.19 ( 8.29 ) & 0.35 ( 0.05 ) & 301.84 & 0.98 & 1.00 & 296.19 & 0.35\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.39 & 0.9 ( 0 )  & 10.2 ( 0.98 )   & 0.1 ( 0.03 )  &  16.10 & 0.52 & 0.90 &  10.20 & 0.10\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.83 & 0.9 ( 0 )  & 1.76 ( 0.26 )   & 0.02 ( 0.01 ) &   7.74 & 0.18 & 0.90 &   1.76 & 0.02\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.14 & 0.96 ( 0 ) & 13.96 ( 0.83 )  & 1.01 ( 0.09 ) &  18.95 & 0.7  & 0.96 &  13.96 & 1.01\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.25 & 0.92 ( 0 ) & 9.38 ( 0.67 )   & 0.68 ( 0.07 ) &  14.70 & 0.58 & 0.92 &   9.38 & 0.68\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 0.89 ( 0 ) & 5.43 ( 0.9 )    & 0.14 ( 0.03 ) &  11.29 & 0.3  & 0.89 &   5.43 & 0.14\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.48 & 0.88 ( 0 ) & 5.03 ( 1 )      & 0.09 ( 0.03 ) &  10.94 & 0.23 & 0.88 &   5.03 & 0.09\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.06 & 0.99 ( 0 ) & 36.33 ( 2.24 )  & 0.9 ( 0.09 )  &  41.43 & 0.85 & 0.99 &  36.33 & 0.90\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.19 & 0.93 ( 0 ) & 16.08 ( 0.95 )  & 0.75 ( 0.07 ) &  21.33 & 0.72 & 0.93 &  16.08 & 0.75\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.37 & 0.89 ( 0 ) & 8.66 ( 1.15 )   & 0.11 ( 0.03 ) &  14.55 & 0.42 & 0.89 &   8.66 & 0.11\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.39 & 0.88 ( 0 ) & 8.45 ( 1.17 )   & 0.12 ( 0.03 ) &  14.33 & 0.39 & 0.88 &   8.45 & 0.12\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.01 & 1 ( 0 )    & 228.37 ( 7.07 ) & 0.6 ( 0.08 )  & 233.77 & 0.97 & 1.00 & 228.37 & 0.60\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.04 & 0.99 ( 0 ) & 114.53 ( 5.78 ) & 0.43 ( 0.06 ) & 120.10 & 0.94 & 0.99 & 114.53 & 0.43\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.31 & 0.89 ( 0 ) & 13.64 ( 2.08 )  & 0.2 ( 0.04 )  &  19.44 & 0.57 & 0.89 &  13.64 & 0.20\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.54 & 0.88 ( 0 ) & 6.3 ( 1.15 )    & 0.09 ( 0.04 ) &  12.21 & 0.38 & 0.88 &   6.30 & 0.09\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.01 & 1 ( 0 )    & 412.73 ( 7.91 ) & 0.88 ( 0.1 )  & 417.85 & 0.99 & 1.00 & 412.73 & 0.88\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.02 & 1 ( 0 )    & 279.68 ( 7.94 ) & 0.45 ( 0.07 ) & 285.23 & 0.98 & 1.00 & 279.68 & 0.45\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.67 & 0.86 ( 0 )    & 1.32 ( 0.25 )    & 2.42 ( 0.06 ) &   4.90 & 0.19 & 0.86 &   1.32 & 2.42\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.74 & 0.85 ( 0 )    & 0.84 ( 0.14 )    & 2.48 ( 0.06 ) &   4.36 & 0.13 & 0.85 &   0.84 & 2.48\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.07 & 0.96 ( 0 )    & 24.54 ( 1.62 )   & 2.08 ( 0.11 ) &  28.46 & 0.84 & 0.96 &  24.54 & 2.08\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.16 & 0.89 ( 0 )    & 14.69 ( 1.28 )   & 1.85 ( 0.08 ) &  18.84 & 0.71 & 0.89 &  14.69 & 1.85\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.55 & 0.86 ( 0 )    & 2.19 ( 0.64 )    & 2.33 ( 0.07 ) &   5.86 & 0.18 & 0.86 &   2.19 & 2.33\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.81 & 0.85 ( 0 )    & 0.51 ( 0.1 )     & 2.56 ( 0.05 ) &   3.95 & 0.09 & 0.85 &   0.51 & 2.56\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 1 ( 0 )       & 161.23 ( 7.83 )  & 1.29 ( 0.1 )  & 165.94 & 0.96 & 1.00 & 161.23 & 1.29\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.06 & 0.96 ( 0 )    & 60.05 ( 4.29 )   & 1.63 ( 0.12 ) &  64.42 & 0.92 & 0.96 &  60.05 & 1.63\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.50 & 0.85 ( 0 )    & 3.12 ( 1.31 )    & 2.68 ( 0.07 ) &   6.44 & 0.19 & 0.85 &   3.12 & 2.68\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.89 & 0.85 ( 0 )    & 0.39 ( 0.06 )    & 2.88 ( 0.04 ) &   3.51 & 0.09 & 0.85 &   0.39 & 2.88\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 1 ( 0 )       & 363.91 ( 11.45 ) & 1.39 ( 0.1 )  & 368.52 & 0.99 & 1.00 & 363.91 & 1.39\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.03 & 0.99 ( 0 )    & 145.9 ( 7.62 )   & 1.24 ( 0.11 ) & 150.66 & 0.96 & 0.99 & 145.90 & 1.24\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.61 & 0.85 ( 0 )    & 2.38 ( 0.4 )     & 2.7 ( 0.06 )  &   5.68 & 0.27 & 0.85 &   2.38 & 2.70\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.87 & 0.85 ( 0 )    & 0.65 ( 0.08 )    & 2.87 ( 0.04 ) &   3.78 & 0.14 & 0.85 &   0.65 & 2.87\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.14 & 0.84 ( 0.01 ) & 9.98 ( 0.78 )    & 2.68 ( 0.11 ) &  13.30 & 0.7  & 0.84 &   9.98 & 2.68\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.25 & 0.8 ( 0 )     & 6.57 ( 0.7 )     & 2.73 ( 0.09 ) &   9.84 & 0.58 & 0.80 &   6.57 & 2.73\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.52 & 0.8 ( 0 )     & 2.9 ( 0.5 )      & 2.75 ( 0.07 ) &   6.15 & 0.33 & 0.80 &   2.90 & 2.75\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.70 & 0.8 ( 0 )     & 1.88 ( 0.19 )    & 2.79 ( 0.05 ) &   5.09 & 0.3  & 0.80 &   1.88 & 2.79\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.10 & 0.87 ( 0.01 ) & 15.93 ( 1 )      & 2.88 ( 0.1 )  &  19.05 & 0.81 & 0.87 &  15.93 & 2.88\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.14 & 0.81 ( 0 )    & 14.34 ( 1.4 )    & 2.75 ( 0.1 )  &  17.59 & 0.73 & 0.81 &  14.34 & 2.75\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.56 & 0.8 ( 0 )     & 3.51 ( 0.44 )    & 2.86 ( 0.04 ) &   6.65 & 0.4  & 0.80 &   3.51 & 2.86\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.57 & 0.8 ( 0 )     & 3.18 ( 0.59 )    & 2.86 ( 0.04 ) &   6.32 & 0.34 & 0.80 &   3.18 & 2.86\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.02 & 0.99 ( 0 )    & 95.52 ( 6.42 )   & 2.37 ( 0.15 ) &  99.15 & 0.95 & 0.99 &  95.52 & 2.37\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.04 & 0.88 ( 0 )    & 53.08 ( 3.93 )   & 2.8 ( 0.12 )  &  56.28 & 0.93 & 0.88 &  53.08 & 2.80\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.40 & 0.79 ( 0 )    & 6.75 ( 0.73 )    & 2.76 ( 0.07 ) &   9.99 & 0.56 & 0.79 &   6.75 & 2.76\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.73 & 0.8 ( 0 )     & 2.48 ( 0.28 )    & 2.79 ( 0.05 ) &   5.69 & 0.37 & 0.80 &   2.48 & 2.79\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.01 & 1 ( 0 )       & 201.98 ( 10.15 ) & 2.34 ( 0.16 ) & 205.64 & 0.98 & 1.00 & 201.98 & 2.34\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.03 & 0.95 ( 0 )    & 70.81 ( 4.4 )    & 3.05 ( 0.12 ) &  73.76 & 0.96 & 0.95 &  70.81 & 3.05\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.38 & 0.79 ( 0 )    & 7.71 ( 1.09 )    & 2.85 ( 0.06 ) &  10.86 & 0.59 & 0.79 &   7.71 & 2.85\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.76 & 0.8 ( 0 )     & 2.41 ( 0.17 )    & 2.77 ( 0.07 ) &   5.64 & 0.39 & 0.80 &   2.41 & 2.77\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.13 | 0.97 ( 0 ) | 14.84 ( 0.9 )   | 0.93 ( 0.09 ) |  19.91 | 0.7  | 0.97 |  14.84 | 0.93 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.33 | 0.93 ( 0 ) | 7.19 ( 0.43 )   | 0.49 ( 0.07 ) |  12.70 | 0.53 | 0.93 |   7.19 | 0.49 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.70 | 0.9 ( 0 )  | 1.93 ( 0.34 )   | 0.16 ( 0.04 ) |   7.77 | 0.16 | 0.90 |   1.93 | 0.16 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.79 | 0.9 ( 0 )  | 1.37 ( 0.42 )   | 0.01 ( 0.01 ) |   7.36 | 0.1  | 0.90 |   1.37 | 0.01 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.07 | 0.99 ( 0 ) | 36.64 ( 2.16 )  | 0.65 ( 0.07 ) |  41.99 | 0.84 | 0.99 |  36.64 | 0.65 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.20 | 0.94 ( 0 ) | 16.16 ( 1.22 )  | 0.47 ( 0.06 ) |  21.69 | 0.7  | 0.94 |  16.16 | 0.47 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.36 | 0.9 ( 0 )  | 8.64 ( 1.35 )   | 0.08 ( 0.03 ) |  14.56 | 0.37 | 0.90 |   8.64 | 0.08 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.47 | 0.9 ( 0 )  | 5.83 ( 1.4 )    | 0.02 ( 0.01 ) |  11.81 | 0.22 | 0.90 |   5.83 | 0.02 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.01 | 1 ( 0 )    | 248.06 ( 7.56 ) | 0.56 ( 0.07 ) | 253.50 | 0.98 | 1.00 | 248.06 | 0.56 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.03 | 1 ( 0 )    | 132.04 ( 7.47 ) | 0.33 ( 0.05 ) | 137.71 | 0.95 | 1.00 | 132.04 | 0.33 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.34 | 0.91 ( 0 ) | 12.55 ( 1.81 )  | 0.04 ( 0.02 ) |  18.51 | 0.52 | 0.91 |  12.55 | 0.04 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.69 | 0.9 ( 0 )  | 3.02 ( 0.6 )    | 0.01 ( 0.01 ) |   9.01 | 0.22 | 0.90 |   3.02 | 0.01 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.01 | 1 ( 0 )    | 398.98 ( 6.9 )  | 0.8 ( 0.08 )  | 404.18 | 0.99 | 1.00 | 398.98 | 0.80 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.02 | 1 ( 0 )    | 296.19 ( 8.29 ) | 0.35 ( 0.05 ) | 301.84 | 0.98 | 1.00 | 296.19 | 0.35 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.39 | 0.9 ( 0 )  | 10.2 ( 0.98 )   | 0.1 ( 0.03 )  |  16.10 | 0.52 | 0.90 |  10.20 | 0.10 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.83 | 0.9 ( 0 )  | 1.76 ( 0.26 )   | 0.02 ( 0.01 ) |   7.74 | 0.18 | 0.90 |   1.76 | 0.02 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.14 | 0.96 ( 0 ) | 13.96 ( 0.83 )  | 1.01 ( 0.09 ) |  18.95 | 0.7  | 0.96 |  13.96 | 1.01 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.25 | 0.92 ( 0 ) | 9.38 ( 0.67 )   | 0.68 ( 0.07 ) |  14.70 | 0.58 | 0.92 |   9.38 | 0.68 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 0.89 ( 0 ) | 5.43 ( 0.9 )    | 0.14 ( 0.03 ) |  11.29 | 0.3  | 0.89 |   5.43 | 0.14 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.48 | 0.88 ( 0 ) | 5.03 ( 1 )      | 0.09 ( 0.03 ) |  10.94 | 0.23 | 0.88 |   5.03 | 0.09 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.06 | 0.99 ( 0 ) | 36.33 ( 2.24 )  | 0.9 ( 0.09 )  |  41.43 | 0.85 | 0.99 |  36.33 | 0.90 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.19 | 0.93 ( 0 ) | 16.08 ( 0.95 )  | 0.75 ( 0.07 ) |  21.33 | 0.72 | 0.93 |  16.08 | 0.75 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.37 | 0.89 ( 0 ) | 8.66 ( 1.15 )   | 0.11 ( 0.03 ) |  14.55 | 0.42 | 0.89 |   8.66 | 0.11 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.39 | 0.88 ( 0 ) | 8.45 ( 1.17 )   | 0.12 ( 0.03 ) |  14.33 | 0.39 | 0.88 |   8.45 | 0.12 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.01 | 1 ( 0 )    | 228.37 ( 7.07 ) | 0.6 ( 0.08 )  | 233.77 | 0.97 | 1.00 | 228.37 | 0.60 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.04 | 0.99 ( 0 ) | 114.53 ( 5.78 ) | 0.43 ( 0.06 ) | 120.10 | 0.94 | 0.99 | 114.53 | 0.43 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.31 | 0.89 ( 0 ) | 13.64 ( 2.08 )  | 0.2 ( 0.04 )  |  19.44 | 0.57 | 0.89 |  13.64 | 0.20 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.54 | 0.88 ( 0 ) | 6.3 ( 1.15 )    | 0.09 ( 0.04 ) |  12.21 | 0.38 | 0.88 |   6.30 | 0.09 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.01 | 1 ( 0 )    | 412.73 ( 7.91 ) | 0.88 ( 0.1 )  | 417.85 | 0.99 | 1.00 | 412.73 | 0.88 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.02 | 1 ( 0 )    | 279.68 ( 7.94 ) | 0.45 ( 0.07 ) | 285.23 | 0.98 | 1.00 | 279.68 | 0.45 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.67 | 0.86 ( 0 )    | 1.32 ( 0.25 )    | 2.42 ( 0.06 ) |   4.90 | 0.19 | 0.86 |   1.32 | 2.42 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.74 | 0.85 ( 0 )    | 0.84 ( 0.14 )    | 2.48 ( 0.06 ) |   4.36 | 0.13 | 0.85 |   0.84 | 2.48 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.07 | 0.96 ( 0 )    | 24.54 ( 1.62 )   | 2.08 ( 0.11 ) |  28.46 | 0.84 | 0.96 |  24.54 | 2.08 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.16 | 0.89 ( 0 )    | 14.69 ( 1.28 )   | 1.85 ( 0.08 ) |  18.84 | 0.71 | 0.89 |  14.69 | 1.85 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.55 | 0.86 ( 0 )    | 2.19 ( 0.64 )    | 2.33 ( 0.07 ) |   5.86 | 0.18 | 0.86 |   2.19 | 2.33 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.81 | 0.85 ( 0 )    | 0.51 ( 0.1 )     | 2.56 ( 0.05 ) |   3.95 | 0.09 | 0.85 |   0.51 | 2.56 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 1 ( 0 )       | 161.23 ( 7.83 )  | 1.29 ( 0.1 )  | 165.94 | 0.96 | 1.00 | 161.23 | 1.29 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.06 | 0.96 ( 0 )    | 60.05 ( 4.29 )   | 1.63 ( 0.12 ) |  64.42 | 0.92 | 0.96 |  60.05 | 1.63 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.50 | 0.85 ( 0 )    | 3.12 ( 1.31 )    | 2.68 ( 0.07 ) |   6.44 | 0.19 | 0.85 |   3.12 | 2.68 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.89 | 0.85 ( 0 )    | 0.39 ( 0.06 )    | 2.88 ( 0.04 ) |   3.51 | 0.09 | 0.85 |   0.39 | 2.88 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 1 ( 0 )       | 363.91 ( 11.45 ) | 1.39 ( 0.1 )  | 368.52 | 0.99 | 1.00 | 363.91 | 1.39 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.03 | 0.99 ( 0 )    | 145.9 ( 7.62 )   | 1.24 ( 0.11 ) | 150.66 | 0.96 | 0.99 | 145.90 | 1.24 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.61 | 0.85 ( 0 )    | 2.38 ( 0.4 )     | 2.7 ( 0.06 )  |   5.68 | 0.27 | 0.85 |   2.38 | 2.70 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.87 | 0.85 ( 0 )    | 0.65 ( 0.08 )    | 2.87 ( 0.04 ) |   3.78 | 0.14 | 0.85 |   0.65 | 2.87 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.14 | 0.84 ( 0.01 ) | 9.98 ( 0.78 )    | 2.68 ( 0.11 ) |  13.30 | 0.7  | 0.84 |   9.98 | 2.68 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.25 | 0.8 ( 0 )     | 6.57 ( 0.7 )     | 2.73 ( 0.09 ) |   9.84 | 0.58 | 0.80 |   6.57 | 2.73 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.52 | 0.8 ( 0 )     | 2.9 ( 0.5 )      | 2.75 ( 0.07 ) |   6.15 | 0.33 | 0.80 |   2.90 | 2.75 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.70 | 0.8 ( 0 )     | 1.88 ( 0.19 )    | 2.79 ( 0.05 ) |   5.09 | 0.3  | 0.80 |   1.88 | 2.79 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.10 | 0.87 ( 0.01 ) | 15.93 ( 1 )      | 2.88 ( 0.1 )  |  19.05 | 0.81 | 0.87 |  15.93 | 2.88 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.14 | 0.81 ( 0 )    | 14.34 ( 1.4 )    | 2.75 ( 0.1 )  |  17.59 | 0.73 | 0.81 |  14.34 | 2.75 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.56 | 0.8 ( 0 )     | 3.51 ( 0.44 )    | 2.86 ( 0.04 ) |   6.65 | 0.4  | 0.80 |   3.51 | 2.86 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.57 | 0.8 ( 0 )     | 3.18 ( 0.59 )    | 2.86 ( 0.04 ) |   6.32 | 0.34 | 0.80 |   3.18 | 2.86 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.02 | 0.99 ( 0 )    | 95.52 ( 6.42 )   | 2.37 ( 0.15 ) |  99.15 | 0.95 | 0.99 |  95.52 | 2.37 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.04 | 0.88 ( 0 )    | 53.08 ( 3.93 )   | 2.8 ( 0.12 )  |  56.28 | 0.93 | 0.88 |  53.08 | 2.80 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.40 | 0.79 ( 0 )    | 6.75 ( 0.73 )    | 2.76 ( 0.07 ) |   9.99 | 0.56 | 0.79 |   6.75 | 2.76 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.73 | 0.8 ( 0 )     | 2.48 ( 0.28 )    | 2.79 ( 0.05 ) |   5.69 | 0.37 | 0.80 |   2.48 | 2.79 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.01 | 1 ( 0 )       | 201.98 ( 10.15 ) | 2.34 ( 0.16 ) | 205.64 | 0.98 | 1.00 | 201.98 | 2.34 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.03 | 0.95 ( 0 )    | 70.81 ( 4.4 )    | 3.05 ( 0.12 ) |  73.76 | 0.96 | 0.95 |  70.81 | 3.05 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.38 | 0.79 ( 0 )    | 7.71 ( 1.09 )    | 2.85 ( 0.06 ) |  10.86 | 0.59 | 0.79 |   7.71 | 2.85 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.76 | 0.8 ( 0 )     | 2.41 ( 0.17 )    | 2.77 ( 0.07 ) |   5.64 | 0.39 | 0.80 |   2.41 | 2.77 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "1    50   50 0.1   1.00 0.13 0.97 ( 0 )    14.84 ( 0.9 )    0.93 ( 0.09 )\n",
+       "2   100   50 0.1   0.50 0.33 0.93 ( 0 )    7.19 ( 0.43 )    0.49 ( 0.07 )\n",
+       "3   500   50 0.1   0.10 0.70 0.9 ( 0 )     1.93 ( 0.34 )    0.16 ( 0.04 )\n",
+       "4  1000   50 0.1   0.05 0.79 0.9 ( 0 )     1.37 ( 0.42 )    0.01 ( 0.01 )\n",
+       "5    50  100 0.1   2.00 0.07 0.99 ( 0 )    36.64 ( 2.16 )   0.65 ( 0.07 )\n",
+       "6   100  100 0.1   1.00 0.20 0.94 ( 0 )    16.16 ( 1.22 )   0.47 ( 0.06 )\n",
+       "7   500  100 0.1   0.20 0.36 0.9 ( 0 )     8.64 ( 1.35 )    0.08 ( 0.03 )\n",
+       "8  1000  100 0.1   0.10 0.47 0.9 ( 0 )     5.83 ( 1.4 )     0.02 ( 0.01 )\n",
+       "9    50  500 0.1  10.00 0.01 1 ( 0 )       248.06 ( 7.56 )  0.56 ( 0.07 )\n",
+       "10  100  500 0.1   5.00 0.03 1 ( 0 )       132.04 ( 7.47 )  0.33 ( 0.05 )\n",
+       "11  500  500 0.1   1.00 0.34 0.91 ( 0 )    12.55 ( 1.81 )   0.04 ( 0.02 )\n",
+       "12 1000  500 0.1   0.50 0.69 0.9 ( 0 )     3.02 ( 0.6 )     0.01 ( 0.01 )\n",
+       "13   50 1000 0.1  20.00 0.01 1 ( 0 )       398.98 ( 6.9 )   0.8 ( 0.08 ) \n",
+       "14  100 1000 0.1  10.00 0.02 1 ( 0 )       296.19 ( 8.29 )  0.35 ( 0.05 )\n",
+       "15  500 1000 0.1   2.00 0.39 0.9 ( 0 )     10.2 ( 0.98 )    0.1 ( 0.03 ) \n",
+       "16 1000 1000 0.1   1.00 0.83 0.9 ( 0 )     1.76 ( 0.26 )    0.02 ( 0.01 )\n",
+       "17   50   50 0.3   1.00 0.14 0.96 ( 0 )    13.96 ( 0.83 )   1.01 ( 0.09 )\n",
+       "18  100   50 0.3   0.50 0.25 0.92 ( 0 )    9.38 ( 0.67 )    0.68 ( 0.07 )\n",
+       "19  500   50 0.3   0.10 0.44 0.89 ( 0 )    5.43 ( 0.9 )     0.14 ( 0.03 )\n",
+       "20 1000   50 0.3   0.05 0.48 0.88 ( 0 )    5.03 ( 1 )       0.09 ( 0.03 )\n",
+       "21   50  100 0.3   2.00 0.06 0.99 ( 0 )    36.33 ( 2.24 )   0.9 ( 0.09 ) \n",
+       "22  100  100 0.3   1.00 0.19 0.93 ( 0 )    16.08 ( 0.95 )   0.75 ( 0.07 )\n",
+       "23  500  100 0.3   0.20 0.37 0.89 ( 0 )    8.66 ( 1.15 )    0.11 ( 0.03 )\n",
+       "24 1000  100 0.3   0.10 0.39 0.88 ( 0 )    8.45 ( 1.17 )    0.12 ( 0.03 )\n",
+       "25   50  500 0.3  10.00 0.01 1 ( 0 )       228.37 ( 7.07 )  0.6 ( 0.08 ) \n",
+       "26  100  500 0.3   5.00 0.04 0.99 ( 0 )    114.53 ( 5.78 )  0.43 ( 0.06 )\n",
+       "27  500  500 0.3   1.00 0.31 0.89 ( 0 )    13.64 ( 2.08 )   0.2 ( 0.04 ) \n",
+       "28 1000  500 0.3   0.50 0.54 0.88 ( 0 )    6.3 ( 1.15 )     0.09 ( 0.04 )\n",
+       "29   50 1000 0.3  20.00 0.01 1 ( 0 )       412.73 ( 7.91 )  0.88 ( 0.1 ) \n",
+       "30  100 1000 0.3  10.00 0.02 1 ( 0 )       279.68 ( 7.94 )  0.45 ( 0.07 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮                ⋮            \n",
+       "51  500   50 0.7   0.10 0.67 0.86 ( 0 )    1.32 ( 0.25 )    2.42 ( 0.06 )\n",
+       "52 1000   50 0.7   0.05 0.74 0.85 ( 0 )    0.84 ( 0.14 )    2.48 ( 0.06 )\n",
+       "53   50  100 0.7   2.00 0.07 0.96 ( 0 )    24.54 ( 1.62 )   2.08 ( 0.11 )\n",
+       "54  100  100 0.7   1.00 0.16 0.89 ( 0 )    14.69 ( 1.28 )   1.85 ( 0.08 )\n",
+       "55  500  100 0.7   0.20 0.55 0.86 ( 0 )    2.19 ( 0.64 )    2.33 ( 0.07 )\n",
+       "56 1000  100 0.7   0.10 0.81 0.85 ( 0 )    0.51 ( 0.1 )     2.56 ( 0.05 )\n",
+       "57   50  500 0.7  10.00 0.02 1 ( 0 )       161.23 ( 7.83 )  1.29 ( 0.1 ) \n",
+       "58  100  500 0.7   5.00 0.06 0.96 ( 0 )    60.05 ( 4.29 )   1.63 ( 0.12 )\n",
+       "59  500  500 0.7   1.00 0.50 0.85 ( 0 )    3.12 ( 1.31 )    2.68 ( 0.07 )\n",
+       "60 1000  500 0.7   0.50 0.89 0.85 ( 0 )    0.39 ( 0.06 )    2.88 ( 0.04 )\n",
+       "61   50 1000 0.7  20.00 0.01 1 ( 0 )       363.91 ( 11.45 ) 1.39 ( 0.1 ) \n",
+       "62  100 1000 0.7  10.00 0.03 0.99 ( 0 )    145.9 ( 7.62 )   1.24 ( 0.11 )\n",
+       "63  500 1000 0.7   2.00 0.61 0.85 ( 0 )    2.38 ( 0.4 )     2.7 ( 0.06 ) \n",
+       "64 1000 1000 0.7   1.00 0.87 0.85 ( 0 )    0.65 ( 0.08 )    2.87 ( 0.04 )\n",
+       "65   50   50 0.9   1.00 0.14 0.84 ( 0.01 ) 9.98 ( 0.78 )    2.68 ( 0.11 )\n",
+       "66  100   50 0.9   0.50 0.25 0.8 ( 0 )     6.57 ( 0.7 )     2.73 ( 0.09 )\n",
+       "67  500   50 0.9   0.10 0.52 0.8 ( 0 )     2.9 ( 0.5 )      2.75 ( 0.07 )\n",
+       "68 1000   50 0.9   0.05 0.70 0.8 ( 0 )     1.88 ( 0.19 )    2.79 ( 0.05 )\n",
+       "69   50  100 0.9   2.00 0.10 0.87 ( 0.01 ) 15.93 ( 1 )      2.88 ( 0.1 ) \n",
+       "70  100  100 0.9   1.00 0.14 0.81 ( 0 )    14.34 ( 1.4 )    2.75 ( 0.1 ) \n",
+       "71  500  100 0.9   0.20 0.56 0.8 ( 0 )     3.51 ( 0.44 )    2.86 ( 0.04 )\n",
+       "72 1000  100 0.9   0.10 0.57 0.8 ( 0 )     3.18 ( 0.59 )    2.86 ( 0.04 )\n",
+       "73   50  500 0.9  10.00 0.02 0.99 ( 0 )    95.52 ( 6.42 )   2.37 ( 0.15 )\n",
+       "74  100  500 0.9   5.00 0.04 0.88 ( 0 )    53.08 ( 3.93 )   2.8 ( 0.12 ) \n",
+       "75  500  500 0.9   1.00 0.40 0.79 ( 0 )    6.75 ( 0.73 )    2.76 ( 0.07 )\n",
+       "76 1000  500 0.9   0.50 0.73 0.8 ( 0 )     2.48 ( 0.28 )    2.79 ( 0.05 )\n",
+       "77   50 1000 0.9  20.00 0.01 1 ( 0 )       201.98 ( 10.15 ) 2.34 ( 0.16 )\n",
+       "78  100 1000 0.9  10.00 0.03 0.95 ( 0 )    70.81 ( 4.4 )    3.05 ( 0.12 )\n",
+       "79  500 1000 0.9   2.00 0.38 0.79 ( 0 )    7.71 ( 1.09 )    2.85 ( 0.06 )\n",
+       "80 1000 1000 0.9   1.00 0.76 0.8 ( 0 )     2.41 ( 0.17 )    2.77 ( 0.07 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   19.91     0.7  0.97      14.84  0.93   \n",
+       "2   12.70     0.53 0.93       7.19  0.49   \n",
+       "3    7.77     0.16 0.90       1.93  0.16   \n",
+       "4    7.36     0.1  0.90       1.37  0.01   \n",
+       "5   41.99     0.84 0.99      36.64  0.65   \n",
+       "6   21.69     0.7  0.94      16.16  0.47   \n",
+       "7   14.56     0.37 0.90       8.64  0.08   \n",
+       "8   11.81     0.22 0.90       5.83  0.02   \n",
+       "9  253.50     0.98 1.00     248.06  0.56   \n",
+       "10 137.71     0.95 1.00     132.04  0.33   \n",
+       "11  18.51     0.52 0.91      12.55  0.04   \n",
+       "12   9.01     0.22 0.90       3.02  0.01   \n",
+       "13 404.18     0.99 1.00     398.98  0.80   \n",
+       "14 301.84     0.98 1.00     296.19  0.35   \n",
+       "15  16.10     0.52 0.90      10.20  0.10   \n",
+       "16   7.74     0.18 0.90       1.76  0.02   \n",
+       "17  18.95     0.7  0.96      13.96  1.01   \n",
+       "18  14.70     0.58 0.92       9.38  0.68   \n",
+       "19  11.29     0.3  0.89       5.43  0.14   \n",
+       "20  10.94     0.23 0.88       5.03  0.09   \n",
+       "21  41.43     0.85 0.99      36.33  0.90   \n",
+       "22  21.33     0.72 0.93      16.08  0.75   \n",
+       "23  14.55     0.42 0.89       8.66  0.11   \n",
+       "24  14.33     0.39 0.88       8.45  0.12   \n",
+       "25 233.77     0.97 1.00     228.37  0.60   \n",
+       "26 120.10     0.94 0.99     114.53  0.43   \n",
+       "27  19.44     0.57 0.89      13.64  0.20   \n",
+       "28  12.21     0.38 0.88       6.30  0.09   \n",
+       "29 417.85     0.99 1.00     412.73  0.88   \n",
+       "30 285.23     0.98 1.00     279.68  0.45   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51   4.90     0.19 0.86       1.32  2.42   \n",
+       "52   4.36     0.13 0.85       0.84  2.48   \n",
+       "53  28.46     0.84 0.96      24.54  2.08   \n",
+       "54  18.84     0.71 0.89      14.69  1.85   \n",
+       "55   5.86     0.18 0.86       2.19  2.33   \n",
+       "56   3.95     0.09 0.85       0.51  2.56   \n",
+       "57 165.94     0.96 1.00     161.23  1.29   \n",
+       "58  64.42     0.92 0.96      60.05  1.63   \n",
+       "59   6.44     0.19 0.85       3.12  2.68   \n",
+       "60   3.51     0.09 0.85       0.39  2.88   \n",
+       "61 368.52     0.99 1.00     363.91  1.39   \n",
+       "62 150.66     0.96 0.99     145.90  1.24   \n",
+       "63   5.68     0.27 0.85       2.38  2.70   \n",
+       "64   3.78     0.14 0.85       0.65  2.87   \n",
+       "65  13.30     0.7  0.84       9.98  2.68   \n",
+       "66   9.84     0.58 0.80       6.57  2.73   \n",
+       "67   6.15     0.33 0.80       2.90  2.75   \n",
+       "68   5.09     0.3  0.80       1.88  2.79   \n",
+       "69  19.05     0.81 0.87      15.93  2.88   \n",
+       "70  17.59     0.73 0.81      14.34  2.75   \n",
+       "71   6.65     0.4  0.80       3.51  2.86   \n",
+       "72   6.32     0.34 0.80       3.18  2.86   \n",
+       "73  99.15     0.95 0.99      95.52  2.37   \n",
+       "74  56.28     0.93 0.88      53.08  2.80   \n",
+       "75   9.99     0.56 0.79       6.75  2.76   \n",
+       "76   5.69     0.37 0.80       2.48  2.79   \n",
+       "77 205.64     0.98 1.00     201.98  2.34   \n",
+       "78  73.76     0.96 0.95      70.81  3.05   \n",
+       "79  10.86     0.59 0.79       7.71  2.85   \n",
+       "80   5.64     0.39 0.80       2.41  2.77   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_bin/sim_toe_GencompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/2.1_sim_block_lasso_binary_update.ipynb b/simulations/notebooks_sim_bin/2.1_sim_block_lasso_binary_update.ipynb
new file mode 100644
index 0000000..484e80b
--- /dev/null
+++ b/simulations/notebooks_sim_bin/2.1_sim_block_lasso_binary_update.ipynb
@@ -0,0 +1,971 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_Lasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_lasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_lasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_lasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 )</td><td>0.59 ( 0.01 )</td><td>0.03</td><td>2.39</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>0.56 ( 0.01 )</td><td>0.11</td><td>2.00</td><td>0.39</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )</td><td>0.63 ( 0 )</td><td>0.28</td><td>6.52</td><td>0.45</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 )</td><td>0.64 ( 0 )</td><td>0.39</td><td>7.64</td><td>0.44</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>0.62 ( 0.01 )</td><td>0.02</td><td>3.48</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>0.56 ( 0.01 )</td><td>0.07</td><td>2.02</td><td>0.57</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 2.69 ( 0.14 ) & 5.3 ( 0.07 ) & 0.59 ( 0.01 ) & 0.03 & 2.39 & 0.67\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 0.56 ( 0.01 ) & 0.11 & 2.00 & 0.39\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 4.52 ( 0.29 ) & 3 ( 0.11 ) & 0.63 ( 0 ) & 0.28 & 6.52 & 0.45\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 4.74 ( 0.22 ) & 2.1 ( 0.09 ) & 0.64 ( 0 ) & 0.39 & 7.64 & 0.44\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 0.62 ( 0.01 ) & 0.02 & 3.48 & 0.78\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 0.56 ( 0.01 ) & 0.07 & 2.02 & 0.57\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 2.69 ( 0.14 ) | 5.3 ( 0.07 ) | 0.59 ( 0.01 ) | 0.03 | 2.39 | 0.67 |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 0.56 ( 0.01 ) | 0.11 | 2.00 | 0.39 |\n",
+       "| 3 | 500 | 50 | 0.1 | 4.52 ( 0.29 ) | 3 ( 0.11 ) | 0.63 ( 0 ) | 0.28 | 6.52 | 0.45 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 4.74 ( 0.22 ) | 2.1 ( 0.09 ) | 0.64 ( 0 ) | 0.39 | 7.64 | 0.44 |\n",
+       "| 5 | 50 | 100 | 0.1 | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 0.62 ( 0.01 ) | 0.02 | 3.48 | 0.78 |\n",
+       "| 6 | 100 | 100 | 0.1 | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 0.56 ( 0.01 ) | 0.07 | 2.02 | 0.57 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 2.69 ( 0.14 ) 5.3 ( 0.07 )  0.59 ( 0.01 ) 0.03 2.39       0.67\n",
+       "2 100  50  0.1 2.02 ( 0.15 ) 5.02 ( 0.08 ) 0.56 ( 0.01 ) 0.11 2.00       0.39\n",
+       "3 500  50  0.1 4.52 ( 0.29 ) 3 ( 0.11 )    0.63 ( 0 )    0.28 6.52       0.45\n",
+       "4 1000 50  0.1 4.74 ( 0.22 ) 2.1 ( 0.09 )  0.64 ( 0 )    0.39 7.64       0.44\n",
+       "5 50   100 0.1 3.82 ( 0.16 ) 5.34 ( 0.07 ) 0.62 ( 0.01 ) 0.02 3.48       0.78\n",
+       "6 100  100 0.1 2.29 ( 0.19 ) 5.27 ( 0.08 ) 0.56 ( 0.01 ) 0.07 2.02       0.57"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.91 ( 0.78 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.19</td><td>29.91</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>14.12 ( 0.43 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.31</td><td>19.12</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>0.64 ( 0.01 )</td><td>0.07</td><td>11.59</td><td>0.84</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>0.74 ( 0.01 )</td><td>0.19</td><td>13.71</td><td>0.65</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>34.98 ( 1.04 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.14</td><td>39.98</td><td>0.83</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>22.21 ( 0.73 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.22</td><td>27.21</td><td>0.76</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.91 ( 0.78 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.19 & 29.91 & 0.78\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 14.12 ( 0.43 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.31 & 19.12 & 0.67\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 0.64 ( 0.01 ) & 0.07 & 11.59 & 0.84\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 0.74 ( 0.01 ) & 0.19 & 13.71 & 0.65\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 34.98 ( 1.04 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.14 & 39.98 & 0.83\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 22.21 ( 0.73 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.22 & 27.21 & 0.76\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.91 ( 0.78 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.19 | 29.91 | 0.78 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 14.12 ( 0.43 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.31 | 19.12 | 0.67 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 0.64 ( 0.01 ) | 0.07 | 11.59 | 0.84 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 0.74 ( 0.01 ) | 0.19 | 13.71 | 0.65 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 34.98 ( 1.04 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.14 | 39.98 | 0.83 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 22.21 ( 0.73 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.22 | 27.21 | 0.76 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 24.91 ( 0.78 ) 0 ( 0 )       0.91 ( 0 )    0.19 29.91     \n",
+       "76 1000 500  0.9 14.12 ( 0.43 ) 0 ( 0 )       0.92 ( 0 )    0.31 19.12     \n",
+       "77 50   1000 0.9 10.81 ( 0.29 ) 4.22 ( 0.08 ) 0.64 ( 0.01 ) 0.07 11.59     \n",
+       "78 100  1000 0.9 11.15 ( 0.75 ) 2.44 ( 0.11 ) 0.74 ( 0.01 ) 0.19 13.71     \n",
+       "79 500  1000 0.9 34.98 ( 1.04 ) 0 ( 0 )       0.91 ( 0 )    0.14 39.98     \n",
+       "80 1000 1000 0.9 22.21 ( 0.73 ) 0 ( 0 )       0.92 ( 0 )    0.22 27.21     \n",
+       "   FDR \n",
+       "75 0.78\n",
+       "76 0.67\n",
+       "77 0.84\n",
+       "78 0.65\n",
+       "79 0.83\n",
+       "80 0.76"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 ) </td><td>2.39</td><td>0.67</td><td>0.59</td><td>2.69</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>2.00</td><td>0.39</td><td>0.56</td><td>2.02</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )   </td><td>6.52</td><td>0.45</td><td>0.63</td><td>4.52</td><td>3.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 ) </td><td>7.64</td><td>0.44</td><td>0.64</td><td>4.74</td><td>2.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>3.48</td><td>0.78</td><td>0.62</td><td>3.82</td><td>5.34</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>2.02</td><td>0.57</td><td>0.56</td><td>2.29</td><td>5.27</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 ) & 5.3 ( 0.07 )  & 2.39 & 0.67 & 0.59 & 2.69 & 5.30\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 2.00 & 0.39 & 0.56 & 2.02 & 5.02\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 ) & 3 ( 0.11 )    & 6.52 & 0.45 & 0.63 & 4.52 & 3.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 ) & 2.1 ( 0.09 )  & 7.64 & 0.44 & 0.64 & 4.74 & 2.10\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 3.48 & 0.78 & 0.62 & 3.82 & 5.34\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 2.02 & 0.57 & 0.56 & 2.29 & 5.27\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 ) | 5.3 ( 0.07 )  | 2.39 | 0.67 | 0.59 | 2.69 | 5.30 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 2.00 | 0.39 | 0.56 | 2.02 | 5.02 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 ) | 3 ( 0.11 )    | 6.52 | 0.45 | 0.63 | 4.52 | 3.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 ) | 2.1 ( 0.09 )  | 7.64 | 0.44 | 0.64 | 4.74 | 2.10 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 3.48 | 0.78 | 0.62 | 3.82 | 5.34 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 2.02 | 0.57 | 0.56 | 2.29 | 5.27 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.03 0.59 ( 0.01 ) 2.69 ( 0.14 ) 5.3 ( 0.07 )  2.39      \n",
+       "2  100  50 0.1  0.50  0.11 0.56 ( 0.01 ) 2.02 ( 0.15 ) 5.02 ( 0.08 ) 2.00      \n",
+       "3  500  50 0.1  0.10  0.28 0.63 ( 0 )    4.52 ( 0.29 ) 3 ( 0.11 )    6.52      \n",
+       "4 1000  50 0.1  0.05  0.39 0.64 ( 0 )    4.74 ( 0.22 ) 2.1 ( 0.09 )  7.64      \n",
+       "5   50 100 0.1  2.00  0.02 0.62 ( 0.01 ) 3.82 ( 0.16 ) 5.34 ( 0.07 ) 3.48      \n",
+       "6  100 100 0.1  1.00  0.07 0.56 ( 0.01 ) 2.29 ( 0.19 ) 5.27 ( 0.08 ) 2.02      \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.67 0.59     2.69    5.30   \n",
+       "2 0.39 0.56     2.02    5.02   \n",
+       "3 0.45 0.63     4.52    3.00   \n",
+       "4 0.44 0.64     4.74    2.10   \n",
+       "5 0.78 0.62     3.82    5.34   \n",
+       "6 0.57 0.56     2.29    5.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.19</td><td>0.91 ( 0 )   </td><td>24.91 ( 0.78 )</td><td>0 ( 0 )      </td><td>29.91</td><td>0.78</td><td>0.91</td><td>24.91</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.31</td><td>0.92 ( 0 )   </td><td>14.12 ( 0.43 )</td><td>0 ( 0 )      </td><td>19.12</td><td>0.67</td><td>0.92</td><td>14.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.07</td><td>0.64 ( 0.01 )</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>11.59</td><td>0.84</td><td>0.64</td><td>10.81</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.19</td><td>0.74 ( 0.01 )</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>13.71</td><td>0.65</td><td>0.74</td><td>11.15</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.14</td><td>0.91 ( 0 )   </td><td>34.98 ( 1.04 )</td><td>0 ( 0 )      </td><td>39.98</td><td>0.83</td><td>0.91</td><td>34.98</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.22</td><td>0.92 ( 0 )   </td><td>22.21 ( 0.73 )</td><td>0 ( 0 )      </td><td>27.21</td><td>0.76</td><td>0.92</td><td>22.21</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.19 & 0.91 ( 0 )    & 24.91 ( 0.78 ) & 0 ( 0 )       & 29.91 & 0.78 & 0.91 & 24.91 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.31 & 0.92 ( 0 )    & 14.12 ( 0.43 ) & 0 ( 0 )       & 19.12 & 0.67 & 0.92 & 14.12 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.07 & 0.64 ( 0.01 ) & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 11.59 & 0.84 & 0.64 & 10.81 & 4.22\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.19 & 0.74 ( 0.01 ) & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 13.71 & 0.65 & 0.74 & 11.15 & 2.44\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.14 & 0.91 ( 0 )    & 34.98 ( 1.04 ) & 0 ( 0 )       & 39.98 & 0.83 & 0.91 & 34.98 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.22 & 0.92 ( 0 )    & 22.21 ( 0.73 ) & 0 ( 0 )       & 27.21 & 0.76 & 0.92 & 22.21 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.19 | 0.91 ( 0 )    | 24.91 ( 0.78 ) | 0 ( 0 )       | 29.91 | 0.78 | 0.91 | 24.91 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.31 | 0.92 ( 0 )    | 14.12 ( 0.43 ) | 0 ( 0 )       | 19.12 | 0.67 | 0.92 | 14.12 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.07 | 0.64 ( 0.01 ) | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 11.59 | 0.84 | 0.64 | 10.81 | 4.22 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.19 | 0.74 ( 0.01 ) | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 13.71 | 0.65 | 0.74 | 11.15 | 2.44 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.14 | 0.91 ( 0 )    | 34.98 ( 1.04 ) | 0 ( 0 )       | 39.98 | 0.83 | 0.91 | 34.98 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.22 | 0.92 ( 0 )    | 22.21 ( 0.73 ) | 0 ( 0 )       | 27.21 | 0.76 | 0.92 | 22.21 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.19 0.91 ( 0 )    24.91 ( 0.78 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.31 0.92 ( 0 )    14.12 ( 0.43 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.07 0.64 ( 0.01 ) 10.81 ( 0.29 ) 4.22 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.19 0.74 ( 0.01 ) 11.15 ( 0.75 ) 2.44 ( 0.11 )\n",
+       "79  500 1000 0.9   2.0  0.14 0.91 ( 0 )    34.98 ( 1.04 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.22 0.92 ( 0 )    22.21 ( 0.73 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 29.91      0.78 0.91     24.91   0.00   \n",
+       "76 19.12      0.67 0.92     14.12   0.00   \n",
+       "77 11.59      0.84 0.64     10.81   4.22   \n",
+       "78 13.71      0.65 0.74     11.15   2.44   \n",
+       "79 39.98      0.83 0.91     34.98   0.00   \n",
+       "80 27.21      0.76 0.92     22.21   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 ) </td><td>5.3 ( 0.07 ) </td><td> 2.39</td><td>0.67</td><td>0.59</td><td> 2.69</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 ) </td><td>5.02 ( 0.08 )</td><td> 2.00</td><td>0.39</td><td>0.56</td><td> 2.02</td><td>5.02</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 ) </td><td>3 ( 0.11 )   </td><td> 6.52</td><td>0.45</td><td>0.63</td><td> 4.52</td><td>3.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 ) </td><td>2.1 ( 0.09 ) </td><td> 7.64</td><td>0.44</td><td>0.64</td><td> 4.74</td><td>2.10</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 ) </td><td>5.34 ( 0.07 )</td><td> 3.48</td><td>0.78</td><td>0.62</td><td> 3.82</td><td>5.34</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 ) </td><td>5.27 ( 0.08 )</td><td> 2.02</td><td>0.57</td><td>0.56</td><td> 2.29</td><td>5.27</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.23</td><td>0.61 ( 0 )   </td><td>5.54 ( 0.52 ) </td><td>3.27 ( 0.12 )</td><td> 7.27</td><td>0.47</td><td>0.61</td><td> 5.54</td><td>3.27</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.27</td><td>0.64 ( 0 )   </td><td>7.69 ( 0.42 ) </td><td>2.25 ( 0.1 ) </td><td>10.44</td><td>0.56</td><td>0.64</td><td> 7.69</td><td>2.25</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.01</td><td>0.6 ( 0.01 ) </td><td>8.69 ( 0.24 ) </td><td>5.55 ( 0.06 )</td><td> 8.14</td><td>0.94</td><td>0.60</td><td> 8.69</td><td>5.55</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.03</td><td>0.56 ( 0.01 )</td><td>4.39 ( 0.34 ) </td><td>5.4 ( 0.07 ) </td><td> 3.99</td><td>0.82</td><td>0.56</td><td> 4.39</td><td>5.40</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.27</td><td>0.61 ( 0.01 )</td><td>3.84 ( 0.28 ) </td><td>3.93 ( 0.08 )</td><td> 4.91</td><td>0.47</td><td>0.61</td><td> 3.84</td><td>3.93</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.20</td><td>0.63 ( 0 )   </td><td>9.87 ( 1.4 )  </td><td>3.18 ( 0.09 )</td><td>11.69</td><td>0.49</td><td>0.63</td><td> 9.87</td><td>3.18</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.00</td><td>0.62 ( 0.01 )</td><td>10.42 ( 0.22 )</td><td>5.56 ( 0.06 )</td><td> 9.86</td><td>0.95</td><td>0.62</td><td>10.42</td><td>5.56</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.01</td><td>0.56 ( 0.01 )</td><td>5.93 ( 0.28 ) </td><td>5.53 ( 0.06 )</td><td> 5.40</td><td>0.9 </td><td>0.56</td><td> 5.93</td><td>5.53</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.24</td><td>0.62 ( 0.01 )</td><td>4.22 ( 0.48 ) </td><td>4.05 ( 0.09 )</td><td> 5.17</td><td>0.46</td><td>0.62</td><td> 4.22</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.33</td><td>0.63 ( 0 )   </td><td>5.03 ( 0.78 ) </td><td>3.35 ( 0.06 )</td><td> 6.68</td><td>0.42</td><td>0.63</td><td> 5.03</td><td>3.35</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.17</td><td>0.65 ( 0.01 )</td><td>3.87 ( 0.23 ) </td><td>3.68 ( 0.12 )</td><td> 5.19</td><td>0.5 </td><td>0.65</td><td> 3.87</td><td>3.68</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.32</td><td>0.78 ( 0.01 )</td><td>6.48 ( 0.32 ) </td><td>1.13 ( 0.11 )</td><td>10.35</td><td>0.49</td><td>0.78</td><td> 6.48</td><td>1.13</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>0.85 ( 0 )   </td><td>6.84 ( 0.26 ) </td><td>0 ( 0 )      </td><td>11.84</td><td>0.47</td><td>0.85</td><td> 6.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.65</td><td>0.86 ( 0 )   </td><td>3.69 ( 0.19 ) </td><td>0 ( 0 )      </td><td> 8.69</td><td>0.28</td><td>0.86</td><td> 3.69</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.14</td><td>0.63 ( 0.01 )</td><td>5.12 ( 0.25 ) </td><td>3.91 ( 0.1 ) </td><td> 6.21</td><td>0.63</td><td>0.63</td><td> 5.12</td><td>3.91</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.26</td><td>0.74 ( 0.01 )</td><td>7.62 ( 0.48 ) </td><td>1.8 ( 0.12 ) </td><td>10.82</td><td>0.53</td><td>0.74</td><td> 7.62</td><td>1.80</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.36</td><td>0.86 ( 0 )   </td><td>10.19 ( 0.4 ) </td><td>0 ( 0 )      </td><td>15.19</td><td>0.57</td><td>0.86</td><td>10.19</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.52</td><td>0.86 ( 0 )   </td><td>6 ( 0.27 )    </td><td>0 ( 0 )      </td><td>11.00</td><td>0.42</td><td>0.86</td><td> 6.00</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.03</td><td>0.61 ( 0.01 )</td><td>10.46 ( 0.31 )</td><td>4.69 ( 0.08 )</td><td>10.77</td><td>0.87</td><td>0.61</td><td>10.46</td><td>4.69</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.17</td><td>0.66 ( 0.01 )</td><td>8.6 ( 0.65 )  </td><td>3.19 ( 0.12 )</td><td>10.41</td><td>0.64</td><td>0.66</td><td> 8.60</td><td>3.19</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.19</td><td>0.85 ( 0 )   </td><td>25.12 ( 1.08 )</td><td>0 ( 0 )      </td><td>30.12</td><td>0.77</td><td>0.85</td><td>25.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.25</td><td>0.86 ( 0 )   </td><td>18.48 ( 0.73 )</td><td>0 ( 0 )      </td><td>23.48</td><td>0.72</td><td>0.86</td><td>18.48</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>0.6 ( 0.01 ) </td><td>12.08 ( 0.26 )</td><td>4.97 ( 0.08 )</td><td>12.11</td><td>0.91</td><td>0.60</td><td>12.08</td><td>4.97</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.13</td><td>0.65 ( 0.01 )</td><td>9 ( 0.66 )    </td><td>3.58 ( 0.12 )</td><td>10.42</td><td>0.72</td><td>0.65</td><td> 9.00</td><td>3.58</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.49</td><td>0.91 ( 0 )   </td><td>5.84 ( 0.26 ) </td><td>0 ( 0 )      </td><td>10.84</td><td>0.42</td><td>0.91</td><td> 5.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.71</td><td>0.92 ( 0 )   </td><td>3.03 ( 0.15 ) </td><td>0 ( 0 )      </td><td> 8.03</td><td>0.23</td><td>0.92</td><td> 3.03</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.21</td><td>0.69 ( 0.01 )</td><td>5.07 ( 0.25 ) </td><td>3.13 ( 0.1 ) </td><td> 6.94</td><td>0.55</td><td>0.69</td><td> 5.07</td><td>3.13</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.28</td><td>0.82 ( 0.01 )</td><td>9.65 ( 0.48 ) </td><td>0.97 ( 0.1 ) </td><td>13.68</td><td>0.59</td><td>0.82</td><td> 9.65</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.36</td><td>0.91 ( 0 )   </td><td>10.19 ( 0.37 )</td><td>0 ( 0 )      </td><td>15.19</td><td>0.58</td><td>0.91</td><td>10.19</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.59</td><td>0.92 ( 0 )   </td><td>4.81 ( 0.19 ) </td><td>0 ( 0 )      </td><td> 9.81</td><td>0.37</td><td>0.92</td><td> 4.81</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.09</td><td>0.66 ( 0.01 )</td><td>9.13 ( 0.25 ) </td><td>4.14 ( 0.08 )</td><td> 9.99</td><td>0.81</td><td>0.66</td><td> 9.13</td><td>4.14</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.21</td><td>0.75 ( 0.01 )</td><td>9.59 ( 0.72 ) </td><td>2.44 ( 0.11 )</td><td>12.15</td><td>0.61</td><td>0.75</td><td> 9.59</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.18</td><td>0.9 ( 0 )    </td><td>26.2 ( 0.86 ) </td><td>0 ( 0 )      </td><td>31.20</td><td>0.79</td><td>0.90</td><td>26.20</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.6 ( 0.55 ) </td><td>0 ( 0 )      </td><td>20.60</td><td>0.68</td><td>0.91</td><td>15.60</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.05</td><td>0.64 ( 0.01 )</td><td>11.44 ( 0.28 )</td><td>4.51 ( 0.08 )</td><td>11.93</td><td>0.87</td><td>0.64</td><td>11.44</td><td>4.51</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.18</td><td>0.71 ( 0.01 )</td><td>10.03 ( 0.71 )</td><td>2.95 ( 0.1 ) </td><td>12.08</td><td>0.65</td><td>0.71</td><td>10.03</td><td>2.95</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.14</td><td>0.9 ( 0 )    </td><td>34.85 ( 1.14 )</td><td>0 ( 0 )      </td><td>39.85</td><td>0.83</td><td>0.90</td><td>34.85</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.20</td><td>0.91 ( 0 )   </td><td>24.83 ( 0.82 )</td><td>0 ( 0 )      </td><td>29.83</td><td>0.78</td><td>0.91</td><td>24.83</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.30</td><td>0.73 ( 0.01 )</td><td>4.4 ( 0.26 )  </td><td>2.55 ( 0.11 )</td><td> 6.85</td><td>0.44</td><td>0.73</td><td> 4.40</td><td>2.55</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.33</td><td>0.85 ( 0.01 )</td><td>8.14 ( 0.35 ) </td><td>0.42 ( 0.06 )</td><td>12.72</td><td>0.53</td><td>0.85</td><td> 8.14</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.54</td><td>0.92 ( 0 )   </td><td>5.15 ( 0.22 ) </td><td>0 ( 0 )      </td><td>10.15</td><td>0.38</td><td>0.92</td><td> 5.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.76</td><td>0.92 ( 0 )   </td><td>2.61 ( 0.13 ) </td><td>0 ( 0 )      </td><td> 7.61</td><td>0.19</td><td>0.92</td><td> 2.61</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.23</td><td>0.7 ( 0.01 ) </td><td>4.84 ( 0.24 ) </td><td>3.3 ( 0.09 ) </td><td> 6.54</td><td>0.54</td><td>0.70</td><td> 4.84</td><td>3.30</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.28</td><td>0.81 ( 0.01 )</td><td>9.92 ( 0.51 ) </td><td>0.87 ( 0.08 )</td><td>14.05</td><td>0.57</td><td>0.81</td><td> 9.92</td><td>0.87</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.37</td><td>0.92 ( 0 )   </td><td>9.65 ( 0.35 ) </td><td>0 ( 0 )      </td><td>14.65</td><td>0.57</td><td>0.92</td><td> 9.65</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.62</td><td>0.93 ( 0 )   </td><td>4.29 ( 0.18 ) </td><td>0 ( 0 )      </td><td> 9.29</td><td>0.33</td><td>0.93</td><td> 4.29</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.08</td><td>0.62 ( 0.01 )</td><td>8.99 ( 0.26 ) </td><td>4.13 ( 0.09 )</td><td> 9.86</td><td>0.8 </td><td>0.62</td><td> 8.99</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.23</td><td>0.74 ( 0.01 )</td><td>8.85 ( 0.62 ) </td><td>2.4 ( 0.12 ) </td><td>11.45</td><td>0.6 </td><td>0.74</td><td> 8.85</td><td>2.40</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.19</td><td>0.91 ( 0 )   </td><td>24.91 ( 0.78 )</td><td>0 ( 0 )      </td><td>29.91</td><td>0.78</td><td>0.91</td><td>24.91</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.31</td><td>0.92 ( 0 )   </td><td>14.12 ( 0.43 )</td><td>0 ( 0 )      </td><td>19.12</td><td>0.67</td><td>0.92</td><td>14.12</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.07</td><td>0.64 ( 0.01 )</td><td>10.81 ( 0.29 )</td><td>4.22 ( 0.08 )</td><td>11.59</td><td>0.84</td><td>0.64</td><td>10.81</td><td>4.22</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.19</td><td>0.74 ( 0.01 )</td><td>11.15 ( 0.75 )</td><td>2.44 ( 0.11 )</td><td>13.71</td><td>0.65</td><td>0.74</td><td>11.15</td><td>2.44</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.14</td><td>0.91 ( 0 )   </td><td>34.98 ( 1.04 )</td><td>0 ( 0 )      </td><td>39.98</td><td>0.83</td><td>0.91</td><td>34.98</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.22</td><td>0.92 ( 0 )   </td><td>22.21 ( 0.73 )</td><td>0 ( 0 )      </td><td>27.21</td><td>0.76</td><td>0.92</td><td>22.21</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 )  & 5.3 ( 0.07 )  &  2.39 & 0.67 & 0.59 &  2.69 & 5.30\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 )  & 5.02 ( 0.08 ) &  2.00 & 0.39 & 0.56 &  2.02 & 5.02\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 )  & 3 ( 0.11 )    &  6.52 & 0.45 & 0.63 &  4.52 & 3.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 )  & 2.1 ( 0.09 )  &  7.64 & 0.44 & 0.64 &  4.74 & 2.10\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 )  & 5.34 ( 0.07 ) &  3.48 & 0.78 & 0.62 &  3.82 & 5.34\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 )  & 5.27 ( 0.08 ) &  2.02 & 0.57 & 0.56 &  2.29 & 5.27\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.23 & 0.61 ( 0 )    & 5.54 ( 0.52 )  & 3.27 ( 0.12 ) &  7.27 & 0.47 & 0.61 &  5.54 & 3.27\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.27 & 0.64 ( 0 )    & 7.69 ( 0.42 )  & 2.25 ( 0.1 )  & 10.44 & 0.56 & 0.64 &  7.69 & 2.25\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.01 & 0.6 ( 0.01 )  & 8.69 ( 0.24 )  & 5.55 ( 0.06 ) &  8.14 & 0.94 & 0.60 &  8.69 & 5.55\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.03 & 0.56 ( 0.01 ) & 4.39 ( 0.34 )  & 5.4 ( 0.07 )  &  3.99 & 0.82 & 0.56 &  4.39 & 5.40\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.27 & 0.61 ( 0.01 ) & 3.84 ( 0.28 )  & 3.93 ( 0.08 ) &  4.91 & 0.47 & 0.61 &  3.84 & 3.93\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.20 & 0.63 ( 0 )    & 9.87 ( 1.4 )   & 3.18 ( 0.09 ) & 11.69 & 0.49 & 0.63 &  9.87 & 3.18\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.00 & 0.62 ( 0.01 ) & 10.42 ( 0.22 ) & 5.56 ( 0.06 ) &  9.86 & 0.95 & 0.62 & 10.42 & 5.56\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.01 & 0.56 ( 0.01 ) & 5.93 ( 0.28 )  & 5.53 ( 0.06 ) &  5.40 & 0.9  & 0.56 &  5.93 & 5.53\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.24 & 0.62 ( 0.01 ) & 4.22 ( 0.48 )  & 4.05 ( 0.09 ) &  5.17 & 0.46 & 0.62 &  4.22 & 4.05\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.33 & 0.63 ( 0 )    & 5.03 ( 0.78 )  & 3.35 ( 0.06 ) &  6.68 & 0.42 & 0.63 &  5.03 & 3.35\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.17 & 0.65 ( 0.01 ) & 3.87 ( 0.23 )  & 3.68 ( 0.12 ) &  5.19 & 0.5  & 0.65 &  3.87 & 3.68\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.32 & 0.78 ( 0.01 ) & 6.48 ( 0.32 )  & 1.13 ( 0.11 ) & 10.35 & 0.49 & 0.78 &  6.48 & 1.13\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.44 & 0.85 ( 0 )    & 6.84 ( 0.26 )  & 0 ( 0 )       & 11.84 & 0.47 & 0.85 &  6.84 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.65 & 0.86 ( 0 )    & 3.69 ( 0.19 )  & 0 ( 0 )       &  8.69 & 0.28 & 0.86 &  3.69 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.14 & 0.63 ( 0.01 ) & 5.12 ( 0.25 )  & 3.91 ( 0.1 )  &  6.21 & 0.63 & 0.63 &  5.12 & 3.91\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.26 & 0.74 ( 0.01 ) & 7.62 ( 0.48 )  & 1.8 ( 0.12 )  & 10.82 & 0.53 & 0.74 &  7.62 & 1.80\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.36 & 0.86 ( 0 )    & 10.19 ( 0.4 )  & 0 ( 0 )       & 15.19 & 0.57 & 0.86 & 10.19 & 0.00\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.52 & 0.86 ( 0 )    & 6 ( 0.27 )     & 0 ( 0 )       & 11.00 & 0.42 & 0.86 &  6.00 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.03 & 0.61 ( 0.01 ) & 10.46 ( 0.31 ) & 4.69 ( 0.08 ) & 10.77 & 0.87 & 0.61 & 10.46 & 4.69\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.17 & 0.66 ( 0.01 ) & 8.6 ( 0.65 )   & 3.19 ( 0.12 ) & 10.41 & 0.64 & 0.66 &  8.60 & 3.19\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.19 & 0.85 ( 0 )    & 25.12 ( 1.08 ) & 0 ( 0 )       & 30.12 & 0.77 & 0.85 & 25.12 & 0.00\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.25 & 0.86 ( 0 )    & 18.48 ( 0.73 ) & 0 ( 0 )       & 23.48 & 0.72 & 0.86 & 18.48 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 0.6 ( 0.01 )  & 12.08 ( 0.26 ) & 4.97 ( 0.08 ) & 12.11 & 0.91 & 0.60 & 12.08 & 4.97\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.13 & 0.65 ( 0.01 ) & 9 ( 0.66 )     & 3.58 ( 0.12 ) & 10.42 & 0.72 & 0.65 &  9.00 & 3.58\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.49 & 0.91 ( 0 )    & 5.84 ( 0.26 )  & 0 ( 0 )       & 10.84 & 0.42 & 0.91 &  5.84 & 0.00\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.71 & 0.92 ( 0 )    & 3.03 ( 0.15 )  & 0 ( 0 )       &  8.03 & 0.23 & 0.92 &  3.03 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.21 & 0.69 ( 0.01 ) & 5.07 ( 0.25 )  & 3.13 ( 0.1 )  &  6.94 & 0.55 & 0.69 &  5.07 & 3.13\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.28 & 0.82 ( 0.01 ) & 9.65 ( 0.48 )  & 0.97 ( 0.1 )  & 13.68 & 0.59 & 0.82 &  9.65 & 0.97\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.36 & 0.91 ( 0 )    & 10.19 ( 0.37 ) & 0 ( 0 )       & 15.19 & 0.58 & 0.91 & 10.19 & 0.00\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.59 & 0.92 ( 0 )    & 4.81 ( 0.19 )  & 0 ( 0 )       &  9.81 & 0.37 & 0.92 &  4.81 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.09 & 0.66 ( 0.01 ) & 9.13 ( 0.25 )  & 4.14 ( 0.08 ) &  9.99 & 0.81 & 0.66 &  9.13 & 4.14\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.21 & 0.75 ( 0.01 ) & 9.59 ( 0.72 )  & 2.44 ( 0.11 ) & 12.15 & 0.61 & 0.75 &  9.59 & 2.44\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.18 & 0.9 ( 0 )     & 26.2 ( 0.86 )  & 0 ( 0 )       & 31.20 & 0.79 & 0.90 & 26.20 & 0.00\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.28 & 0.91 ( 0 )    & 15.6 ( 0.55 )  & 0 ( 0 )       & 20.60 & 0.68 & 0.91 & 15.60 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.05 & 0.64 ( 0.01 ) & 11.44 ( 0.28 ) & 4.51 ( 0.08 ) & 11.93 & 0.87 & 0.64 & 11.44 & 4.51\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.18 & 0.71 ( 0.01 ) & 10.03 ( 0.71 ) & 2.95 ( 0.1 )  & 12.08 & 0.65 & 0.71 & 10.03 & 2.95\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.14 & 0.9 ( 0 )     & 34.85 ( 1.14 ) & 0 ( 0 )       & 39.85 & 0.83 & 0.90 & 34.85 & 0.00\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.20 & 0.91 ( 0 )    & 24.83 ( 0.82 ) & 0 ( 0 )       & 29.83 & 0.78 & 0.91 & 24.83 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.30 & 0.73 ( 0.01 ) & 4.4 ( 0.26 )   & 2.55 ( 0.11 ) &  6.85 & 0.44 & 0.73 &  4.40 & 2.55\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.33 & 0.85 ( 0.01 ) & 8.14 ( 0.35 )  & 0.42 ( 0.06 ) & 12.72 & 0.53 & 0.85 &  8.14 & 0.42\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.54 & 0.92 ( 0 )    & 5.15 ( 0.22 )  & 0 ( 0 )       & 10.15 & 0.38 & 0.92 &  5.15 & 0.00\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.76 & 0.92 ( 0 )    & 2.61 ( 0.13 )  & 0 ( 0 )       &  7.61 & 0.19 & 0.92 &  2.61 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.23 & 0.7 ( 0.01 )  & 4.84 ( 0.24 )  & 3.3 ( 0.09 )  &  6.54 & 0.54 & 0.70 &  4.84 & 3.30\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.28 & 0.81 ( 0.01 ) & 9.92 ( 0.51 )  & 0.87 ( 0.08 ) & 14.05 & 0.57 & 0.81 &  9.92 & 0.87\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.37 & 0.92 ( 0 )    & 9.65 ( 0.35 )  & 0 ( 0 )       & 14.65 & 0.57 & 0.92 &  9.65 & 0.00\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.62 & 0.93 ( 0 )    & 4.29 ( 0.18 )  & 0 ( 0 )       &  9.29 & 0.33 & 0.93 &  4.29 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.08 & 0.62 ( 0.01 ) & 8.99 ( 0.26 )  & 4.13 ( 0.09 ) &  9.86 & 0.8  & 0.62 &  8.99 & 4.13\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.23 & 0.74 ( 0.01 ) & 8.85 ( 0.62 )  & 2.4 ( 0.12 )  & 11.45 & 0.6  & 0.74 &  8.85 & 2.40\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.19 & 0.91 ( 0 )    & 24.91 ( 0.78 ) & 0 ( 0 )       & 29.91 & 0.78 & 0.91 & 24.91 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.31 & 0.92 ( 0 )    & 14.12 ( 0.43 ) & 0 ( 0 )       & 19.12 & 0.67 & 0.92 & 14.12 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.07 & 0.64 ( 0.01 ) & 10.81 ( 0.29 ) & 4.22 ( 0.08 ) & 11.59 & 0.84 & 0.64 & 10.81 & 4.22\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.19 & 0.74 ( 0.01 ) & 11.15 ( 0.75 ) & 2.44 ( 0.11 ) & 13.71 & 0.65 & 0.74 & 11.15 & 2.44\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.14 & 0.91 ( 0 )    & 34.98 ( 1.04 ) & 0 ( 0 )       & 39.98 & 0.83 & 0.91 & 34.98 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.22 & 0.92 ( 0 )    & 22.21 ( 0.73 ) & 0 ( 0 )       & 27.21 & 0.76 & 0.92 & 22.21 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 )  | 5.3 ( 0.07 )  |  2.39 | 0.67 | 0.59 |  2.69 | 5.30 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 )  | 5.02 ( 0.08 ) |  2.00 | 0.39 | 0.56 |  2.02 | 5.02 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 )  | 3 ( 0.11 )    |  6.52 | 0.45 | 0.63 |  4.52 | 3.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 )  | 2.1 ( 0.09 )  |  7.64 | 0.44 | 0.64 |  4.74 | 2.10 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 )  | 5.34 ( 0.07 ) |  3.48 | 0.78 | 0.62 |  3.82 | 5.34 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 )  | 5.27 ( 0.08 ) |  2.02 | 0.57 | 0.56 |  2.29 | 5.27 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.23 | 0.61 ( 0 )    | 5.54 ( 0.52 )  | 3.27 ( 0.12 ) |  7.27 | 0.47 | 0.61 |  5.54 | 3.27 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.27 | 0.64 ( 0 )    | 7.69 ( 0.42 )  | 2.25 ( 0.1 )  | 10.44 | 0.56 | 0.64 |  7.69 | 2.25 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.01 | 0.6 ( 0.01 )  | 8.69 ( 0.24 )  | 5.55 ( 0.06 ) |  8.14 | 0.94 | 0.60 |  8.69 | 5.55 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.03 | 0.56 ( 0.01 ) | 4.39 ( 0.34 )  | 5.4 ( 0.07 )  |  3.99 | 0.82 | 0.56 |  4.39 | 5.40 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.27 | 0.61 ( 0.01 ) | 3.84 ( 0.28 )  | 3.93 ( 0.08 ) |  4.91 | 0.47 | 0.61 |  3.84 | 3.93 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.20 | 0.63 ( 0 )    | 9.87 ( 1.4 )   | 3.18 ( 0.09 ) | 11.69 | 0.49 | 0.63 |  9.87 | 3.18 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.00 | 0.62 ( 0.01 ) | 10.42 ( 0.22 ) | 5.56 ( 0.06 ) |  9.86 | 0.95 | 0.62 | 10.42 | 5.56 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.01 | 0.56 ( 0.01 ) | 5.93 ( 0.28 )  | 5.53 ( 0.06 ) |  5.40 | 0.9  | 0.56 |  5.93 | 5.53 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.24 | 0.62 ( 0.01 ) | 4.22 ( 0.48 )  | 4.05 ( 0.09 ) |  5.17 | 0.46 | 0.62 |  4.22 | 4.05 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.33 | 0.63 ( 0 )    | 5.03 ( 0.78 )  | 3.35 ( 0.06 ) |  6.68 | 0.42 | 0.63 |  5.03 | 3.35 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.17 | 0.65 ( 0.01 ) | 3.87 ( 0.23 )  | 3.68 ( 0.12 ) |  5.19 | 0.5  | 0.65 |  3.87 | 3.68 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.32 | 0.78 ( 0.01 ) | 6.48 ( 0.32 )  | 1.13 ( 0.11 ) | 10.35 | 0.49 | 0.78 |  6.48 | 1.13 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.44 | 0.85 ( 0 )    | 6.84 ( 0.26 )  | 0 ( 0 )       | 11.84 | 0.47 | 0.85 |  6.84 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.65 | 0.86 ( 0 )    | 3.69 ( 0.19 )  | 0 ( 0 )       |  8.69 | 0.28 | 0.86 |  3.69 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.14 | 0.63 ( 0.01 ) | 5.12 ( 0.25 )  | 3.91 ( 0.1 )  |  6.21 | 0.63 | 0.63 |  5.12 | 3.91 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.26 | 0.74 ( 0.01 ) | 7.62 ( 0.48 )  | 1.8 ( 0.12 )  | 10.82 | 0.53 | 0.74 |  7.62 | 1.80 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.36 | 0.86 ( 0 )    | 10.19 ( 0.4 )  | 0 ( 0 )       | 15.19 | 0.57 | 0.86 | 10.19 | 0.00 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.52 | 0.86 ( 0 )    | 6 ( 0.27 )     | 0 ( 0 )       | 11.00 | 0.42 | 0.86 |  6.00 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.03 | 0.61 ( 0.01 ) | 10.46 ( 0.31 ) | 4.69 ( 0.08 ) | 10.77 | 0.87 | 0.61 | 10.46 | 4.69 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.17 | 0.66 ( 0.01 ) | 8.6 ( 0.65 )   | 3.19 ( 0.12 ) | 10.41 | 0.64 | 0.66 |  8.60 | 3.19 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.19 | 0.85 ( 0 )    | 25.12 ( 1.08 ) | 0 ( 0 )       | 30.12 | 0.77 | 0.85 | 25.12 | 0.00 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.25 | 0.86 ( 0 )    | 18.48 ( 0.73 ) | 0 ( 0 )       | 23.48 | 0.72 | 0.86 | 18.48 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 0.6 ( 0.01 )  | 12.08 ( 0.26 ) | 4.97 ( 0.08 ) | 12.11 | 0.91 | 0.60 | 12.08 | 4.97 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.13 | 0.65 ( 0.01 ) | 9 ( 0.66 )     | 3.58 ( 0.12 ) | 10.42 | 0.72 | 0.65 |  9.00 | 3.58 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.49 | 0.91 ( 0 )    | 5.84 ( 0.26 )  | 0 ( 0 )       | 10.84 | 0.42 | 0.91 |  5.84 | 0.00 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.71 | 0.92 ( 0 )    | 3.03 ( 0.15 )  | 0 ( 0 )       |  8.03 | 0.23 | 0.92 |  3.03 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.21 | 0.69 ( 0.01 ) | 5.07 ( 0.25 )  | 3.13 ( 0.1 )  |  6.94 | 0.55 | 0.69 |  5.07 | 3.13 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.28 | 0.82 ( 0.01 ) | 9.65 ( 0.48 )  | 0.97 ( 0.1 )  | 13.68 | 0.59 | 0.82 |  9.65 | 0.97 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.36 | 0.91 ( 0 )    | 10.19 ( 0.37 ) | 0 ( 0 )       | 15.19 | 0.58 | 0.91 | 10.19 | 0.00 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.59 | 0.92 ( 0 )    | 4.81 ( 0.19 )  | 0 ( 0 )       |  9.81 | 0.37 | 0.92 |  4.81 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.09 | 0.66 ( 0.01 ) | 9.13 ( 0.25 )  | 4.14 ( 0.08 ) |  9.99 | 0.81 | 0.66 |  9.13 | 4.14 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.21 | 0.75 ( 0.01 ) | 9.59 ( 0.72 )  | 2.44 ( 0.11 ) | 12.15 | 0.61 | 0.75 |  9.59 | 2.44 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.18 | 0.9 ( 0 )     | 26.2 ( 0.86 )  | 0 ( 0 )       | 31.20 | 0.79 | 0.90 | 26.20 | 0.00 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.28 | 0.91 ( 0 )    | 15.6 ( 0.55 )  | 0 ( 0 )       | 20.60 | 0.68 | 0.91 | 15.60 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.05 | 0.64 ( 0.01 ) | 11.44 ( 0.28 ) | 4.51 ( 0.08 ) | 11.93 | 0.87 | 0.64 | 11.44 | 4.51 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.18 | 0.71 ( 0.01 ) | 10.03 ( 0.71 ) | 2.95 ( 0.1 )  | 12.08 | 0.65 | 0.71 | 10.03 | 2.95 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.14 | 0.9 ( 0 )     | 34.85 ( 1.14 ) | 0 ( 0 )       | 39.85 | 0.83 | 0.90 | 34.85 | 0.00 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.20 | 0.91 ( 0 )    | 24.83 ( 0.82 ) | 0 ( 0 )       | 29.83 | 0.78 | 0.91 | 24.83 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.30 | 0.73 ( 0.01 ) | 4.4 ( 0.26 )   | 2.55 ( 0.11 ) |  6.85 | 0.44 | 0.73 |  4.40 | 2.55 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.33 | 0.85 ( 0.01 ) | 8.14 ( 0.35 )  | 0.42 ( 0.06 ) | 12.72 | 0.53 | 0.85 |  8.14 | 0.42 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.54 | 0.92 ( 0 )    | 5.15 ( 0.22 )  | 0 ( 0 )       | 10.15 | 0.38 | 0.92 |  5.15 | 0.00 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.76 | 0.92 ( 0 )    | 2.61 ( 0.13 )  | 0 ( 0 )       |  7.61 | 0.19 | 0.92 |  2.61 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.23 | 0.7 ( 0.01 )  | 4.84 ( 0.24 )  | 3.3 ( 0.09 )  |  6.54 | 0.54 | 0.70 |  4.84 | 3.30 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.28 | 0.81 ( 0.01 ) | 9.92 ( 0.51 )  | 0.87 ( 0.08 ) | 14.05 | 0.57 | 0.81 |  9.92 | 0.87 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.37 | 0.92 ( 0 )    | 9.65 ( 0.35 )  | 0 ( 0 )       | 14.65 | 0.57 | 0.92 |  9.65 | 0.00 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.62 | 0.93 ( 0 )    | 4.29 ( 0.18 )  | 0 ( 0 )       |  9.29 | 0.33 | 0.93 |  4.29 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.08 | 0.62 ( 0.01 ) | 8.99 ( 0.26 )  | 4.13 ( 0.09 ) |  9.86 | 0.8  | 0.62 |  8.99 | 4.13 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.23 | 0.74 ( 0.01 ) | 8.85 ( 0.62 )  | 2.4 ( 0.12 )  | 11.45 | 0.6  | 0.74 |  8.85 | 2.40 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.19 | 0.91 ( 0 )    | 24.91 ( 0.78 ) | 0 ( 0 )       | 29.91 | 0.78 | 0.91 | 24.91 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.31 | 0.92 ( 0 )    | 14.12 ( 0.43 ) | 0 ( 0 )       | 19.12 | 0.67 | 0.92 | 14.12 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.07 | 0.64 ( 0.01 ) | 10.81 ( 0.29 ) | 4.22 ( 0.08 ) | 11.59 | 0.84 | 0.64 | 10.81 | 4.22 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.19 | 0.74 ( 0.01 ) | 11.15 ( 0.75 ) | 2.44 ( 0.11 ) | 13.71 | 0.65 | 0.74 | 11.15 | 2.44 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.14 | 0.91 ( 0 )    | 34.98 ( 1.04 ) | 0 ( 0 )       | 39.98 | 0.83 | 0.91 | 34.98 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.22 | 0.92 ( 0 )    | 22.21 ( 0.73 ) | 0 ( 0 )       | 27.21 | 0.76 | 0.92 | 22.21 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "1    50   50 0.1   1.00 0.03 0.59 ( 0.01 ) 2.69 ( 0.14 )  5.3 ( 0.07 ) \n",
+       "2   100   50 0.1   0.50 0.11 0.56 ( 0.01 ) 2.02 ( 0.15 )  5.02 ( 0.08 )\n",
+       "3   500   50 0.1   0.10 0.28 0.63 ( 0 )    4.52 ( 0.29 )  3 ( 0.11 )   \n",
+       "4  1000   50 0.1   0.05 0.39 0.64 ( 0 )    4.74 ( 0.22 )  2.1 ( 0.09 ) \n",
+       "5    50  100 0.1   2.00 0.02 0.62 ( 0.01 ) 3.82 ( 0.16 )  5.34 ( 0.07 )\n",
+       "6   100  100 0.1   1.00 0.07 0.56 ( 0.01 ) 2.29 ( 0.19 )  5.27 ( 0.08 )\n",
+       "7   500  100 0.1   0.20 0.23 0.61 ( 0 )    5.54 ( 0.52 )  3.27 ( 0.12 )\n",
+       "8  1000  100 0.1   0.10 0.27 0.64 ( 0 )    7.69 ( 0.42 )  2.25 ( 0.1 ) \n",
+       "9    50  500 0.1  10.00 0.01 0.6 ( 0.01 )  8.69 ( 0.24 )  5.55 ( 0.06 )\n",
+       "10  100  500 0.1   5.00 0.03 0.56 ( 0.01 ) 4.39 ( 0.34 )  5.4 ( 0.07 ) \n",
+       "11  500  500 0.1   1.00 0.27 0.61 ( 0.01 ) 3.84 ( 0.28 )  3.93 ( 0.08 )\n",
+       "12 1000  500 0.1   0.50 0.20 0.63 ( 0 )    9.87 ( 1.4 )   3.18 ( 0.09 )\n",
+       "13   50 1000 0.1  20.00 0.00 0.62 ( 0.01 ) 10.42 ( 0.22 ) 5.56 ( 0.06 )\n",
+       "14  100 1000 0.1  10.00 0.01 0.56 ( 0.01 ) 5.93 ( 0.28 )  5.53 ( 0.06 )\n",
+       "15  500 1000 0.1   2.00 0.24 0.62 ( 0.01 ) 4.22 ( 0.48 )  4.05 ( 0.09 )\n",
+       "16 1000 1000 0.1   1.00 0.33 0.63 ( 0 )    5.03 ( 0.78 )  3.35 ( 0.06 )\n",
+       "17   50   50 0.3   1.00 0.17 0.65 ( 0.01 ) 3.87 ( 0.23 )  3.68 ( 0.12 )\n",
+       "18  100   50 0.3   0.50 0.32 0.78 ( 0.01 ) 6.48 ( 0.32 )  1.13 ( 0.11 )\n",
+       "19  500   50 0.3   0.10 0.44 0.85 ( 0 )    6.84 ( 0.26 )  0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.65 0.86 ( 0 )    3.69 ( 0.19 )  0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.14 0.63 ( 0.01 ) 5.12 ( 0.25 )  3.91 ( 0.1 ) \n",
+       "22  100  100 0.3   1.00 0.26 0.74 ( 0.01 ) 7.62 ( 0.48 )  1.8 ( 0.12 ) \n",
+       "23  500  100 0.3   0.20 0.36 0.86 ( 0 )    10.19 ( 0.4 )  0 ( 0 )      \n",
+       "24 1000  100 0.3   0.10 0.52 0.86 ( 0 )    6 ( 0.27 )     0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.03 0.61 ( 0.01 ) 10.46 ( 0.31 ) 4.69 ( 0.08 )\n",
+       "26  100  500 0.3   5.00 0.17 0.66 ( 0.01 ) 8.6 ( 0.65 )   3.19 ( 0.12 )\n",
+       "27  500  500 0.3   1.00 0.19 0.85 ( 0 )    25.12 ( 1.08 ) 0 ( 0 )      \n",
+       "28 1000  500 0.3   0.50 0.25 0.86 ( 0 )    18.48 ( 0.73 ) 0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.02 0.6 ( 0.01 )  12.08 ( 0.26 ) 4.97 ( 0.08 )\n",
+       "30  100 1000 0.3  10.00 0.13 0.65 ( 0.01 ) 9 ( 0.66 )     3.58 ( 0.12 )\n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮              ⋮            \n",
+       "51  500   50 0.7   0.10 0.49 0.91 ( 0 )    5.84 ( 0.26 )  0 ( 0 )      \n",
+       "52 1000   50 0.7   0.05 0.71 0.92 ( 0 )    3.03 ( 0.15 )  0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.21 0.69 ( 0.01 ) 5.07 ( 0.25 )  3.13 ( 0.1 ) \n",
+       "54  100  100 0.7   1.00 0.28 0.82 ( 0.01 ) 9.65 ( 0.48 )  0.97 ( 0.1 ) \n",
+       "55  500  100 0.7   0.20 0.36 0.91 ( 0 )    10.19 ( 0.37 ) 0 ( 0 )      \n",
+       "56 1000  100 0.7   0.10 0.59 0.92 ( 0 )    4.81 ( 0.19 )  0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.09 0.66 ( 0.01 ) 9.13 ( 0.25 )  4.14 ( 0.08 )\n",
+       "58  100  500 0.7   5.00 0.21 0.75 ( 0.01 ) 9.59 ( 0.72 )  2.44 ( 0.11 )\n",
+       "59  500  500 0.7   1.00 0.18 0.9 ( 0 )     26.2 ( 0.86 )  0 ( 0 )      \n",
+       "60 1000  500 0.7   0.50 0.28 0.91 ( 0 )    15.6 ( 0.55 )  0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.05 0.64 ( 0.01 ) 11.44 ( 0.28 ) 4.51 ( 0.08 )\n",
+       "62  100 1000 0.7  10.00 0.18 0.71 ( 0.01 ) 10.03 ( 0.71 ) 2.95 ( 0.1 ) \n",
+       "63  500 1000 0.7   2.00 0.14 0.9 ( 0 )     34.85 ( 1.14 ) 0 ( 0 )      \n",
+       "64 1000 1000 0.7   1.00 0.20 0.91 ( 0 )    24.83 ( 0.82 ) 0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.30 0.73 ( 0.01 ) 4.4 ( 0.26 )   2.55 ( 0.11 )\n",
+       "66  100   50 0.9   0.50 0.33 0.85 ( 0.01 ) 8.14 ( 0.35 )  0.42 ( 0.06 )\n",
+       "67  500   50 0.9   0.10 0.54 0.92 ( 0 )    5.15 ( 0.22 )  0 ( 0 )      \n",
+       "68 1000   50 0.9   0.05 0.76 0.92 ( 0 )    2.61 ( 0.13 )  0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.23 0.7 ( 0.01 )  4.84 ( 0.24 )  3.3 ( 0.09 ) \n",
+       "70  100  100 0.9   1.00 0.28 0.81 ( 0.01 ) 9.92 ( 0.51 )  0.87 ( 0.08 )\n",
+       "71  500  100 0.9   0.20 0.37 0.92 ( 0 )    9.65 ( 0.35 )  0 ( 0 )      \n",
+       "72 1000  100 0.9   0.10 0.62 0.93 ( 0 )    4.29 ( 0.18 )  0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.08 0.62 ( 0.01 ) 8.99 ( 0.26 )  4.13 ( 0.09 )\n",
+       "74  100  500 0.9   5.00 0.23 0.74 ( 0.01 ) 8.85 ( 0.62 )  2.4 ( 0.12 ) \n",
+       "75  500  500 0.9   1.00 0.19 0.91 ( 0 )    24.91 ( 0.78 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.50 0.31 0.92 ( 0 )    14.12 ( 0.43 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.07 0.64 ( 0.01 ) 10.81 ( 0.29 ) 4.22 ( 0.08 )\n",
+       "78  100 1000 0.9  10.00 0.19 0.74 ( 0.01 ) 11.15 ( 0.75 ) 2.44 ( 0.11 )\n",
+       "79  500 1000 0.9   2.00 0.14 0.91 ( 0 )    34.98 ( 1.04 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.00 0.22 0.92 ( 0 )    22.21 ( 0.73 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   2.39      0.67 0.59      2.69   5.30   \n",
+       "2   2.00      0.39 0.56      2.02   5.02   \n",
+       "3   6.52      0.45 0.63      4.52   3.00   \n",
+       "4   7.64      0.44 0.64      4.74   2.10   \n",
+       "5   3.48      0.78 0.62      3.82   5.34   \n",
+       "6   2.02      0.57 0.56      2.29   5.27   \n",
+       "7   7.27      0.47 0.61      5.54   3.27   \n",
+       "8  10.44      0.56 0.64      7.69   2.25   \n",
+       "9   8.14      0.94 0.60      8.69   5.55   \n",
+       "10  3.99      0.82 0.56      4.39   5.40   \n",
+       "11  4.91      0.47 0.61      3.84   3.93   \n",
+       "12 11.69      0.49 0.63      9.87   3.18   \n",
+       "13  9.86      0.95 0.62     10.42   5.56   \n",
+       "14  5.40      0.9  0.56      5.93   5.53   \n",
+       "15  5.17      0.46 0.62      4.22   4.05   \n",
+       "16  6.68      0.42 0.63      5.03   3.35   \n",
+       "17  5.19      0.5  0.65      3.87   3.68   \n",
+       "18 10.35      0.49 0.78      6.48   1.13   \n",
+       "19 11.84      0.47 0.85      6.84   0.00   \n",
+       "20  8.69      0.28 0.86      3.69   0.00   \n",
+       "21  6.21      0.63 0.63      5.12   3.91   \n",
+       "22 10.82      0.53 0.74      7.62   1.80   \n",
+       "23 15.19      0.57 0.86     10.19   0.00   \n",
+       "24 11.00      0.42 0.86      6.00   0.00   \n",
+       "25 10.77      0.87 0.61     10.46   4.69   \n",
+       "26 10.41      0.64 0.66      8.60   3.19   \n",
+       "27 30.12      0.77 0.85     25.12   0.00   \n",
+       "28 23.48      0.72 0.86     18.48   0.00   \n",
+       "29 12.11      0.91 0.60     12.08   4.97   \n",
+       "30 10.42      0.72 0.65      9.00   3.58   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 10.84      0.42 0.91      5.84   0.00   \n",
+       "52  8.03      0.23 0.92      3.03   0.00   \n",
+       "53  6.94      0.55 0.69      5.07   3.13   \n",
+       "54 13.68      0.59 0.82      9.65   0.97   \n",
+       "55 15.19      0.58 0.91     10.19   0.00   \n",
+       "56  9.81      0.37 0.92      4.81   0.00   \n",
+       "57  9.99      0.81 0.66      9.13   4.14   \n",
+       "58 12.15      0.61 0.75      9.59   2.44   \n",
+       "59 31.20      0.79 0.90     26.20   0.00   \n",
+       "60 20.60      0.68 0.91     15.60   0.00   \n",
+       "61 11.93      0.87 0.64     11.44   4.51   \n",
+       "62 12.08      0.65 0.71     10.03   2.95   \n",
+       "63 39.85      0.83 0.90     34.85   0.00   \n",
+       "64 29.83      0.78 0.91     24.83   0.00   \n",
+       "65  6.85      0.44 0.73      4.40   2.55   \n",
+       "66 12.72      0.53 0.85      8.14   0.42   \n",
+       "67 10.15      0.38 0.92      5.15   0.00   \n",
+       "68  7.61      0.19 0.92      2.61   0.00   \n",
+       "69  6.54      0.54 0.70      4.84   3.30   \n",
+       "70 14.05      0.57 0.81      9.92   0.87   \n",
+       "71 14.65      0.57 0.92      9.65   0.00   \n",
+       "72  9.29      0.33 0.93      4.29   0.00   \n",
+       "73  9.86      0.8  0.62      8.99   4.13   \n",
+       "74 11.45      0.6  0.74      8.85   2.40   \n",
+       "75 29.91      0.78 0.91     24.91   0.00   \n",
+       "76 19.12      0.67 0.92     14.12   0.00   \n",
+       "77 11.59      0.84 0.64     10.81   4.22   \n",
+       "78 13.71      0.65 0.74     11.15   2.44   \n",
+       "79 39.98      0.83 0.91     34.98   0.00   \n",
+       "80 27.21      0.76 0.92     22.21   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_lasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/2.2_sim_block_elnet_binary_update.ipynb b/simulations/notebooks_sim_bin/2.2_sim_block_elnet_binary_update.ipynb
new file mode 100644
index 0000000..26fb822
--- /dev/null
+++ b/simulations/notebooks_sim_bin/2.2_sim_block_elnet_binary_update.ipynb
@@ -0,0 +1,972 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_Elnet_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_elnet[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_elnet$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_elnet$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>17.05 ( 1.31 )</td><td>3.17 ( 0.19 )</td><td>0.61 ( 0.01 )</td><td>0.01</td><td>18.88</td><td>0.80</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>13.62 ( 1.31 )</td><td>2.9 ( 0.17 )</td><td>0.59 ( 0.01 )</td><td>0.04</td><td>15.72</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>16.89 ( 1.14 )</td><td>1.1 ( 0.13 )</td><td>0.64 ( 0 )</td><td>0.11</td><td>20.79</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>19.43 ( 1.02 )</td><td>0.42 ( 0.08 )</td><td>0.66 ( 0 )</td><td>0.12</td><td>24.01</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>27.39 ( 2.11 )</td><td>3.38 ( 0.17 )</td><td>0.63 ( 0.01 )</td><td>0.01</td><td>29.01</td><td>0.87</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>26.17 ( 2.4 )</td><td>3.34 ( 0.16 )</td><td>0.59 ( 0.01 )</td><td>0.02</td><td>27.83</td><td>0.84</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 17.05 ( 1.31 ) & 3.17 ( 0.19 ) & 0.61 ( 0.01 ) & 0.01 & 18.88 & 0.80\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 13.62 ( 1.31 ) & 2.9 ( 0.17 ) & 0.59 ( 0.01 ) & 0.04 & 15.72 & 0.64\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 16.89 ( 1.14 ) & 1.1 ( 0.13 ) & 0.64 ( 0 ) & 0.11 & 20.79 & 0.68\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 19.43 ( 1.02 ) & 0.42 ( 0.08 ) & 0.66 ( 0 ) & 0.12 & 24.01 & 0.71\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 27.39 ( 2.11 ) & 3.38 ( 0.17 ) & 0.63 ( 0.01 ) & 0.01 & 29.01 & 0.87\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 26.17 ( 2.4 ) & 3.34 ( 0.16 ) & 0.59 ( 0.01 ) & 0.02 & 27.83 & 0.84\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 17.05 ( 1.31 ) | 3.17 ( 0.19 ) | 0.61 ( 0.01 ) | 0.01 | 18.88 | 0.80 |\n",
+       "| 2 | 100 | 50 | 0.1 | 13.62 ( 1.31 ) | 2.9 ( 0.17 ) | 0.59 ( 0.01 ) | 0.04 | 15.72 | 0.64 |\n",
+       "| 3 | 500 | 50 | 0.1 | 16.89 ( 1.14 ) | 1.1 ( 0.13 ) | 0.64 ( 0 ) | 0.11 | 20.79 | 0.68 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 19.43 ( 1.02 ) | 0.42 ( 0.08 ) | 0.66 ( 0 ) | 0.12 | 24.01 | 0.71 |\n",
+       "| 5 | 50 | 100 | 0.1 | 27.39 ( 2.11 ) | 3.38 ( 0.17 ) | 0.63 ( 0.01 ) | 0.01 | 29.01 | 0.87 |\n",
+       "| 6 | 100 | 100 | 0.1 | 26.17 ( 2.4 ) | 3.34 ( 0.16 ) | 0.59 ( 0.01 ) | 0.02 | 27.83 | 0.84 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 17.05 ( 1.31 ) 3.17 ( 0.19 ) 0.61 ( 0.01 ) 0.01 18.88      0.80\n",
+       "2 100  50  0.1 13.62 ( 1.31 ) 2.9 ( 0.17 )  0.59 ( 0.01 ) 0.04 15.72      0.64\n",
+       "3 500  50  0.1 16.89 ( 1.14 ) 1.1 ( 0.13 )  0.64 ( 0 )    0.11 20.79      0.68\n",
+       "4 1000 50  0.1 19.43 ( 1.02 ) 0.42 ( 0.08 ) 0.66 ( 0 )    0.12 24.01      0.71\n",
+       "5 50   100 0.1 27.39 ( 2.11 ) 3.38 ( 0.17 ) 0.63 ( 0.01 ) 0.01 29.01      0.87\n",
+       "6 100  100 0.1 26.17 ( 2.4 )  3.34 ( 0.16 ) 0.59 ( 0.01 ) 0.02 27.83      0.84"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>15.52 ( 1.4 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.28</td><td>20.52</td><td>0.57</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>14.84 ( 1.41 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.29</td><td>19.84</td><td>0.56</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>0.63 ( 0.01 )</td><td>0.02</td><td>80.57</td><td>0.92</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>0.74 ( 0.01 )</td><td>0.06</td><td>46.93</td><td>0.79</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>18.95 ( 1.93 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.25</td><td>23.95</td><td>0.60</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>15.24 ( 1.68 )</td><td>0 ( 0 )</td><td>0.92 ( 0 )</td><td>0.29</td><td>20.24</td><td>0.52</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 15.52 ( 1.4 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.28 & 20.52 & 0.57\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 14.84 ( 1.41 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.29 & 19.84 & 0.56\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 0.63 ( 0.01 ) & 0.02 & 80.57 & 0.92\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 0.74 ( 0.01 ) & 0.06 & 46.93 & 0.79\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 18.95 ( 1.93 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.25 & 23.95 & 0.60\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 15.24 ( 1.68 ) & 0 ( 0 ) & 0.92 ( 0 ) & 0.29 & 20.24 & 0.52\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 15.52 ( 1.4 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.28 | 20.52 | 0.57 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 14.84 ( 1.41 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.29 | 19.84 | 0.56 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 0.63 ( 0.01 ) | 0.02 | 80.57 | 0.92 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 0.74 ( 0.01 ) | 0.06 | 46.93 | 0.79 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 18.95 ( 1.93 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.25 | 23.95 | 0.60 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 15.24 ( 1.68 ) | 0 ( 0 ) | 0.92 ( 0 ) | 0.29 | 20.24 | 0.52 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC           Stab num_select\n",
+       "75 500  500  0.9 15.52 ( 1.4 )  0 ( 0 )       0.91 ( 0 )    0.28 20.52     \n",
+       "76 1000 500  0.9 14.84 ( 1.41 ) 0 ( 0 )       0.92 ( 0 )    0.29 19.84     \n",
+       "77 50   1000 0.9 78.65 ( 8.46 ) 3.08 ( 0.12 ) 0.63 ( 0.01 ) 0.02 80.57     \n",
+       "78 100  1000 0.9 43.94 ( 6.95 ) 2.01 ( 0.11 ) 0.74 ( 0.01 ) 0.06 46.93     \n",
+       "79 500  1000 0.9 18.95 ( 1.93 ) 0 ( 0 )       0.91 ( 0 )    0.25 23.95     \n",
+       "80 1000 1000 0.9 15.24 ( 1.68 ) 0 ( 0 )       0.92 ( 0 )    0.29 20.24     \n",
+       "   FDR \n",
+       "75 0.57\n",
+       "76 0.56\n",
+       "77 0.92\n",
+       "78 0.79\n",
+       "79 0.60\n",
+       "80 0.52"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 0 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 0 × 13\n",
+       "\\begin{tabular}{lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       " <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 0 × 13\n",
+       "\n",
+       "| N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab ROC FP FN num_select FDR ROC_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.01</td><td>0.61 ( 0.01 )</td><td>17.05 ( 1.31 )</td><td>3.17 ( 0.19 )</td><td>18.88</td><td>0.8 </td><td>0.61</td><td>17.05</td><td>3.17</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.04</td><td>0.59 ( 0.01 )</td><td>13.62 ( 1.31 )</td><td>2.9 ( 0.17 ) </td><td>15.72</td><td>0.64</td><td>0.59</td><td>13.62</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.11</td><td>0.64 ( 0 )   </td><td>16.89 ( 1.14 )</td><td>1.1 ( 0.13 ) </td><td>20.79</td><td>0.68</td><td>0.64</td><td>16.89</td><td>1.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.12</td><td>0.66 ( 0 )   </td><td>19.43 ( 1.02 )</td><td>0.42 ( 0.08 )</td><td>24.01</td><td>0.71</td><td>0.66</td><td>19.43</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>27.39 ( 2.11 )</td><td>3.38 ( 0.17 )</td><td>29.01</td><td>0.87</td><td>0.63</td><td>27.39</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.02</td><td>0.59 ( 0.01 )</td><td>26.17 ( 2.4 ) </td><td>3.34 ( 0.16 )</td><td>27.83</td><td>0.84</td><td>0.59</td><td>26.17</td><td>3.34</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.01 & 0.61 ( 0.01 ) & 17.05 ( 1.31 ) & 3.17 ( 0.19 ) & 18.88 & 0.8  & 0.61 & 17.05 & 3.17\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.04 & 0.59 ( 0.01 ) & 13.62 ( 1.31 ) & 2.9 ( 0.17 )  & 15.72 & 0.64 & 0.59 & 13.62 & 2.90\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.11 & 0.64 ( 0 )    & 16.89 ( 1.14 ) & 1.1 ( 0.13 )  & 20.79 & 0.68 & 0.64 & 16.89 & 1.10\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.12 & 0.66 ( 0 )    & 19.43 ( 1.02 ) & 0.42 ( 0.08 ) & 24.01 & 0.71 & 0.66 & 19.43 & 0.42\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.01 & 0.63 ( 0.01 ) & 27.39 ( 2.11 ) & 3.38 ( 0.17 ) & 29.01 & 0.87 & 0.63 & 27.39 & 3.38\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.02 & 0.59 ( 0.01 ) & 26.17 ( 2.4 )  & 3.34 ( 0.16 ) & 27.83 & 0.84 & 0.59 & 26.17 & 3.34\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.01 | 0.61 ( 0.01 ) | 17.05 ( 1.31 ) | 3.17 ( 0.19 ) | 18.88 | 0.8  | 0.61 | 17.05 | 3.17 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.04 | 0.59 ( 0.01 ) | 13.62 ( 1.31 ) | 2.9 ( 0.17 )  | 15.72 | 0.64 | 0.59 | 13.62 | 2.90 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.11 | 0.64 ( 0 )    | 16.89 ( 1.14 ) | 1.1 ( 0.13 )  | 20.79 | 0.68 | 0.64 | 16.89 | 1.10 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.12 | 0.66 ( 0 )    | 19.43 ( 1.02 ) | 0.42 ( 0.08 ) | 24.01 | 0.71 | 0.66 | 19.43 | 0.42 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.01 | 0.63 ( 0.01 ) | 27.39 ( 2.11 ) | 3.38 ( 0.17 ) | 29.01 | 0.87 | 0.63 | 27.39 | 3.38 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.02 | 0.59 ( 0.01 ) | 26.17 ( 2.4 )  | 3.34 ( 0.16 ) | 27.83 | 0.84 | 0.59 | 26.17 | 3.34 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.01 0.61 ( 0.01 ) 17.05 ( 1.31 ) 3.17 ( 0.19 )\n",
+       "2  100  50 0.1  0.50  0.04 0.59 ( 0.01 ) 13.62 ( 1.31 ) 2.9 ( 0.17 ) \n",
+       "3  500  50 0.1  0.10  0.11 0.64 ( 0 )    16.89 ( 1.14 ) 1.1 ( 0.13 ) \n",
+       "4 1000  50 0.1  0.05  0.12 0.66 ( 0 )    19.43 ( 1.02 ) 0.42 ( 0.08 )\n",
+       "5   50 100 0.1  2.00  0.01 0.63 ( 0.01 ) 27.39 ( 2.11 ) 3.38 ( 0.17 )\n",
+       "6  100 100 0.1  1.00  0.02 0.59 ( 0.01 ) 26.17 ( 2.4 )  3.34 ( 0.16 )\n",
+       "  num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1 18.88      0.8  0.61     17.05   3.17   \n",
+       "2 15.72      0.64 0.59     13.62   2.90   \n",
+       "3 20.79      0.68 0.64     16.89   1.10   \n",
+       "4 24.01      0.71 0.66     19.43   0.42   \n",
+       "5 29.01      0.87 0.63     27.39   3.38   \n",
+       "6 27.83      0.84 0.59     26.17   3.34   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.52 ( 1.4 ) </td><td>0 ( 0 )      </td><td>20.52</td><td>0.57</td><td>0.91</td><td>15.52</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.29</td><td>0.92 ( 0 )   </td><td>14.84 ( 1.41 )</td><td>0 ( 0 )      </td><td>19.84</td><td>0.56</td><td>0.92</td><td>14.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.02</td><td>0.63 ( 0.01 )</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>80.57</td><td>0.92</td><td>0.63</td><td>78.65</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.06</td><td>0.74 ( 0.01 )</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>46.93</td><td>0.79</td><td>0.74</td><td>43.94</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.25</td><td>0.91 ( 0 )   </td><td>18.95 ( 1.93 )</td><td>0 ( 0 )      </td><td>23.95</td><td>0.6 </td><td>0.91</td><td>18.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.29</td><td>0.92 ( 0 )   </td><td>15.24 ( 1.68 )</td><td>0 ( 0 )      </td><td>20.24</td><td>0.52</td><td>0.92</td><td>15.24</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.28 & 0.91 ( 0 )    & 15.52 ( 1.4 )  & 0 ( 0 )       & 20.52 & 0.57 & 0.91 & 15.52 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.29 & 0.92 ( 0 )    & 14.84 ( 1.41 ) & 0 ( 0 )       & 19.84 & 0.56 & 0.92 & 14.84 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.02 & 0.63 ( 0.01 ) & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 80.57 & 0.92 & 0.63 & 78.65 & 3.08\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.06 & 0.74 ( 0.01 ) & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 46.93 & 0.79 & 0.74 & 43.94 & 2.01\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.25 & 0.91 ( 0 )    & 18.95 ( 1.93 ) & 0 ( 0 )       & 23.95 & 0.6  & 0.91 & 18.95 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.29 & 0.92 ( 0 )    & 15.24 ( 1.68 ) & 0 ( 0 )       & 20.24 & 0.52 & 0.92 & 15.24 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.28 | 0.91 ( 0 )    | 15.52 ( 1.4 )  | 0 ( 0 )       | 20.52 | 0.57 | 0.91 | 15.52 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.29 | 0.92 ( 0 )    | 14.84 ( 1.41 ) | 0 ( 0 )       | 19.84 | 0.56 | 0.92 | 14.84 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.02 | 0.63 ( 0.01 ) | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 80.57 | 0.92 | 0.63 | 78.65 | 3.08 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.06 | 0.74 ( 0.01 ) | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 46.93 | 0.79 | 0.74 | 43.94 | 2.01 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.25 | 0.91 ( 0 )    | 18.95 ( 1.93 ) | 0 ( 0 )       | 23.95 | 0.6  | 0.91 | 18.95 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.29 | 0.92 ( 0 )    | 15.24 ( 1.68 ) | 0 ( 0 )       | 20.24 | 0.52 | 0.92 | 15.24 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.28 0.91 ( 0 )    15.52 ( 1.4 )  0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.29 0.92 ( 0 )    14.84 ( 1.41 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.02 0.63 ( 0.01 ) 78.65 ( 8.46 ) 3.08 ( 0.12 )\n",
+       "78  100 1000 0.9  10.0  0.06 0.74 ( 0.01 ) 43.94 ( 6.95 ) 2.01 ( 0.11 )\n",
+       "79  500 1000 0.9   2.0  0.25 0.91 ( 0 )    18.95 ( 1.93 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.29 0.92 ( 0 )    15.24 ( 1.68 ) 0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75 20.52      0.57 0.91     15.52   0.00   \n",
+       "76 19.84      0.56 0.92     14.84   0.00   \n",
+       "77 80.57      0.92 0.63     78.65   3.08   \n",
+       "78 46.93      0.79 0.74     43.94   2.01   \n",
+       "79 23.95      0.6  0.91     18.95   0.00   \n",
+       "80 20.24      0.52 0.92     15.24   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.01</td><td>0.61 ( 0.01 )</td><td>17.05 ( 1.31 )  </td><td>3.17 ( 0.19 )</td><td> 18.88</td><td>0.8 </td><td>0.61</td><td> 17.05</td><td>3.17</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.04</td><td>0.59 ( 0.01 )</td><td>13.62 ( 1.31 )  </td><td>2.9 ( 0.17 ) </td><td> 15.72</td><td>0.64</td><td>0.59</td><td> 13.62</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.11</td><td>0.64 ( 0 )   </td><td>16.89 ( 1.14 )  </td><td>1.1 ( 0.13 ) </td><td> 20.79</td><td>0.68</td><td>0.64</td><td> 16.89</td><td>1.10</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.12</td><td>0.66 ( 0 )   </td><td>19.43 ( 1.02 )  </td><td>0.42 ( 0.08 )</td><td> 24.01</td><td>0.71</td><td>0.66</td><td> 19.43</td><td>0.42</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>27.39 ( 2.11 )  </td><td>3.38 ( 0.17 )</td><td> 29.01</td><td>0.87</td><td>0.63</td><td> 27.39</td><td>3.38</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.02</td><td>0.59 ( 0.01 )</td><td>26.17 ( 2.4 )   </td><td>3.34 ( 0.16 )</td><td> 27.83</td><td>0.84</td><td>0.59</td><td> 26.17</td><td>3.34</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.09</td><td>0.62 ( 0 )   </td><td>22.75 ( 1.87 )  </td><td>1.61 ( 0.13 )</td><td> 26.14</td><td>0.69</td><td>0.62</td><td> 22.75</td><td>1.61</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.10</td><td>0.64 ( 0 )   </td><td>28.32 ( 1.88 )  </td><td>0.66 ( 0.1 ) </td><td> 32.66</td><td>0.77</td><td>0.64</td><td> 28.32</td><td>0.66</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.61 ( 0.01 )</td><td>69.03 ( 7.45 )  </td><td>4.13 ( 0.19 )</td><td> 69.90</td><td>0.96</td><td>0.61</td><td> 69.03</td><td>4.13</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.00</td><td>0.57 ( 0.01 )</td><td>70.64 ( 9.13 )  </td><td>3.91 ( 0.17 )</td><td> 71.73</td><td>0.91</td><td>0.57</td><td> 70.64</td><td>3.91</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.10</td><td>0.61 ( 0.01 )</td><td>20.55 ( 4.33 )  </td><td>3.15 ( 0.1 ) </td><td> 22.40</td><td>0.62</td><td>0.61</td><td> 20.55</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.12</td><td>0.63 ( 0 )   </td><td>26.03 ( 3.45 )  </td><td>2.38 ( 0.11 )</td><td> 28.65</td><td>0.62</td><td>0.63</td><td> 26.03</td><td>2.38</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.00</td><td>0.59 ( 0.01 )</td><td>103.61 ( 10.54 )</td><td>4.34 ( 0.18 )</td><td>104.27</td><td>0.98</td><td>0.59</td><td>103.61</td><td>4.34</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.56 ( 0.01 )</td><td>103.21 ( 12.75 )</td><td>4.15 ( 0.17 )</td><td>104.06</td><td>0.95</td><td>0.56</td><td>103.21</td><td>4.15</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.07</td><td>0.61 ( 0 )   </td><td>29.41 ( 7.4 )   </td><td>3.25 ( 0.09 )</td><td> 31.16</td><td>0.62</td><td>0.61</td><td> 29.41</td><td>3.25</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.18</td><td>0.62 ( 0 )   </td><td>16.15 ( 2.77 )  </td><td>2.73 ( 0.09 )</td><td> 18.42</td><td>0.53</td><td>0.62</td><td> 16.15</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.08</td><td>0.68 ( 0.01 )</td><td>14.8 ( 1.01 )   </td><td>1.73 ( 0.15 )</td><td> 18.07</td><td>0.69</td><td>0.68</td><td> 14.80</td><td>1.73</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.15</td><td>0.77 ( 0.01 )</td><td>15.15 ( 1 )     </td><td>0.64 ( 0.09 )</td><td> 19.51</td><td>0.65</td><td>0.77</td><td> 15.15</td><td>0.64</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.35</td><td>0.85 ( 0 )   </td><td>8.95 ( 0.69 )   </td><td>0 ( 0 )      </td><td> 13.95</td><td>0.46</td><td>0.85</td><td>  8.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.33</td><td>0.86 ( 0 )   </td><td>9.66 ( 0.93 )   </td><td>0 ( 0 )      </td><td> 14.66</td><td>0.44</td><td>0.86</td><td>  9.66</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.05</td><td>0.67 ( 0.01 )</td><td>23.92 ( 2 )     </td><td>2.13 ( 0.15 )</td><td> 26.79</td><td>0.77</td><td>0.67</td><td> 23.92</td><td>2.13</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.74 ( 0.01 )</td><td>17.75 ( 1.51 )  </td><td>1.13 ( 0.11 )</td><td> 21.62</td><td>0.67</td><td>0.74</td><td> 17.75</td><td>1.13</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.32</td><td>0.86 ( 0 )   </td><td>11.53 ( 1.02 )  </td><td>0.01 ( 0.01 )</td><td> 16.52</td><td>0.51</td><td>0.86</td><td> 11.53</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.33</td><td>0.86 ( 0 )   </td><td>11.15 ( 1.03 )  </td><td>0 ( 0 )      </td><td> 16.15</td><td>0.49</td><td>0.86</td><td> 11.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.01</td><td>0.6 ( 0.01 ) </td><td>81.07 ( 6.91 )  </td><td>2.92 ( 0.14 )</td><td> 83.15</td><td>0.93</td><td>0.60</td><td> 81.07</td><td>2.92</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.06</td><td>0.66 ( 0.01 )</td><td>38.14 ( 5.11 )  </td><td>2.16 ( 0.13 )</td><td> 40.98</td><td>0.78</td><td>0.66</td><td> 38.14</td><td>2.16</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.22</td><td>0.84 ( 0 )   </td><td>20.87 ( 1.93 )  </td><td>0.02 ( 0.01 )</td><td> 25.85</td><td>0.63</td><td>0.84</td><td> 20.87</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.29</td><td>0.86 ( 0 )   </td><td>15.35 ( 1.6 )   </td><td>0 ( 0 )      </td><td> 20.35</td><td>0.53</td><td>0.86</td><td> 15.35</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.01</td><td>0.63 ( 0.01 )</td><td>104.78 ( 9.53 ) </td><td>3.37 ( 0.14 )</td><td>106.41</td><td>0.96</td><td>0.63</td><td>104.78</td><td>3.37</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.04</td><td>0.64 ( 0.01 )</td><td>55.95 ( 7.57 )  </td><td>2.5 ( 0.13 ) </td><td> 58.45</td><td>0.85</td><td>0.64</td><td> 55.95</td><td>2.50</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.34</td><td>0.91 ( 0 )   </td><td>9.34 ( 0.72 ) </td><td>0 ( 0 )      </td><td>14.34</td><td>0.48</td><td>0.91</td><td> 9.34</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.36</td><td>0.91 ( 0 )   </td><td>8.58 ( 0.78 ) </td><td>0 ( 0 )      </td><td>13.58</td><td>0.42</td><td>0.91</td><td> 8.58</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.07</td><td>0.69 ( 0.01 )</td><td>24.95 ( 1.99 )</td><td>1.47 ( 0.1 ) </td><td>28.48</td><td>0.76</td><td>0.69</td><td>24.95</td><td>1.47</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.17</td><td>0.79 ( 0.01 )</td><td>15.63 ( 1.26 )</td><td>0.96 ( 0.11 )</td><td>19.67</td><td>0.63</td><td>0.79</td><td>15.63</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.34</td><td>0.9 ( 0 )    </td><td>10.78 ( 0.81 )</td><td>0.01 ( 0.01 )</td><td>15.77</td><td>0.5 </td><td>0.90</td><td>10.78</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.38</td><td>0.91 ( 0 )   </td><td>9.42 ( 0.9 )  </td><td>0 ( 0 )      </td><td>14.42</td><td>0.44</td><td>0.91</td><td> 9.42</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>66.01 ( 6.05 )</td><td>2.75 ( 0.12 )</td><td>68.26</td><td>0.92</td><td>0.62</td><td>66.01</td><td>2.75</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.10</td><td>0.74 ( 0.01 )</td><td>25.15 ( 2.8 ) </td><td>1.84 ( 0.12 )</td><td>28.31</td><td>0.71</td><td>0.74</td><td>25.15</td><td>1.84</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.25</td><td>0.9 ( 0 )    </td><td>17.68 ( 1.71 )</td><td>0 ( 0 )      </td><td>22.68</td><td>0.59</td><td>0.90</td><td>17.68</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.31</td><td>0.91 ( 0 )   </td><td>13.6 ( 1.43 ) </td><td>0 ( 0 )      </td><td>18.60</td><td>0.51</td><td>0.91</td><td>13.60</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>0.62 ( 0.01 )</td><td>86.38 ( 8.91 )</td><td>3.09 ( 0.13 )</td><td>88.29</td><td>0.93</td><td>0.62</td><td>86.38</td><td>3.09</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.06</td><td>0.69 ( 0.01 )</td><td>44.56 ( 6.27 )</td><td>2.12 ( 0.11 )</td><td>47.44</td><td>0.82</td><td>0.69</td><td>44.56</td><td>2.12</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.23</td><td>0.9 ( 0 )    </td><td>20.2 ( 1.89 ) </td><td>0.03 ( 0.02 )</td><td>25.17</td><td>0.62</td><td>0.90</td><td>20.20</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.27</td><td>0.91 ( 0 )   </td><td>16.62 ( 1.81 )</td><td>0 ( 0 )      </td><td>21.62</td><td>0.54</td><td>0.91</td><td>16.62</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.14</td><td>0.74 ( 0.01 )</td><td>12.38 ( 0.9 ) </td><td>1.46 ( 0.1 ) </td><td>15.92</td><td>0.63</td><td>0.74</td><td>12.38</td><td>1.46</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.21</td><td>0.84 ( 0.01 )</td><td>12.36 ( 0.84 )</td><td>0.46 ( 0.08 )</td><td>16.90</td><td>0.58</td><td>0.84</td><td>12.36</td><td>0.46</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.39</td><td>0.92 ( 0 )   </td><td>7.89 ( 0.68 ) </td><td>0 ( 0 )      </td><td>12.89</td><td>0.42</td><td>0.92</td><td> 7.89</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.41</td><td>0.92 ( 0 )   </td><td>7.38 ( 0.78 ) </td><td>0 ( 0 )      </td><td>12.38</td><td>0.38</td><td>0.92</td><td> 7.38</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.09</td><td>0.7 ( 0.01 ) </td><td>19.37 ( 1.75 )</td><td>2.08 ( 0.13 )</td><td>22.29</td><td>0.75</td><td>0.70</td><td>19.37</td><td>2.08</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.20</td><td>0.81 ( 0.01 )</td><td>14.28 ( 1.07 )</td><td>0.93 ( 0.09 )</td><td>18.35</td><td>0.61</td><td>0.81</td><td>14.28</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.36</td><td>0.92 ( 0 )   </td><td>10.15 ( 0.82 )</td><td>0 ( 0 )      </td><td>15.15</td><td>0.48</td><td>0.92</td><td>10.15</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.38</td><td>0.93 ( 0 )   </td><td>9.4 ( 1.07 )  </td><td>0 ( 0 )      </td><td>14.40</td><td>0.42</td><td>0.93</td><td> 9.40</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.02</td><td>0.62 ( 0.01 )</td><td>62.39 ( 5.92 )</td><td>2.69 ( 0.14 )</td><td>64.70</td><td>0.9 </td><td>0.62</td><td>62.39</td><td>2.69</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.12</td><td>0.74 ( 0.01 )</td><td>21.21 ( 2.91 )</td><td>2.02 ( 0.12 )</td><td>24.19</td><td>0.69</td><td>0.74</td><td>21.21</td><td>2.02</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.28</td><td>0.91 ( 0 )   </td><td>15.52 ( 1.4 ) </td><td>0 ( 0 )      </td><td>20.52</td><td>0.57</td><td>0.91</td><td>15.52</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.29</td><td>0.92 ( 0 )   </td><td>14.84 ( 1.41 )</td><td>0 ( 0 )      </td><td>19.84</td><td>0.56</td><td>0.92</td><td>14.84</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.02</td><td>0.63 ( 0.01 )</td><td>78.65 ( 8.46 )</td><td>3.08 ( 0.12 )</td><td>80.57</td><td>0.92</td><td>0.63</td><td>78.65</td><td>3.08</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.06</td><td>0.74 ( 0.01 )</td><td>43.94 ( 6.95 )</td><td>2.01 ( 0.11 )</td><td>46.93</td><td>0.79</td><td>0.74</td><td>43.94</td><td>2.01</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.25</td><td>0.91 ( 0 )   </td><td>18.95 ( 1.93 )</td><td>0 ( 0 )      </td><td>23.95</td><td>0.6 </td><td>0.91</td><td>18.95</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.29</td><td>0.92 ( 0 )   </td><td>15.24 ( 1.68 )</td><td>0 ( 0 )      </td><td>20.24</td><td>0.52</td><td>0.92</td><td>15.24</td><td>0.00</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.01 & 0.61 ( 0.01 ) & 17.05 ( 1.31 )   & 3.17 ( 0.19 ) &  18.88 & 0.8  & 0.61 &  17.05 & 3.17\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.04 & 0.59 ( 0.01 ) & 13.62 ( 1.31 )   & 2.9 ( 0.17 )  &  15.72 & 0.64 & 0.59 &  13.62 & 2.90\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.11 & 0.64 ( 0 )    & 16.89 ( 1.14 )   & 1.1 ( 0.13 )  &  20.79 & 0.68 & 0.64 &  16.89 & 1.10\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.12 & 0.66 ( 0 )    & 19.43 ( 1.02 )   & 0.42 ( 0.08 ) &  24.01 & 0.71 & 0.66 &  19.43 & 0.42\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.01 & 0.63 ( 0.01 ) & 27.39 ( 2.11 )   & 3.38 ( 0.17 ) &  29.01 & 0.87 & 0.63 &  27.39 & 3.38\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.02 & 0.59 ( 0.01 ) & 26.17 ( 2.4 )    & 3.34 ( 0.16 ) &  27.83 & 0.84 & 0.59 &  26.17 & 3.34\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.09 & 0.62 ( 0 )    & 22.75 ( 1.87 )   & 1.61 ( 0.13 ) &  26.14 & 0.69 & 0.62 &  22.75 & 1.61\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.10 & 0.64 ( 0 )    & 28.32 ( 1.88 )   & 0.66 ( 0.1 )  &  32.66 & 0.77 & 0.64 &  28.32 & 0.66\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.00 & 0.61 ( 0.01 ) & 69.03 ( 7.45 )   & 4.13 ( 0.19 ) &  69.90 & 0.96 & 0.61 &  69.03 & 4.13\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.00 & 0.57 ( 0.01 ) & 70.64 ( 9.13 )   & 3.91 ( 0.17 ) &  71.73 & 0.91 & 0.57 &  70.64 & 3.91\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.10 & 0.61 ( 0.01 ) & 20.55 ( 4.33 )   & 3.15 ( 0.1 )  &  22.40 & 0.62 & 0.61 &  20.55 & 3.15\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.12 & 0.63 ( 0 )    & 26.03 ( 3.45 )   & 2.38 ( 0.11 ) &  28.65 & 0.62 & 0.63 &  26.03 & 2.38\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.00 & 0.59 ( 0.01 ) & 103.61 ( 10.54 ) & 4.34 ( 0.18 ) & 104.27 & 0.98 & 0.59 & 103.61 & 4.34\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.00 & 0.56 ( 0.01 ) & 103.21 ( 12.75 ) & 4.15 ( 0.17 ) & 104.06 & 0.95 & 0.56 & 103.21 & 4.15\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.07 & 0.61 ( 0 )    & 29.41 ( 7.4 )    & 3.25 ( 0.09 ) &  31.16 & 0.62 & 0.61 &  29.41 & 3.25\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.18 & 0.62 ( 0 )    & 16.15 ( 2.77 )   & 2.73 ( 0.09 ) &  18.42 & 0.53 & 0.62 &  16.15 & 2.73\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.08 & 0.68 ( 0.01 ) & 14.8 ( 1.01 )    & 1.73 ( 0.15 ) &  18.07 & 0.69 & 0.68 &  14.80 & 1.73\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.15 & 0.77 ( 0.01 ) & 15.15 ( 1 )      & 0.64 ( 0.09 ) &  19.51 & 0.65 & 0.77 &  15.15 & 0.64\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.35 & 0.85 ( 0 )    & 8.95 ( 0.69 )    & 0 ( 0 )       &  13.95 & 0.46 & 0.85 &   8.95 & 0.00\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.33 & 0.86 ( 0 )    & 9.66 ( 0.93 )    & 0 ( 0 )       &  14.66 & 0.44 & 0.86 &   9.66 & 0.00\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.05 & 0.67 ( 0.01 ) & 23.92 ( 2 )      & 2.13 ( 0.15 ) &  26.79 & 0.77 & 0.67 &  23.92 & 2.13\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.14 & 0.74 ( 0.01 ) & 17.75 ( 1.51 )   & 1.13 ( 0.11 ) &  21.62 & 0.67 & 0.74 &  17.75 & 1.13\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.32 & 0.86 ( 0 )    & 11.53 ( 1.02 )   & 0.01 ( 0.01 ) &  16.52 & 0.51 & 0.86 &  11.53 & 0.01\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.33 & 0.86 ( 0 )    & 11.15 ( 1.03 )   & 0 ( 0 )       &  16.15 & 0.49 & 0.86 &  11.15 & 0.00\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.01 & 0.6 ( 0.01 )  & 81.07 ( 6.91 )   & 2.92 ( 0.14 ) &  83.15 & 0.93 & 0.60 &  81.07 & 2.92\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.06 & 0.66 ( 0.01 ) & 38.14 ( 5.11 )   & 2.16 ( 0.13 ) &  40.98 & 0.78 & 0.66 &  38.14 & 2.16\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.22 & 0.84 ( 0 )    & 20.87 ( 1.93 )   & 0.02 ( 0.01 ) &  25.85 & 0.63 & 0.84 &  20.87 & 0.02\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.29 & 0.86 ( 0 )    & 15.35 ( 1.6 )    & 0 ( 0 )       &  20.35 & 0.53 & 0.86 &  15.35 & 0.00\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.01 & 0.63 ( 0.01 ) & 104.78 ( 9.53 )  & 3.37 ( 0.14 ) & 106.41 & 0.96 & 0.63 & 104.78 & 3.37\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.04 & 0.64 ( 0.01 ) & 55.95 ( 7.57 )   & 2.5 ( 0.13 )  &  58.45 & 0.85 & 0.64 &  55.95 & 2.50\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.34 & 0.91 ( 0 )    & 9.34 ( 0.72 )  & 0 ( 0 )       & 14.34 & 0.48 & 0.91 &  9.34 & 0.00\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.36 & 0.91 ( 0 )    & 8.58 ( 0.78 )  & 0 ( 0 )       & 13.58 & 0.42 & 0.91 &  8.58 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.07 & 0.69 ( 0.01 ) & 24.95 ( 1.99 ) & 1.47 ( 0.1 )  & 28.48 & 0.76 & 0.69 & 24.95 & 1.47\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.17 & 0.79 ( 0.01 ) & 15.63 ( 1.26 ) & 0.96 ( 0.11 ) & 19.67 & 0.63 & 0.79 & 15.63 & 0.96\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.34 & 0.9 ( 0 )     & 10.78 ( 0.81 ) & 0.01 ( 0.01 ) & 15.77 & 0.5  & 0.90 & 10.78 & 0.01\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.38 & 0.91 ( 0 )    & 9.42 ( 0.9 )   & 0 ( 0 )       & 14.42 & 0.44 & 0.91 &  9.42 & 0.00\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 0.62 ( 0.01 ) & 66.01 ( 6.05 ) & 2.75 ( 0.12 ) & 68.26 & 0.92 & 0.62 & 66.01 & 2.75\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.10 & 0.74 ( 0.01 ) & 25.15 ( 2.8 )  & 1.84 ( 0.12 ) & 28.31 & 0.71 & 0.74 & 25.15 & 1.84\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.25 & 0.9 ( 0 )     & 17.68 ( 1.71 ) & 0 ( 0 )       & 22.68 & 0.59 & 0.90 & 17.68 & 0.00\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.31 & 0.91 ( 0 )    & 13.6 ( 1.43 )  & 0 ( 0 )       & 18.60 & 0.51 & 0.91 & 13.60 & 0.00\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 0.62 ( 0.01 ) & 86.38 ( 8.91 ) & 3.09 ( 0.13 ) & 88.29 & 0.93 & 0.62 & 86.38 & 3.09\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.06 & 0.69 ( 0.01 ) & 44.56 ( 6.27 ) & 2.12 ( 0.11 ) & 47.44 & 0.82 & 0.69 & 44.56 & 2.12\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.23 & 0.9 ( 0 )     & 20.2 ( 1.89 )  & 0.03 ( 0.02 ) & 25.17 & 0.62 & 0.90 & 20.20 & 0.03\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.27 & 0.91 ( 0 )    & 16.62 ( 1.81 ) & 0 ( 0 )       & 21.62 & 0.54 & 0.91 & 16.62 & 0.00\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.14 & 0.74 ( 0.01 ) & 12.38 ( 0.9 )  & 1.46 ( 0.1 )  & 15.92 & 0.63 & 0.74 & 12.38 & 1.46\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.21 & 0.84 ( 0.01 ) & 12.36 ( 0.84 ) & 0.46 ( 0.08 ) & 16.90 & 0.58 & 0.84 & 12.36 & 0.46\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.39 & 0.92 ( 0 )    & 7.89 ( 0.68 )  & 0 ( 0 )       & 12.89 & 0.42 & 0.92 &  7.89 & 0.00\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.41 & 0.92 ( 0 )    & 7.38 ( 0.78 )  & 0 ( 0 )       & 12.38 & 0.38 & 0.92 &  7.38 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.09 & 0.7 ( 0.01 )  & 19.37 ( 1.75 ) & 2.08 ( 0.13 ) & 22.29 & 0.75 & 0.70 & 19.37 & 2.08\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.20 & 0.81 ( 0.01 ) & 14.28 ( 1.07 ) & 0.93 ( 0.09 ) & 18.35 & 0.61 & 0.81 & 14.28 & 0.93\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.36 & 0.92 ( 0 )    & 10.15 ( 0.82 ) & 0 ( 0 )       & 15.15 & 0.48 & 0.92 & 10.15 & 0.00\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.38 & 0.93 ( 0 )    & 9.4 ( 1.07 )   & 0 ( 0 )       & 14.40 & 0.42 & 0.93 &  9.40 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.02 & 0.62 ( 0.01 ) & 62.39 ( 5.92 ) & 2.69 ( 0.14 ) & 64.70 & 0.9  & 0.62 & 62.39 & 2.69\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.12 & 0.74 ( 0.01 ) & 21.21 ( 2.91 ) & 2.02 ( 0.12 ) & 24.19 & 0.69 & 0.74 & 21.21 & 2.02\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.28 & 0.91 ( 0 )    & 15.52 ( 1.4 )  & 0 ( 0 )       & 20.52 & 0.57 & 0.91 & 15.52 & 0.00\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.29 & 0.92 ( 0 )    & 14.84 ( 1.41 ) & 0 ( 0 )       & 19.84 & 0.56 & 0.92 & 14.84 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.02 & 0.63 ( 0.01 ) & 78.65 ( 8.46 ) & 3.08 ( 0.12 ) & 80.57 & 0.92 & 0.63 & 78.65 & 3.08\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.06 & 0.74 ( 0.01 ) & 43.94 ( 6.95 ) & 2.01 ( 0.11 ) & 46.93 & 0.79 & 0.74 & 43.94 & 2.01\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.25 & 0.91 ( 0 )    & 18.95 ( 1.93 ) & 0 ( 0 )       & 23.95 & 0.6  & 0.91 & 18.95 & 0.00\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.29 & 0.92 ( 0 )    & 15.24 ( 1.68 ) & 0 ( 0 )       & 20.24 & 0.52 & 0.92 & 15.24 & 0.00\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.01 | 0.61 ( 0.01 ) | 17.05 ( 1.31 )   | 3.17 ( 0.19 ) |  18.88 | 0.8  | 0.61 |  17.05 | 3.17 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.04 | 0.59 ( 0.01 ) | 13.62 ( 1.31 )   | 2.9 ( 0.17 )  |  15.72 | 0.64 | 0.59 |  13.62 | 2.90 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.11 | 0.64 ( 0 )    | 16.89 ( 1.14 )   | 1.1 ( 0.13 )  |  20.79 | 0.68 | 0.64 |  16.89 | 1.10 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.12 | 0.66 ( 0 )    | 19.43 ( 1.02 )   | 0.42 ( 0.08 ) |  24.01 | 0.71 | 0.66 |  19.43 | 0.42 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.01 | 0.63 ( 0.01 ) | 27.39 ( 2.11 )   | 3.38 ( 0.17 ) |  29.01 | 0.87 | 0.63 |  27.39 | 3.38 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.02 | 0.59 ( 0.01 ) | 26.17 ( 2.4 )    | 3.34 ( 0.16 ) |  27.83 | 0.84 | 0.59 |  26.17 | 3.34 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.09 | 0.62 ( 0 )    | 22.75 ( 1.87 )   | 1.61 ( 0.13 ) |  26.14 | 0.69 | 0.62 |  22.75 | 1.61 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.10 | 0.64 ( 0 )    | 28.32 ( 1.88 )   | 0.66 ( 0.1 )  |  32.66 | 0.77 | 0.64 |  28.32 | 0.66 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.00 | 0.61 ( 0.01 ) | 69.03 ( 7.45 )   | 4.13 ( 0.19 ) |  69.90 | 0.96 | 0.61 |  69.03 | 4.13 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.00 | 0.57 ( 0.01 ) | 70.64 ( 9.13 )   | 3.91 ( 0.17 ) |  71.73 | 0.91 | 0.57 |  70.64 | 3.91 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.10 | 0.61 ( 0.01 ) | 20.55 ( 4.33 )   | 3.15 ( 0.1 )  |  22.40 | 0.62 | 0.61 |  20.55 | 3.15 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.12 | 0.63 ( 0 )    | 26.03 ( 3.45 )   | 2.38 ( 0.11 ) |  28.65 | 0.62 | 0.63 |  26.03 | 2.38 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.00 | 0.59 ( 0.01 ) | 103.61 ( 10.54 ) | 4.34 ( 0.18 ) | 104.27 | 0.98 | 0.59 | 103.61 | 4.34 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.00 | 0.56 ( 0.01 ) | 103.21 ( 12.75 ) | 4.15 ( 0.17 ) | 104.06 | 0.95 | 0.56 | 103.21 | 4.15 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.07 | 0.61 ( 0 )    | 29.41 ( 7.4 )    | 3.25 ( 0.09 ) |  31.16 | 0.62 | 0.61 |  29.41 | 3.25 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.18 | 0.62 ( 0 )    | 16.15 ( 2.77 )   | 2.73 ( 0.09 ) |  18.42 | 0.53 | 0.62 |  16.15 | 2.73 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.08 | 0.68 ( 0.01 ) | 14.8 ( 1.01 )    | 1.73 ( 0.15 ) |  18.07 | 0.69 | 0.68 |  14.80 | 1.73 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.15 | 0.77 ( 0.01 ) | 15.15 ( 1 )      | 0.64 ( 0.09 ) |  19.51 | 0.65 | 0.77 |  15.15 | 0.64 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.35 | 0.85 ( 0 )    | 8.95 ( 0.69 )    | 0 ( 0 )       |  13.95 | 0.46 | 0.85 |   8.95 | 0.00 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.33 | 0.86 ( 0 )    | 9.66 ( 0.93 )    | 0 ( 0 )       |  14.66 | 0.44 | 0.86 |   9.66 | 0.00 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.05 | 0.67 ( 0.01 ) | 23.92 ( 2 )      | 2.13 ( 0.15 ) |  26.79 | 0.77 | 0.67 |  23.92 | 2.13 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.14 | 0.74 ( 0.01 ) | 17.75 ( 1.51 )   | 1.13 ( 0.11 ) |  21.62 | 0.67 | 0.74 |  17.75 | 1.13 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.32 | 0.86 ( 0 )    | 11.53 ( 1.02 )   | 0.01 ( 0.01 ) |  16.52 | 0.51 | 0.86 |  11.53 | 0.01 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.33 | 0.86 ( 0 )    | 11.15 ( 1.03 )   | 0 ( 0 )       |  16.15 | 0.49 | 0.86 |  11.15 | 0.00 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.01 | 0.6 ( 0.01 )  | 81.07 ( 6.91 )   | 2.92 ( 0.14 ) |  83.15 | 0.93 | 0.60 |  81.07 | 2.92 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.06 | 0.66 ( 0.01 ) | 38.14 ( 5.11 )   | 2.16 ( 0.13 ) |  40.98 | 0.78 | 0.66 |  38.14 | 2.16 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.22 | 0.84 ( 0 )    | 20.87 ( 1.93 )   | 0.02 ( 0.01 ) |  25.85 | 0.63 | 0.84 |  20.87 | 0.02 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.29 | 0.86 ( 0 )    | 15.35 ( 1.6 )    | 0 ( 0 )       |  20.35 | 0.53 | 0.86 |  15.35 | 0.00 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.01 | 0.63 ( 0.01 ) | 104.78 ( 9.53 )  | 3.37 ( 0.14 ) | 106.41 | 0.96 | 0.63 | 104.78 | 3.37 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.04 | 0.64 ( 0.01 ) | 55.95 ( 7.57 )   | 2.5 ( 0.13 )  |  58.45 | 0.85 | 0.64 |  55.95 | 2.50 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.34 | 0.91 ( 0 )    | 9.34 ( 0.72 )  | 0 ( 0 )       | 14.34 | 0.48 | 0.91 |  9.34 | 0.00 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.36 | 0.91 ( 0 )    | 8.58 ( 0.78 )  | 0 ( 0 )       | 13.58 | 0.42 | 0.91 |  8.58 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.07 | 0.69 ( 0.01 ) | 24.95 ( 1.99 ) | 1.47 ( 0.1 )  | 28.48 | 0.76 | 0.69 | 24.95 | 1.47 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.17 | 0.79 ( 0.01 ) | 15.63 ( 1.26 ) | 0.96 ( 0.11 ) | 19.67 | 0.63 | 0.79 | 15.63 | 0.96 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.34 | 0.9 ( 0 )     | 10.78 ( 0.81 ) | 0.01 ( 0.01 ) | 15.77 | 0.5  | 0.90 | 10.78 | 0.01 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.38 | 0.91 ( 0 )    | 9.42 ( 0.9 )   | 0 ( 0 )       | 14.42 | 0.44 | 0.91 |  9.42 | 0.00 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 0.62 ( 0.01 ) | 66.01 ( 6.05 ) | 2.75 ( 0.12 ) | 68.26 | 0.92 | 0.62 | 66.01 | 2.75 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.10 | 0.74 ( 0.01 ) | 25.15 ( 2.8 )  | 1.84 ( 0.12 ) | 28.31 | 0.71 | 0.74 | 25.15 | 1.84 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.25 | 0.9 ( 0 )     | 17.68 ( 1.71 ) | 0 ( 0 )       | 22.68 | 0.59 | 0.90 | 17.68 | 0.00 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.31 | 0.91 ( 0 )    | 13.6 ( 1.43 )  | 0 ( 0 )       | 18.60 | 0.51 | 0.91 | 13.60 | 0.00 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 0.62 ( 0.01 ) | 86.38 ( 8.91 ) | 3.09 ( 0.13 ) | 88.29 | 0.93 | 0.62 | 86.38 | 3.09 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.06 | 0.69 ( 0.01 ) | 44.56 ( 6.27 ) | 2.12 ( 0.11 ) | 47.44 | 0.82 | 0.69 | 44.56 | 2.12 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.23 | 0.9 ( 0 )     | 20.2 ( 1.89 )  | 0.03 ( 0.02 ) | 25.17 | 0.62 | 0.90 | 20.20 | 0.03 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.27 | 0.91 ( 0 )    | 16.62 ( 1.81 ) | 0 ( 0 )       | 21.62 | 0.54 | 0.91 | 16.62 | 0.00 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.14 | 0.74 ( 0.01 ) | 12.38 ( 0.9 )  | 1.46 ( 0.1 )  | 15.92 | 0.63 | 0.74 | 12.38 | 1.46 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.21 | 0.84 ( 0.01 ) | 12.36 ( 0.84 ) | 0.46 ( 0.08 ) | 16.90 | 0.58 | 0.84 | 12.36 | 0.46 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.39 | 0.92 ( 0 )    | 7.89 ( 0.68 )  | 0 ( 0 )       | 12.89 | 0.42 | 0.92 |  7.89 | 0.00 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.41 | 0.92 ( 0 )    | 7.38 ( 0.78 )  | 0 ( 0 )       | 12.38 | 0.38 | 0.92 |  7.38 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.09 | 0.7 ( 0.01 )  | 19.37 ( 1.75 ) | 2.08 ( 0.13 ) | 22.29 | 0.75 | 0.70 | 19.37 | 2.08 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.20 | 0.81 ( 0.01 ) | 14.28 ( 1.07 ) | 0.93 ( 0.09 ) | 18.35 | 0.61 | 0.81 | 14.28 | 0.93 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.36 | 0.92 ( 0 )    | 10.15 ( 0.82 ) | 0 ( 0 )       | 15.15 | 0.48 | 0.92 | 10.15 | 0.00 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.38 | 0.93 ( 0 )    | 9.4 ( 1.07 )   | 0 ( 0 )       | 14.40 | 0.42 | 0.93 |  9.40 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.02 | 0.62 ( 0.01 ) | 62.39 ( 5.92 ) | 2.69 ( 0.14 ) | 64.70 | 0.9  | 0.62 | 62.39 | 2.69 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.12 | 0.74 ( 0.01 ) | 21.21 ( 2.91 ) | 2.02 ( 0.12 ) | 24.19 | 0.69 | 0.74 | 21.21 | 2.02 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.28 | 0.91 ( 0 )    | 15.52 ( 1.4 )  | 0 ( 0 )       | 20.52 | 0.57 | 0.91 | 15.52 | 0.00 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.29 | 0.92 ( 0 )    | 14.84 ( 1.41 ) | 0 ( 0 )       | 19.84 | 0.56 | 0.92 | 14.84 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.02 | 0.63 ( 0.01 ) | 78.65 ( 8.46 ) | 3.08 ( 0.12 ) | 80.57 | 0.92 | 0.63 | 78.65 | 3.08 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.06 | 0.74 ( 0.01 ) | 43.94 ( 6.95 ) | 2.01 ( 0.11 ) | 46.93 | 0.79 | 0.74 | 43.94 | 2.01 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.25 | 0.91 ( 0 )    | 18.95 ( 1.93 ) | 0 ( 0 )       | 23.95 | 0.6  | 0.91 | 18.95 | 0.00 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.29 | 0.92 ( 0 )    | 15.24 ( 1.68 ) | 0 ( 0 )       | 20.24 | 0.52 | 0.92 | 15.24 | 0.00 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "1    50   50 0.1   1.00 0.01 0.61 ( 0.01 ) 17.05 ( 1.31 )   3.17 ( 0.19 )\n",
+       "2   100   50 0.1   0.50 0.04 0.59 ( 0.01 ) 13.62 ( 1.31 )   2.9 ( 0.17 ) \n",
+       "3   500   50 0.1   0.10 0.11 0.64 ( 0 )    16.89 ( 1.14 )   1.1 ( 0.13 ) \n",
+       "4  1000   50 0.1   0.05 0.12 0.66 ( 0 )    19.43 ( 1.02 )   0.42 ( 0.08 )\n",
+       "5    50  100 0.1   2.00 0.01 0.63 ( 0.01 ) 27.39 ( 2.11 )   3.38 ( 0.17 )\n",
+       "6   100  100 0.1   1.00 0.02 0.59 ( 0.01 ) 26.17 ( 2.4 )    3.34 ( 0.16 )\n",
+       "7   500  100 0.1   0.20 0.09 0.62 ( 0 )    22.75 ( 1.87 )   1.61 ( 0.13 )\n",
+       "8  1000  100 0.1   0.10 0.10 0.64 ( 0 )    28.32 ( 1.88 )   0.66 ( 0.1 ) \n",
+       "9    50  500 0.1  10.00 0.00 0.61 ( 0.01 ) 69.03 ( 7.45 )   4.13 ( 0.19 )\n",
+       "10  100  500 0.1   5.00 0.00 0.57 ( 0.01 ) 70.64 ( 9.13 )   3.91 ( 0.17 )\n",
+       "11  500  500 0.1   1.00 0.10 0.61 ( 0.01 ) 20.55 ( 4.33 )   3.15 ( 0.1 ) \n",
+       "12 1000  500 0.1   0.50 0.12 0.63 ( 0 )    26.03 ( 3.45 )   2.38 ( 0.11 )\n",
+       "13   50 1000 0.1  20.00 0.00 0.59 ( 0.01 ) 103.61 ( 10.54 ) 4.34 ( 0.18 )\n",
+       "14  100 1000 0.1  10.00 0.00 0.56 ( 0.01 ) 103.21 ( 12.75 ) 4.15 ( 0.17 )\n",
+       "15  500 1000 0.1   2.00 0.07 0.61 ( 0 )    29.41 ( 7.4 )    3.25 ( 0.09 )\n",
+       "16 1000 1000 0.1   1.00 0.18 0.62 ( 0 )    16.15 ( 2.77 )   2.73 ( 0.09 )\n",
+       "17   50   50 0.3   1.00 0.08 0.68 ( 0.01 ) 14.8 ( 1.01 )    1.73 ( 0.15 )\n",
+       "18  100   50 0.3   0.50 0.15 0.77 ( 0.01 ) 15.15 ( 1 )      0.64 ( 0.09 )\n",
+       "19  500   50 0.3   0.10 0.35 0.85 ( 0 )    8.95 ( 0.69 )    0 ( 0 )      \n",
+       "20 1000   50 0.3   0.05 0.33 0.86 ( 0 )    9.66 ( 0.93 )    0 ( 0 )      \n",
+       "21   50  100 0.3   2.00 0.05 0.67 ( 0.01 ) 23.92 ( 2 )      2.13 ( 0.15 )\n",
+       "22  100  100 0.3   1.00 0.14 0.74 ( 0.01 ) 17.75 ( 1.51 )   1.13 ( 0.11 )\n",
+       "23  500  100 0.3   0.20 0.32 0.86 ( 0 )    11.53 ( 1.02 )   0.01 ( 0.01 )\n",
+       "24 1000  100 0.3   0.10 0.33 0.86 ( 0 )    11.15 ( 1.03 )   0 ( 0 )      \n",
+       "25   50  500 0.3  10.00 0.01 0.6 ( 0.01 )  81.07 ( 6.91 )   2.92 ( 0.14 )\n",
+       "26  100  500 0.3   5.00 0.06 0.66 ( 0.01 ) 38.14 ( 5.11 )   2.16 ( 0.13 )\n",
+       "27  500  500 0.3   1.00 0.22 0.84 ( 0 )    20.87 ( 1.93 )   0.02 ( 0.01 )\n",
+       "28 1000  500 0.3   0.50 0.29 0.86 ( 0 )    15.35 ( 1.6 )    0 ( 0 )      \n",
+       "29   50 1000 0.3  20.00 0.01 0.63 ( 0.01 ) 104.78 ( 9.53 )  3.37 ( 0.14 )\n",
+       "30  100 1000 0.3  10.00 0.04 0.64 ( 0.01 ) 55.95 ( 7.57 )   2.5 ( 0.13 ) \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮                ⋮            \n",
+       "51  500   50 0.7   0.10 0.34 0.91 ( 0 )    9.34 ( 0.72 )    0 ( 0 )      \n",
+       "52 1000   50 0.7   0.05 0.36 0.91 ( 0 )    8.58 ( 0.78 )    0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.07 0.69 ( 0.01 ) 24.95 ( 1.99 )   1.47 ( 0.1 ) \n",
+       "54  100  100 0.7   1.00 0.17 0.79 ( 0.01 ) 15.63 ( 1.26 )   0.96 ( 0.11 )\n",
+       "55  500  100 0.7   0.20 0.34 0.9 ( 0 )     10.78 ( 0.81 )   0.01 ( 0.01 )\n",
+       "56 1000  100 0.7   0.10 0.38 0.91 ( 0 )    9.42 ( 0.9 )     0 ( 0 )      \n",
+       "57   50  500 0.7  10.00 0.02 0.62 ( 0.01 ) 66.01 ( 6.05 )   2.75 ( 0.12 )\n",
+       "58  100  500 0.7   5.00 0.10 0.74 ( 0.01 ) 25.15 ( 2.8 )    1.84 ( 0.12 )\n",
+       "59  500  500 0.7   1.00 0.25 0.9 ( 0 )     17.68 ( 1.71 )   0 ( 0 )      \n",
+       "60 1000  500 0.7   0.50 0.31 0.91 ( 0 )    13.6 ( 1.43 )    0 ( 0 )      \n",
+       "61   50 1000 0.7  20.00 0.01 0.62 ( 0.01 ) 86.38 ( 8.91 )   3.09 ( 0.13 )\n",
+       "62  100 1000 0.7  10.00 0.06 0.69 ( 0.01 ) 44.56 ( 6.27 )   2.12 ( 0.11 )\n",
+       "63  500 1000 0.7   2.00 0.23 0.9 ( 0 )     20.2 ( 1.89 )    0.03 ( 0.02 )\n",
+       "64 1000 1000 0.7   1.00 0.27 0.91 ( 0 )    16.62 ( 1.81 )   0 ( 0 )      \n",
+       "65   50   50 0.9   1.00 0.14 0.74 ( 0.01 ) 12.38 ( 0.9 )    1.46 ( 0.1 ) \n",
+       "66  100   50 0.9   0.50 0.21 0.84 ( 0.01 ) 12.36 ( 0.84 )   0.46 ( 0.08 )\n",
+       "67  500   50 0.9   0.10 0.39 0.92 ( 0 )    7.89 ( 0.68 )    0 ( 0 )      \n",
+       "68 1000   50 0.9   0.05 0.41 0.92 ( 0 )    7.38 ( 0.78 )    0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.09 0.7 ( 0.01 )  19.37 ( 1.75 )   2.08 ( 0.13 )\n",
+       "70  100  100 0.9   1.00 0.20 0.81 ( 0.01 ) 14.28 ( 1.07 )   0.93 ( 0.09 )\n",
+       "71  500  100 0.9   0.20 0.36 0.92 ( 0 )    10.15 ( 0.82 )   0 ( 0 )      \n",
+       "72 1000  100 0.9   0.10 0.38 0.93 ( 0 )    9.4 ( 1.07 )     0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.02 0.62 ( 0.01 ) 62.39 ( 5.92 )   2.69 ( 0.14 )\n",
+       "74  100  500 0.9   5.00 0.12 0.74 ( 0.01 ) 21.21 ( 2.91 )   2.02 ( 0.12 )\n",
+       "75  500  500 0.9   1.00 0.28 0.91 ( 0 )    15.52 ( 1.4 )    0 ( 0 )      \n",
+       "76 1000  500 0.9   0.50 0.29 0.92 ( 0 )    14.84 ( 1.41 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.02 0.63 ( 0.01 ) 78.65 ( 8.46 )   3.08 ( 0.12 )\n",
+       "78  100 1000 0.9  10.00 0.06 0.74 ( 0.01 ) 43.94 ( 6.95 )   2.01 ( 0.11 )\n",
+       "79  500 1000 0.9   2.00 0.25 0.91 ( 0 )    18.95 ( 1.93 )   0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.00 0.29 0.92 ( 0 )    15.24 ( 1.68 )   0 ( 0 )      \n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1   18.88     0.8  0.61      17.05  3.17   \n",
+       "2   15.72     0.64 0.59      13.62  2.90   \n",
+       "3   20.79     0.68 0.64      16.89  1.10   \n",
+       "4   24.01     0.71 0.66      19.43  0.42   \n",
+       "5   29.01     0.87 0.63      27.39  3.38   \n",
+       "6   27.83     0.84 0.59      26.17  3.34   \n",
+       "7   26.14     0.69 0.62      22.75  1.61   \n",
+       "8   32.66     0.77 0.64      28.32  0.66   \n",
+       "9   69.90     0.96 0.61      69.03  4.13   \n",
+       "10  71.73     0.91 0.57      70.64  3.91   \n",
+       "11  22.40     0.62 0.61      20.55  3.15   \n",
+       "12  28.65     0.62 0.63      26.03  2.38   \n",
+       "13 104.27     0.98 0.59     103.61  4.34   \n",
+       "14 104.06     0.95 0.56     103.21  4.15   \n",
+       "15  31.16     0.62 0.61      29.41  3.25   \n",
+       "16  18.42     0.53 0.62      16.15  2.73   \n",
+       "17  18.07     0.69 0.68      14.80  1.73   \n",
+       "18  19.51     0.65 0.77      15.15  0.64   \n",
+       "19  13.95     0.46 0.85       8.95  0.00   \n",
+       "20  14.66     0.44 0.86       9.66  0.00   \n",
+       "21  26.79     0.77 0.67      23.92  2.13   \n",
+       "22  21.62     0.67 0.74      17.75  1.13   \n",
+       "23  16.52     0.51 0.86      11.53  0.01   \n",
+       "24  16.15     0.49 0.86      11.15  0.00   \n",
+       "25  83.15     0.93 0.60      81.07  2.92   \n",
+       "26  40.98     0.78 0.66      38.14  2.16   \n",
+       "27  25.85     0.63 0.84      20.87  0.02   \n",
+       "28  20.35     0.53 0.86      15.35  0.00   \n",
+       "29 106.41     0.96 0.63     104.78  3.37   \n",
+       "30  58.45     0.85 0.64      55.95  2.50   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51 14.34      0.48 0.91      9.34   0.00   \n",
+       "52 13.58      0.42 0.91      8.58   0.00   \n",
+       "53 28.48      0.76 0.69     24.95   1.47   \n",
+       "54 19.67      0.63 0.79     15.63   0.96   \n",
+       "55 15.77      0.5  0.90     10.78   0.01   \n",
+       "56 14.42      0.44 0.91      9.42   0.00   \n",
+       "57 68.26      0.92 0.62     66.01   2.75   \n",
+       "58 28.31      0.71 0.74     25.15   1.84   \n",
+       "59 22.68      0.59 0.90     17.68   0.00   \n",
+       "60 18.60      0.51 0.91     13.60   0.00   \n",
+       "61 88.29      0.93 0.62     86.38   3.09   \n",
+       "62 47.44      0.82 0.69     44.56   2.12   \n",
+       "63 25.17      0.62 0.90     20.20   0.03   \n",
+       "64 21.62      0.54 0.91     16.62   0.00   \n",
+       "65 15.92      0.63 0.74     12.38   1.46   \n",
+       "66 16.90      0.58 0.84     12.36   0.46   \n",
+       "67 12.89      0.42 0.92      7.89   0.00   \n",
+       "68 12.38      0.38 0.92      7.38   0.00   \n",
+       "69 22.29      0.75 0.70     19.37   2.08   \n",
+       "70 18.35      0.61 0.81     14.28   0.93   \n",
+       "71 15.15      0.48 0.92     10.15   0.00   \n",
+       "72 14.40      0.42 0.93      9.40   0.00   \n",
+       "73 64.70      0.9  0.62     62.39   2.69   \n",
+       "74 24.19      0.69 0.74     21.21   2.02   \n",
+       "75 20.52      0.57 0.91     15.52   0.00   \n",
+       "76 19.84      0.56 0.92     14.84   0.00   \n",
+       "77 80.57      0.92 0.63     78.65   3.08   \n",
+       "78 46.93      0.79 0.74     43.94   2.01   \n",
+       "79 23.95      0.6  0.91     18.95   0.00   \n",
+       "80 20.24      0.52 0.92     15.24   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_Elnet_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/2.3_sim_block_rf_binary_update.ipynb b/simulations/notebooks_sim_bin/2.3_sim_block_rf_binary_update.ipynb
new file mode 100644
index 0000000..6322392
--- /dev/null
+++ b/simulations/notebooks_sim_bin/2.3_sim_block_rf_binary_update.ipynb
@@ -0,0 +1,1067 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 62\"\n",
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+      "[1] \"indx: 64\"\n",
+      "[1] \"indx: 65\"\n",
+      "[1] \"indx: 66\"\n",
+      "[1] \"indx: 67\"\n",
+      "[1] \"indx: 68\"\n",
+      "[1] \"indx: 69\"\n",
+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    #if(i == 16) next \n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_RF_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_rf[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_rf$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_rf$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>2.4 ( 0.16 )</td><td>5.3 ( 0.11 )</td><td>1 ( 0 )</td><td>0.01</td><td>3.00</td><td>0.78</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>2.96 ( 0.21 )</td><td>4.29 ( 0.15 )</td><td>1 ( 0 )</td><td>0.12</td><td>4.63</td><td>0.62</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>3.28 ( 0.21 )</td><td>3.04 ( 0.12 )</td><td>1 ( 0 )</td><td>0.33</td><td>6.24</td><td>0.49</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>5.09 ( 0.21 )</td><td>5.6 ( 0.09 )</td><td>1 ( 0 )</td><td>0</td><td>5.48</td><td>0.93</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 2.4 ( 0.16 ) & 5.3 ( 0.11 ) & 1 ( 0 ) & 0.01 & 3.00 & 0.78\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 2.96 ( 0.21 ) & 4.29 ( 0.15 ) & 1 ( 0 ) & 0.12 & 4.63 & 0.62\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 3.28 ( 0.21 ) & 3.04 ( 0.12 ) & 1 ( 0 ) & 0.33 & 6.24 & 0.49\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 5.09 ( 0.21 ) & 5.6 ( 0.09 ) & 1 ( 0 ) & 0 & 5.48 & 0.93\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 2 | 100 | 50 | 0.1 | 2.4 ( 0.16 ) | 5.3 ( 0.11 ) | 1 ( 0 ) | 0.01 | 3.00 | 0.78 |\n",
+       "| 3 | 500 | 50 | 0.1 | 2.96 ( 0.21 ) | 4.29 ( 0.15 ) | 1 ( 0 ) | 0.12 | 4.63 | 0.62 |\n",
+       "| 4 | 1000 | 50 | 0.1 | 3.28 ( 0.21 ) | 3.04 ( 0.12 ) | 1 ( 0 ) | 0.33 | 6.24 | 0.49 |\n",
+       "| 5 | 50 | 100 | 0.1 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 6 | 100 | 100 | 0.1 | 5.09 ( 0.21 ) | 5.6 ( 0.09 ) | 1 ( 0 ) | 0 | 5.48 | 0.93 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC     Stab num_select FDR \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "2 100  50  0.1 2.4 ( 0.16 )  5.3 ( 0.11 )  1 ( 0 ) 0.01 3.00       0.78\n",
+       "3 500  50  0.1 2.96 ( 0.21 ) 4.29 ( 0.15 ) 1 ( 0 ) 0.12 4.63       0.62\n",
+       "4 1000 50  0.1 3.28 ( 0.21 ) 3.04 ( 0.12 ) 1 ( 0 ) 0.33 6.24       0.49\n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1 ( 0 ) NaN  0.00        NaN\n",
+       "6 100  100 0.1 5.09 ( 0.21 ) 5.6 ( 0.09 )  1 ( 0 ) 0    5.48       0.93"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>1 ( 0 )</td><td>0.07</td><td>27.64</td><td>0.88</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>1 ( 0 )</td><td>0.13</td><td>25.93</td><td>0.82</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>1 ( 0 )</td><td>NaN</td><td> 0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>1 ( 0 )</td><td>0</td><td>48.53</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>48.09 ( 0.7 )</td><td>2.94 ( 0.09 )</td><td>1 ( 0 )</td><td>0.04</td><td>51.15</td><td>0.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>45.6 ( 0.72 )</td><td>1.63 ( 0.08 )</td><td>1 ( 0 )</td><td>0.07</td><td>49.97</td><td>0.91</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 1 ( 0 ) & 0.07 & 27.64 & 0.88\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 1 ( 0 ) & 0.13 & 25.93 & 0.82\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 1 ( 0 ) & 6 ( 0 ) & 1 ( 0 ) & NaN &  0.00 &  NaN\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 1 ( 0 ) & 0 & 48.53 & 0.98\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 48.09 ( 0.7 ) & 2.94 ( 0.09 ) & 1 ( 0 ) & 0.04 & 51.15 & 0.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 45.6 ( 0.72 ) & 1.63 ( 0.08 ) & 1 ( 0 ) & 0.07 & 49.97 & 0.91\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 1 ( 0 ) | 0.07 | 27.64 | 0.88 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 1 ( 0 ) | 0.13 | 25.93 | 0.82 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 1 ( 0 ) | 6 ( 0 ) | 1 ( 0 ) | NaN |  0.00 |  NaN |\n",
+       "| 78 | 100 | 1000 | 0.9 | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 1 ( 0 ) | 0 | 48.53 | 0.98 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 48.09 ( 0.7 ) | 2.94 ( 0.09 ) | 1 ( 0 ) | 0.04 | 51.15 | 0.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 45.6 ( 0.72 ) | 1.63 ( 0.08 ) | 1 ( 0 ) | 0.07 | 49.97 | 0.91 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP             FN            ROC     Stab num_select FDR \n",
+       "75 500  500  0.9 24.29 ( 0.42 ) 2.65 ( 0.09 ) 1 ( 0 ) 0.07 27.64      0.88\n",
+       "76 1000 500  0.9 21.42 ( 0.42 ) 1.49 ( 0.08 ) 1 ( 0 ) 0.13 25.93      0.82\n",
+       "77 50   1000 0.9 1 ( 0 )        6 ( 0 )       1 ( 0 ) NaN   0.00       NaN\n",
+       "78 100  1000 0.9 47.48 ( 0.72 ) 4.95 ( 0.08 ) 1 ( 0 ) 0    48.53      0.98\n",
+       "79 500  1000 0.9 48.09 ( 0.7 )  2.94 ( 0.09 ) 1 ( 0 ) 0.04 51.15      0.94\n",
+       "80 1000 1000 0.9 45.6 ( 0.72 )  1.63 ( 0.08 ) 1 ( 0 ) 0.07 49.97      0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NaN</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 &   50 & 0.1 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t5 & 50 &  100 & 0.1 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t9 & 50 &  500 & 0.1 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t13 & 50 & 1000 & 0.1 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t17 & 50 &   50 & 0.3 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t21 & 50 &  100 & 0.3 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t25 & 50 &  500 & 0.3 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t29 & 50 & 1000 & 0.3 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t33 & 50 &   50 & 0.5 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t37 & 50 &  100 & 0.5 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t41 & 50 &  500 & 0.5 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t45 & 50 & 1000 & 0.5 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t49 & 50 &   50 & 0.7 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t53 & 50 &  100 & 0.7 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t57 & 50 &  500 & 0.7 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t61 & 50 & 1000 & 0.7 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t65 & 50 &   50 & 0.9 &  1 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t69 & 50 &  100 & 0.9 &  2 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t73 & 50 &  500 & 0.9 & 10 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 20 & NaN & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 |   50 | 0.1 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 5 | 50 |  100 | 0.1 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 9 | 50 |  500 | 0.1 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 13 | 50 | 1000 | 0.1 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 17 | 50 |   50 | 0.3 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 21 | 50 |  100 | 0.3 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 25 | 50 |  500 | 0.3 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 29 | 50 | 1000 | 0.3 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 33 | 50 |   50 | 0.5 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 37 | 50 |  100 | 0.5 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 41 | 50 |  500 | 0.5 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 45 | 50 | 1000 | 0.5 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 49 | 50 |   50 | 0.7 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 53 | 50 |  100 | 0.7 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 57 | 50 |  500 | 0.7 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 61 | 50 | 1000 | 0.7 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 65 | 50 |   50 | 0.9 |  1 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 69 | 50 |  100 | 0.9 |  2 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 73 | 50 |  500 | 0.9 | 10 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 20 | NaN | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "1  50   50 0.1   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "5  50  100 0.1   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "9  50  500 0.1  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "13 50 1000 0.1  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "17 50   50 0.3   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "21 50  100 0.3   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "25 50  500 0.3  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "29 50 1000 0.3  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "33 50   50 0.5   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0.5   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0.5  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0.5  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "49 50   50 0.7   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "53 50  100 0.7   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "57 50  500 0.7  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "61 50 1000 0.7  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "65 50   50 0.9   1    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "69 50  100 0.9   2    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "73 50  500 0.9  10    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "77 50 1000 0.9  20    NaN  1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean\n",
+       "1  1       6      \n",
+       "5  1       6      \n",
+       "9  1       6      \n",
+       "13 1       6      \n",
+       "17 1       6      \n",
+       "21 1       6      \n",
+       "25 1       6      \n",
+       "29 1       6      \n",
+       "33 1       6      \n",
+       "37 1       6      \n",
+       "41 1       6      \n",
+       "45 1       6      \n",
+       "49 1       6      \n",
+       "53 1       6      \n",
+       "57 1       6      \n",
+       "61 1       6      \n",
+       "65 1       6      \n",
+       "69 1       6      \n",
+       "73 1       6      \n",
+       "77 1       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.01</td><td>1 ( 0 )</td><td>2.4 ( 0.16 ) </td><td>5.3 ( 0.11 ) </td><td>3.00</td><td>0.78</td><td>1</td><td>2.40</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td>0.12</td><td>1 ( 0 )</td><td>2.96 ( 0.21 )</td><td>4.29 ( 0.15 )</td><td>4.63</td><td>0.62</td><td>1</td><td>2.96</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td>0.33</td><td>1 ( 0 )</td><td>3.28 ( 0.21 )</td><td>3.04 ( 0.12 )</td><td>6.24</td><td>0.49</td><td>1</td><td>3.28</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>1</td><td>1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.00</td><td>1 ( 0 )</td><td>5.09 ( 0.21 )</td><td>5.6 ( 0.09 ) </td><td>5.48</td><td>0.93</td><td>1</td><td>5.09</td><td>5.60</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.01 & 1 ( 0 ) & 2.4 ( 0.16 )  & 5.3 ( 0.11 )  & 3.00 & 0.78 & 1 & 2.40 & 5.30\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 & 0.12 & 1 ( 0 ) & 2.96 ( 0.21 ) & 4.29 ( 0.15 ) & 4.63 & 0.62 & 1 & 2.96 & 4.29\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 & 0.33 & 1 ( 0 ) & 3.28 ( 0.21 ) & 3.04 ( 0.12 ) & 6.24 & 0.49 & 1 & 3.28 & 3.04\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 1 & 1.00 & 6.00\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.00 & 1 ( 0 ) & 5.09 ( 0.21 ) & 5.6 ( 0.09 )  & 5.48 & 0.93 & 1 & 5.09 & 5.60\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.01 | 1 ( 0 ) | 2.4 ( 0.16 )  | 5.3 ( 0.11 )  | 3.00 | 0.78 | 1 | 2.40 | 5.30 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 | 0.12 | 1 ( 0 ) | 2.96 ( 0.21 ) | 4.29 ( 0.15 ) | 4.63 | 0.62 | 1 | 2.96 | 4.29 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 | 0.33 | 1 ( 0 ) | 3.28 ( 0.21 ) | 3.04 ( 0.12 ) | 6.24 | 0.49 | 1 | 3.28 | 3.04 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 1 | 1.00 | 6.00 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.00 | 1 ( 0 ) | 5.09 ( 0.21 ) | 5.6 ( 0.09 )  | 5.48 | 0.93 | 1 | 5.09 | 5.60 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC     FP            FN            num_select FDR \n",
+       "1   50  50 0.1  1.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "2  100  50 0.1  0.50  0.01 1 ( 0 ) 2.4 ( 0.16 )  5.3 ( 0.11 )  3.00       0.78\n",
+       "3  500  50 0.1  0.10  0.12 1 ( 0 ) 2.96 ( 0.21 ) 4.29 ( 0.15 ) 4.63       0.62\n",
+       "4 1000  50 0.1  0.05  0.33 1 ( 0 ) 3.28 ( 0.21 ) 3.04 ( 0.12 ) 6.24       0.49\n",
+       "5   50 100 0.1  2.00   NaN 1 ( 0 ) 1 ( 0 )       6 ( 0 )       0.00       NaN \n",
+       "6  100 100 0.1  1.00  0.00 1 ( 0 ) 5.09 ( 0.21 ) 5.6 ( 0.09 )  5.48       0.93\n",
+       "  ROC_mean FP_mean FN_mean\n",
+       "1 1        1.00    6.00   \n",
+       "2 1        2.40    5.30   \n",
+       "3 1        2.96    4.29   \n",
+       "4 1        3.28    3.04   \n",
+       "5 1        1.00    6.00   \n",
+       "6 1        5.09    5.60   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>27.64</td><td>0.88</td><td>1</td><td>24.29</td><td>2.65</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.13</td><td>1 ( 0 )</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>25.93</td><td>0.82</td><td>1</td><td>21.42</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.00</td><td>1 ( 0 )</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>48.53</td><td>0.98</td><td>1</td><td>47.48</td><td>4.95</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.04</td><td>1 ( 0 )</td><td>48.09 ( 0.7 ) </td><td>2.94 ( 0.09 )</td><td>51.15</td><td>0.94</td><td>1</td><td>48.09</td><td>2.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.07</td><td>1 ( 0 )</td><td>45.6 ( 0.72 ) </td><td>1.63 ( 0.08 )</td><td>49.97</td><td>0.91</td><td>1</td><td>45.60</td><td>1.63</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 27.64 & 0.88 & 1 & 24.29 & 2.65\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.13 & 1 ( 0 ) & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 25.93 & 0.82 & 1 & 21.42 & 1.49\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.00 & 1 ( 0 ) & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 48.53 & 0.98 & 1 & 47.48 & 4.95\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.04 & 1 ( 0 ) & 48.09 ( 0.7 )  & 2.94 ( 0.09 ) & 51.15 & 0.94 & 1 & 48.09 & 2.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.07 & 1 ( 0 ) & 45.6 ( 0.72 )  & 1.63 ( 0.08 ) & 49.97 & 0.91 & 1 & 45.60 & 1.63\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 27.64 | 0.88 | 1 | 24.29 | 2.65 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.13 | 1 ( 0 ) | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 25.93 | 0.82 | 1 | 21.42 | 1.49 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.00 | 1 ( 0 ) | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 48.53 | 0.98 | 1 | 47.48 | 4.95 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.04 | 1 ( 0 ) | 48.09 ( 0.7 )  | 2.94 ( 0.09 ) | 51.15 | 0.94 | 1 | 48.09 | 2.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.07 | 1 ( 0 ) | 45.6 ( 0.72 )  | 1.63 ( 0.08 ) | 49.97 | 0.91 | 1 | 45.60 | 1.63 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "75  500  500 0.9   1.0  0.07 1 ( 0 ) 24.29 ( 0.42 ) 2.65 ( 0.09 ) 27.64     \n",
+       "76 1000  500 0.9   0.5  0.13 1 ( 0 ) 21.42 ( 0.42 ) 1.49 ( 0.08 ) 25.93     \n",
+       "77   50 1000 0.9  20.0   NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.0  0.00 1 ( 0 ) 47.48 ( 0.72 ) 4.95 ( 0.08 ) 48.53     \n",
+       "79  500 1000 0.9   2.0  0.04 1 ( 0 ) 48.09 ( 0.7 )  2.94 ( 0.09 ) 51.15     \n",
+       "80 1000 1000 0.9   1.0  0.07 1 ( 0 ) 45.6 ( 0.72 )  1.63 ( 0.08 ) 49.97     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "75 0.88 1        24.29   2.65   \n",
+       "76 0.82 1        21.42   1.49   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.48   4.95   \n",
+       "79 0.94 1        48.09   2.94   \n",
+       "80 0.91 1        45.60   1.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.01</td><td>1 ( 0 )</td><td>2.4 ( 0.16 )  </td><td>5.3 ( 0.11 ) </td><td> 3.00</td><td>0.78</td><td>1</td><td> 2.40</td><td>5.30</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>0.12</td><td>1 ( 0 )</td><td>2.96 ( 0.21 ) </td><td>4.29 ( 0.15 )</td><td> 4.63</td><td>0.62</td><td>1</td><td> 2.96</td><td>4.29</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>0.33</td><td>1 ( 0 )</td><td>3.28 ( 0.21 ) </td><td>3.04 ( 0.12 )</td><td> 6.24</td><td>0.49</td><td>1</td><td> 3.28</td><td>3.04</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.00</td><td>1 ( 0 )</td><td>5.09 ( 0.21 ) </td><td>5.6 ( 0.09 ) </td><td> 5.48</td><td>0.93</td><td>1</td><td> 5.09</td><td>5.60</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>0.10</td><td>1 ( 0 )</td><td>5.39 ( 0.29 ) </td><td>4.05 ( 0.16 )</td><td> 7.34</td><td>0.72</td><td>1</td><td> 5.39</td><td>4.05</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>0.25</td><td>1 ( 0 )</td><td>5.43 ( 0.26 ) </td><td>2.89 ( 0.14 )</td><td> 8.54</td><td>0.62</td><td>1</td><td> 5.43</td><td>2.89</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.00</td><td>1 ( 0 )</td><td>24.14 ( 0.56 )</td><td>5.35 ( 0.11 )</td><td>24.79</td><td>0.97</td><td>1</td><td>24.14</td><td>5.35</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>1 ( 0 )</td><td>24.48 ( 0.54 )</td><td>4.25 ( 0.16 )</td><td>26.23</td><td>0.93</td><td>1</td><td>24.48</td><td>4.25</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>0.07</td><td>1 ( 0 )</td><td>24.08 ( 0.54 )</td><td>3.1 ( 0.16 ) </td><td>26.98</td><td>0.89</td><td>1</td><td>24.08</td><td>3.10</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>48.22 ( 0.62 )</td><td>5.53 ( 0.09 )</td><td>48.69</td><td>0.99</td><td>1</td><td>48.22</td><td>5.53</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.01</td><td>1 ( 0 )</td><td>50.18 ( 0.78 )</td><td>4.63 ( 0.12 )</td><td>51.55</td><td>0.97</td><td>1</td><td>50.18</td><td>4.63</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>0.04</td><td>1 ( 0 )</td><td>49.43 ( 0.66 )</td><td>3.23 ( 0.14 )</td><td>52.20</td><td>0.95</td><td>1</td><td>49.43</td><td>3.23</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.02</td><td>1 ( 0 )</td><td>2.13 ( 0.12 ) </td><td>5.14 ( 0.08 )</td><td> 2.96</td><td>0.71</td><td>1</td><td> 2.13</td><td>5.14</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.46</td><td>1 ( 0 )</td><td>1.43 ( 0.11 ) </td><td>2.59 ( 0.11 )</td><td> 4.84</td><td>0.27</td><td>1</td><td> 1.43</td><td>2.59</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.60</td><td>1 ( 0 )</td><td>1.44 ( 0.13 ) </td><td>1.35 ( 0.08 )</td><td> 6.09</td><td>0.21</td><td>1</td><td> 1.44</td><td>1.35</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.01</td><td>1 ( 0 )</td><td>4.84 ( 0.18 ) </td><td>5.1 ( 0.08 ) </td><td> 5.73</td><td>0.84</td><td>1</td><td> 4.84</td><td>5.10</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.28</td><td>1 ( 0 )</td><td>3.77 ( 0.17 ) </td><td>2.76 ( 0.1 ) </td><td> 7.01</td><td>0.52</td><td>1</td><td> 3.77</td><td>2.76</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.44</td><td>1 ( 0 )</td><td>3.43 ( 0.18 ) </td><td>1.5 ( 0.09 ) </td><td> 7.93</td><td>0.41</td><td>1</td><td> 3.43</td><td>1.50</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.00</td><td>1 ( 0 )</td><td>24.49 ( 0.47 )</td><td>5.11 ( 0.08 )</td><td>25.38</td><td>0.96</td><td>1</td><td>24.49</td><td>5.11</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>23.05 ( 0.49 )</td><td>3.15 ( 0.09 )</td><td>25.90</td><td>0.89</td><td>1</td><td>23.05</td><td>3.15</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.11</td><td>1 ( 0 )</td><td>22.18 ( 0.49 )</td><td>1.91 ( 0.09 )</td><td>26.27</td><td>0.84</td><td>1</td><td>22.18</td><td>1.91</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>46.96 ( 0.66 )</td><td>5.18 ( 0.08 )</td><td>47.78</td><td>0.98</td><td>1</td><td>46.96</td><td>5.18</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>1.29 ( 0.11 ) </td><td>2.25 ( 0.08 )</td><td> 5.04</td><td>0.23</td><td>1</td><td> 1.29</td><td>2.25</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.63</td><td>1 ( 0 )</td><td>1.29 ( 0.11 ) </td><td>1.3 ( 0.08 ) </td><td> 5.99</td><td>0.19</td><td>1</td><td> 1.29</td><td>1.30</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.03</td><td>1 ( 0 )</td><td>4.35 ( 0.18 ) </td><td>4.98 ( 0.07 )</td><td> 5.37</td><td>0.8 </td><td>1</td><td> 4.35</td><td>4.98</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.34</td><td>1 ( 0 )</td><td>3.46 ( 0.18 ) </td><td>2.53 ( 0.09 )</td><td> 6.93</td><td>0.47</td><td>1</td><td> 3.46</td><td>2.53</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.48</td><td>1 ( 0 )</td><td>3.04 ( 0.15 ) </td><td>1.27 ( 0.08 )</td><td> 7.77</td><td>0.37</td><td>1</td><td> 3.04</td><td>1.27</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.78 ( 0.44 )</td><td>4.93 ( 0.08 )</td><td>24.85</td><td>0.96</td><td>1</td><td>23.78</td><td>4.93</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.08</td><td>1 ( 0 )</td><td>23.14 ( 0.52 )</td><td>2.73 ( 0.09 )</td><td>26.41</td><td>0.87</td><td>1</td><td>23.14</td><td>2.73</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>21.58 ( 0.49 )</td><td>1.57 ( 0.08 )</td><td>26.01</td><td>0.82</td><td>1</td><td>21.58</td><td>1.57</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.84 ( 0.72 )</td><td>5 ( 0.09 )   </td><td>48.84</td><td>0.98</td><td>1</td><td>47.84</td><td>5.00</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>47.31 ( 0.68 )</td><td>2.9 ( 0.1 )  </td><td>50.41</td><td>0.94</td><td>1</td><td>47.31</td><td>2.90</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.06</td><td>1 ( 0 )</td><td>46.57 ( 0.73 )</td><td>1.71 ( 0.08 )</td><td>50.86</td><td>0.91</td><td>1</td><td>46.57</td><td>1.71</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.05</td><td>1 ( 0 )</td><td>2.2 ( 0.13 )  </td><td>5 ( 0.08 )   </td><td> 3.19</td><td>0.66</td><td>1</td><td> 2.20</td><td>5.00</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>1.28 ( 0.1 )  </td><td>2.32 ( 0.08 )</td><td> 4.96</td><td>0.23</td><td>1</td><td> 1.28</td><td>2.32</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>0.67</td><td>1 ( 0 )</td><td>0.93 ( 0.09 ) </td><td>1.31 ( 0.09 )</td><td> 5.62</td><td>0.15</td><td>1</td><td> 0.93</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.04</td><td>1 ( 0 )</td><td>4.24 ( 0.16 ) </td><td>4.83 ( 0.09 )</td><td> 5.40</td><td>0.79</td><td>1</td><td> 4.24</td><td>4.83</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.36</td><td>1 ( 0 )</td><td>3.54 ( 0.19 ) </td><td>2.29 ( 0.09 )</td><td> 7.25</td><td>0.46</td><td>1</td><td> 3.54</td><td>2.29</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.53</td><td>1 ( 0 )</td><td>2.53 ( 0.14 ) </td><td>1.31 ( 0.08 )</td><td> 7.22</td><td>0.33</td><td>1</td><td> 2.53</td><td>1.31</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.01</td><td>1 ( 0 )</td><td>23.63 ( 0.45 )</td><td>5.04 ( 0.08 )</td><td>24.59</td><td>0.96</td><td>1</td><td>23.63</td><td>5.04</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>24.29 ( 0.42 )</td><td>2.65 ( 0.09 )</td><td>27.64</td><td>0.88</td><td>1</td><td>24.29</td><td>2.65</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.13</td><td>1 ( 0 )</td><td>21.42 ( 0.42 )</td><td>1.49 ( 0.08 )</td><td>25.93</td><td>0.82</td><td>1</td><td>21.42</td><td>1.49</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td> NaN</td><td>1 ( 0 )</td><td>1 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td>NaN </td><td>1</td><td> 1.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.00</td><td>1 ( 0 )</td><td>47.48 ( 0.72 )</td><td>4.95 ( 0.08 )</td><td>48.53</td><td>0.98</td><td>1</td><td>47.48</td><td>4.95</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.04</td><td>1 ( 0 )</td><td>48.09 ( 0.7 ) </td><td>2.94 ( 0.09 )</td><td>51.15</td><td>0.94</td><td>1</td><td>48.09</td><td>2.94</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.07</td><td>1 ( 0 )</td><td>45.6 ( 0.72 ) </td><td>1.63 ( 0.08 )</td><td>49.97</td><td>0.91</td><td>1</td><td>45.60</td><td>1.63</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.01 & 1 ( 0 ) & 2.4 ( 0.16 )   & 5.3 ( 0.11 )  &  3.00 & 0.78 & 1 &  2.40 & 5.30\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 & 0.12 & 1 ( 0 ) & 2.96 ( 0.21 )  & 4.29 ( 0.15 ) &  4.63 & 0.62 & 1 &  2.96 & 4.29\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 & 0.33 & 1 ( 0 ) & 3.28 ( 0.21 )  & 3.04 ( 0.12 ) &  6.24 & 0.49 & 1 &  3.28 & 3.04\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.00 & 1 ( 0 ) & 5.09 ( 0.21 )  & 5.6 ( 0.09 )  &  5.48 & 0.93 & 1 &  5.09 & 5.60\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 & 0.10 & 1 ( 0 ) & 5.39 ( 0.29 )  & 4.05 ( 0.16 ) &  7.34 & 0.72 & 1 &  5.39 & 4.05\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 & 0.25 & 1 ( 0 ) & 5.43 ( 0.26 )  & 2.89 ( 0.14 ) &  8.54 & 0.62 & 1 &  5.43 & 2.89\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.00 & 1 ( 0 ) & 24.14 ( 0.56 ) & 5.35 ( 0.11 ) & 24.79 & 0.97 & 1 & 24.14 & 5.35\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 & 0.03 & 1 ( 0 ) & 24.48 ( 0.54 ) & 4.25 ( 0.16 ) & 26.23 & 0.93 & 1 & 24.48 & 4.25\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 & 0.07 & 1 ( 0 ) & 24.08 ( 0.54 ) & 3.1 ( 0.16 )  & 26.98 & 0.89 & 1 & 24.08 & 3.10\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.00 & 1 ( 0 ) & 48.22 ( 0.62 ) & 5.53 ( 0.09 ) & 48.69 & 0.99 & 1 & 48.22 & 5.53\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.01 & 1 ( 0 ) & 50.18 ( 0.78 ) & 4.63 ( 0.12 ) & 51.55 & 0.97 & 1 & 50.18 & 4.63\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 & 0.04 & 1 ( 0 ) & 49.43 ( 0.66 ) & 3.23 ( 0.14 ) & 52.20 & 0.95 & 1 & 49.43 & 3.23\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.02 & 1 ( 0 ) & 2.13 ( 0.12 )  & 5.14 ( 0.08 ) &  2.96 & 0.71 & 1 &  2.13 & 5.14\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.46 & 1 ( 0 ) & 1.43 ( 0.11 )  & 2.59 ( 0.11 ) &  4.84 & 0.27 & 1 &  1.43 & 2.59\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.60 & 1 ( 0 ) & 1.44 ( 0.13 )  & 1.35 ( 0.08 ) &  6.09 & 0.21 & 1 &  1.44 & 1.35\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.01 & 1 ( 0 ) & 4.84 ( 0.18 )  & 5.1 ( 0.08 )  &  5.73 & 0.84 & 1 &  4.84 & 5.10\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.28 & 1 ( 0 ) & 3.77 ( 0.17 )  & 2.76 ( 0.1 )  &  7.01 & 0.52 & 1 &  3.77 & 2.76\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.44 & 1 ( 0 ) & 3.43 ( 0.18 )  & 1.5 ( 0.09 )  &  7.93 & 0.41 & 1 &  3.43 & 1.50\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.00 & 1 ( 0 ) & 24.49 ( 0.47 ) & 5.11 ( 0.08 ) & 25.38 & 0.96 & 1 & 24.49 & 5.11\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.06 & 1 ( 0 ) & 23.05 ( 0.49 ) & 3.15 ( 0.09 ) & 25.90 & 0.89 & 1 & 23.05 & 3.15\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.11 & 1 ( 0 ) & 22.18 ( 0.49 ) & 1.91 ( 0.09 ) & 26.27 & 0.84 & 1 & 22.18 & 1.91\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.00 & 1 ( 0 ) & 46.96 ( 0.66 ) & 5.18 ( 0.08 ) & 47.78 & 0.98 & 1 & 46.96 & 5.18\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.53 & 1 ( 0 ) & 1.29 ( 0.11 )  & 2.25 ( 0.08 ) &  5.04 & 0.23 & 1 &  1.29 & 2.25\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.63 & 1 ( 0 ) & 1.29 ( 0.11 )  & 1.3 ( 0.08 )  &  5.99 & 0.19 & 1 &  1.29 & 1.30\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.03 & 1 ( 0 ) & 4.35 ( 0.18 )  & 4.98 ( 0.07 ) &  5.37 & 0.8  & 1 &  4.35 & 4.98\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.34 & 1 ( 0 ) & 3.46 ( 0.18 )  & 2.53 ( 0.09 ) &  6.93 & 0.47 & 1 &  3.46 & 2.53\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.48 & 1 ( 0 ) & 3.04 ( 0.15 )  & 1.27 ( 0.08 ) &  7.77 & 0.37 & 1 &  3.04 & 1.27\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.01 & 1 ( 0 ) & 23.78 ( 0.44 ) & 4.93 ( 0.08 ) & 24.85 & 0.96 & 1 & 23.78 & 4.93\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.08 & 1 ( 0 ) & 23.14 ( 0.52 ) & 2.73 ( 0.09 ) & 26.41 & 0.87 & 1 & 23.14 & 2.73\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.13 & 1 ( 0 ) & 21.58 ( 0.49 ) & 1.57 ( 0.08 ) & 26.01 & 0.82 & 1 & 21.58 & 1.57\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.00 & 1 ( 0 ) & 47.84 ( 0.72 ) & 5 ( 0.09 )    & 48.84 & 0.98 & 1 & 47.84 & 5.00\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.04 & 1 ( 0 ) & 47.31 ( 0.68 ) & 2.9 ( 0.1 )   & 50.41 & 0.94 & 1 & 47.31 & 2.90\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.06 & 1 ( 0 ) & 46.57 ( 0.73 ) & 1.71 ( 0.08 ) & 50.86 & 0.91 & 1 & 46.57 & 1.71\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.05 & 1 ( 0 ) & 2.2 ( 0.13 )   & 5 ( 0.08 )    &  3.19 & 0.66 & 1 &  2.20 & 5.00\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.53 & 1 ( 0 ) & 1.28 ( 0.1 )   & 2.32 ( 0.08 ) &  4.96 & 0.23 & 1 &  1.28 & 2.32\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 0.67 & 1 ( 0 ) & 0.93 ( 0.09 )  & 1.31 ( 0.09 ) &  5.62 & 0.15 & 1 &  0.93 & 1.31\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.04 & 1 ( 0 ) & 4.24 ( 0.16 )  & 4.83 ( 0.09 ) &  5.40 & 0.79 & 1 &  4.24 & 4.83\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.36 & 1 ( 0 ) & 3.54 ( 0.19 )  & 2.29 ( 0.09 ) &  7.25 & 0.46 & 1 &  3.54 & 2.29\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.53 & 1 ( 0 ) & 2.53 ( 0.14 )  & 1.31 ( 0.08 ) &  7.22 & 0.33 & 1 &  2.53 & 1.31\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.01 & 1 ( 0 ) & 23.63 ( 0.45 ) & 5.04 ( 0.08 ) & 24.59 & 0.96 & 1 & 23.63 & 5.04\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 24.29 ( 0.42 ) & 2.65 ( 0.09 ) & 27.64 & 0.88 & 1 & 24.29 & 2.65\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.13 & 1 ( 0 ) & 21.42 ( 0.42 ) & 1.49 ( 0.08 ) & 25.93 & 0.82 & 1 & 21.42 & 1.49\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 &  NaN & 1 ( 0 ) & 1 ( 0 )        & 6 ( 0 )       &  0.00 & NaN  & 1 &  1.00 & 6.00\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.00 & 1 ( 0 ) & 47.48 ( 0.72 ) & 4.95 ( 0.08 ) & 48.53 & 0.98 & 1 & 47.48 & 4.95\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.04 & 1 ( 0 ) & 48.09 ( 0.7 )  & 2.94 ( 0.09 ) & 51.15 & 0.94 & 1 & 48.09 & 2.94\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.07 & 1 ( 0 ) & 45.6 ( 0.72 )  & 1.63 ( 0.08 ) & 49.97 & 0.91 & 1 & 45.60 & 1.63\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.01 | 1 ( 0 ) | 2.4 ( 0.16 )   | 5.3 ( 0.11 )  |  3.00 | 0.78 | 1 |  2.40 | 5.30 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 | 0.12 | 1 ( 0 ) | 2.96 ( 0.21 )  | 4.29 ( 0.15 ) |  4.63 | 0.62 | 1 |  2.96 | 4.29 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 | 0.33 | 1 ( 0 ) | 3.28 ( 0.21 )  | 3.04 ( 0.12 ) |  6.24 | 0.49 | 1 |  3.28 | 3.04 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.00 | 1 ( 0 ) | 5.09 ( 0.21 )  | 5.6 ( 0.09 )  |  5.48 | 0.93 | 1 |  5.09 | 5.60 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 | 0.10 | 1 ( 0 ) | 5.39 ( 0.29 )  | 4.05 ( 0.16 ) |  7.34 | 0.72 | 1 |  5.39 | 4.05 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 | 0.25 | 1 ( 0 ) | 5.43 ( 0.26 )  | 2.89 ( 0.14 ) |  8.54 | 0.62 | 1 |  5.43 | 2.89 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.00 | 1 ( 0 ) | 24.14 ( 0.56 ) | 5.35 ( 0.11 ) | 24.79 | 0.97 | 1 | 24.14 | 5.35 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 | 0.03 | 1 ( 0 ) | 24.48 ( 0.54 ) | 4.25 ( 0.16 ) | 26.23 | 0.93 | 1 | 24.48 | 4.25 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 | 0.07 | 1 ( 0 ) | 24.08 ( 0.54 ) | 3.1 ( 0.16 )  | 26.98 | 0.89 | 1 | 24.08 | 3.10 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.00 | 1 ( 0 ) | 48.22 ( 0.62 ) | 5.53 ( 0.09 ) | 48.69 | 0.99 | 1 | 48.22 | 5.53 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.01 | 1 ( 0 ) | 50.18 ( 0.78 ) | 4.63 ( 0.12 ) | 51.55 | 0.97 | 1 | 50.18 | 4.63 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 | 0.04 | 1 ( 0 ) | 49.43 ( 0.66 ) | 3.23 ( 0.14 ) | 52.20 | 0.95 | 1 | 49.43 | 3.23 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.02 | 1 ( 0 ) | 2.13 ( 0.12 )  | 5.14 ( 0.08 ) |  2.96 | 0.71 | 1 |  2.13 | 5.14 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.46 | 1 ( 0 ) | 1.43 ( 0.11 )  | 2.59 ( 0.11 ) |  4.84 | 0.27 | 1 |  1.43 | 2.59 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.60 | 1 ( 0 ) | 1.44 ( 0.13 )  | 1.35 ( 0.08 ) |  6.09 | 0.21 | 1 |  1.44 | 1.35 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.01 | 1 ( 0 ) | 4.84 ( 0.18 )  | 5.1 ( 0.08 )  |  5.73 | 0.84 | 1 |  4.84 | 5.10 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.28 | 1 ( 0 ) | 3.77 ( 0.17 )  | 2.76 ( 0.1 )  |  7.01 | 0.52 | 1 |  3.77 | 2.76 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.44 | 1 ( 0 ) | 3.43 ( 0.18 )  | 1.5 ( 0.09 )  |  7.93 | 0.41 | 1 |  3.43 | 1.50 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.00 | 1 ( 0 ) | 24.49 ( 0.47 ) | 5.11 ( 0.08 ) | 25.38 | 0.96 | 1 | 24.49 | 5.11 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.06 | 1 ( 0 ) | 23.05 ( 0.49 ) | 3.15 ( 0.09 ) | 25.90 | 0.89 | 1 | 23.05 | 3.15 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.11 | 1 ( 0 ) | 22.18 ( 0.49 ) | 1.91 ( 0.09 ) | 26.27 | 0.84 | 1 | 22.18 | 1.91 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.00 | 1 ( 0 ) | 46.96 ( 0.66 ) | 5.18 ( 0.08 ) | 47.78 | 0.98 | 1 | 46.96 | 5.18 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.53 | 1 ( 0 ) | 1.29 ( 0.11 )  | 2.25 ( 0.08 ) |  5.04 | 0.23 | 1 |  1.29 | 2.25 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.63 | 1 ( 0 ) | 1.29 ( 0.11 )  | 1.3 ( 0.08 )  |  5.99 | 0.19 | 1 |  1.29 | 1.30 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.03 | 1 ( 0 ) | 4.35 ( 0.18 )  | 4.98 ( 0.07 ) |  5.37 | 0.8  | 1 |  4.35 | 4.98 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.34 | 1 ( 0 ) | 3.46 ( 0.18 )  | 2.53 ( 0.09 ) |  6.93 | 0.47 | 1 |  3.46 | 2.53 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.48 | 1 ( 0 ) | 3.04 ( 0.15 )  | 1.27 ( 0.08 ) |  7.77 | 0.37 | 1 |  3.04 | 1.27 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.01 | 1 ( 0 ) | 23.78 ( 0.44 ) | 4.93 ( 0.08 ) | 24.85 | 0.96 | 1 | 23.78 | 4.93 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.08 | 1 ( 0 ) | 23.14 ( 0.52 ) | 2.73 ( 0.09 ) | 26.41 | 0.87 | 1 | 23.14 | 2.73 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.13 | 1 ( 0 ) | 21.58 ( 0.49 ) | 1.57 ( 0.08 ) | 26.01 | 0.82 | 1 | 21.58 | 1.57 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.00 | 1 ( 0 ) | 47.84 ( 0.72 ) | 5 ( 0.09 )    | 48.84 | 0.98 | 1 | 47.84 | 5.00 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.04 | 1 ( 0 ) | 47.31 ( 0.68 ) | 2.9 ( 0.1 )   | 50.41 | 0.94 | 1 | 47.31 | 2.90 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.06 | 1 ( 0 ) | 46.57 ( 0.73 ) | 1.71 ( 0.08 ) | 50.86 | 0.91 | 1 | 46.57 | 1.71 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.05 | 1 ( 0 ) | 2.2 ( 0.13 )   | 5 ( 0.08 )    |  3.19 | 0.66 | 1 |  2.20 | 5.00 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.53 | 1 ( 0 ) | 1.28 ( 0.1 )   | 2.32 ( 0.08 ) |  4.96 | 0.23 | 1 |  1.28 | 2.32 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 0.67 | 1 ( 0 ) | 0.93 ( 0.09 )  | 1.31 ( 0.09 ) |  5.62 | 0.15 | 1 |  0.93 | 1.31 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.04 | 1 ( 0 ) | 4.24 ( 0.16 )  | 4.83 ( 0.09 ) |  5.40 | 0.79 | 1 |  4.24 | 4.83 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.36 | 1 ( 0 ) | 3.54 ( 0.19 )  | 2.29 ( 0.09 ) |  7.25 | 0.46 | 1 |  3.54 | 2.29 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.53 | 1 ( 0 ) | 2.53 ( 0.14 )  | 1.31 ( 0.08 ) |  7.22 | 0.33 | 1 |  2.53 | 1.31 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.01 | 1 ( 0 ) | 23.63 ( 0.45 ) | 5.04 ( 0.08 ) | 24.59 | 0.96 | 1 | 23.63 | 5.04 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 24.29 ( 0.42 ) | 2.65 ( 0.09 ) | 27.64 | 0.88 | 1 | 24.29 | 2.65 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.13 | 1 ( 0 ) | 21.42 ( 0.42 ) | 1.49 ( 0.08 ) | 25.93 | 0.82 | 1 | 21.42 | 1.49 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 |  NaN | 1 ( 0 ) | 1 ( 0 )        | 6 ( 0 )       |  0.00 | NaN  | 1 |  1.00 | 6.00 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.00 | 1 ( 0 ) | 47.48 ( 0.72 ) | 4.95 ( 0.08 ) | 48.53 | 0.98 | 1 | 47.48 | 4.95 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.04 | 1 ( 0 ) | 48.09 ( 0.7 )  | 2.94 ( 0.09 ) | 51.15 | 0.94 | 1 | 48.09 | 2.94 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.07 | 1 ( 0 ) | 45.6 ( 0.72 )  | 1.63 ( 0.08 ) | 49.97 | 0.91 | 1 | 45.60 | 1.63 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC     FP             FN            num_select\n",
+       "1    50   50 0.1   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "2   100   50 0.1   0.50 0.01 1 ( 0 ) 2.4 ( 0.16 )   5.3 ( 0.11 )   3.00     \n",
+       "3   500   50 0.1   0.10 0.12 1 ( 0 ) 2.96 ( 0.21 )  4.29 ( 0.15 )  4.63     \n",
+       "4  1000   50 0.1   0.05 0.33 1 ( 0 ) 3.28 ( 0.21 )  3.04 ( 0.12 )  6.24     \n",
+       "5    50  100 0.1   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "6   100  100 0.1   1.00 0.00 1 ( 0 ) 5.09 ( 0.21 )  5.6 ( 0.09 )   5.48     \n",
+       "7   500  100 0.1   0.20 0.10 1 ( 0 ) 5.39 ( 0.29 )  4.05 ( 0.16 )  7.34     \n",
+       "8  1000  100 0.1   0.10 0.25 1 ( 0 ) 5.43 ( 0.26 )  2.89 ( 0.14 )  8.54     \n",
+       "9    50  500 0.1  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "10  100  500 0.1   5.00 0.00 1 ( 0 ) 24.14 ( 0.56 ) 5.35 ( 0.11 ) 24.79     \n",
+       "11  500  500 0.1   1.00 0.03 1 ( 0 ) 24.48 ( 0.54 ) 4.25 ( 0.16 ) 26.23     \n",
+       "12 1000  500 0.1   0.50 0.07 1 ( 0 ) 24.08 ( 0.54 ) 3.1 ( 0.16 )  26.98     \n",
+       "13   50 1000 0.1  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "14  100 1000 0.1  10.00 0.00 1 ( 0 ) 48.22 ( 0.62 ) 5.53 ( 0.09 ) 48.69     \n",
+       "15  500 1000 0.1   2.00 0.01 1 ( 0 ) 50.18 ( 0.78 ) 4.63 ( 0.12 ) 51.55     \n",
+       "16 1000 1000 0.1   1.00 0.04 1 ( 0 ) 49.43 ( 0.66 ) 3.23 ( 0.14 ) 52.20     \n",
+       "17   50   50 0.3   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "18  100   50 0.3   0.50 0.02 1 ( 0 ) 2.13 ( 0.12 )  5.14 ( 0.08 )  2.96     \n",
+       "19  500   50 0.3   0.10 0.46 1 ( 0 ) 1.43 ( 0.11 )  2.59 ( 0.11 )  4.84     \n",
+       "20 1000   50 0.3   0.05 0.60 1 ( 0 ) 1.44 ( 0.13 )  1.35 ( 0.08 )  6.09     \n",
+       "21   50  100 0.3   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "22  100  100 0.3   1.00 0.01 1 ( 0 ) 4.84 ( 0.18 )  5.1 ( 0.08 )   5.73     \n",
+       "23  500  100 0.3   0.20 0.28 1 ( 0 ) 3.77 ( 0.17 )  2.76 ( 0.1 )   7.01     \n",
+       "24 1000  100 0.3   0.10 0.44 1 ( 0 ) 3.43 ( 0.18 )  1.5 ( 0.09 )   7.93     \n",
+       "25   50  500 0.3  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "26  100  500 0.3   5.00 0.00 1 ( 0 ) 24.49 ( 0.47 ) 5.11 ( 0.08 ) 25.38     \n",
+       "27  500  500 0.3   1.00 0.06 1 ( 0 ) 23.05 ( 0.49 ) 3.15 ( 0.09 ) 25.90     \n",
+       "28 1000  500 0.3   0.50 0.11 1 ( 0 ) 22.18 ( 0.49 ) 1.91 ( 0.09 ) 26.27     \n",
+       "29   50 1000 0.3  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "30  100 1000 0.3  10.00 0.00 1 ( 0 ) 46.96 ( 0.66 ) 5.18 ( 0.08 ) 47.78     \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮       ⋮              ⋮             ⋮         \n",
+       "51  500   50 0.7   0.10 0.53 1 ( 0 ) 1.29 ( 0.11 )  2.25 ( 0.08 )  5.04     \n",
+       "52 1000   50 0.7   0.05 0.63 1 ( 0 ) 1.29 ( 0.11 )  1.3 ( 0.08 )   5.99     \n",
+       "53   50  100 0.7   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "54  100  100 0.7   1.00 0.03 1 ( 0 ) 4.35 ( 0.18 )  4.98 ( 0.07 )  5.37     \n",
+       "55  500  100 0.7   0.20 0.34 1 ( 0 ) 3.46 ( 0.18 )  2.53 ( 0.09 )  6.93     \n",
+       "56 1000  100 0.7   0.10 0.48 1 ( 0 ) 3.04 ( 0.15 )  1.27 ( 0.08 )  7.77     \n",
+       "57   50  500 0.7  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "58  100  500 0.7   5.00 0.01 1 ( 0 ) 23.78 ( 0.44 ) 4.93 ( 0.08 ) 24.85     \n",
+       "59  500  500 0.7   1.00 0.08 1 ( 0 ) 23.14 ( 0.52 ) 2.73 ( 0.09 ) 26.41     \n",
+       "60 1000  500 0.7   0.50 0.13 1 ( 0 ) 21.58 ( 0.49 ) 1.57 ( 0.08 ) 26.01     \n",
+       "61   50 1000 0.7  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "62  100 1000 0.7  10.00 0.00 1 ( 0 ) 47.84 ( 0.72 ) 5 ( 0.09 )    48.84     \n",
+       "63  500 1000 0.7   2.00 0.04 1 ( 0 ) 47.31 ( 0.68 ) 2.9 ( 0.1 )   50.41     \n",
+       "64 1000 1000 0.7   1.00 0.06 1 ( 0 ) 46.57 ( 0.73 ) 1.71 ( 0.08 ) 50.86     \n",
+       "65   50   50 0.9   1.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "66  100   50 0.9   0.50 0.05 1 ( 0 ) 2.2 ( 0.13 )   5 ( 0.08 )     3.19     \n",
+       "67  500   50 0.9   0.10 0.53 1 ( 0 ) 1.28 ( 0.1 )   2.32 ( 0.08 )  4.96     \n",
+       "68 1000   50 0.9   0.05 0.67 1 ( 0 ) 0.93 ( 0.09 )  1.31 ( 0.09 )  5.62     \n",
+       "69   50  100 0.9   2.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "70  100  100 0.9   1.00 0.04 1 ( 0 ) 4.24 ( 0.16 )  4.83 ( 0.09 )  5.40     \n",
+       "71  500  100 0.9   0.20 0.36 1 ( 0 ) 3.54 ( 0.19 )  2.29 ( 0.09 )  7.25     \n",
+       "72 1000  100 0.9   0.10 0.53 1 ( 0 ) 2.53 ( 0.14 )  1.31 ( 0.08 )  7.22     \n",
+       "73   50  500 0.9  10.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "74  100  500 0.9   5.00 0.01 1 ( 0 ) 23.63 ( 0.45 ) 5.04 ( 0.08 ) 24.59     \n",
+       "75  500  500 0.9   1.00 0.07 1 ( 0 ) 24.29 ( 0.42 ) 2.65 ( 0.09 ) 27.64     \n",
+       "76 1000  500 0.9   0.50 0.13 1 ( 0 ) 21.42 ( 0.42 ) 1.49 ( 0.08 ) 25.93     \n",
+       "77   50 1000 0.9  20.00  NaN 1 ( 0 ) 1 ( 0 )        6 ( 0 )        0.00     \n",
+       "78  100 1000 0.9  10.00 0.00 1 ( 0 ) 47.48 ( 0.72 ) 4.95 ( 0.08 ) 48.53     \n",
+       "79  500 1000 0.9   2.00 0.04 1 ( 0 ) 48.09 ( 0.7 )  2.94 ( 0.09 ) 51.15     \n",
+       "80 1000 1000 0.9   1.00 0.07 1 ( 0 ) 45.6 ( 0.72 )  1.63 ( 0.08 ) 49.97     \n",
+       "   FDR  ROC_mean FP_mean FN_mean\n",
+       "1  NaN  1         1.00   6.00   \n",
+       "2  0.78 1         2.40   5.30   \n",
+       "3  0.62 1         2.96   4.29   \n",
+       "4  0.49 1         3.28   3.04   \n",
+       "5  NaN  1         1.00   6.00   \n",
+       "6  0.93 1         5.09   5.60   \n",
+       "7  0.72 1         5.39   4.05   \n",
+       "8  0.62 1         5.43   2.89   \n",
+       "9  NaN  1         1.00   6.00   \n",
+       "10 0.97 1        24.14   5.35   \n",
+       "11 0.93 1        24.48   4.25   \n",
+       "12 0.89 1        24.08   3.10   \n",
+       "13 NaN  1         1.00   6.00   \n",
+       "14 0.99 1        48.22   5.53   \n",
+       "15 0.97 1        50.18   4.63   \n",
+       "16 0.95 1        49.43   3.23   \n",
+       "17 NaN  1         1.00   6.00   \n",
+       "18 0.71 1         2.13   5.14   \n",
+       "19 0.27 1         1.43   2.59   \n",
+       "20 0.21 1         1.44   1.35   \n",
+       "21 NaN  1         1.00   6.00   \n",
+       "22 0.84 1         4.84   5.10   \n",
+       "23 0.52 1         3.77   2.76   \n",
+       "24 0.41 1         3.43   1.50   \n",
+       "25 NaN  1         1.00   6.00   \n",
+       "26 0.96 1        24.49   5.11   \n",
+       "27 0.89 1        23.05   3.15   \n",
+       "28 0.84 1        22.18   1.91   \n",
+       "29 NaN  1         1.00   6.00   \n",
+       "30 0.98 1        46.96   5.18   \n",
+       "⋮  ⋮    ⋮        ⋮       ⋮      \n",
+       "51 0.23 1         1.29   2.25   \n",
+       "52 0.19 1         1.29   1.30   \n",
+       "53 NaN  1         1.00   6.00   \n",
+       "54 0.8  1         4.35   4.98   \n",
+       "55 0.47 1         3.46   2.53   \n",
+       "56 0.37 1         3.04   1.27   \n",
+       "57 NaN  1         1.00   6.00   \n",
+       "58 0.96 1        23.78   4.93   \n",
+       "59 0.87 1        23.14   2.73   \n",
+       "60 0.82 1        21.58   1.57   \n",
+       "61 NaN  1         1.00   6.00   \n",
+       "62 0.98 1        47.84   5.00   \n",
+       "63 0.94 1        47.31   2.90   \n",
+       "64 0.91 1        46.57   1.71   \n",
+       "65 NaN  1         1.00   6.00   \n",
+       "66 0.66 1         2.20   5.00   \n",
+       "67 0.23 1         1.28   2.32   \n",
+       "68 0.15 1         0.93   1.31   \n",
+       "69 NaN  1         1.00   6.00   \n",
+       "70 0.79 1         4.24   4.83   \n",
+       "71 0.46 1         3.54   2.29   \n",
+       "72 0.33 1         2.53   1.31   \n",
+       "73 NaN  1         1.00   6.00   \n",
+       "74 0.96 1        23.63   5.04   \n",
+       "75 0.88 1        24.29   2.65   \n",
+       "76 0.82 1        21.42   1.49   \n",
+       "77 NaN  1         1.00   6.00   \n",
+       "78 0.98 1        47.48   4.95   \n",
+       "79 0.94 1        48.09   2.94   \n",
+       "80 0.91 1        45.60   1.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_RF_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/2.4_sim_block_GencompLasso_binary_update.ipynb b/simulations/notebooks_sim_bin/2.4_sim_block_GencompLasso_binary_update.ipynb
new file mode 100644
index 0000000..b3900c2
--- /dev/null
+++ b/simulations/notebooks_sim_bin/2.4_sim_block_GencompLasso_binary_update.ipynb
@@ -0,0 +1,1003 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Block Simulation Scenarios for binary outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "80"
+      ],
+      "text/latex": [
+       "80"
+      ],
+      "text/markdown": [
+       "80"
+      ],
+      "text/plain": [
+       "[1] 80"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "length(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"indx: 1\"\n",
+      "[1] \"indx: 2\"\n",
+      "[1] \"indx: 3\"\n",
+      "[1] \"indx: 4\"\n",
+      "[1] \"indx: 5\"\n",
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+      "[1] \"indx: 70\"\n",
+      "[1] \"indx: 71\"\n",
+      "[1] \"indx: 72\"\n",
+      "[1] \"indx: 73\"\n",
+      "[1] \"indx: 74\"\n",
+      "[1] \"indx: 75\"\n",
+      "[1] \"indx: 76\"\n",
+      "[1] \"indx: 77\"\n",
+      "[1] \"indx: 78\"\n",
+      "[1] \"indx: 79\"\n",
+      "[1] \"indx: 80\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg_FDR = NULL\n",
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(files))\n",
+    "for (i in 1:length(files)){\n",
+    "    print(paste0('indx: ', i))\n",
+    "    load(paste0(dir, '/binary_update/block_GenCompLasso_binary_', i, '.RData')) \n",
+    "    \n",
+    "    table_block = rbind(table_block, results_block_GenCompLasso[c('n', 'p', 'rou', 'FP', 'FN', 'ROC', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_GenCompLasso$Stab.table))\n",
+    "    \n",
+    "    # calculate FDR\n",
+    "    load(file_name, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_GenCompLasso$Stab.table\n",
+    "    FDR = NULL # false positive rate\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50</td><td>50</td><td>0.1</td><td>1.68 ( 0.53 )</td><td>5.42 ( 0.1 )</td><td>0.58 ( 0.01 )</td><td>0.02</td><td>2.26</td><td>0.58</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100</td><td>50</td><td>0.1</td><td>0.08 ( 0.04 )</td><td>5.65 ( 0.08 )</td><td>0.51 ( 0 )</td><td>0.06</td><td>0.43</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500</td><td>50</td><td>0.1</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0.5 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50</td><td>0.1</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0.5 ( 0 )</td><td>NaN</td><td>0.00</td><td> NaN</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50</td><td>100</td><td>0.1</td><td>3.43 ( 0.63 )</td><td>5.23 ( 0.1 )</td><td>0.61 ( 0.01 )</td><td>0.04</td><td>4.20</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100</td><td>100</td><td>0.1</td><td>0.31 ( 0.1 )</td><td>5.8 ( 0.05 )</td><td>0.51 ( 0 )</td><td>0.03</td><td>0.51</td><td>0.53</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50 & 50 & 0.1 & 1.68 ( 0.53 ) & 5.42 ( 0.1 ) & 0.58 ( 0.01 ) & 0.02 & 2.26 & 0.58\\\\\n",
+       "\t2 & 100 & 50 & 0.1 & 0.08 ( 0.04 ) & 5.65 ( 0.08 ) & 0.51 ( 0 ) & 0.06 & 0.43 & 0.18\\\\\n",
+       "\t3 & 500 & 50 & 0.1 & 0 ( 0 ) & 6 ( 0 ) & 0.5 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t4 & 1000 & 50 & 0.1 & 0 ( 0 ) & 6 ( 0 ) & 0.5 ( 0 ) & NaN & 0.00 &  NaN\\\\\n",
+       "\t5 & 50 & 100 & 0.1 & 3.43 ( 0.63 ) & 5.23 ( 0.1 ) & 0.61 ( 0.01 ) & 0.04 & 4.20 & 0.71\\\\\n",
+       "\t6 & 100 & 100 & 0.1 & 0.31 ( 0.1 ) & 5.8 ( 0.05 ) & 0.51 ( 0 ) & 0.03 & 0.51 & 0.53\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50 | 50 | 0.1 | 1.68 ( 0.53 ) | 5.42 ( 0.1 ) | 0.58 ( 0.01 ) | 0.02 | 2.26 | 0.58 |\n",
+       "| 2 | 100 | 50 | 0.1 | 0.08 ( 0.04 ) | 5.65 ( 0.08 ) | 0.51 ( 0 ) | 0.06 | 0.43 | 0.18 |\n",
+       "| 3 | 500 | 50 | 0.1 | 0 ( 0 ) | 6 ( 0 ) | 0.5 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 4 | 1000 | 50 | 0.1 | 0 ( 0 ) | 6 ( 0 ) | 0.5 ( 0 ) | NaN | 0.00 |  NaN |\n",
+       "| 5 | 50 | 100 | 0.1 | 3.43 ( 0.63 ) | 5.23 ( 0.1 ) | 0.61 ( 0.01 ) | 0.04 | 4.20 | 0.71 |\n",
+       "| 6 | 100 | 100 | 0.1 | 0.31 ( 0.1 ) | 5.8 ( 0.05 ) | 0.51 ( 0 ) | 0.03 | 0.51 | 0.53 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            ROC           Stab num_select FDR \n",
+       "1 50   50  0.1 1.68 ( 0.53 ) 5.42 ( 0.1 )  0.58 ( 0.01 ) 0.02 2.26       0.58\n",
+       "2 100  50  0.1 0.08 ( 0.04 ) 5.65 ( 0.08 ) 0.51 ( 0 )    0.06 0.43       0.18\n",
+       "3 500  50  0.1 0 ( 0 )       6 ( 0 )       0.5 ( 0 )     NaN  0.00        NaN\n",
+       "4 1000 50  0.1 0 ( 0 )       6 ( 0 )       0.5 ( 0 )     NaN  0.00        NaN\n",
+       "5 50   100 0.1 3.43 ( 0.63 ) 5.23 ( 0.1 )  0.61 ( 0.01 ) 0.04 4.20       0.71\n",
+       "6 100  100 0.1 0.31 ( 0.1 )  5.8 ( 0.05 )  0.51 ( 0 )    0.03 0.51       0.53"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 9</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>ROC</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;list&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td>500</td><td>500</td><td>0.9</td><td>1.08 ( 0.33 )</td><td>0.01 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.85</td><td>  7.07</td><td>0.08</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td>500</td><td>0.9</td><td>0.14 ( 0.07 )</td><td>0 ( 0 )</td><td>0.91 ( 0 )</td><td>0.98</td><td>  6.14</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50</td><td>1000</td><td>0.9</td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>1 ( 0 )</td><td>0.01</td><td>335.54</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td>100</td><td>1000</td><td>0.9</td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>1 ( 0 )</td><td>0.02</td><td>223.07</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td>500</td><td>1000</td><td>0.9</td><td>1.91 ( 0.48 )</td><td>0.02 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.76</td><td>  7.89</td><td>0.12</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td>0.27 ( 0.11 )</td><td>0.02 ( 0.01 )</td><td>0.91 ( 0 )</td><td>0.95</td><td>  6.25</td><td>0.03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 9\n",
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & n & p & rou & FP & FN & ROC & Stab & num\\_select & FDR\\\\\n",
+       "  & <list> & <list> & <list> & <list> & <list> & <list> & <list> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 & 500 & 500 & 0.9 & 1.08 ( 0.33 ) & 0.01 ( 0.01 ) & 0.91 ( 0 ) & 0.85 &   7.07 & 0.08\\\\\n",
+       "\t76 & 1000 & 500 & 0.9 & 0.14 ( 0.07 ) & 0 ( 0 ) & 0.91 ( 0 ) & 0.98 &   6.14 & 0.02\\\\\n",
+       "\t77 & 50 & 1000 & 0.9 & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 1 ( 0 ) & 0.01 & 335.54 & 0.98\\\\\n",
+       "\t78 & 100 & 1000 & 0.9 & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 1 ( 0 ) & 0.02 & 223.07 & 0.97\\\\\n",
+       "\t79 & 500 & 1000 & 0.9 & 1.91 ( 0.48 ) & 0.02 ( 0.01 ) & 0.91 ( 0 ) & 0.76 &   7.89 & 0.12\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 & 0.27 ( 0.11 ) & 0.02 ( 0.01 ) & 0.91 ( 0 ) & 0.95 &   6.25 & 0.03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 9\n",
+       "\n",
+       "| <!--/--> | n &lt;list&gt; | p &lt;list&gt; | rou &lt;list&gt; | FP &lt;list&gt; | FN &lt;list&gt; | ROC &lt;list&gt; | Stab &lt;list&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 | 500 | 500 | 0.9 | 1.08 ( 0.33 ) | 0.01 ( 0.01 ) | 0.91 ( 0 ) | 0.85 |   7.07 | 0.08 |\n",
+       "| 76 | 1000 | 500 | 0.9 | 0.14 ( 0.07 ) | 0 ( 0 ) | 0.91 ( 0 ) | 0.98 |   6.14 | 0.02 |\n",
+       "| 77 | 50 | 1000 | 0.9 | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 1 ( 0 ) | 0.01 | 335.54 | 0.98 |\n",
+       "| 78 | 100 | 1000 | 0.9 | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 1 ( 0 ) | 0.02 | 223.07 | 0.97 |\n",
+       "| 79 | 500 | 1000 | 0.9 | 1.91 ( 0.48 ) | 0.02 ( 0.01 ) | 0.91 ( 0 ) | 0.76 |   7.89 | 0.12 |\n",
+       "| 80 | 1000 | 1000 | 0.9 | 0.27 ( 0.11 ) | 0.02 ( 0.01 ) | 0.91 ( 0 ) | 0.95 |   6.25 | 0.03 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n    p    rou FP              FN            ROC        Stab num_select FDR \n",
+       "75 500  500  0.9 1.08 ( 0.33 )   0.01 ( 0.01 ) 0.91 ( 0 ) 0.85   7.07     0.08\n",
+       "76 1000 500  0.9 0.14 ( 0.07 )   0 ( 0 )       0.91 ( 0 ) 0.98   6.14     0.02\n",
+       "77 50   1000 0.9 330.51 ( 8.59 ) 0.97 ( 0.08 ) 1 ( 0 )    0.01 335.54     0.98\n",
+       "78 100  1000 0.9 217.74 ( 6.88 ) 0.67 ( 0.07 ) 1 ( 0 )    0.02 223.07     0.97\n",
+       "79 500  1000 0.9 1.91 ( 0.48 )   0.02 ( 0.01 ) 0.91 ( 0 ) 0.76   7.89     0.12\n",
+       "80 1000 1000 0.9 0.27 ( 0.11 )   0.02 ( 0.01 ) 0.91 ( 0 ) 0.95   6.25     0.03"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'ROC', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$ROC_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$ROC))\n",
+    "result.table_block$FP_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FP))\n",
+    "result.table_block$FN_mean = as.numeric(sub(\"\\\\(.*\", \"\", result.table_block$FN))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 7 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td>0.10</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td>0.05</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td>0.20</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td>0.10</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td>1.00</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td>0.50</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td>1.00</td><td>NaN</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 7 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t3 &  500 &   50 & 0.1 & 0.10 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 & 0.05 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t7 &  500 &  100 & 0.1 & 0.20 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 & 0.10 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t11 &  500 &  500 & 0.1 & 1.00 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 & 0.50 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 & 1.00 & NaN & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 7 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 3 |  500 |   50 | 0.1 | 0.10 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 4 | 1000 |   50 | 0.1 | 0.05 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 7 |  500 |  100 | 0.1 | 0.20 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 8 | 1000 |  100 | 0.1 | 0.10 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 11 |  500 |  500 | 0.1 | 1.00 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 12 | 1000 |  500 | 0.1 | 0.50 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "| 16 | 1000 | 1000 | 0.1 | 1.00 | NaN | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC       FP      FN      num_select FDR ROC_mean\n",
+       "3   500   50 0.1  0.10  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "4  1000   50 0.1  0.05  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "7   500  100 0.1  0.20  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "8  1000  100 0.1  0.10  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "11  500  500 0.1  1.00  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "12 1000  500 0.1  0.50  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "16 1000 1000 0.1  1.00  NaN  0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "   FP_mean FN_mean\n",
+       "3  0       6      \n",
+       "4  0       6      \n",
+       "7  0       6      \n",
+       "8  0       6      \n",
+       "11 0       6      \n",
+       "12 0       6      \n",
+       "16 0       6      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td> 50</td><td>0.1</td><td>1.00</td><td>0.02</td><td>0.58 ( 0.01 )</td><td>1.68 ( 0.53 )</td><td>5.42 ( 0.1 ) </td><td>2.26</td><td>0.58</td><td>0.58</td><td>1.68</td><td>5.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td> 50</td><td>0.1</td><td>0.50</td><td>0.06</td><td>0.51 ( 0 )   </td><td>0.08 ( 0.04 )</td><td>5.65 ( 0.08 )</td><td>0.43</td><td>0.18</td><td>0.51</td><td>0.08</td><td>5.65</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td> 50</td><td>0.1</td><td>0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>0.50</td><td>0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td> 50</td><td>0.1</td><td>0.05</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )      </td><td>6 ( 0 )      </td><td>0.00</td><td>NaN </td><td>0.50</td><td>0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td>100</td><td>0.1</td><td>2.00</td><td>0.04</td><td>0.61 ( 0.01 )</td><td>3.43 ( 0.63 )</td><td>5.23 ( 0.1 ) </td><td>4.20</td><td>0.71</td><td>0.61</td><td>3.43</td><td>5.23</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td>100</td><td>0.1</td><td>1.00</td><td>0.03</td><td>0.51 ( 0 )   </td><td>0.31 ( 0.1 ) </td><td>5.8 ( 0.05 ) </td><td>0.51</td><td>0.53</td><td>0.51</td><td>0.31</td><td>5.80</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &  50 & 0.1 & 1.00 & 0.02 & 0.58 ( 0.01 ) & 1.68 ( 0.53 ) & 5.42 ( 0.1 )  & 2.26 & 0.58 & 0.58 & 1.68 & 5.42\\\\\n",
+       "\t2 &  100 &  50 & 0.1 & 0.50 & 0.06 & 0.51 ( 0 )    & 0.08 ( 0.04 ) & 5.65 ( 0.08 ) & 0.43 & 0.18 & 0.51 & 0.08 & 5.65\\\\\n",
+       "\t3 &  500 &  50 & 0.1 & 0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 0.50 & 0.00 & 6.00\\\\\n",
+       "\t4 & 1000 &  50 & 0.1 & 0.05 &  NaN & 0.5 ( 0 )     & 0 ( 0 )       & 6 ( 0 )       & 0.00 & NaN  & 0.50 & 0.00 & 6.00\\\\\n",
+       "\t5 &   50 & 100 & 0.1 & 2.00 & 0.04 & 0.61 ( 0.01 ) & 3.43 ( 0.63 ) & 5.23 ( 0.1 )  & 4.20 & 0.71 & 0.61 & 3.43 & 5.23\\\\\n",
+       "\t6 &  100 & 100 & 0.1 & 1.00 & 0.03 & 0.51 ( 0 )    & 0.31 ( 0.1 )  & 5.8 ( 0.05 )  & 0.51 & 0.53 & 0.51 & 0.31 & 5.80\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |  50 | 0.1 | 1.00 | 0.02 | 0.58 ( 0.01 ) | 1.68 ( 0.53 ) | 5.42 ( 0.1 )  | 2.26 | 0.58 | 0.58 | 1.68 | 5.42 |\n",
+       "| 2 |  100 |  50 | 0.1 | 0.50 | 0.06 | 0.51 ( 0 )    | 0.08 ( 0.04 ) | 5.65 ( 0.08 ) | 0.43 | 0.18 | 0.51 | 0.08 | 5.65 |\n",
+       "| 3 |  500 |  50 | 0.1 | 0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 0.50 | 0.00 | 6.00 |\n",
+       "| 4 | 1000 |  50 | 0.1 | 0.05 |  NaN | 0.5 ( 0 )     | 0 ( 0 )       | 6 ( 0 )       | 0.00 | NaN  | 0.50 | 0.00 | 6.00 |\n",
+       "| 5 |   50 | 100 | 0.1 | 2.00 | 0.04 | 0.61 ( 0.01 ) | 3.43 ( 0.63 ) | 5.23 ( 0.1 )  | 4.20 | 0.71 | 0.61 | 3.43 | 5.23 |\n",
+       "| 6 |  100 | 100 | 0.1 | 1.00 | 0.03 | 0.51 ( 0 )    | 0.31 ( 0.1 )  | 5.8 ( 0.05 )  | 0.51 | 0.53 | 0.51 | 0.31 | 5.80 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.02 0.58 ( 0.01 ) 1.68 ( 0.53 ) 5.42 ( 0.1 )  2.26      \n",
+       "2  100  50 0.1  0.50  0.06 0.51 ( 0 )    0.08 ( 0.04 ) 5.65 ( 0.08 ) 0.43      \n",
+       "3  500  50 0.1  0.10   NaN 0.5 ( 0 )     0 ( 0 )       6 ( 0 )       0.00      \n",
+       "4 1000  50 0.1  0.05   NaN 0.5 ( 0 )     0 ( 0 )       6 ( 0 )       0.00      \n",
+       "5   50 100 0.1  2.00  0.04 0.61 ( 0.01 ) 3.43 ( 0.63 ) 5.23 ( 0.1 )  4.20      \n",
+       "6  100 100 0.1  1.00  0.03 0.51 ( 0 )    0.31 ( 0.1 )  5.8 ( 0.05 )  0.51      \n",
+       "  FDR  ROC_mean FP_mean FN_mean\n",
+       "1 0.58 0.58     1.68    5.42   \n",
+       "2 0.18 0.51     0.08    5.65   \n",
+       "3 NaN  0.50     0.00    6.00   \n",
+       "4 NaN  0.50     0.00    6.00   \n",
+       "5 0.71 0.61     3.43    5.23   \n",
+       "6 0.53 0.51     0.31    5.80   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.0</td><td>0.85</td><td>0.91 ( 0 )</td><td>1.08 ( 0.33 )  </td><td>0.01 ( 0.01 )</td><td>  7.07</td><td>0.08</td><td>0.91</td><td>  1.08</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.5</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.14 ( 0.07 )  </td><td>0 ( 0 )      </td><td>  6.14</td><td>0.02</td><td>0.91</td><td>  0.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.0</td><td>0.01</td><td>1 ( 0 )   </td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>335.54</td><td>0.98</td><td>1.00</td><td>330.51</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.0</td><td>0.02</td><td>1 ( 0 )   </td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>223.07</td><td>0.97</td><td>1.00</td><td>217.74</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.0</td><td>0.76</td><td>0.91 ( 0 )</td><td>1.91 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>  7.89</td><td>0.12</td><td>0.91</td><td>  1.91</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.0</td><td>0.95</td><td>0.91 ( 0 )</td><td>0.27 ( 0.11 )  </td><td>0.02 ( 0.01 )</td><td>  6.25</td><td>0.03</td><td>0.91</td><td>  0.27</td><td>0.02</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t75 &  500 &  500 & 0.9 &  1.0 & 0.85 & 0.91 ( 0 ) & 1.08 ( 0.33 )   & 0.01 ( 0.01 ) &   7.07 & 0.08 & 0.91 &   1.08 & 0.01\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.5 & 0.98 & 0.91 ( 0 ) & 0.14 ( 0.07 )   & 0 ( 0 )       &   6.14 & 0.02 & 0.91 &   0.14 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.0 & 0.01 & 1 ( 0 )    & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 335.54 & 0.98 & 1.00 & 330.51 & 0.97\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.0 & 0.02 & 1 ( 0 )    & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 223.07 & 0.97 & 1.00 & 217.74 & 0.67\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.0 & 0.76 & 0.91 ( 0 ) & 1.91 ( 0.48 )   & 0.02 ( 0.01 ) &   7.89 & 0.12 & 0.91 &   1.91 & 0.02\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.0 & 0.95 & 0.91 ( 0 ) & 0.27 ( 0.11 )   & 0.02 ( 0.01 ) &   6.25 & 0.03 & 0.91 &   0.27 & 0.02\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500 |  500 | 0.9 |  1.0 | 0.85 | 0.91 ( 0 ) | 1.08 ( 0.33 )   | 0.01 ( 0.01 ) |   7.07 | 0.08 | 0.91 |   1.08 | 0.01 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.5 | 0.98 | 0.91 ( 0 ) | 0.14 ( 0.07 )   | 0 ( 0 )       |   6.14 | 0.02 | 0.91 |   0.14 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.0 | 0.01 | 1 ( 0 )    | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 335.54 | 0.98 | 1.00 | 330.51 | 0.97 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.0 | 0.02 | 1 ( 0 )    | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 223.07 | 0.97 | 1.00 | 217.74 | 0.67 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.0 | 0.76 | 0.91 ( 0 ) | 1.91 ( 0.48 )   | 0.02 ( 0.01 ) |   7.89 | 0.12 | 0.91 |   1.91 | 0.02 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.0 | 0.95 | 0.91 ( 0 ) | 0.27 ( 0.11 )   | 0.02 ( 0.01 ) |   6.25 | 0.03 | 0.91 |   0.27 | 0.02 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC        FP              FN           \n",
+       "75  500  500 0.9   1.0  0.85 0.91 ( 0 ) 1.08 ( 0.33 )   0.01 ( 0.01 )\n",
+       "76 1000  500 0.9   0.5  0.98 0.91 ( 0 ) 0.14 ( 0.07 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.01 1 ( 0 )    330.51 ( 8.59 ) 0.97 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.02 1 ( 0 )    217.74 ( 6.88 ) 0.67 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.76 0.91 ( 0 ) 1.91 ( 0.48 )   0.02 ( 0.01 )\n",
+       "80 1000 1000 0.9   1.0  0.95 0.91 ( 0 ) 0.27 ( 0.11 )   0.02 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "75   7.07     0.08 0.91       1.08  0.01   \n",
+       "76   6.14     0.02 0.91       0.14  0.00   \n",
+       "77 335.54     0.98 1.00     330.51  0.97   \n",
+       "78 223.07     0.97 1.00     217.74  0.67   \n",
+       "79   7.89     0.12 0.91       1.91  0.02   \n",
+       "80   6.25     0.03 0.91       0.27  0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 80 × 13</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>0.02</td><td>0.58 ( 0.01 )</td><td>1.68 ( 0.53 )  </td><td>5.42 ( 0.1 ) </td><td>  2.26</td><td>0.58</td><td>0.58</td><td>  1.68</td><td>5.42</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td> 100</td><td>  50</td><td>0.1</td><td> 0.50</td><td>0.06</td><td>0.51 ( 0 )   </td><td>0.08 ( 0.04 )  </td><td>5.65 ( 0.08 )</td><td>  0.43</td><td>0.18</td><td>0.51</td><td>  0.08</td><td>5.65</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>0.04</td><td>0.61 ( 0.01 )</td><td>3.43 ( 0.63 )  </td><td>5.23 ( 0.1 ) </td><td>  4.20</td><td>0.71</td><td>0.61</td><td>  3.43</td><td>5.23</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td> 100</td><td> 100</td><td>0.1</td><td> 1.00</td><td>0.03</td><td>0.51 ( 0 )   </td><td>0.31 ( 0.1 )   </td><td>5.8 ( 0.05 ) </td><td>  0.51</td><td>0.53</td><td>0.51</td><td>  0.31</td><td>5.80</td></tr>\n",
+       "\t<tr><th scope=row>7</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>0.00</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 ) </td><td>5.17 ( 0.13 )</td><td> 30.45</td><td>0.9 </td><td>0.73</td><td> 29.62</td><td>5.17</td></tr>\n",
+       "\t<tr><th scope=row>10</th><td> 100</td><td> 500</td><td>0.1</td><td> 5.00</td><td>0.04</td><td>0.53 ( 0.01 )</td><td>0.53 ( 0.1 )   </td><td>5.63 ( 0.08 )</td><td>  0.90</td><td>0.57</td><td>0.53</td><td>  0.53</td><td>5.63</td></tr>\n",
+       "\t<tr><th scope=row>11</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>0.01</td><td>0.83 ( 0.01 )</td><td>17.45 ( 2.89 ) </td><td>5.37 ( 0.1 ) </td><td> 18.08</td><td>0.94</td><td>0.83</td><td> 17.45</td><td>5.37</td></tr>\n",
+       "\t<tr><th scope=row>14</th><td> 100</td><td>1000</td><td>0.1</td><td>10.00</td><td>0.03</td><td>0.56 ( 0.01 )</td><td>1.27 ( 0.25 )  </td><td>5.59 ( 0.08 )</td><td>  1.68</td><td>0.73</td><td>0.56</td><td>  1.27</td><td>5.59</td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500</td><td>1000</td><td>0.1</td><td> 2.00</td><td>0.00</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>5.98 ( 0.02 )</td><td>  0.02</td><td>0   </td><td>0.50</td><td>  0.00</td><td>5.98</td></tr>\n",
+       "\t<tr><th scope=row>16</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td> NaN</td><td>0.5 ( 0 )    </td><td>0 ( 0 )        </td><td>6 ( 0 )      </td><td>  0.00</td><td>NaN </td><td>0.50</td><td>  0.00</td><td>6.00</td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>0.14</td><td>0.86 ( 0.01 )</td><td>9.81 ( 1.05 )  </td><td>1.78 ( 0.11 )</td><td> 14.03</td><td>0.6 </td><td>0.86</td><td>  9.81</td><td>1.78</td></tr>\n",
+       "\t<tr><th scope=row>18</th><td> 100</td><td>  50</td><td>0.3</td><td> 0.50</td><td>0.37</td><td>0.83 ( 0.01 )</td><td>3.64 ( 0.75 )  </td><td>1.7 ( 0.09 ) </td><td>  7.94</td><td>0.27</td><td>0.83</td><td>  3.64</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>19</th><td> 500</td><td>  50</td><td>0.3</td><td> 0.10</td><td>0.81</td><td>0.81 ( 0 )   </td><td>0.02 ( 0.01 )  </td><td>1.75 ( 0.09 )</td><td>  4.27</td><td>0   </td><td>0.81</td><td>  0.02</td><td>1.75</td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000</td><td>  50</td><td>0.3</td><td> 0.05</td><td>0.81</td><td>0.8 ( 0 )    </td><td>0.06 ( 0.05 )  </td><td>1.7 ( 0.09 ) </td><td>  4.36</td><td>0.01</td><td>0.80</td><td>  0.06</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>0.10</td><td>0.9 ( 0 )    </td><td>16.32 ( 1.58 ) </td><td>2.2 ( 0.11 ) </td><td> 20.12</td><td>0.74</td><td>0.90</td><td> 16.32</td><td>2.20</td></tr>\n",
+       "\t<tr><th scope=row>22</th><td> 100</td><td> 100</td><td>0.3</td><td> 1.00</td><td>0.31</td><td>0.84 ( 0 )   </td><td>5.71 ( 1.02 )  </td><td>1.72 ( 0.1 ) </td><td>  9.99</td><td>0.41</td><td>0.84</td><td>  5.71</td><td>1.72</td></tr>\n",
+       "\t<tr><th scope=row>23</th><td> 500</td><td> 100</td><td>0.3</td><td> 0.20</td><td>0.80</td><td>0.81 ( 0 )   </td><td>0.12 ( 0.06 )  </td><td>1.7 ( 0.09 ) </td><td>  4.42</td><td>0.01</td><td>0.81</td><td>  0.12</td><td>1.70</td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000</td><td> 100</td><td>0.3</td><td> 0.10</td><td>0.74</td><td>0.8 ( 0 )    </td><td>0.49 ( 0.26 )  </td><td>2.05 ( 0.08 )</td><td>  4.44</td><td>0.04</td><td>0.80</td><td>  0.49</td><td>2.05</td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>0.02</td><td>1 ( 0 )      </td><td>105.94 ( 7.57 )</td><td>2.04 ( 0.11 )</td><td>109.90</td><td>0.95</td><td>1.00</td><td>105.94</td><td>2.04</td></tr>\n",
+       "\t<tr><th scope=row>26</th><td> 100</td><td> 500</td><td>0.3</td><td> 5.00</td><td>0.12</td><td>0.88 ( 0.01 )</td><td>23.82 ( 3.36 ) </td><td>1.9 ( 0.09 ) </td><td> 27.92</td><td>0.75</td><td>0.88</td><td> 23.82</td><td>1.90</td></tr>\n",
+       "\t<tr><th scope=row>27</th><td> 500</td><td> 500</td><td>0.3</td><td> 1.00</td><td>0.79</td><td>0.8 ( 0 )    </td><td>0.24 ( 0.08 )  </td><td>1.86 ( 0.09 )</td><td>  4.38</td><td>0.03</td><td>0.80</td><td>  0.24</td><td>1.86</td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000</td><td> 500</td><td>0.3</td><td> 0.50</td><td>0.73</td><td>0.8 ( 0 )    </td><td>0.63 ( 0.15 )  </td><td>1.85 ( 0.09 )</td><td>  4.78</td><td>0.08</td><td>0.80</td><td>  0.63</td><td>1.85</td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>0.02</td><td>1 ( 0 )      </td><td>154.21 ( 5.09 )</td><td>2.09 ( 0.1 ) </td><td>158.12</td><td>0.97</td><td>1.00</td><td>154.21</td><td>2.09</td></tr>\n",
+       "\t<tr><th scope=row>30</th><td> 100</td><td>1000</td><td>0.3</td><td>10.00</td><td>0.07</td><td>0.91 ( 0 )   </td><td>36.7 ( 6.19 )  </td><td>2.26 ( 0.1 ) </td><td> 40.44</td><td>0.84</td><td>0.91</td><td> 36.70</td><td>2.26</td></tr>\n",
+       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
+       "\t<tr><th scope=row>51</th><td> 500</td><td>  50</td><td>0.7</td><td> 0.10</td><td>0.96</td><td>0.9 ( 0 ) </td><td>0.17 ( 0.08 )  </td><td>0.04 ( 0.02 )</td><td>  6.13</td><td>0.02</td><td>0.90</td><td>  0.17</td><td>0.04</td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000</td><td>  50</td><td>0.7</td><td> 0.05</td><td>0.98</td><td>0.89 ( 0 )</td><td>0.1 ( 0.06 )   </td><td>0 ( 0 )      </td><td>  6.10</td><td>0.01</td><td>0.89</td><td>  0.10</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>35.59 ( 2.41 ) </td><td>0.94 ( 0.09 )</td><td> 40.65</td><td>0.83</td><td>0.99</td><td> 35.59</td><td>0.94</td></tr>\n",
+       "\t<tr><th scope=row>54</th><td> 100</td><td> 100</td><td>0.7</td><td> 1.00</td><td>0.26</td><td>0.92 ( 0 )</td><td>11.64 ( 1.38 ) </td><td>0.53 ( 0.06 )</td><td> 17.11</td><td>0.57</td><td>0.92</td><td> 11.64</td><td>0.53</td></tr>\n",
+       "\t<tr><th scope=row>55</th><td> 500</td><td> 100</td><td>0.7</td><td> 0.20</td><td>0.97</td><td>0.9 ( 0 ) </td><td>0.15 ( 0.07 )  </td><td>0.05 ( 0.02 )</td><td>  6.10</td><td>0.02</td><td>0.90</td><td>  0.15</td><td>0.05</td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000</td><td> 100</td><td>0.7</td><td> 0.10</td><td>0.93</td><td>0.9 ( 0 ) </td><td>0.41 ( 0.14 )  </td><td>0.03 ( 0.02 )</td><td>  6.38</td><td>0.04</td><td>0.90</td><td>  0.41</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>171.29 ( 6.32 )</td><td>1.12 ( 0.09 )</td><td>176.17</td><td>0.97</td><td>1.00</td><td>171.29</td><td>1.12</td></tr>\n",
+       "\t<tr><th scope=row>58</th><td> 100</td><td> 500</td><td>0.7</td><td> 5.00</td><td>0.06</td><td>0.98 ( 0 )</td><td>68.31 ( 6.57 ) </td><td>0.98 ( 0.09 )</td><td> 73.33</td><td>0.87</td><td>0.98</td><td> 68.31</td><td>0.98</td></tr>\n",
+       "\t<tr><th scope=row>59</th><td> 500</td><td> 500</td><td>0.7</td><td> 1.00</td><td>0.93</td><td>0.89 ( 0 )</td><td>0.37 ( 0.12 )  </td><td>0.06 ( 0.02 )</td><td>  6.31</td><td>0.04</td><td>0.89</td><td>  0.37</td><td>0.06</td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000</td><td> 500</td><td>0.7</td><td> 0.50</td><td>0.98</td><td>0.9 ( 0 ) </td><td>0.13 ( 0.07 )  </td><td>0.01 ( 0.01 )</td><td>  6.12</td><td>0.01</td><td>0.90</td><td>  0.13</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>275.88 ( 8.89 )</td><td>1.39 ( 0.09 )</td><td>280.49</td><td>0.98</td><td>1.00</td><td>275.88</td><td>1.39</td></tr>\n",
+       "\t<tr><th scope=row>62</th><td> 100</td><td>1000</td><td>0.7</td><td>10.00</td><td>0.03</td><td>1 ( 0 )   </td><td>155.38 ( 7.45 )</td><td>0.93 ( 0.08 )</td><td>160.45</td><td>0.95</td><td>1.00</td><td>155.38</td><td>0.93</td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500</td><td>1000</td><td>0.7</td><td> 2.00</td><td>0.84</td><td>0.9 ( 0 ) </td><td>1.07 ( 0.26 )  </td><td>0.1 ( 0.03 ) </td><td>  6.97</td><td>0.09</td><td>0.90</td><td>  1.07</td><td>0.10</td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000</td><td>1000</td><td>0.7</td><td> 1.00</td><td>0.95</td><td>0.9 ( 0 ) </td><td>0.3 ( 0.11 )   </td><td>0.02 ( 0.01 )</td><td>  6.28</td><td>0.03</td><td>0.90</td><td>  0.30</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>0.13</td><td>0.97 ( 0 )</td><td>15.75 ( 1.04 ) </td><td>0.71 ( 0.07 )</td><td> 21.04</td><td>0.7 </td><td>0.97</td><td> 15.75</td><td>0.71</td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100</td><td>  50</td><td>0.9</td><td> 0.50</td><td>0.44</td><td>0.93 ( 0 )</td><td>5.06 ( 0.38 )  </td><td>0.34 ( 0.05 )</td><td> 10.72</td><td>0.42</td><td>0.93</td><td>  5.06</td><td>0.34</td></tr>\n",
+       "\t<tr><th scope=row>67</th><td> 500</td><td>  50</td><td>0.9</td><td> 0.10</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.07 ( 0.04 )  </td><td>0.03 ( 0.02 )</td><td>  6.04</td><td>0.01</td><td>0.91</td><td>  0.07</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>68</th><td>1000</td><td>  50</td><td>0.9</td><td> 0.05</td><td>1.00</td><td>0.91 ( 0 )</td><td>0 ( 0 )        </td><td>0 ( 0 )      </td><td>  6.00</td><td>0   </td><td>0.91</td><td>  0.00</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>0.06</td><td>0.99 ( 0 )</td><td>37.69 ( 2.33 ) </td><td>0.96 ( 0.09 )</td><td> 42.73</td><td>0.84</td><td>0.99</td><td> 37.69</td><td>0.96</td></tr>\n",
+       "\t<tr><th scope=row>70</th><td> 100</td><td> 100</td><td>0.9</td><td> 1.00</td><td>0.26</td><td>0.94 ( 0 )</td><td>11.53 ( 1.15 ) </td><td>0.52 ( 0.06 )</td><td> 17.01</td><td>0.61</td><td>0.94</td><td> 11.53</td><td>0.52</td></tr>\n",
+       "\t<tr><th scope=row>71</th><td> 500</td><td> 100</td><td>0.9</td><td> 0.20</td><td>0.96</td><td>0.91 ( 0 )</td><td>0.18 ( 0.06 )  </td><td>0.03 ( 0.02 )</td><td>  6.15</td><td>0.02</td><td>0.91</td><td>  0.18</td><td>0.03</td></tr>\n",
+       "\t<tr><th scope=row>72</th><td>1000</td><td> 100</td><td>0.9</td><td> 0.10</td><td>0.97</td><td>0.91 ( 0 )</td><td>0.17 ( 0.09 )  </td><td>0 ( 0 )      </td><td>  6.17</td><td>0.02</td><td>0.91</td><td>  0.17</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>0.01</td><td>1 ( 0 )   </td><td>189.13 ( 6.24 )</td><td>0.91 ( 0.1 ) </td><td>194.22</td><td>0.97</td><td>1.00</td><td>189.13</td><td>0.91</td></tr>\n",
+       "\t<tr><th scope=row>74</th><td> 100</td><td> 500</td><td>0.9</td><td> 5.00</td><td>0.05</td><td>0.99 ( 0 )</td><td>88.92 ( 5.92 ) </td><td>0.68 ( 0.07 )</td><td> 94.24</td><td>0.92</td><td>0.99</td><td> 88.92</td><td>0.68</td></tr>\n",
+       "\t<tr><th scope=row>75</th><td> 500</td><td> 500</td><td>0.9</td><td> 1.00</td><td>0.85</td><td>0.91 ( 0 )</td><td>1.08 ( 0.33 )  </td><td>0.01 ( 0.01 )</td><td>  7.07</td><td>0.08</td><td>0.91</td><td>  1.08</td><td>0.01</td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000</td><td> 500</td><td>0.9</td><td> 0.50</td><td>0.98</td><td>0.91 ( 0 )</td><td>0.14 ( 0.07 )  </td><td>0 ( 0 )      </td><td>  6.14</td><td>0.02</td><td>0.91</td><td>  0.14</td><td>0.00</td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>0.01</td><td>1 ( 0 )   </td><td>330.51 ( 8.59 )</td><td>0.97 ( 0.08 )</td><td>335.54</td><td>0.98</td><td>1.00</td><td>330.51</td><td>0.97</td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100</td><td>1000</td><td>0.9</td><td>10.00</td><td>0.02</td><td>1 ( 0 )   </td><td>217.74 ( 6.88 )</td><td>0.67 ( 0.07 )</td><td>223.07</td><td>0.97</td><td>1.00</td><td>217.74</td><td>0.67</td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500</td><td>1000</td><td>0.9</td><td> 2.00</td><td>0.76</td><td>0.91 ( 0 )</td><td>1.91 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>  7.89</td><td>0.12</td><td>0.91</td><td>  1.91</td><td>0.02</td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000</td><td>1000</td><td>0.9</td><td> 1.00</td><td>0.95</td><td>0.91 ( 0 )</td><td>0.27 ( 0.11 )  </td><td>0.02 ( 0.01 )</td><td>  6.25</td><td>0.03</td><td>0.91</td><td>  0.27</td><td>0.02</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 80 × 13\n",
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "  & <dbl> & <dbl> & <fct> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 &   50 &   50 & 0.1 &  1.00 & 0.02 & 0.58 ( 0.01 ) & 1.68 ( 0.53 )   & 5.42 ( 0.1 )  &   2.26 & 0.58 & 0.58 &   1.68 & 5.42\\\\\n",
+       "\t2 &  100 &   50 & 0.1 &  0.50 & 0.06 & 0.51 ( 0 )    & 0.08 ( 0.04 )   & 5.65 ( 0.08 ) &   0.43 & 0.18 & 0.51 &   0.08 & 5.65\\\\\n",
+       "\t3 &  500 &   50 & 0.1 &  0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t4 & 1000 &   50 & 0.1 &  0.05 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t5 &   50 &  100 & 0.1 &  2.00 & 0.04 & 0.61 ( 0.01 ) & 3.43 ( 0.63 )   & 5.23 ( 0.1 )  &   4.20 & 0.71 & 0.61 &   3.43 & 5.23\\\\\n",
+       "\t6 &  100 &  100 & 0.1 &  1.00 & 0.03 & 0.51 ( 0 )    & 0.31 ( 0.1 )    & 5.8 ( 0.05 )  &   0.51 & 0.53 & 0.51 &   0.31 & 5.80\\\\\n",
+       "\t7 &  500 &  100 & 0.1 &  0.20 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t8 & 1000 &  100 & 0.1 &  0.10 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t9 &   50 &  500 & 0.1 & 10.00 & 0.00 & 0.73 ( 0.01 ) & 29.62 ( 7.94 )  & 5.17 ( 0.13 ) &  30.45 & 0.9  & 0.73 &  29.62 & 5.17\\\\\n",
+       "\t10 &  100 &  500 & 0.1 &  5.00 & 0.04 & 0.53 ( 0.01 ) & 0.53 ( 0.1 )    & 5.63 ( 0.08 ) &   0.90 & 0.57 & 0.53 &   0.53 & 5.63\\\\\n",
+       "\t11 &  500 &  500 & 0.1 &  1.00 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t12 & 1000 &  500 & 0.1 &  0.50 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t13 &   50 & 1000 & 0.1 & 20.00 & 0.01 & 0.83 ( 0.01 ) & 17.45 ( 2.89 )  & 5.37 ( 0.1 )  &  18.08 & 0.94 & 0.83 &  17.45 & 5.37\\\\\n",
+       "\t14 &  100 & 1000 & 0.1 & 10.00 & 0.03 & 0.56 ( 0.01 ) & 1.27 ( 0.25 )   & 5.59 ( 0.08 ) &   1.68 & 0.73 & 0.56 &   1.27 & 5.59\\\\\n",
+       "\t15 &  500 & 1000 & 0.1 &  2.00 & 0.00 & 0.5 ( 0 )     & 0 ( 0 )         & 5.98 ( 0.02 ) &   0.02 & 0    & 0.50 &   0.00 & 5.98\\\\\n",
+       "\t16 & 1000 & 1000 & 0.1 &  1.00 &  NaN & 0.5 ( 0 )     & 0 ( 0 )         & 6 ( 0 )       &   0.00 & NaN  & 0.50 &   0.00 & 6.00\\\\\n",
+       "\t17 &   50 &   50 & 0.3 &  1.00 & 0.14 & 0.86 ( 0.01 ) & 9.81 ( 1.05 )   & 1.78 ( 0.11 ) &  14.03 & 0.6  & 0.86 &   9.81 & 1.78\\\\\n",
+       "\t18 &  100 &   50 & 0.3 &  0.50 & 0.37 & 0.83 ( 0.01 ) & 3.64 ( 0.75 )   & 1.7 ( 0.09 )  &   7.94 & 0.27 & 0.83 &   3.64 & 1.70\\\\\n",
+       "\t19 &  500 &   50 & 0.3 &  0.10 & 0.81 & 0.81 ( 0 )    & 0.02 ( 0.01 )   & 1.75 ( 0.09 ) &   4.27 & 0    & 0.81 &   0.02 & 1.75\\\\\n",
+       "\t20 & 1000 &   50 & 0.3 &  0.05 & 0.81 & 0.8 ( 0 )     & 0.06 ( 0.05 )   & 1.7 ( 0.09 )  &   4.36 & 0.01 & 0.80 &   0.06 & 1.70\\\\\n",
+       "\t21 &   50 &  100 & 0.3 &  2.00 & 0.10 & 0.9 ( 0 )     & 16.32 ( 1.58 )  & 2.2 ( 0.11 )  &  20.12 & 0.74 & 0.90 &  16.32 & 2.20\\\\\n",
+       "\t22 &  100 &  100 & 0.3 &  1.00 & 0.31 & 0.84 ( 0 )    & 5.71 ( 1.02 )   & 1.72 ( 0.1 )  &   9.99 & 0.41 & 0.84 &   5.71 & 1.72\\\\\n",
+       "\t23 &  500 &  100 & 0.3 &  0.20 & 0.80 & 0.81 ( 0 )    & 0.12 ( 0.06 )   & 1.7 ( 0.09 )  &   4.42 & 0.01 & 0.81 &   0.12 & 1.70\\\\\n",
+       "\t24 & 1000 &  100 & 0.3 &  0.10 & 0.74 & 0.8 ( 0 )     & 0.49 ( 0.26 )   & 2.05 ( 0.08 ) &   4.44 & 0.04 & 0.80 &   0.49 & 2.05\\\\\n",
+       "\t25 &   50 &  500 & 0.3 & 10.00 & 0.02 & 1 ( 0 )       & 105.94 ( 7.57 ) & 2.04 ( 0.11 ) & 109.90 & 0.95 & 1.00 & 105.94 & 2.04\\\\\n",
+       "\t26 &  100 &  500 & 0.3 &  5.00 & 0.12 & 0.88 ( 0.01 ) & 23.82 ( 3.36 )  & 1.9 ( 0.09 )  &  27.92 & 0.75 & 0.88 &  23.82 & 1.90\\\\\n",
+       "\t27 &  500 &  500 & 0.3 &  1.00 & 0.79 & 0.8 ( 0 )     & 0.24 ( 0.08 )   & 1.86 ( 0.09 ) &   4.38 & 0.03 & 0.80 &   0.24 & 1.86\\\\\n",
+       "\t28 & 1000 &  500 & 0.3 &  0.50 & 0.73 & 0.8 ( 0 )     & 0.63 ( 0.15 )   & 1.85 ( 0.09 ) &   4.78 & 0.08 & 0.80 &   0.63 & 1.85\\\\\n",
+       "\t29 &   50 & 1000 & 0.3 & 20.00 & 0.02 & 1 ( 0 )       & 154.21 ( 5.09 ) & 2.09 ( 0.1 )  & 158.12 & 0.97 & 1.00 & 154.21 & 2.09\\\\\n",
+       "\t30 &  100 & 1000 & 0.3 & 10.00 & 0.07 & 0.91 ( 0 )    & 36.7 ( 6.19 )   & 2.26 ( 0.1 )  &  40.44 & 0.84 & 0.91 &  36.70 & 2.26\\\\\n",
+       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
+       "\t51 &  500 &   50 & 0.7 &  0.10 & 0.96 & 0.9 ( 0 )  & 0.17 ( 0.08 )   & 0.04 ( 0.02 ) &   6.13 & 0.02 & 0.90 &   0.17 & 0.04\\\\\n",
+       "\t52 & 1000 &   50 & 0.7 &  0.05 & 0.98 & 0.89 ( 0 ) & 0.1 ( 0.06 )    & 0 ( 0 )       &   6.10 & 0.01 & 0.89 &   0.10 & 0.00\\\\\n",
+       "\t53 &   50 &  100 & 0.7 &  2.00 & 0.06 & 0.99 ( 0 ) & 35.59 ( 2.41 )  & 0.94 ( 0.09 ) &  40.65 & 0.83 & 0.99 &  35.59 & 0.94\\\\\n",
+       "\t54 &  100 &  100 & 0.7 &  1.00 & 0.26 & 0.92 ( 0 ) & 11.64 ( 1.38 )  & 0.53 ( 0.06 ) &  17.11 & 0.57 & 0.92 &  11.64 & 0.53\\\\\n",
+       "\t55 &  500 &  100 & 0.7 &  0.20 & 0.97 & 0.9 ( 0 )  & 0.15 ( 0.07 )   & 0.05 ( 0.02 ) &   6.10 & 0.02 & 0.90 &   0.15 & 0.05\\\\\n",
+       "\t56 & 1000 &  100 & 0.7 &  0.10 & 0.93 & 0.9 ( 0 )  & 0.41 ( 0.14 )   & 0.03 ( 0.02 ) &   6.38 & 0.04 & 0.90 &   0.41 & 0.03\\\\\n",
+       "\t57 &   50 &  500 & 0.7 & 10.00 & 0.02 & 1 ( 0 )    & 171.29 ( 6.32 ) & 1.12 ( 0.09 ) & 176.17 & 0.97 & 1.00 & 171.29 & 1.12\\\\\n",
+       "\t58 &  100 &  500 & 0.7 &  5.00 & 0.06 & 0.98 ( 0 ) & 68.31 ( 6.57 )  & 0.98 ( 0.09 ) &  73.33 & 0.87 & 0.98 &  68.31 & 0.98\\\\\n",
+       "\t59 &  500 &  500 & 0.7 &  1.00 & 0.93 & 0.89 ( 0 ) & 0.37 ( 0.12 )   & 0.06 ( 0.02 ) &   6.31 & 0.04 & 0.89 &   0.37 & 0.06\\\\\n",
+       "\t60 & 1000 &  500 & 0.7 &  0.50 & 0.98 & 0.9 ( 0 )  & 0.13 ( 0.07 )   & 0.01 ( 0.01 ) &   6.12 & 0.01 & 0.90 &   0.13 & 0.01\\\\\n",
+       "\t61 &   50 & 1000 & 0.7 & 20.00 & 0.01 & 1 ( 0 )    & 275.88 ( 8.89 ) & 1.39 ( 0.09 ) & 280.49 & 0.98 & 1.00 & 275.88 & 1.39\\\\\n",
+       "\t62 &  100 & 1000 & 0.7 & 10.00 & 0.03 & 1 ( 0 )    & 155.38 ( 7.45 ) & 0.93 ( 0.08 ) & 160.45 & 0.95 & 1.00 & 155.38 & 0.93\\\\\n",
+       "\t63 &  500 & 1000 & 0.7 &  2.00 & 0.84 & 0.9 ( 0 )  & 1.07 ( 0.26 )   & 0.1 ( 0.03 )  &   6.97 & 0.09 & 0.90 &   1.07 & 0.10\\\\\n",
+       "\t64 & 1000 & 1000 & 0.7 &  1.00 & 0.95 & 0.9 ( 0 )  & 0.3 ( 0.11 )    & 0.02 ( 0.01 ) &   6.28 & 0.03 & 0.90 &   0.30 & 0.02\\\\\n",
+       "\t65 &   50 &   50 & 0.9 &  1.00 & 0.13 & 0.97 ( 0 ) & 15.75 ( 1.04 )  & 0.71 ( 0.07 ) &  21.04 & 0.7  & 0.97 &  15.75 & 0.71\\\\\n",
+       "\t66 &  100 &   50 & 0.9 &  0.50 & 0.44 & 0.93 ( 0 ) & 5.06 ( 0.38 )   & 0.34 ( 0.05 ) &  10.72 & 0.42 & 0.93 &   5.06 & 0.34\\\\\n",
+       "\t67 &  500 &   50 & 0.9 &  0.10 & 0.98 & 0.91 ( 0 ) & 0.07 ( 0.04 )   & 0.03 ( 0.02 ) &   6.04 & 0.01 & 0.91 &   0.07 & 0.03\\\\\n",
+       "\t68 & 1000 &   50 & 0.9 &  0.05 & 1.00 & 0.91 ( 0 ) & 0 ( 0 )         & 0 ( 0 )       &   6.00 & 0    & 0.91 &   0.00 & 0.00\\\\\n",
+       "\t69 &   50 &  100 & 0.9 &  2.00 & 0.06 & 0.99 ( 0 ) & 37.69 ( 2.33 )  & 0.96 ( 0.09 ) &  42.73 & 0.84 & 0.99 &  37.69 & 0.96\\\\\n",
+       "\t70 &  100 &  100 & 0.9 &  1.00 & 0.26 & 0.94 ( 0 ) & 11.53 ( 1.15 )  & 0.52 ( 0.06 ) &  17.01 & 0.61 & 0.94 &  11.53 & 0.52\\\\\n",
+       "\t71 &  500 &  100 & 0.9 &  0.20 & 0.96 & 0.91 ( 0 ) & 0.18 ( 0.06 )   & 0.03 ( 0.02 ) &   6.15 & 0.02 & 0.91 &   0.18 & 0.03\\\\\n",
+       "\t72 & 1000 &  100 & 0.9 &  0.10 & 0.97 & 0.91 ( 0 ) & 0.17 ( 0.09 )   & 0 ( 0 )       &   6.17 & 0.02 & 0.91 &   0.17 & 0.00\\\\\n",
+       "\t73 &   50 &  500 & 0.9 & 10.00 & 0.01 & 1 ( 0 )    & 189.13 ( 6.24 ) & 0.91 ( 0.1 )  & 194.22 & 0.97 & 1.00 & 189.13 & 0.91\\\\\n",
+       "\t74 &  100 &  500 & 0.9 &  5.00 & 0.05 & 0.99 ( 0 ) & 88.92 ( 5.92 )  & 0.68 ( 0.07 ) &  94.24 & 0.92 & 0.99 &  88.92 & 0.68\\\\\n",
+       "\t75 &  500 &  500 & 0.9 &  1.00 & 0.85 & 0.91 ( 0 ) & 1.08 ( 0.33 )   & 0.01 ( 0.01 ) &   7.07 & 0.08 & 0.91 &   1.08 & 0.01\\\\\n",
+       "\t76 & 1000 &  500 & 0.9 &  0.50 & 0.98 & 0.91 ( 0 ) & 0.14 ( 0.07 )   & 0 ( 0 )       &   6.14 & 0.02 & 0.91 &   0.14 & 0.00\\\\\n",
+       "\t77 &   50 & 1000 & 0.9 & 20.00 & 0.01 & 1 ( 0 )    & 330.51 ( 8.59 ) & 0.97 ( 0.08 ) & 335.54 & 0.98 & 1.00 & 330.51 & 0.97\\\\\n",
+       "\t78 &  100 & 1000 & 0.9 & 10.00 & 0.02 & 1 ( 0 )    & 217.74 ( 6.88 ) & 0.67 ( 0.07 ) & 223.07 & 0.97 & 1.00 & 217.74 & 0.67\\\\\n",
+       "\t79 &  500 & 1000 & 0.9 &  2.00 & 0.76 & 0.91 ( 0 ) & 1.91 ( 0.48 )   & 0.02 ( 0.01 ) &   7.89 & 0.12 & 0.91 &   1.91 & 0.02\\\\\n",
+       "\t80 & 1000 & 1000 & 0.9 &  1.00 & 0.95 & 0.91 ( 0 ) & 0.27 ( 0.11 )   & 0.02 ( 0.01 ) &   6.25 & 0.03 & 0.91 &   0.27 & 0.02\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 80 × 13\n",
+       "\n",
+       "| <!--/--> | N &lt;dbl&gt; | P &lt;dbl&gt; | Corr &lt;fct&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;fct&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |   50 |   50 | 0.1 |  1.00 | 0.02 | 0.58 ( 0.01 ) | 1.68 ( 0.53 )   | 5.42 ( 0.1 )  |   2.26 | 0.58 | 0.58 |   1.68 | 5.42 |\n",
+       "| 2 |  100 |   50 | 0.1 |  0.50 | 0.06 | 0.51 ( 0 )    | 0.08 ( 0.04 )   | 5.65 ( 0.08 ) |   0.43 | 0.18 | 0.51 |   0.08 | 5.65 |\n",
+       "| 3 |  500 |   50 | 0.1 |  0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 4 | 1000 |   50 | 0.1 |  0.05 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 5 |   50 |  100 | 0.1 |  2.00 | 0.04 | 0.61 ( 0.01 ) | 3.43 ( 0.63 )   | 5.23 ( 0.1 )  |   4.20 | 0.71 | 0.61 |   3.43 | 5.23 |\n",
+       "| 6 |  100 |  100 | 0.1 |  1.00 | 0.03 | 0.51 ( 0 )    | 0.31 ( 0.1 )    | 5.8 ( 0.05 )  |   0.51 | 0.53 | 0.51 |   0.31 | 5.80 |\n",
+       "| 7 |  500 |  100 | 0.1 |  0.20 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 8 | 1000 |  100 | 0.1 |  0.10 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 9 |   50 |  500 | 0.1 | 10.00 | 0.00 | 0.73 ( 0.01 ) | 29.62 ( 7.94 )  | 5.17 ( 0.13 ) |  30.45 | 0.9  | 0.73 |  29.62 | 5.17 |\n",
+       "| 10 |  100 |  500 | 0.1 |  5.00 | 0.04 | 0.53 ( 0.01 ) | 0.53 ( 0.1 )    | 5.63 ( 0.08 ) |   0.90 | 0.57 | 0.53 |   0.53 | 5.63 |\n",
+       "| 11 |  500 |  500 | 0.1 |  1.00 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 12 | 1000 |  500 | 0.1 |  0.50 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 13 |   50 | 1000 | 0.1 | 20.00 | 0.01 | 0.83 ( 0.01 ) | 17.45 ( 2.89 )  | 5.37 ( 0.1 )  |  18.08 | 0.94 | 0.83 |  17.45 | 5.37 |\n",
+       "| 14 |  100 | 1000 | 0.1 | 10.00 | 0.03 | 0.56 ( 0.01 ) | 1.27 ( 0.25 )   | 5.59 ( 0.08 ) |   1.68 | 0.73 | 0.56 |   1.27 | 5.59 |\n",
+       "| 15 |  500 | 1000 | 0.1 |  2.00 | 0.00 | 0.5 ( 0 )     | 0 ( 0 )         | 5.98 ( 0.02 ) |   0.02 | 0    | 0.50 |   0.00 | 5.98 |\n",
+       "| 16 | 1000 | 1000 | 0.1 |  1.00 |  NaN | 0.5 ( 0 )     | 0 ( 0 )         | 6 ( 0 )       |   0.00 | NaN  | 0.50 |   0.00 | 6.00 |\n",
+       "| 17 |   50 |   50 | 0.3 |  1.00 | 0.14 | 0.86 ( 0.01 ) | 9.81 ( 1.05 )   | 1.78 ( 0.11 ) |  14.03 | 0.6  | 0.86 |   9.81 | 1.78 |\n",
+       "| 18 |  100 |   50 | 0.3 |  0.50 | 0.37 | 0.83 ( 0.01 ) | 3.64 ( 0.75 )   | 1.7 ( 0.09 )  |   7.94 | 0.27 | 0.83 |   3.64 | 1.70 |\n",
+       "| 19 |  500 |   50 | 0.3 |  0.10 | 0.81 | 0.81 ( 0 )    | 0.02 ( 0.01 )   | 1.75 ( 0.09 ) |   4.27 | 0    | 0.81 |   0.02 | 1.75 |\n",
+       "| 20 | 1000 |   50 | 0.3 |  0.05 | 0.81 | 0.8 ( 0 )     | 0.06 ( 0.05 )   | 1.7 ( 0.09 )  |   4.36 | 0.01 | 0.80 |   0.06 | 1.70 |\n",
+       "| 21 |   50 |  100 | 0.3 |  2.00 | 0.10 | 0.9 ( 0 )     | 16.32 ( 1.58 )  | 2.2 ( 0.11 )  |  20.12 | 0.74 | 0.90 |  16.32 | 2.20 |\n",
+       "| 22 |  100 |  100 | 0.3 |  1.00 | 0.31 | 0.84 ( 0 )    | 5.71 ( 1.02 )   | 1.72 ( 0.1 )  |   9.99 | 0.41 | 0.84 |   5.71 | 1.72 |\n",
+       "| 23 |  500 |  100 | 0.3 |  0.20 | 0.80 | 0.81 ( 0 )    | 0.12 ( 0.06 )   | 1.7 ( 0.09 )  |   4.42 | 0.01 | 0.81 |   0.12 | 1.70 |\n",
+       "| 24 | 1000 |  100 | 0.3 |  0.10 | 0.74 | 0.8 ( 0 )     | 0.49 ( 0.26 )   | 2.05 ( 0.08 ) |   4.44 | 0.04 | 0.80 |   0.49 | 2.05 |\n",
+       "| 25 |   50 |  500 | 0.3 | 10.00 | 0.02 | 1 ( 0 )       | 105.94 ( 7.57 ) | 2.04 ( 0.11 ) | 109.90 | 0.95 | 1.00 | 105.94 | 2.04 |\n",
+       "| 26 |  100 |  500 | 0.3 |  5.00 | 0.12 | 0.88 ( 0.01 ) | 23.82 ( 3.36 )  | 1.9 ( 0.09 )  |  27.92 | 0.75 | 0.88 |  23.82 | 1.90 |\n",
+       "| 27 |  500 |  500 | 0.3 |  1.00 | 0.79 | 0.8 ( 0 )     | 0.24 ( 0.08 )   | 1.86 ( 0.09 ) |   4.38 | 0.03 | 0.80 |   0.24 | 1.86 |\n",
+       "| 28 | 1000 |  500 | 0.3 |  0.50 | 0.73 | 0.8 ( 0 )     | 0.63 ( 0.15 )   | 1.85 ( 0.09 ) |   4.78 | 0.08 | 0.80 |   0.63 | 1.85 |\n",
+       "| 29 |   50 | 1000 | 0.3 | 20.00 | 0.02 | 1 ( 0 )       | 154.21 ( 5.09 ) | 2.09 ( 0.1 )  | 158.12 | 0.97 | 1.00 | 154.21 | 2.09 |\n",
+       "| 30 |  100 | 1000 | 0.3 | 10.00 | 0.07 | 0.91 ( 0 )    | 36.7 ( 6.19 )   | 2.26 ( 0.1 )  |  40.44 | 0.84 | 0.91 |  36.70 | 2.26 |\n",
+       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
+       "| 51 |  500 |   50 | 0.7 |  0.10 | 0.96 | 0.9 ( 0 )  | 0.17 ( 0.08 )   | 0.04 ( 0.02 ) |   6.13 | 0.02 | 0.90 |   0.17 | 0.04 |\n",
+       "| 52 | 1000 |   50 | 0.7 |  0.05 | 0.98 | 0.89 ( 0 ) | 0.1 ( 0.06 )    | 0 ( 0 )       |   6.10 | 0.01 | 0.89 |   0.10 | 0.00 |\n",
+       "| 53 |   50 |  100 | 0.7 |  2.00 | 0.06 | 0.99 ( 0 ) | 35.59 ( 2.41 )  | 0.94 ( 0.09 ) |  40.65 | 0.83 | 0.99 |  35.59 | 0.94 |\n",
+       "| 54 |  100 |  100 | 0.7 |  1.00 | 0.26 | 0.92 ( 0 ) | 11.64 ( 1.38 )  | 0.53 ( 0.06 ) |  17.11 | 0.57 | 0.92 |  11.64 | 0.53 |\n",
+       "| 55 |  500 |  100 | 0.7 |  0.20 | 0.97 | 0.9 ( 0 )  | 0.15 ( 0.07 )   | 0.05 ( 0.02 ) |   6.10 | 0.02 | 0.90 |   0.15 | 0.05 |\n",
+       "| 56 | 1000 |  100 | 0.7 |  0.10 | 0.93 | 0.9 ( 0 )  | 0.41 ( 0.14 )   | 0.03 ( 0.02 ) |   6.38 | 0.04 | 0.90 |   0.41 | 0.03 |\n",
+       "| 57 |   50 |  500 | 0.7 | 10.00 | 0.02 | 1 ( 0 )    | 171.29 ( 6.32 ) | 1.12 ( 0.09 ) | 176.17 | 0.97 | 1.00 | 171.29 | 1.12 |\n",
+       "| 58 |  100 |  500 | 0.7 |  5.00 | 0.06 | 0.98 ( 0 ) | 68.31 ( 6.57 )  | 0.98 ( 0.09 ) |  73.33 | 0.87 | 0.98 |  68.31 | 0.98 |\n",
+       "| 59 |  500 |  500 | 0.7 |  1.00 | 0.93 | 0.89 ( 0 ) | 0.37 ( 0.12 )   | 0.06 ( 0.02 ) |   6.31 | 0.04 | 0.89 |   0.37 | 0.06 |\n",
+       "| 60 | 1000 |  500 | 0.7 |  0.50 | 0.98 | 0.9 ( 0 )  | 0.13 ( 0.07 )   | 0.01 ( 0.01 ) |   6.12 | 0.01 | 0.90 |   0.13 | 0.01 |\n",
+       "| 61 |   50 | 1000 | 0.7 | 20.00 | 0.01 | 1 ( 0 )    | 275.88 ( 8.89 ) | 1.39 ( 0.09 ) | 280.49 | 0.98 | 1.00 | 275.88 | 1.39 |\n",
+       "| 62 |  100 | 1000 | 0.7 | 10.00 | 0.03 | 1 ( 0 )    | 155.38 ( 7.45 ) | 0.93 ( 0.08 ) | 160.45 | 0.95 | 1.00 | 155.38 | 0.93 |\n",
+       "| 63 |  500 | 1000 | 0.7 |  2.00 | 0.84 | 0.9 ( 0 )  | 1.07 ( 0.26 )   | 0.1 ( 0.03 )  |   6.97 | 0.09 | 0.90 |   1.07 | 0.10 |\n",
+       "| 64 | 1000 | 1000 | 0.7 |  1.00 | 0.95 | 0.9 ( 0 )  | 0.3 ( 0.11 )    | 0.02 ( 0.01 ) |   6.28 | 0.03 | 0.90 |   0.30 | 0.02 |\n",
+       "| 65 |   50 |   50 | 0.9 |  1.00 | 0.13 | 0.97 ( 0 ) | 15.75 ( 1.04 )  | 0.71 ( 0.07 ) |  21.04 | 0.7  | 0.97 |  15.75 | 0.71 |\n",
+       "| 66 |  100 |   50 | 0.9 |  0.50 | 0.44 | 0.93 ( 0 ) | 5.06 ( 0.38 )   | 0.34 ( 0.05 ) |  10.72 | 0.42 | 0.93 |   5.06 | 0.34 |\n",
+       "| 67 |  500 |   50 | 0.9 |  0.10 | 0.98 | 0.91 ( 0 ) | 0.07 ( 0.04 )   | 0.03 ( 0.02 ) |   6.04 | 0.01 | 0.91 |   0.07 | 0.03 |\n",
+       "| 68 | 1000 |   50 | 0.9 |  0.05 | 1.00 | 0.91 ( 0 ) | 0 ( 0 )         | 0 ( 0 )       |   6.00 | 0    | 0.91 |   0.00 | 0.00 |\n",
+       "| 69 |   50 |  100 | 0.9 |  2.00 | 0.06 | 0.99 ( 0 ) | 37.69 ( 2.33 )  | 0.96 ( 0.09 ) |  42.73 | 0.84 | 0.99 |  37.69 | 0.96 |\n",
+       "| 70 |  100 |  100 | 0.9 |  1.00 | 0.26 | 0.94 ( 0 ) | 11.53 ( 1.15 )  | 0.52 ( 0.06 ) |  17.01 | 0.61 | 0.94 |  11.53 | 0.52 |\n",
+       "| 71 |  500 |  100 | 0.9 |  0.20 | 0.96 | 0.91 ( 0 ) | 0.18 ( 0.06 )   | 0.03 ( 0.02 ) |   6.15 | 0.02 | 0.91 |   0.18 | 0.03 |\n",
+       "| 72 | 1000 |  100 | 0.9 |  0.10 | 0.97 | 0.91 ( 0 ) | 0.17 ( 0.09 )   | 0 ( 0 )       |   6.17 | 0.02 | 0.91 |   0.17 | 0.00 |\n",
+       "| 73 |   50 |  500 | 0.9 | 10.00 | 0.01 | 1 ( 0 )    | 189.13 ( 6.24 ) | 0.91 ( 0.1 )  | 194.22 | 0.97 | 1.00 | 189.13 | 0.91 |\n",
+       "| 74 |  100 |  500 | 0.9 |  5.00 | 0.05 | 0.99 ( 0 ) | 88.92 ( 5.92 )  | 0.68 ( 0.07 ) |  94.24 | 0.92 | 0.99 |  88.92 | 0.68 |\n",
+       "| 75 |  500 |  500 | 0.9 |  1.00 | 0.85 | 0.91 ( 0 ) | 1.08 ( 0.33 )   | 0.01 ( 0.01 ) |   7.07 | 0.08 | 0.91 |   1.08 | 0.01 |\n",
+       "| 76 | 1000 |  500 | 0.9 |  0.50 | 0.98 | 0.91 ( 0 ) | 0.14 ( 0.07 )   | 0 ( 0 )       |   6.14 | 0.02 | 0.91 |   0.14 | 0.00 |\n",
+       "| 77 |   50 | 1000 | 0.9 | 20.00 | 0.01 | 1 ( 0 )    | 330.51 ( 8.59 ) | 0.97 ( 0.08 ) | 335.54 | 0.98 | 1.00 | 330.51 | 0.97 |\n",
+       "| 78 |  100 | 1000 | 0.9 | 10.00 | 0.02 | 1 ( 0 )    | 217.74 ( 6.88 ) | 0.67 ( 0.07 ) | 223.07 | 0.97 | 1.00 | 217.74 | 0.67 |\n",
+       "| 79 |  500 | 1000 | 0.9 |  2.00 | 0.76 | 0.91 ( 0 ) | 1.91 ( 0.48 )   | 0.02 ( 0.01 ) |   7.89 | 0.12 | 0.91 |   1.91 | 0.02 |\n",
+       "| 80 | 1000 | 1000 | 0.9 |  1.00 | 0.95 | 0.91 ( 0 ) | 0.27 ( 0.11 )   | 0.02 ( 0.01 ) |   6.25 | 0.03 | 0.91 |   0.27 | 0.02 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab ROC           FP              FN           \n",
+       "1    50   50 0.1   1.00 0.02 0.58 ( 0.01 ) 1.68 ( 0.53 )   5.42 ( 0.1 ) \n",
+       "2   100   50 0.1   0.50 0.06 0.51 ( 0 )    0.08 ( 0.04 )   5.65 ( 0.08 )\n",
+       "3   500   50 0.1   0.10  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "4  1000   50 0.1   0.05  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "5    50  100 0.1   2.00 0.04 0.61 ( 0.01 ) 3.43 ( 0.63 )   5.23 ( 0.1 ) \n",
+       "6   100  100 0.1   1.00 0.03 0.51 ( 0 )    0.31 ( 0.1 )    5.8 ( 0.05 ) \n",
+       "7   500  100 0.1   0.20  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "8  1000  100 0.1   0.10  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "9    50  500 0.1  10.00 0.00 0.73 ( 0.01 ) 29.62 ( 7.94 )  5.17 ( 0.13 )\n",
+       "10  100  500 0.1   5.00 0.04 0.53 ( 0.01 ) 0.53 ( 0.1 )    5.63 ( 0.08 )\n",
+       "11  500  500 0.1   1.00  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "12 1000  500 0.1   0.50  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "13   50 1000 0.1  20.00 0.01 0.83 ( 0.01 ) 17.45 ( 2.89 )  5.37 ( 0.1 ) \n",
+       "14  100 1000 0.1  10.00 0.03 0.56 ( 0.01 ) 1.27 ( 0.25 )   5.59 ( 0.08 )\n",
+       "15  500 1000 0.1   2.00 0.00 0.5 ( 0 )     0 ( 0 )         5.98 ( 0.02 )\n",
+       "16 1000 1000 0.1   1.00  NaN 0.5 ( 0 )     0 ( 0 )         6 ( 0 )      \n",
+       "17   50   50 0.3   1.00 0.14 0.86 ( 0.01 ) 9.81 ( 1.05 )   1.78 ( 0.11 )\n",
+       "18  100   50 0.3   0.50 0.37 0.83 ( 0.01 ) 3.64 ( 0.75 )   1.7 ( 0.09 ) \n",
+       "19  500   50 0.3   0.10 0.81 0.81 ( 0 )    0.02 ( 0.01 )   1.75 ( 0.09 )\n",
+       "20 1000   50 0.3   0.05 0.81 0.8 ( 0 )     0.06 ( 0.05 )   1.7 ( 0.09 ) \n",
+       "21   50  100 0.3   2.00 0.10 0.9 ( 0 )     16.32 ( 1.58 )  2.2 ( 0.11 ) \n",
+       "22  100  100 0.3   1.00 0.31 0.84 ( 0 )    5.71 ( 1.02 )   1.72 ( 0.1 ) \n",
+       "23  500  100 0.3   0.20 0.80 0.81 ( 0 )    0.12 ( 0.06 )   1.7 ( 0.09 ) \n",
+       "24 1000  100 0.3   0.10 0.74 0.8 ( 0 )     0.49 ( 0.26 )   2.05 ( 0.08 )\n",
+       "25   50  500 0.3  10.00 0.02 1 ( 0 )       105.94 ( 7.57 ) 2.04 ( 0.11 )\n",
+       "26  100  500 0.3   5.00 0.12 0.88 ( 0.01 ) 23.82 ( 3.36 )  1.9 ( 0.09 ) \n",
+       "27  500  500 0.3   1.00 0.79 0.8 ( 0 )     0.24 ( 0.08 )   1.86 ( 0.09 )\n",
+       "28 1000  500 0.3   0.50 0.73 0.8 ( 0 )     0.63 ( 0.15 )   1.85 ( 0.09 )\n",
+       "29   50 1000 0.3  20.00 0.02 1 ( 0 )       154.21 ( 5.09 ) 2.09 ( 0.1 ) \n",
+       "30  100 1000 0.3  10.00 0.07 0.91 ( 0 )    36.7 ( 6.19 )   2.26 ( 0.1 ) \n",
+       "⋮  ⋮    ⋮    ⋮    ⋮     ⋮    ⋮             ⋮               ⋮            \n",
+       "51  500   50 0.7   0.10 0.96 0.9 ( 0 )     0.17 ( 0.08 )   0.04 ( 0.02 )\n",
+       "52 1000   50 0.7   0.05 0.98 0.89 ( 0 )    0.1 ( 0.06 )    0 ( 0 )      \n",
+       "53   50  100 0.7   2.00 0.06 0.99 ( 0 )    35.59 ( 2.41 )  0.94 ( 0.09 )\n",
+       "54  100  100 0.7   1.00 0.26 0.92 ( 0 )    11.64 ( 1.38 )  0.53 ( 0.06 )\n",
+       "55  500  100 0.7   0.20 0.97 0.9 ( 0 )     0.15 ( 0.07 )   0.05 ( 0.02 )\n",
+       "56 1000  100 0.7   0.10 0.93 0.9 ( 0 )     0.41 ( 0.14 )   0.03 ( 0.02 )\n",
+       "57   50  500 0.7  10.00 0.02 1 ( 0 )       171.29 ( 6.32 ) 1.12 ( 0.09 )\n",
+       "58  100  500 0.7   5.00 0.06 0.98 ( 0 )    68.31 ( 6.57 )  0.98 ( 0.09 )\n",
+       "59  500  500 0.7   1.00 0.93 0.89 ( 0 )    0.37 ( 0.12 )   0.06 ( 0.02 )\n",
+       "60 1000  500 0.7   0.50 0.98 0.9 ( 0 )     0.13 ( 0.07 )   0.01 ( 0.01 )\n",
+       "61   50 1000 0.7  20.00 0.01 1 ( 0 )       275.88 ( 8.89 ) 1.39 ( 0.09 )\n",
+       "62  100 1000 0.7  10.00 0.03 1 ( 0 )       155.38 ( 7.45 ) 0.93 ( 0.08 )\n",
+       "63  500 1000 0.7   2.00 0.84 0.9 ( 0 )     1.07 ( 0.26 )   0.1 ( 0.03 ) \n",
+       "64 1000 1000 0.7   1.00 0.95 0.9 ( 0 )     0.3 ( 0.11 )    0.02 ( 0.01 )\n",
+       "65   50   50 0.9   1.00 0.13 0.97 ( 0 )    15.75 ( 1.04 )  0.71 ( 0.07 )\n",
+       "66  100   50 0.9   0.50 0.44 0.93 ( 0 )    5.06 ( 0.38 )   0.34 ( 0.05 )\n",
+       "67  500   50 0.9   0.10 0.98 0.91 ( 0 )    0.07 ( 0.04 )   0.03 ( 0.02 )\n",
+       "68 1000   50 0.9   0.05 1.00 0.91 ( 0 )    0 ( 0 )         0 ( 0 )      \n",
+       "69   50  100 0.9   2.00 0.06 0.99 ( 0 )    37.69 ( 2.33 )  0.96 ( 0.09 )\n",
+       "70  100  100 0.9   1.00 0.26 0.94 ( 0 )    11.53 ( 1.15 )  0.52 ( 0.06 )\n",
+       "71  500  100 0.9   0.20 0.96 0.91 ( 0 )    0.18 ( 0.06 )   0.03 ( 0.02 )\n",
+       "72 1000  100 0.9   0.10 0.97 0.91 ( 0 )    0.17 ( 0.09 )   0 ( 0 )      \n",
+       "73   50  500 0.9  10.00 0.01 1 ( 0 )       189.13 ( 6.24 ) 0.91 ( 0.1 ) \n",
+       "74  100  500 0.9   5.00 0.05 0.99 ( 0 )    88.92 ( 5.92 )  0.68 ( 0.07 )\n",
+       "75  500  500 0.9   1.00 0.85 0.91 ( 0 )    1.08 ( 0.33 )   0.01 ( 0.01 )\n",
+       "76 1000  500 0.9   0.50 0.98 0.91 ( 0 )    0.14 ( 0.07 )   0 ( 0 )      \n",
+       "77   50 1000 0.9  20.00 0.01 1 ( 0 )       330.51 ( 8.59 ) 0.97 ( 0.08 )\n",
+       "78  100 1000 0.9  10.00 0.02 1 ( 0 )       217.74 ( 6.88 ) 0.67 ( 0.07 )\n",
+       "79  500 1000 0.9   2.00 0.76 0.91 ( 0 )    1.91 ( 0.48 )   0.02 ( 0.01 )\n",
+       "80 1000 1000 0.9   1.00 0.95 0.91 ( 0 )    0.27 ( 0.11 )   0.02 ( 0.01 )\n",
+       "   num_select FDR  ROC_mean FP_mean FN_mean\n",
+       "1    2.26     0.58 0.58       1.68  5.42   \n",
+       "2    0.43     0.18 0.51       0.08  5.65   \n",
+       "3    0.00     NaN  0.50       0.00  6.00   \n",
+       "4    0.00     NaN  0.50       0.00  6.00   \n",
+       "5    4.20     0.71 0.61       3.43  5.23   \n",
+       "6    0.51     0.53 0.51       0.31  5.80   \n",
+       "7    0.00     NaN  0.50       0.00  6.00   \n",
+       "8    0.00     NaN  0.50       0.00  6.00   \n",
+       "9   30.45     0.9  0.73      29.62  5.17   \n",
+       "10   0.90     0.57 0.53       0.53  5.63   \n",
+       "11   0.00     NaN  0.50       0.00  6.00   \n",
+       "12   0.00     NaN  0.50       0.00  6.00   \n",
+       "13  18.08     0.94 0.83      17.45  5.37   \n",
+       "14   1.68     0.73 0.56       1.27  5.59   \n",
+       "15   0.02     0    0.50       0.00  5.98   \n",
+       "16   0.00     NaN  0.50       0.00  6.00   \n",
+       "17  14.03     0.6  0.86       9.81  1.78   \n",
+       "18   7.94     0.27 0.83       3.64  1.70   \n",
+       "19   4.27     0    0.81       0.02  1.75   \n",
+       "20   4.36     0.01 0.80       0.06  1.70   \n",
+       "21  20.12     0.74 0.90      16.32  2.20   \n",
+       "22   9.99     0.41 0.84       5.71  1.72   \n",
+       "23   4.42     0.01 0.81       0.12  1.70   \n",
+       "24   4.44     0.04 0.80       0.49  2.05   \n",
+       "25 109.90     0.95 1.00     105.94  2.04   \n",
+       "26  27.92     0.75 0.88      23.82  1.90   \n",
+       "27   4.38     0.03 0.80       0.24  1.86   \n",
+       "28   4.78     0.08 0.80       0.63  1.85   \n",
+       "29 158.12     0.97 1.00     154.21  2.09   \n",
+       "30  40.44     0.84 0.91      36.70  2.26   \n",
+       "⋮  ⋮          ⋮    ⋮        ⋮       ⋮      \n",
+       "51   6.13     0.02 0.90       0.17  0.04   \n",
+       "52   6.10     0.01 0.89       0.10  0.00   \n",
+       "53  40.65     0.83 0.99      35.59  0.94   \n",
+       "54  17.11     0.57 0.92      11.64  0.53   \n",
+       "55   6.10     0.02 0.90       0.15  0.05   \n",
+       "56   6.38     0.04 0.90       0.41  0.03   \n",
+       "57 176.17     0.97 1.00     171.29  1.12   \n",
+       "58  73.33     0.87 0.98      68.31  0.98   \n",
+       "59   6.31     0.04 0.89       0.37  0.06   \n",
+       "60   6.12     0.01 0.90       0.13  0.01   \n",
+       "61 280.49     0.98 1.00     275.88  1.39   \n",
+       "62 160.45     0.95 1.00     155.38  0.93   \n",
+       "63   6.97     0.09 0.90       1.07  0.10   \n",
+       "64   6.28     0.03 0.90       0.30  0.02   \n",
+       "65  21.04     0.7  0.97      15.75  0.71   \n",
+       "66  10.72     0.42 0.93       5.06  0.34   \n",
+       "67   6.04     0.01 0.91       0.07  0.03   \n",
+       "68   6.00     0    0.91       0.00  0.00   \n",
+       "69  42.73     0.84 0.99      37.69  0.96   \n",
+       "70  17.01     0.61 0.94      11.53  0.52   \n",
+       "71   6.15     0.02 0.91       0.18  0.03   \n",
+       "72   6.17     0.02 0.91       0.17  0.00   \n",
+       "73 194.22     0.97 1.00     189.13  0.91   \n",
+       "74  94.24     0.92 0.99      88.92  0.68   \n",
+       "75   7.07     0.08 0.91       1.08  0.01   \n",
+       "76   6.14     0.02 0.91       0.14  0.00   \n",
+       "77 335.54     0.98 1.00     330.51  0.97   \n",
+       "78 223.07     0.97 1.00     217.74  0.67   \n",
+       "79   7.89     0.12 0.91       1.91  0.02   \n",
+       "80   6.25     0.03 0.91       0.27  0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_bin/sim_block_GenCompLasso_binary.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/3_sim_binary_summary_update.ipynb b/simulations/notebooks_sim_bin/3_sim_binary_summary_update.ipynb
new file mode 100644
index 0000000..52874ac
--- /dev/null
+++ b/simulations/notebooks_sim_bin/3_sim_binary_summary_update.ipynb
@@ -0,0 +1,1052 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize method comparison results across 4 feature selection methods in different simulation scenarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Independent results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_lasso = read.csv('../results_summary_bin/sim_ind_lasso_binary.txt', sep='\\t')\n",
+    "ind_elnet = read.csv('../results_summary_bin/sim_ind_Elnet_binary.txt', sep='\\t')\n",
+    "ind_rf = read.csv('../results_summary_bin/sim_ind_RF_binary.txt', sep='\\t')\n",
+    "ind_compLasso = read.csv('../results_summary_bin/sim_ind_GencompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "ind_lasso$method = rep('lasso', dim(ind_lasso)[1])\n",
+    "ind_elnet$method = rep('elnet', dim(ind_elnet)[1])\n",
+    "ind_rf$method = rep('rf', dim(ind_rf)[1])\n",
+    "ind_compLasso$method = rep('compLasso', dim(ind_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>16</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(ind_lasso)\n",
+    "dim(ind_elnet)\n",
+    "dim(ind_rf)\n",
+    "dim(ind_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>64</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 64\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 64\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 64 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind = rbind(ind_lasso, ind_elnet)\n",
+    "ind = rbind(ind, ind_rf)\n",
+    "ind = rbind(ind, ind_compLasso)\n",
+    "dim(ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 4 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>33</th><td>50</td><td>  50</td><td>0</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50</td><td> 100</td><td>0</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50</td><td> 500</td><td>0</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50</td><td>1000</td><td>0</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 4 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <int> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t33 & 50 &   50 & 0 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t37 & 50 &  100 & 0 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t41 & 50 &  500 & 0 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t45 & 50 & 1000 & 0 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 4 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;int&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 33 | 50 |   50 | 0 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 37 | 50 |  100 | 0 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 41 | 50 |  500 | 0 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 45 | 50 | 1000 | 0 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "33 50   50 0     1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "37 50  100 0     2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "41 50  500 0    10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "45 50 1000 0    20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "   FP_mean FN_mean method\n",
+       "33 1       6       rf    \n",
+       "37 1       6       rf    \n",
+       "41 1       6       rf    \n",
+       "45 1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind[ind$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "ind$FP_mean[ind$num_select ==0] = 0\n",
+    "write.table(ind, '../results_summary_bin/table_ind_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Toeplitz results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_lasso = read.csv('../results_summary_bin/sim_toe_lasso_binary.txt', sep='\\t')\n",
+    "toe_elnet = read.csv('../results_summary_bin/sim_toe_Elnet_binary.txt', sep='\\t')\n",
+    "toe_rf = read.csv('../results_summary_bin/sim_toe_RF_binary.txt', sep='\\t')\n",
+    "toe_compLasso = read.csv('../results_summary_bin/sim_toe_GencompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "toe_lasso$method = rep('lasso', dim(toe_lasso)[1])\n",
+    "toe_elnet$method = rep('elnet', dim(toe_elnet)[1])\n",
+    "toe_rf$method = rep('rf', dim(toe_rf)[1])\n",
+    "toe_compLasso$method = rep('compLasso', dim(toe_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(toe_lasso)\n",
+    "dim(toe_elnet)\n",
+    "dim(toe_rf)\n",
+    "dim(toe_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>320</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe = rbind(toe_lasso, toe_elnet)\n",
+    "toe = rbind(toe, toe_rf)\n",
+    "toe = rbind(toe, toe_compLasso)\n",
+    "dim(toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 20 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>50</td><td>  50</td><td>0.1</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50</td><td> 100</td><td>0.1</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50</td><td> 500</td><td>0.1</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50</td><td>1000</td><td>0.1</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>50</td><td>  50</td><td>0.3</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>50</td><td> 100</td><td>0.3</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>50</td><td> 500</td><td>0.3</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>50</td><td>1000</td><td>0.3</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>50</td><td>  50</td><td>0.5</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>50</td><td> 100</td><td>0.5</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>50</td><td> 500</td><td>0.5</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>50</td><td>1000</td><td>0.5</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>50</td><td>  50</td><td>0.7</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>50</td><td> 100</td><td>0.7</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>50</td><td> 500</td><td>0.7</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>50</td><td>1000</td><td>0.7</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>50</td><td>  50</td><td>0.9</td><td> 1</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>50</td><td> 100</td><td>0.9</td><td> 2</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>50</td><td> 500</td><td>0.9</td><td>10</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>50</td><td>1000</td><td>0.9</td><td>20</td><td>NA</td><td>1 ( 0 )</td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1</td><td>1</td><td>6</td><td>rf</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 20 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <dbl> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t161 & 50 &   50 & 0.1 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t165 & 50 &  100 & 0.1 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t169 & 50 &  500 & 0.1 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t173 & 50 & 1000 & 0.1 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t177 & 50 &   50 & 0.3 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t181 & 50 &  100 & 0.3 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t185 & 50 &  500 & 0.3 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t189 & 50 & 1000 & 0.3 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t193 & 50 &   50 & 0.5 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t197 & 50 &  100 & 0.5 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t201 & 50 &  500 & 0.5 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t205 & 50 & 1000 & 0.5 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t209 & 50 &   50 & 0.7 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t213 & 50 &  100 & 0.7 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t217 & 50 &  500 & 0.7 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t221 & 50 & 1000 & 0.7 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t225 & 50 &   50 & 0.9 &  1 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t229 & 50 &  100 & 0.9 &  2 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t233 & 50 &  500 & 0.9 & 10 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\t237 & 50 & 1000 & 0.9 & 20 & NA & 1 ( 0 ) & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1 & 1 & 6 & rf\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 20 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;dbl&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 | 50 |   50 | 0.1 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 165 | 50 |  100 | 0.1 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 169 | 50 |  500 | 0.1 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 173 | 50 | 1000 | 0.1 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 177 | 50 |   50 | 0.3 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 181 | 50 |  100 | 0.3 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 185 | 50 |  500 | 0.3 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 189 | 50 | 1000 | 0.3 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 193 | 50 |   50 | 0.5 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 197 | 50 |  100 | 0.5 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 201 | 50 |  500 | 0.5 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 205 | 50 | 1000 | 0.5 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 209 | 50 |   50 | 0.7 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 213 | 50 |  100 | 0.7 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 217 | 50 |  500 | 0.7 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 221 | 50 | 1000 | 0.7 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 225 | 50 |   50 | 0.9 |  1 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 229 | 50 |  100 | 0.9 |  2 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 233 | 50 |  500 | 0.9 | 10 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "| 237 | 50 | 1000 | 0.9 | 20 | NA | 1 ( 0 ) | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1 | 1 | 6 | rf |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N  P    Corr Ratio Stab ROC     FP      FN      num_select FDR ROC_mean\n",
+       "161 50   50 0.1   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "165 50  100 0.1   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "169 50  500 0.1  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "173 50 1000 0.1  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "177 50   50 0.3   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "181 50  100 0.3   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "185 50  500 0.3  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "189 50 1000 0.3  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "193 50   50 0.5   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "197 50  100 0.5   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "201 50  500 0.5  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "205 50 1000 0.5  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "209 50   50 0.7   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "213 50  100 0.7   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "217 50  500 0.7  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "221 50 1000 0.7  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "225 50   50 0.9   1    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "229 50  100 0.9   2    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "233 50  500 0.9  10    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "237 50 1000 0.9  20    NA   1 ( 0 ) 1 ( 0 ) 6 ( 0 ) 0          NaN 1       \n",
+       "    FP_mean FN_mean method\n",
+       "161 1       6       rf    \n",
+       "165 1       6       rf    \n",
+       "169 1       6       rf    \n",
+       "173 1       6       rf    \n",
+       "177 1       6       rf    \n",
+       "181 1       6       rf    \n",
+       "185 1       6       rf    \n",
+       "189 1       6       rf    \n",
+       "193 1       6       rf    \n",
+       "197 1       6       rf    \n",
+       "201 1       6       rf    \n",
+       "205 1       6       rf    \n",
+       "209 1       6       rf    \n",
+       "213 1       6       rf    \n",
+       "217 1       6       rf    \n",
+       "221 1       6       rf    \n",
+       "225 1       6       rf    \n",
+       "229 1       6       rf    \n",
+       "233 1       6       rf    \n",
+       "237 1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe[toe$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "toe$FP_mean[toe$num_select ==0] = 0\n",
+    "write.table(toe, '../results_summary_bin/table_toe_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize Block results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block_lasso = read.csv('../results_summary_bin/sim_block_lasso_binary.txt', sep='\\t')\n",
+    "block_elnet = read.csv('../results_summary_bin/sim_block_Elnet_binary.txt', sep='\\t')\n",
+    "block_rf = read.csv('../results_summary_bin/sim_block_RF_binary.txt', sep='\\t')\n",
+    "block_compLasso = read.csv('../results_summary_bin/sim_block_GenCompLasso_binary.txt', sep='\\t')\n",
+    "\n",
+    "block_lasso$method = rep('lasso', dim(block_lasso)[1])\n",
+    "block_elnet$method = rep('elnet', dim(block_elnet)[1])\n",
+    "block_rf$method = rep('rf', dim(block_rf)[1])\n",
+    "block_compLasso$method = rep('compLasso', dim(block_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>80</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(block_lasso)\n",
+    "dim(block_elnet)\n",
+    "dim(block_rf)\n",
+    "dim(block_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>320</li><li>14</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block = rbind(block_lasso, block_elnet)\n",
+    "block = rbind(block, block_rf)\n",
+    "block = rbind(block, block_compLasso)\n",
+    "dim(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 27 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>  50</td><td>  50</td><td>0.1</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>  50</td><td> 100</td><td>0.1</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>  50</td><td> 500</td><td>0.1</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>  50</td><td>1000</td><td>0.1</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>  50</td><td>  50</td><td>0.3</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>  50</td><td> 100</td><td>0.3</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>  50</td><td> 500</td><td>0.3</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>  50</td><td>1000</td><td>0.3</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>  50</td><td>  50</td><td>0.5</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>  50</td><td> 100</td><td>0.5</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>  50</td><td> 500</td><td>0.5</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>  50</td><td>1000</td><td>0.5</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>  50</td><td>  50</td><td>0.7</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>  50</td><td> 100</td><td>0.7</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>  50</td><td> 500</td><td>0.7</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>  50</td><td>1000</td><td>0.7</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>  50</td><td>  50</td><td>0.9</td><td> 1.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>  50</td><td> 100</td><td>0.9</td><td> 2.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>  50</td><td> 500</td><td>0.9</td><td>10.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>  50</td><td>1000</td><td>0.9</td><td>20.00</td><td>NA</td><td>1 ( 0 )  </td><td>1 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>1.0</td><td>1</td><td>6</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>243</th><td> 500</td><td>  50</td><td>0.1</td><td> 0.10</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>  50</td><td>0.1</td><td> 0.05</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td> 500</td><td> 100</td><td>0.1</td><td> 0.20</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td> 100</td><td>0.1</td><td> 0.10</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td> 500</td><td> 500</td><td>0.1</td><td> 1.00</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td> 500</td><td>0.1</td><td> 0.50</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td> 1.00</td><td>NA</td><td>0.5 ( 0 )</td><td>0 ( 0 )</td><td>6 ( 0 )</td><td>0</td><td>NaN</td><td>0.5</td><td>0</td><td>6</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 27 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <int> & <int> & <dbl> & <dbl> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n",
+       "\\hline\n",
+       "\t161 &   50 &   50 & 0.1 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t165 &   50 &  100 & 0.1 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t169 &   50 &  500 & 0.1 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t173 &   50 & 1000 & 0.1 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t177 &   50 &   50 & 0.3 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t181 &   50 &  100 & 0.3 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t185 &   50 &  500 & 0.3 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t189 &   50 & 1000 & 0.3 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t193 &   50 &   50 & 0.5 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t197 &   50 &  100 & 0.5 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t201 &   50 &  500 & 0.5 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t205 &   50 & 1000 & 0.5 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t209 &   50 &   50 & 0.7 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t213 &   50 &  100 & 0.7 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t217 &   50 &  500 & 0.7 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t221 &   50 & 1000 & 0.7 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t225 &   50 &   50 & 0.9 &  1.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t229 &   50 &  100 & 0.9 &  2.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t233 &   50 &  500 & 0.9 & 10.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t237 &   50 & 1000 & 0.9 & 20.00 & NA & 1 ( 0 )   & 1 ( 0 ) & 6 ( 0 ) & 0 & NaN & 1.0 & 1 & 6 & rf       \\\\\n",
+       "\t243 &  500 &   50 & 0.1 &  0.10 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t244 & 1000 &   50 & 0.1 &  0.05 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t247 &  500 &  100 & 0.1 &  0.20 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t248 & 1000 &  100 & 0.1 &  0.10 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t251 &  500 &  500 & 0.1 &  1.00 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t252 & 1000 &  500 & 0.1 &  0.50 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 &  1.00 & NA & 0.5 ( 0 ) & 0 ( 0 ) & 6 ( 0 ) & 0 & NaN & 0.5 & 0 & 6 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 27 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;int&gt; | P &lt;int&gt; | Corr &lt;dbl&gt; | Ratio &lt;dbl&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;chr&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 |   50 |   50 | 0.1 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 165 |   50 |  100 | 0.1 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 169 |   50 |  500 | 0.1 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 173 |   50 | 1000 | 0.1 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 177 |   50 |   50 | 0.3 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 181 |   50 |  100 | 0.3 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 185 |   50 |  500 | 0.3 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 189 |   50 | 1000 | 0.3 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 193 |   50 |   50 | 0.5 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 197 |   50 |  100 | 0.5 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 201 |   50 |  500 | 0.5 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 205 |   50 | 1000 | 0.5 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 209 |   50 |   50 | 0.7 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 213 |   50 |  100 | 0.7 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 217 |   50 |  500 | 0.7 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 221 |   50 | 1000 | 0.7 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 225 |   50 |   50 | 0.9 |  1.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 229 |   50 |  100 | 0.9 |  2.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 233 |   50 |  500 | 0.9 | 10.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 237 |   50 | 1000 | 0.9 | 20.00 | NA | 1 ( 0 )   | 1 ( 0 ) | 6 ( 0 ) | 0 | NaN | 1.0 | 1 | 6 | rf        |\n",
+       "| 243 |  500 |   50 | 0.1 |  0.10 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 244 | 1000 |   50 | 0.1 |  0.05 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 247 |  500 |  100 | 0.1 |  0.20 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 248 | 1000 |  100 | 0.1 |  0.10 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 251 |  500 |  500 | 0.1 |  1.00 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 252 | 1000 |  500 | 0.1 |  0.50 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 |  1.00 | NA | 0.5 ( 0 ) | 0 ( 0 ) | 6 ( 0 ) | 0 | NaN | 0.5 | 0 | 6 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC       FP      FN      num_select FDR ROC_mean\n",
+       "161   50   50 0.1   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "165   50  100 0.1   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "169   50  500 0.1  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "173   50 1000 0.1  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "177   50   50 0.3   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "181   50  100 0.3   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "185   50  500 0.3  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "189   50 1000 0.3  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "193   50   50 0.5   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "197   50  100 0.5   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "201   50  500 0.5  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "205   50 1000 0.5  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "209   50   50 0.7   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "213   50  100 0.7   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "217   50  500 0.7  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "221   50 1000 0.7  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "225   50   50 0.9   1.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "229   50  100 0.9   2.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "233   50  500 0.9  10.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "237   50 1000 0.9  20.00 NA   1 ( 0 )   1 ( 0 ) 6 ( 0 ) 0          NaN 1.0     \n",
+       "243  500   50 0.1   0.10 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "244 1000   50 0.1   0.05 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "247  500  100 0.1   0.20 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "248 1000  100 0.1   0.10 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "251  500  500 0.1   1.00 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "252 1000  500 0.1   0.50 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "256 1000 1000 0.1   1.00 NA   0.5 ( 0 ) 0 ( 0 ) 6 ( 0 ) 0          NaN 0.5     \n",
+       "    FP_mean FN_mean method   \n",
+       "161 1       6       rf       \n",
+       "165 1       6       rf       \n",
+       "169 1       6       rf       \n",
+       "173 1       6       rf       \n",
+       "177 1       6       rf       \n",
+       "181 1       6       rf       \n",
+       "185 1       6       rf       \n",
+       "189 1       6       rf       \n",
+       "193 1       6       rf       \n",
+       "197 1       6       rf       \n",
+       "201 1       6       rf       \n",
+       "205 1       6       rf       \n",
+       "209 1       6       rf       \n",
+       "213 1       6       rf       \n",
+       "217 1       6       rf       \n",
+       "221 1       6       rf       \n",
+       "225 1       6       rf       \n",
+       "229 1       6       rf       \n",
+       "233 1       6       rf       \n",
+       "237 1       6       rf       \n",
+       "243 0       6       compLasso\n",
+       "244 0       6       compLasso\n",
+       "247 0       6       compLasso\n",
+       "248 0       6       compLasso\n",
+       "251 0       6       compLasso\n",
+       "252 0       6       compLasso\n",
+       "256 0       6       compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block[block$num_select ==0, ] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "block$FP_mean[block$num_select ==0] = 0\n",
+    "write.table(block, '../results_summary_bin/table_block_binary_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/4_sim_binary_hypothesis_testing.ipynb b/simulations/notebooks_sim_bin/4_sim_binary_hypothesis_testing.ipynb
new file mode 100644
index 0000000..2357762
--- /dev/null
+++ b/simulations/notebooks_sim_bin/4_sim_binary_hypothesis_testing.ipynb
@@ -0,0 +1,768 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hypothesis testing based on boostrap for selected simulation scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, 'binary_update/boot_toe_RF_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_block_RF_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_toe_genCompLasso_binary.RData'))\n",
+    "load(paste0(dir, 'binary_update/boot_block_genCompLasso_binary.RData'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing based on Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 4</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>toe_rf</th><th scope=col>toe_genCompLasso</th><th scope=col>block_rf</th><th scope=col>block_genCompLasso</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>0.01</td><td>0.19</td><td>0.01</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>0.01</td><td>0.19</td><td>0.01</td><td>0.18</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>0.01</td><td>0.19</td><td>0.00</td><td>0.16</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>0.01</td><td>0.19</td><td>0.00</td><td>0.20</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>0.01</td><td>0.17</td><td>0.01</td><td>0.17</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>0.01</td><td>0.18</td><td>0.01</td><td>0.18</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 4\n",
+       "\\begin{tabular}{r|llll}\n",
+       "  & toe\\_rf & toe\\_genCompLasso & block\\_rf & block\\_genCompLasso\\\\\n",
+       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t1 & 0.01 & 0.19 & 0.01 & 0.18\\\\\n",
+       "\t2 & 0.01 & 0.19 & 0.01 & 0.18\\\\\n",
+       "\t3 & 0.01 & 0.19 & 0.00 & 0.16\\\\\n",
+       "\t4 & 0.01 & 0.19 & 0.00 & 0.20\\\\\n",
+       "\t5 & 0.01 & 0.17 & 0.01 & 0.17\\\\\n",
+       "\t6 & 0.01 & 0.18 & 0.01 & 0.18\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 4\n",
+       "\n",
+       "| <!--/--> | toe_rf &lt;dbl&gt; | toe_genCompLasso &lt;dbl&gt; | block_rf &lt;dbl&gt; | block_genCompLasso &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|\n",
+       "| 1 | 0.01 | 0.19 | 0.01 | 0.18 |\n",
+       "| 2 | 0.01 | 0.19 | 0.01 | 0.18 |\n",
+       "| 3 | 0.01 | 0.19 | 0.00 | 0.16 |\n",
+       "| 4 | 0.01 | 0.19 | 0.00 | 0.20 |\n",
+       "| 5 | 0.01 | 0.17 | 0.01 | 0.17 |\n",
+       "| 6 | 0.01 | 0.18 | 0.01 | 0.18 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  toe_rf toe_genCompLasso block_rf block_genCompLasso\n",
+       "1 0.01   0.19             0.01     0.18              \n",
+       "2 0.01   0.19             0.01     0.18              \n",
+       "3 0.01   0.19             0.00     0.16              \n",
+       "4 0.01   0.19             0.00     0.20              \n",
+       "5 0.01   0.17             0.01     0.17              \n",
+       "6 0.01   0.18             0.01     0.18              "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = as.data.frame(cbind(toe_rf$stab_index, toe_genCompLasso$stab_index, block_rf$stab_index, block_genCompLasso$stab_index))\n",
+    "colnames(table) = c('toe_rf', 'toe_genCompLasso', 'block_rf', 'block_genCompLasso')\n",
+    "head(table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1732"
+      ],
+      "text/latex": [
+       "0.1732"
+      ],
+      "text/markdown": [
+       "0.1732"
+      ],
+      "text/plain": [
+       "[1] 0.1732"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.0097"
+      ],
+      "text/latex": [
+       "0.0097"
+      ],
+      "text/markdown": [
+       "0.0097"
+      ],
+      "text/plain": [
+       "[1] 0.0097"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean(table$toe_genCompLasso)\n",
+    "mean(table$toe_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1635"
+      ],
+      "text/latex": [
+       "0.1635"
+      ],
+      "text/markdown": [
+       "0.1635"
+      ],
+      "text/plain": [
+       "[1] 0.1635"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.14</dd><dt>97.5%</dt><dd>0.18</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.14\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.18\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1497.5%\n",
+       ":   0.18\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.14  0.18 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (table$toe_genCompLasso - table$toe_rf)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1782"
+      ],
+      "text/latex": [
+       "0.1782"
+      ],
+      "text/markdown": [
+       "0.1782"
+      ],
+      "text/plain": [
+       "[1] 0.1782"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.0079"
+      ],
+      "text/latex": [
+       "0.0079"
+      ],
+      "text/markdown": [
+       "0.0079"
+      ],
+      "text/plain": [
+       "[1] 0.0079"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean(table$block_genCompLasso)\n",
+    "mean(table$block_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.1703"
+      ],
+      "text/latex": [
+       "0.1703"
+      ],
+      "text/markdown": [
+       "0.1703"
+      ],
+      "text/plain": [
+       "[1] 0.1703"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>0.15</dd><dt>97.5%</dt><dd>0.19525</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.15\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.19525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1597.5%\n",
+       ":   0.19525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.15000 0.19525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (table$block_genCompLasso - table$block_rf)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### hypothesis testing based on ROC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### block 0.5, p = 1000, n = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/block_RF_binary_', i, '.RData')) \n",
+    "results_block_rf[c('n', 'p', 'rou')]\n",
+    "block_rf_rocs = results_block_rf$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/block_GenCompLasso_binary_', i, '.RData')) \n",
+    "results_block_GenCompLasso[c('n', 'p', 'rou')]\n",
+    "block_GenCompLasso_rocs = results_block_GenCompLasso$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.0181"
+      ],
+      "text/latex": [
+       "-0.0181"
+      ],
+      "text/markdown": [
+       "-0.0181"
+      ],
+      "text/plain": [
+       "[1] -0.0181"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.0700000000000001</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.0700000000000001\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.070000000000000197.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       "-0.07  0.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (block_GenCompLasso_rocs - block_rf_rocs)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diff_block\n",
+       "              -0.18 -0.0800000000000001 -0.0700000000000001 -0.0600000000000001 \n",
+       "                  1                   1                   2                   2 \n",
+       "              -0.05               -0.04               -0.03               -0.02 \n",
+       "                  7                   8                  10                  15 \n",
+       "              -0.01                   0 \n",
+       "                  2                  52 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table(diff_block)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Toeplitz 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/toe_RF_binary_', i, '.RData')) \n",
+    "results_toe_rf[c('n', 'p', 'rou')]\n",
+    "toe_rf_rocs = results_toe_rf$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$n</dt>\n",
+       "\t\t<dd><strong>N:</strong> 100</dd>\n",
+       "\t<dt>$p</dt>\n",
+       "\t\t<dd><strong>P:</strong> 1000</dd>\n",
+       "\t<dt>$rou</dt>\n",
+       "\t\t<dd>0.5</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$n] \\textbf{N:} 100\n",
+       "\\item[\\$p] \\textbf{P:} 1000\n",
+       "\\item[\\$rou] 0.5\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$n\n",
+       ":   **N:** 100\n",
+       "$p\n",
+       ":   **P:** 1000\n",
+       "$rou\n",
+       ":   0.5\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$n\n",
+       "  N \n",
+       "100 \n",
+       "\n",
+       "$p\n",
+       "   P \n",
+       "1000 \n",
+       "\n",
+       "$rou\n",
+       "[1] 0.5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 46\n",
+    "load(paste0(dir, '/binary_update/toe_GenCompLasso_binary_', i, '.RData')) \n",
+    "results_toe_GenCompLasso[c('n', 'p', 'rou')]\n",
+    "toe_GenCompLasso_rocs = results_toe_GenCompLasso$ROC.list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-6e-04"
+      ],
+      "text/latex": [
+       "-6e-04"
+      ],
+      "text/markdown": [
+       "-6e-04"
+      ],
+      "text/plain": [
+       "[1] -6e-04"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".dl-inline {width: auto; margin:0; padding: 0}\n",
+       ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n",
+       ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n",
+       ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n",
+       "</style><dl class=dl-inline><dt>2.5%</dt><dd>-0.01</dd><dt>97.5%</dt><dd>0</dd></dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.01\n",
+       "\\item[97.5\\textbackslash{}\\%] 0\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.0197.5%\n",
+       ":   0\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       "-0.01  0.00 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (toe_GenCompLasso_rocs - toe_rf_rocs)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "diff_toe\n",
+       "-0.02 -0.01     0 \n",
+       "    2     2    96 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table(diff_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_bin/6_make_figures_combined.ipynb b/simulations/notebooks_sim_bin/6_make_figures_combined.ipynb
new file mode 100644
index 0000000..992fa99
--- /dev/null
+++ b/simulations/notebooks_sim_bin/6_make_figures_combined.ipynb
@@ -0,0 +1,1937 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate updated figures for both continuous and binary outcomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading required package: magrittr\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "library(ggplot2)\n",
+    "library(gridExtra)\n",
+    "library(ggpubr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### load continuous results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts = read.csv('../results_summary_cts/table_ind_all.txt', sep='\\t')\n",
+    "toe_cts = read.csv('../results_summary_cts/table_toe_all.txt', sep='\\t')\n",
+    "block_cts = read.csv('../results_summary_cts/table_block_all.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts$Ratio = as.factor(ind_cts$Ratio)\n",
+    "toe_cts$Ratio = as.factor(toe_cts$Ratio)\n",
+    "block_cts$Ratio = as.factor(block_cts$Ratio)\n",
+    "\n",
+    "toe_cts$N = as.factor(toe_cts$N)\n",
+    "toe_cts$P = as.factor(toe_cts$P)\n",
+    "\n",
+    "toe_cts_sub1 = toe_cts[toe_cts$Corr %in% 0.1, ]\n",
+    "toe_cts_sub3 = toe_cts[toe_cts$Corr %in% 0.3, ]\n",
+    "toe_cts_sub5 = toe_cts[toe_cts$Corr %in% 0.5, ]\n",
+    "toe_cts_sub7 = toe_cts[toe_cts$Corr %in% 0.7, ]\n",
+    "toe_cts_sub9 = toe_cts[toe_cts$Corr %in% 0.9, ]\n",
+    "\n",
+    "block_cts$N = as.factor(block_cts$N)\n",
+    "block_cts$P = as.factor(block_cts$P)\n",
+    "block_cts_sub1 = block_cts[block_cts$Corr %in% 0.1, ]\n",
+    "block_cts_sub3 = block_cts[block_cts$Corr %in% 0.3, ]\n",
+    "block_cts_sub5 = block_cts[block_cts$Corr %in% 0.5, ]\n",
+    "block_cts_sub7 = block_cts[block_cts$Corr %in% 0.7, ]\n",
+    "block_cts_sub9 = block_cts[block_cts$Corr %in% 0.9, ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### load binary results (not that should be AUC in stead of ROC -- code checked)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin = read.csv('../results_summary_bin/table_ind_binary_all.txt', sep='\\t')\n",
+    "toe_bin = read.csv('../results_summary_bin/table_toe_binary_all.txt', sep='\\t')\n",
+    "block_bin = read.csv('../results_summary_bin/table_block_binary_all.txt', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin$Ratio = as.factor(ind_bin$Ratio)\n",
+    "toe_bin$Ratio = as.factor(toe_bin$Ratio)\n",
+    "block_bin$Ratio = as.factor(block_bin$Ratio)\n",
+    "\n",
+    "toe_bin$N = as.factor(toe_bin$N)\n",
+    "toe_bin$P = as.factor(toe_bin$P)\n",
+    "toe_bin_sub1 = toe_bin[toe_bin$Corr %in% 0.1, ]\n",
+    "toe_bin_sub3 = toe_bin[toe_bin$Corr %in% 0.3, ]\n",
+    "toe_bin_sub5 = toe_bin[toe_bin$Corr %in% 0.5, ]\n",
+    "toe_bin_sub7 = toe_bin[toe_bin$Corr %in% 0.7, ]\n",
+    "toe_bin_sub9 = toe_bin[toe_bin$Corr %in% 0.9, ]\n",
+    "\n",
+    "block_bin$N = as.factor(block_bin$N)\n",
+    "block_bin$P = as.factor(block_bin$P)\n",
+    "block_bin_sub1 = block_bin[block_bin$Corr %in% 0.1, ]\n",
+    "block_bin_sub3 = block_bin[block_bin$Corr %in% 0.3, ]\n",
+    "block_bin_sub5 = block_bin[block_bin$Corr %in% 0.5, ]\n",
+    "block_bin_sub7 = block_bin[block_bin$Corr %in% 0.7, ]\n",
+    "block_bin_sub9 = block_bin[block_bin$Corr %in% 0.9, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 6 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50  </td><td>50 </td><td>0.1</td><td>1   </td><td>0.03</td><td>0.59 ( 0.01 )</td><td>2.69 ( 0.14 )</td><td>5.3 ( 0.07 ) </td><td>2.39</td><td>0.67</td><td>0.59</td><td>2.69</td><td>5.30</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>2</th><td>100 </td><td>50 </td><td>0.1</td><td>0.5 </td><td>0.11</td><td>0.56 ( 0.01 )</td><td>2.02 ( 0.15 )</td><td>5.02 ( 0.08 )</td><td>2.00</td><td>0.39</td><td>0.56</td><td>2.02</td><td>5.02</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>3</th><td>500 </td><td>50 </td><td>0.1</td><td>0.1 </td><td>0.28</td><td>0.63 ( 0 )   </td><td>4.52 ( 0.29 )</td><td>3 ( 0.11 )   </td><td>6.52</td><td>0.45</td><td>0.63</td><td>4.52</td><td>3.00</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000</td><td>50 </td><td>0.1</td><td>0.05</td><td>0.39</td><td>0.64 ( 0 )   </td><td>4.74 ( 0.22 )</td><td>2.1 ( 0.09 ) </td><td>7.64</td><td>0.44</td><td>0.64</td><td>4.74</td><td>2.10</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50  </td><td>100</td><td>0.1</td><td>2   </td><td>0.02</td><td>0.62 ( 0.01 )</td><td>3.82 ( 0.16 )</td><td>5.34 ( 0.07 )</td><td>3.48</td><td>0.78</td><td>0.62</td><td>3.82</td><td>5.34</td><td>lasso</td></tr>\n",
+       "\t<tr><th scope=row>6</th><td>100 </td><td>100</td><td>0.1</td><td>1   </td><td>0.07</td><td>0.56 ( 0.01 )</td><td>2.29 ( 0.19 )</td><td>5.27 ( 0.08 )</td><td>2.02</td><td>0.57</td><td>0.56</td><td>2.29</td><td>5.27</td><td>lasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 6 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t1 & 50   & 50  & 0.1 & 1    & 0.03 & 0.59 ( 0.01 ) & 2.69 ( 0.14 ) & 5.3 ( 0.07 )  & 2.39 & 0.67 & 0.59 & 2.69 & 5.30 & lasso\\\\\n",
+       "\t2 & 100  & 50  & 0.1 & 0.5  & 0.11 & 0.56 ( 0.01 ) & 2.02 ( 0.15 ) & 5.02 ( 0.08 ) & 2.00 & 0.39 & 0.56 & 2.02 & 5.02 & lasso\\\\\n",
+       "\t3 & 500  & 50  & 0.1 & 0.1  & 0.28 & 0.63 ( 0 )    & 4.52 ( 0.29 ) & 3 ( 0.11 )    & 6.52 & 0.45 & 0.63 & 4.52 & 3.00 & lasso\\\\\n",
+       "\t4 & 1000 & 50  & 0.1 & 0.05 & 0.39 & 0.64 ( 0 )    & 4.74 ( 0.22 ) & 2.1 ( 0.09 )  & 7.64 & 0.44 & 0.64 & 4.74 & 2.10 & lasso\\\\\n",
+       "\t5 & 50   & 100 & 0.1 & 2    & 0.02 & 0.62 ( 0.01 ) & 3.82 ( 0.16 ) & 5.34 ( 0.07 ) & 3.48 & 0.78 & 0.62 & 3.82 & 5.34 & lasso\\\\\n",
+       "\t6 & 100  & 100 & 0.1 & 1    & 0.07 & 0.56 ( 0.01 ) & 2.29 ( 0.19 ) & 5.27 ( 0.08 ) & 2.02 & 0.57 & 0.56 & 2.29 & 5.27 & lasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 6 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50   | 50  | 0.1 | 1    | 0.03 | 0.59 ( 0.01 ) | 2.69 ( 0.14 ) | 5.3 ( 0.07 )  | 2.39 | 0.67 | 0.59 | 2.69 | 5.30 | lasso |\n",
+       "| 2 | 100  | 50  | 0.1 | 0.5  | 0.11 | 0.56 ( 0.01 ) | 2.02 ( 0.15 ) | 5.02 ( 0.08 ) | 2.00 | 0.39 | 0.56 | 2.02 | 5.02 | lasso |\n",
+       "| 3 | 500  | 50  | 0.1 | 0.1  | 0.28 | 0.63 ( 0 )    | 4.52 ( 0.29 ) | 3 ( 0.11 )    | 6.52 | 0.45 | 0.63 | 4.52 | 3.00 | lasso |\n",
+       "| 4 | 1000 | 50  | 0.1 | 0.05 | 0.39 | 0.64 ( 0 )    | 4.74 ( 0.22 ) | 2.1 ( 0.09 )  | 7.64 | 0.44 | 0.64 | 4.74 | 2.10 | lasso |\n",
+       "| 5 | 50   | 100 | 0.1 | 2    | 0.02 | 0.62 ( 0.01 ) | 3.82 ( 0.16 ) | 5.34 ( 0.07 ) | 3.48 | 0.78 | 0.62 | 3.82 | 5.34 | lasso |\n",
+       "| 6 | 100  | 100 | 0.1 | 1    | 0.07 | 0.56 ( 0.01 ) | 2.29 ( 0.19 ) | 5.27 ( 0.08 ) | 2.02 | 0.57 | 0.56 | 2.29 | 5.27 | lasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab ROC           FP            FN            num_select\n",
+       "1 50   50  0.1  1     0.03 0.59 ( 0.01 ) 2.69 ( 0.14 ) 5.3 ( 0.07 )  2.39      \n",
+       "2 100  50  0.1  0.5   0.11 0.56 ( 0.01 ) 2.02 ( 0.15 ) 5.02 ( 0.08 ) 2.00      \n",
+       "3 500  50  0.1  0.1   0.28 0.63 ( 0 )    4.52 ( 0.29 ) 3 ( 0.11 )    6.52      \n",
+       "4 1000 50  0.1  0.05  0.39 0.64 ( 0 )    4.74 ( 0.22 ) 2.1 ( 0.09 )  7.64      \n",
+       "5 50   100 0.1  2     0.02 0.62 ( 0.01 ) 3.82 ( 0.16 ) 5.34 ( 0.07 ) 3.48      \n",
+       "6 100  100 0.1  1     0.07 0.56 ( 0.01 ) 2.29 ( 0.19 ) 5.27 ( 0.08 ) 2.02      \n",
+       "  FDR  ROC_mean FP_mean FN_mean method\n",
+       "1 0.67 0.59     2.69    5.30    lasso \n",
+       "2 0.39 0.56     2.02    5.02    lasso \n",
+       "3 0.45 0.63     4.52    3.00    lasso \n",
+       "4 0.44 0.64     4.74    2.10    lasso \n",
+       "5 0.78 0.62     3.82    5.34    lasso \n",
+       "6 0.57 0.56     2.29    5.27    lasso "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(block_bin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 1: MSE/AUC vs. Stability (evaludated by FPR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 9 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>243</th><td>500 </td><td>50  </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>50  </td><td>0.1</td><td>0.05</td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td>500 </td><td>100 </td><td>0.1</td><td>0.2 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td>100 </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>249</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 )</td><td>5.17 ( 0.13 )</td><td>30.45</td><td>0.9</td><td>0.73</td><td>29.62</td><td>5.17</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td>500 </td><td>500 </td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td>500 </td><td>0.1</td><td>0.5 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>255</th><td>500 </td><td>1000</td><td>0.1</td><td>2   </td><td> 0</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>5.98 ( 0.02 )</td><td> 0.02</td><td>0.0</td><td>0.50</td><td> 0.00</td><td>5.98</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )       </td><td>6 ( 0 )      </td><td> 0.00</td><td> NA</td><td>0.50</td><td> 0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 9 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t243 & 500  & 50   & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t244 & 1000 & 50   & 0.1 & 0.05 & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t247 & 500  & 100  & 0.1 & 0.2  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t248 & 1000 & 100  & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t249 & 50   & 500  & 0.1 & 10   &  0 & 0.73 ( 0.01 ) & 29.62 ( 7.94 ) & 5.17 ( 0.13 ) & 30.45 & 0.9 & 0.73 & 29.62 & 5.17 & compLasso\\\\\n",
+       "\t251 & 500  & 500  & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t252 & 1000 & 500  & 0.1 & 0.5  & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\t255 & 500  & 1000 & 0.1 & 2    &  0 & 0.5 ( 0 )     & 0 ( 0 )        & 5.98 ( 0.02 ) &  0.02 & 0.0 & 0.50 &  0.00 & 5.98 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )        & 6 ( 0 )       &  0.00 &  NA & 0.50 &  0.00 & 6.00 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 9 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 243 | 500  | 50   | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 244 | 1000 | 50   | 0.1 | 0.05 | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 247 | 500  | 100  | 0.1 | 0.2  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 248 | 1000 | 100  | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 249 | 50   | 500  | 0.1 | 10   |  0 | 0.73 ( 0.01 ) | 29.62 ( 7.94 ) | 5.17 ( 0.13 ) | 30.45 | 0.9 | 0.73 | 29.62 | 5.17 | compLasso |\n",
+       "| 251 | 500  | 500  | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 252 | 1000 | 500  | 0.1 | 0.5  | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "| 255 | 500  | 1000 | 0.1 | 2    |  0 | 0.5 ( 0 )     | 0 ( 0 )        | 5.98 ( 0.02 ) |  0.02 | 0.0 | 0.50 |  0.00 | 5.98 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )        | 6 ( 0 )       |  0.00 |  NA | 0.50 |  0.00 | 6.00 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC           FP             FN           \n",
+       "243 500  50   0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "244 1000 50   0.1  0.05  NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "247 500  100  0.1  0.2   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "248 1000 100  0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "249 50   500  0.1  10     0   0.73 ( 0.01 ) 29.62 ( 7.94 ) 5.17 ( 0.13 )\n",
+       "251 500  500  0.1  1     NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "252 1000 500  0.1  0.5   NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "255 500  1000 0.1  2      0   0.5 ( 0 )     0 ( 0 )        5.98 ( 0.02 )\n",
+       "256 1000 1000 0.1  1     NA   0.5 ( 0 )     0 ( 0 )        6 ( 0 )      \n",
+       "    num_select FDR ROC_mean FP_mean FN_mean method   \n",
+       "243  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "244  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "247  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "248  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "249 30.45      0.9 0.73     29.62   5.17    compLasso\n",
+       "251  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "252  0.00       NA 0.50      0.00   6.00    compLasso\n",
+       "255  0.02      0.0 0.50      0.00   5.98    compLasso\n",
+       "256  0.00       NA 0.50      0.00   6.00    compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block_bin[block_bin$method=='compLasso' & block_bin$Stab %in% c(NA, 0),]\n",
+    "\n",
+    "# composittional Lasso perform poor (often select nothing) at Block Corr 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<caption>A data.frame: 21 × 14</caption>\n",
+       "<thead>\n",
+       "\t<tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>ROC</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>ROC_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr>\n",
+       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>13</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td> 0</td><td>0.62 ( 0.01 )</td><td>10.42 ( 0.22 )  </td><td>5.56 ( 0.06 )</td><td>  9.86</td><td>0.95</td><td>0.62</td><td> 10.42</td><td>5.56</td><td>lasso    </td></tr>\n",
+       "\t<tr><th scope=row>89</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.61 ( 0.01 )</td><td>69.03 ( 7.45 )  </td><td>4.13 ( 0.19 )</td><td> 69.90</td><td>0.96</td><td>0.61</td><td> 69.03</td><td>4.13</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>90</th><td>100 </td><td>500 </td><td>0.1</td><td>5   </td><td> 0</td><td>0.57 ( 0.01 )</td><td>70.64 ( 9.13 )  </td><td>3.91 ( 0.17 )</td><td> 71.73</td><td>0.91</td><td>0.57</td><td> 70.64</td><td>3.91</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>93</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td> 0</td><td>0.59 ( 0.01 )</td><td>103.61 ( 10.54 )</td><td>4.34 ( 0.18 )</td><td>104.27</td><td>0.98</td><td>0.59</td><td>103.61</td><td>4.34</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>94</th><td>100 </td><td>1000</td><td>0.1</td><td>10  </td><td> 0</td><td>0.56 ( 0.01 )</td><td>103.21 ( 12.75 )</td><td>4.15 ( 0.17 )</td><td>104.06</td><td>0.95</td><td>0.56</td><td>103.21</td><td>4.15</td><td>elnet    </td></tr>\n",
+       "\t<tr><th scope=row>161</th><td>50  </td><td>50  </td><td>0.1</td><td>1   </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50  </td><td>100 </td><td>0.1</td><td>2   </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>166</th><td>100 </td><td>100 </td><td>0.1</td><td>1   </td><td> 0</td><td>1 ( 0 )      </td><td>5.09 ( 0.21 )   </td><td>5.6 ( 0.09 ) </td><td>  5.48</td><td>0.93</td><td>1.00</td><td>  5.09</td><td>5.60</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>170</th><td>100 </td><td>500 </td><td>0.1</td><td>5   </td><td> 0</td><td>1 ( 0 )      </td><td>24.14 ( 0.56 )  </td><td>5.35 ( 0.11 )</td><td> 24.79</td><td>0.97</td><td>1.00</td><td> 24.14</td><td>5.35</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50  </td><td>1000</td><td>0.1</td><td>20  </td><td>NA</td><td>1 ( 0 )      </td><td>1 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>1.00</td><td>  0.00</td><td>6.00</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>174</th><td>100 </td><td>1000</td><td>0.1</td><td>10  </td><td> 0</td><td>1 ( 0 )      </td><td>48.22 ( 0.62 )  </td><td>5.53 ( 0.09 )</td><td> 48.69</td><td>0.99</td><td>1.00</td><td> 48.22</td><td>5.53</td><td>rf       </td></tr>\n",
+       "\t<tr><th scope=row>243</th><td>500 </td><td>50  </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>244</th><td>1000</td><td>50  </td><td>0.1</td><td>0.05</td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>247</th><td>500 </td><td>100 </td><td>0.1</td><td>0.2 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>248</th><td>1000</td><td>100 </td><td>0.1</td><td>0.1 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>249</th><td>50  </td><td>500 </td><td>0.1</td><td>10  </td><td> 0</td><td>0.73 ( 0.01 )</td><td>29.62 ( 7.94 )  </td><td>5.17 ( 0.13 )</td><td> 30.45</td><td>0.90</td><td>0.73</td><td> 29.62</td><td>5.17</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>251</th><td>500 </td><td>500 </td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>252</th><td>1000</td><td>500 </td><td>0.1</td><td>0.5 </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>255</th><td>500 </td><td>1000</td><td>0.1</td><td>2   </td><td> 0</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>5.98 ( 0.02 )</td><td>  0.02</td><td>0.00</td><td>0.50</td><td>  0.00</td><td>5.98</td><td>compLasso</td></tr>\n",
+       "\t<tr><th scope=row>256</th><td>1000</td><td>1000</td><td>0.1</td><td>1   </td><td>NA</td><td>0.5 ( 0 )    </td><td>0 ( 0 )         </td><td>6 ( 0 )      </td><td>  0.00</td><td>  NA</td><td>0.50</td><td>  0.00</td><td>6.00</td><td>compLasso</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "A data.frame: 21 × 14\n",
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & ROC & FP & FN & num\\_select & FDR & ROC\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "  & <fct> & <fct> & <dbl> & <fct> & <dbl> & <fct> & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t13 & 50   & 1000 & 0.1 & 20   &  0 & 0.62 ( 0.01 ) & 10.42 ( 0.22 )   & 5.56 ( 0.06 ) &   9.86 & 0.95 & 0.62 &  10.42 & 5.56 & lasso    \\\\\n",
+       "\t89 & 50   & 500  & 0.1 & 10   &  0 & 0.61 ( 0.01 ) & 69.03 ( 7.45 )   & 4.13 ( 0.19 ) &  69.90 & 0.96 & 0.61 &  69.03 & 4.13 & elnet    \\\\\n",
+       "\t90 & 100  & 500  & 0.1 & 5    &  0 & 0.57 ( 0.01 ) & 70.64 ( 9.13 )   & 3.91 ( 0.17 ) &  71.73 & 0.91 & 0.57 &  70.64 & 3.91 & elnet    \\\\\n",
+       "\t93 & 50   & 1000 & 0.1 & 20   &  0 & 0.59 ( 0.01 ) & 103.61 ( 10.54 ) & 4.34 ( 0.18 ) & 104.27 & 0.98 & 0.59 & 103.61 & 4.34 & elnet    \\\\\n",
+       "\t94 & 100  & 1000 & 0.1 & 10   &  0 & 0.56 ( 0.01 ) & 103.21 ( 12.75 ) & 4.15 ( 0.17 ) & 104.06 & 0.95 & 0.56 & 103.21 & 4.15 & elnet    \\\\\n",
+       "\t161 & 50   & 50   & 0.1 & 1    & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t165 & 50   & 100  & 0.1 & 2    & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t166 & 100  & 100  & 0.1 & 1    &  0 & 1 ( 0 )       & 5.09 ( 0.21 )    & 5.6 ( 0.09 )  &   5.48 & 0.93 & 1.00 &   5.09 & 5.60 & rf       \\\\\n",
+       "\t169 & 50   & 500  & 0.1 & 10   & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t170 & 100  & 500  & 0.1 & 5    &  0 & 1 ( 0 )       & 24.14 ( 0.56 )   & 5.35 ( 0.11 ) &  24.79 & 0.97 & 1.00 &  24.14 & 5.35 & rf       \\\\\n",
+       "\t173 & 50   & 1000 & 0.1 & 20   & NA & 1 ( 0 )       & 1 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 1.00 &   0.00 & 6.00 & rf       \\\\\n",
+       "\t174 & 100  & 1000 & 0.1 & 10   &  0 & 1 ( 0 )       & 48.22 ( 0.62 )   & 5.53 ( 0.09 ) &  48.69 & 0.99 & 1.00 &  48.22 & 5.53 & rf       \\\\\n",
+       "\t243 & 500  & 50   & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t244 & 1000 & 50   & 0.1 & 0.05 & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t247 & 500  & 100  & 0.1 & 0.2  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t248 & 1000 & 100  & 0.1 & 0.1  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t249 & 50   & 500  & 0.1 & 10   &  0 & 0.73 ( 0.01 ) & 29.62 ( 7.94 )   & 5.17 ( 0.13 ) &  30.45 & 0.90 & 0.73 &  29.62 & 5.17 & compLasso\\\\\n",
+       "\t251 & 500  & 500  & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t252 & 1000 & 500  & 0.1 & 0.5  & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\t255 & 500  & 1000 & 0.1 & 2    &  0 & 0.5 ( 0 )     & 0 ( 0 )          & 5.98 ( 0.02 ) &   0.02 & 0.00 & 0.50 &   0.00 & 5.98 & compLasso\\\\\n",
+       "\t256 & 1000 & 1000 & 0.1 & 1    & NA & 0.5 ( 0 )     & 0 ( 0 )          & 6 ( 0 )       &   0.00 &   NA & 0.50 &   0.00 & 6.00 & compLasso\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "A data.frame: 21 × 14\n",
+       "\n",
+       "| <!--/--> | N &lt;fct&gt; | P &lt;fct&gt; | Corr &lt;dbl&gt; | Ratio &lt;fct&gt; | Stab &lt;dbl&gt; | ROC &lt;fct&gt; | FP &lt;fct&gt; | FN &lt;fct&gt; | num_select &lt;dbl&gt; | FDR &lt;dbl&gt; | ROC_mean &lt;dbl&gt; | FP_mean &lt;dbl&gt; | FN_mean &lt;dbl&gt; | method &lt;fct&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 13 | 50   | 1000 | 0.1 | 20   |  0 | 0.62 ( 0.01 ) | 10.42 ( 0.22 )   | 5.56 ( 0.06 ) |   9.86 | 0.95 | 0.62 |  10.42 | 5.56 | lasso     |\n",
+       "| 89 | 50   | 500  | 0.1 | 10   |  0 | 0.61 ( 0.01 ) | 69.03 ( 7.45 )   | 4.13 ( 0.19 ) |  69.90 | 0.96 | 0.61 |  69.03 | 4.13 | elnet     |\n",
+       "| 90 | 100  | 500  | 0.1 | 5    |  0 | 0.57 ( 0.01 ) | 70.64 ( 9.13 )   | 3.91 ( 0.17 ) |  71.73 | 0.91 | 0.57 |  70.64 | 3.91 | elnet     |\n",
+       "| 93 | 50   | 1000 | 0.1 | 20   |  0 | 0.59 ( 0.01 ) | 103.61 ( 10.54 ) | 4.34 ( 0.18 ) | 104.27 | 0.98 | 0.59 | 103.61 | 4.34 | elnet     |\n",
+       "| 94 | 100  | 1000 | 0.1 | 10   |  0 | 0.56 ( 0.01 ) | 103.21 ( 12.75 ) | 4.15 ( 0.17 ) | 104.06 | 0.95 | 0.56 | 103.21 | 4.15 | elnet     |\n",
+       "| 161 | 50   | 50   | 0.1 | 1    | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 165 | 50   | 100  | 0.1 | 2    | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 166 | 100  | 100  | 0.1 | 1    |  0 | 1 ( 0 )       | 5.09 ( 0.21 )    | 5.6 ( 0.09 )  |   5.48 | 0.93 | 1.00 |   5.09 | 5.60 | rf        |\n",
+       "| 169 | 50   | 500  | 0.1 | 10   | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 170 | 100  | 500  | 0.1 | 5    |  0 | 1 ( 0 )       | 24.14 ( 0.56 )   | 5.35 ( 0.11 ) |  24.79 | 0.97 | 1.00 |  24.14 | 5.35 | rf        |\n",
+       "| 173 | 50   | 1000 | 0.1 | 20   | NA | 1 ( 0 )       | 1 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 1.00 |   0.00 | 6.00 | rf        |\n",
+       "| 174 | 100  | 1000 | 0.1 | 10   |  0 | 1 ( 0 )       | 48.22 ( 0.62 )   | 5.53 ( 0.09 ) |  48.69 | 0.99 | 1.00 |  48.22 | 5.53 | rf        |\n",
+       "| 243 | 500  | 50   | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 244 | 1000 | 50   | 0.1 | 0.05 | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 247 | 500  | 100  | 0.1 | 0.2  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 248 | 1000 | 100  | 0.1 | 0.1  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 249 | 50   | 500  | 0.1 | 10   |  0 | 0.73 ( 0.01 ) | 29.62 ( 7.94 )   | 5.17 ( 0.13 ) |  30.45 | 0.90 | 0.73 |  29.62 | 5.17 | compLasso |\n",
+       "| 251 | 500  | 500  | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 252 | 1000 | 500  | 0.1 | 0.5  | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "| 255 | 500  | 1000 | 0.1 | 2    |  0 | 0.5 ( 0 )     | 0 ( 0 )          | 5.98 ( 0.02 ) |   0.02 | 0.00 | 0.50 |   0.00 | 5.98 | compLasso |\n",
+       "| 256 | 1000 | 1000 | 0.1 | 1    | NA | 0.5 ( 0 )     | 0 ( 0 )          | 6 ( 0 )       |   0.00 |   NA | 0.50 |   0.00 | 6.00 | compLasso |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab ROC           FP               FN           \n",
+       "13  50   1000 0.1  20     0   0.62 ( 0.01 ) 10.42 ( 0.22 )   5.56 ( 0.06 )\n",
+       "89  50   500  0.1  10     0   0.61 ( 0.01 ) 69.03 ( 7.45 )   4.13 ( 0.19 )\n",
+       "90  100  500  0.1  5      0   0.57 ( 0.01 ) 70.64 ( 9.13 )   3.91 ( 0.17 )\n",
+       "93  50   1000 0.1  20     0   0.59 ( 0.01 ) 103.61 ( 10.54 ) 4.34 ( 0.18 )\n",
+       "94  100  1000 0.1  10     0   0.56 ( 0.01 ) 103.21 ( 12.75 ) 4.15 ( 0.17 )\n",
+       "161 50   50   0.1  1     NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "165 50   100  0.1  2     NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "166 100  100  0.1  1      0   1 ( 0 )       5.09 ( 0.21 )    5.6 ( 0.09 ) \n",
+       "169 50   500  0.1  10    NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "170 100  500  0.1  5      0   1 ( 0 )       24.14 ( 0.56 )   5.35 ( 0.11 )\n",
+       "173 50   1000 0.1  20    NA   1 ( 0 )       1 ( 0 )          6 ( 0 )      \n",
+       "174 100  1000 0.1  10     0   1 ( 0 )       48.22 ( 0.62 )   5.53 ( 0.09 )\n",
+       "243 500  50   0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "244 1000 50   0.1  0.05  NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "247 500  100  0.1  0.2   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "248 1000 100  0.1  0.1   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "249 50   500  0.1  10     0   0.73 ( 0.01 ) 29.62 ( 7.94 )   5.17 ( 0.13 )\n",
+       "251 500  500  0.1  1     NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "252 1000 500  0.1  0.5   NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "255 500  1000 0.1  2      0   0.5 ( 0 )     0 ( 0 )          5.98 ( 0.02 )\n",
+       "256 1000 1000 0.1  1     NA   0.5 ( 0 )     0 ( 0 )          6 ( 0 )      \n",
+       "    num_select FDR  ROC_mean FP_mean FN_mean method   \n",
+       "13    9.86     0.95 0.62      10.42  5.56    lasso    \n",
+       "89   69.90     0.96 0.61      69.03  4.13    elnet    \n",
+       "90   71.73     0.91 0.57      70.64  3.91    elnet    \n",
+       "93  104.27     0.98 0.59     103.61  4.34    elnet    \n",
+       "94  104.06     0.95 0.56     103.21  4.15    elnet    \n",
+       "161   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "165   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "166   5.48     0.93 1.00       5.09  5.60    rf       \n",
+       "169   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "170  24.79     0.97 1.00      24.14  5.35    rf       \n",
+       "173   0.00       NA 1.00       0.00  6.00    rf       \n",
+       "174  48.69     0.99 1.00      48.22  5.53    rf       \n",
+       "243   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "244   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "247   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "248   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "249  30.45     0.90 0.73      29.62  5.17    compLasso\n",
+       "251   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "252   0.00       NA 0.50       0.00  6.00    compLasso\n",
+       "255   0.02     0.00 0.50       0.00  5.98    compLasso\n",
+       "256   0.00       NA 0.50       0.00  6.00    compLasso"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check other method results at Block Corr 0.1\n",
+    "block_bin[block_bin$Corr == 0.1 & block_bin$Stab %in% c(NA, 0),]\n",
+    "\n",
+    "# other method: Stab = 0 or NA (if Stab = 0, then high FPR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "ind_cts_mse_fpr <- ggplot(ind_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_cts_stab_fpr <- ggplot(ind_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_mse_fpr <- ggplot(toe_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_stab_fpr <- ggplot(toe_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_mse_fpr <- ggplot(block_cts, aes(x=FDR, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_stab_fpr <- ggplot(block_cts, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "fig1_cts <- ggarrange(ind_cts_mse_fpr, ind_cts_stab_fpr, toe_cts_mse_fpr, toe_cts_stab_fpr, \n",
+    "          block_cts_mse_fpr, block_cts_stab_fpr,       \n",
+    "          nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "ind_bin_ROC_fpr <- ggplot(ind_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "ind_bin_stab_fpr <- ggplot(ind_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_ROC_fpr <- ggplot(toe_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_stab_fpr <- ggplot(toe_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_ROC_fpr <- ggplot(block_bin, aes(x=FDR, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_stab_fpr <- ggplot(block_bin, aes(x=FDR, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Positive Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "fig1_bin <- ggarrange(ind_bin_ROC_fpr, ind_bin_stab_fpr, toe_bin_ROC_fpr, toe_bin_stab_fpr, block_bin_ROC_fpr, block_bin_stab_fpr,       \n",
+    "  nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Feh1tIPbAsBQZpptwZRCwesyuy6kpjXCLU6dk7vWtBMrBw2Iq7rMctyuvuYWYgD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\niPM1Ho/X1taOGDEig1c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iPb2xmSYNA4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5sY5EsiPja69ILfuuGEoVzchI0kOADJgE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\noc/bw8PjuOSiosJv7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AASmQJkkVsNBX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Y5Hq21I7yPGVWlB0IQbk8mk20vEAG4gi+15K7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\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VoRU0JsGGCjTdpOyAZBAO2kRcKxSTr2QMYZkElkdWtpdVf1F24rV1z/fAyFXopt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\ncJtExw/OHS/hsLCfY4f0CICjnTm3UfXmtCGQUNKGDs79XF3ybZ1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YQKNjZzSNCwBoa72z0xWWNVXq2zwzndedWev/UDoPTjqyFKXmmF0x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\n4Wxh0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umA3WhEJ1x6NQ3TEbrAmF6o5H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",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1_combined <- ggarrange(ind_cts_mse_fpr, ind_cts_stab_fpr, ind_bin_ROC_fpr, ind_bin_stab_fpr, \n",
+    "                           toe_cts_mse_fpr, toe_cts_stab_fpr, toe_bin_ROC_fpr, toe_bin_stab_fpr, \n",
+    "                           block_cts_mse_fpr, block_cts_stab_fpr,  block_bin_ROC_fpr, block_bin_stab_fpr,            \n",
+    "                           nrow=3, ncol = 4, align = \"hv\", common.legend = T, legend = \"bottom\",\n",
+    "                           labels = \"AUTO\") \n",
+    "fig1_combined\n",
+    "\n",
+    "ggsave(\"../figures_combined/fig1_combined.png\", fig1_combined, dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S1: MSE/AUC vs. Stability (evaludated by FNR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_cts_mse_fnr <- ggplot(ind_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='MSE')  + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_cts_stab_fnr <- ggplot(ind_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_mse_fnr <- ggplot(toe_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_cts_stab_fnr <- ggplot(toe_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_mse_fnr <- ggplot(block_cts, aes(x=FN_mean/6, y=MSE_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='MSE') + ylim(0, 4) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_cts_stab_fnr <- ggplot(block_cts, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "# ggarrange(ind_cts_mse_fnr, ind_cts_stab_fnr, toe_cts_mse_fnr, toe_cts_stab_fnr, block_cts_mse_fnr, block_cts_stab_fnr,       \n",
+    "#   nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_bin_ROC_fnr <- ggplot(ind_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='AUC')  + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "ind_bin_stab_fnr <- ggplot(ind_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.8) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Independent', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "\n",
+    "toe_bin_ROC_fnr <- ggplot(toe_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "toe_bin_stab_fnr <- ggplot(toe_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Toeplitz', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_ROC_fnr <- ggplot(block_bin, aes(x=FN_mean/6, y=ROC_mean, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='AUC') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "block_bin_stab_fnr <- ggplot(block_bin, aes(x=FN_mean/6, y=Stab, color=method, size=Ratio)) + geom_point(alpha=0.3) + \n",
+    "         theme(legend.position=\"bottom\", legend.box=\"vertical\", legend.margin=margin()) +\n",
+    "         scale_size_manual(values=c(0.1, 0.5, 1, 1.5, 2, 4, 5.5, 7.5, 8.5)) + \n",
+    "         labs(title='Block', x='False Negative Rate', y='Stability') + ylim(0, 1) +\n",
+    "        theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "              axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "              axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "              panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "              legend.text = element_text(size = 8))\n",
+    "\n",
+    "\n",
+    "# ggarrange(ind_bin_ROC_fnr, ind_bin_stab_fnr, toe_bin_ROC_fnr, toe_bin_stab_fnr, block_bin_ROC_fnr, block_bin_stab_fnr,       \n",
+    "#   nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 20 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 27 rows containing missing values (geom_point).”\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Zvfkes+les/4y/Ww7EAoqI8sWCxmLuA8guG2kDF6MTtmRQb3Qdh0W0ZgKfhNJ18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\nfvZXu+wiqugFr6fLzc3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6eT0hsiJxiv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ngoGxa8wixlia6oTnjdzbiu/zYPErhC5hFA\nIgaKRQBxgGOXOngw4pxyKmCG5hS9jpNUspoTj13syapBhQC49Xm49awMgTjto5FijMkWZjwx\ngIMYgEEKwiUva3CsktrteYd1CaTgOrDJhfnrAHkH6Sqi8fVNxTGvG+g267n11y8VdqCrWDjW\nz4ZcxQRy3e0i13TVDbhx2LnCbnFx8TOf+cwnP/lJuorV++Mf/7gx/aSusbGxazi1ATc2y8vL\nWutyuVw89TyvXC6vrKzEcXzF7Zvfe+mqe+G4e9MbuRQiSaTVQtxFFJFSVKm6ao2efUZMrn/6\n3SiVruUBDtiBbOWqkwuKwzFtCpwjgZAAGyME6BufHGhT1uq6U3Lj2lo8LvTg+iCBQJjQzydw\nkCJkzwEQMITBGlksvgLBkbIUqKTniTOqrqRrKASoH+jWD+ADXD/FoW9HE4G4/mywbhwrtilk\nqJV7Z1xpxNLERiSc2AtnhhjSA7j/Ec6A9QUX6gXRuvnppqPub3BX1mgb54cuy6jYPOa6y7uX\nYCtX3YAbhx0q7Jxzn/rUp372Z3/20KFDx48fv+KY3/7t3y7uJ5588slPfepT13aCA25gROTy\nuwVjzNW2b3566aorl7kUSrcjzTYVP0ftFpgEisbGaGpajj1nKzX1rp/e1iMasPPZylVHVKRD\niJMLmRPAujAjEghdiKUTAoEvxPuvUygSt26RuiC/ipeJ+l7MwqTV9026C0F7JAKCVgp58bFE\ngB9wmnpmUUluuCob3sr1xFLwesoqQYgJVpj6M7BFei6I11WokPWHdb4MGiVSKKThphB/Z0H+\nukWwULEbuk2w+dxsTo+4yEiJTXbLzacSFxVAIYHwZdpOLtrtDmQrV92AG4cdKuy+8pWvtFqt\nhx56aH5+/vz58wDOnj07MTExPDy8Mebnfu7nigda606nc+UdDRhwGSMjI3mex3Fc+Bestd1u\nd2xsLIqiK27f/N5LV532JEmwuoo4dpD1HyOhoIK4i/oQjY7b73xL/cgbMT19jQ9zwI5iK1cd\nEzMcHG02sKEfVVdIjeLRRfJl0+/4ZjPWhfwBKexrF8EEVxS527CqsSpsWcQCD4oUsp4VXyyx\nB12CcX6QL+WICnFWuFkvfDStfyQDDsTiRBUjiQEGE5wrzIJQAZwX+r25cGSaeFgcxEIHF/Zm\nU5TGEI70j5r15gou67r0ita7TXVMNrJlL7LkrZsVGf19XqGY3VXMeDuHrVx1A24cdqgV+dy5\nc/Pz8x/5yEc+9KEPPfLIIwA+9rGPffazn73e8xpwM7Bnz54wDJ9+up/ccOTIEWbev3//1ba/\nzO7abel0YQ3lBsYgNzAOvY6srsIY+IGEoX3h+W09ogE7n61cdcQilJarqZAVlwpyYgNxF6VG\n0ubAf1ckFWyUalsXOYWkKyxWanOBOwCAIoGAFBcRfSTrY1n1I9iYiYmYEAT+EKsAJgHBEmVK\nW1YA+qqOFVj3MyRAYB/EYHasnAo90nAWpKFCsA8wyIPyoYoKJrrkUQuAy6FD6PWYVRHYBP4w\nWMHm2Di2Cz7ndZ0qAtf3LffPybpVErRumbso6XXd+LdxruxlWac3RKmGLb7WDbhB2KEWuw99\n6EMf+tCHisfHjx//zd/8zc9//vODOnYDXg//+I//mGXZww8/HEXRu971rr/4i78YHR1l5s98\n5jNvf/vbh4aGAFxt+0vRiyGWROBpWAsGgSW3cF1JE9IVLgWytHgtjnDAzmM7Vp1lPl2pNhcX\nx32uxwSXOSm8s8JEECgikQte2nWDlPDF3rfNNqrigQKkn3NQSBsqrIMi4ghiLIjB66P9wFkC\nwAxdJjUOOJgErmvdmq9sLFEVqrDaQSyIoYN+jJ2zAMDk8vKYinzKkXXWw+4UtL9ejs5AhyCO\nPHRzhzxGbR/8fmAYshZqtyEcRXUflp6CV+3n1ZIPJMB6tbmNkDuSC4Y3Wg/mu2CxvPjU0EaQ\nIgEA46L+E5f7tXcU23WtG3CDsEOF3U2LtZL0kOWwBgCYyfMQhLhSYckBW8vjjz/e7XYffvhh\nAO9///uZ+Q/+4A+ccw8++OCv/uqvFmOutv0lcGnGDkLAeuV3YiEFJ47yHIB4AWX5S+5jwE3L\ndqy6laR32janaVXMqmXRFo7BDkTsxPVD6jZZlNYNVFRUa9vMhlbpVz8RAW0YvEQEsCJKFfIO\nUMSACJhAjID7FW1Th4CJAAWvDFHO5uRS+FUH5sLXmbfByoGFiEDMDi7NVblcmgiDEWUSNE9A\neQiG+7MxXdgUyi+yaJWIy9tWB6o81Z+r6SIYQjQFZtT2o30SeQwikAIcoPpSstCRuEzCblA0\npeVLTsfG+SH00042bXfY6U7YbbrWDbhRoJug9u9XvvKVnd7JzlpprEmzgTSVudPQHphBECdk\nDKzh/QdRqdLQ8CB98kahWHW//ws//7/fdZCSRDZClIpfPZAj8K7dNDLm4pjrNf2L//P1nvKA\nG55i1f2vv/ju/+nn7xhaMGmeH2iYWuY5IuUgxLzhYoTw5qCyy/qfYv1VWTfd2aKzFvXteuKE\niMAMZlFMgFjbD9tjJdqjMABRP/SuXqdK36Mi3S6Sno3z3B/nQLPkYlLk1rVjZoDhVGSd8nyD\nqGT3vtH51bwDUkhXYC28CljBWdgeTA/sA1Z043Rr8p1Dh0O/CnHI2wiGEU3BrwGA6eHF/xvd\necDC5nAWVPSKvaxM8UUUlkwNydHXw8WWi+Vd3+x3FRNdESv44O+/ji91x3MD/MIOuJiBxW6b\ncU5WV2R5Sc6eRTmisES7ZjdepI0xraY7d456Md99DyYmKAiv03QHvDqEiFhtlHQlovVQbCEh\n8jwAlKcSll92VwMGvEKa/spqknSxsi8VA9+SUo4tCUtRR3G91odsKvxBAOAI7C7KkNjwxhY+\nyQs5nkVyLRExwVlWSghFJWRRuu9wSAo7IMHzsakbFRFERHlA2Uoc99vaKsXlkjNkM1EsHve4\n1zFhzbRMrqUyS8EQ8i6659A5BfagvL63NG1AaQorXD/oACSrsAnq+1Gagl6/TOoSpt+Cc99E\nZw5ctLXICq0K4qsEwxUaTgEC9uCy/rlycqk1rm/wu0zVOay3b9uhkeoDbl0Gwm47ibtubg5z\np6Vep6mpqw5jRiniUiTG2JMnVKctI6M0MbmjgzgGAChsHqqIa4IDyAkTCDCAYnLaY8D1Yj05\nqAI1YMtYxFqt0Z3s2GrGPS8JnFe1ESCMfg07KUrGFQY8gev3EZOiasfGHeXmZIn1cDTq6yAB\nuO9/BbN4nrCiJBU4skaUoiAsOpgJQHmKPMeGs4EY1iLwVXcV2rP1qKhjAoBD6EJXGsArq6GS\ntierMzM8NAZWfhVeBeVppA24FGJRUmAPKhC14sxuDR86gK70W4ptJhrH6D0goHUSzkL5kBSi\n4Uzfqiib7XCFeuNNelaDcsgVKxVfbOe8kE08kHQDdioDYbdtrK26hXPSbNLk1NVqLF8CaU2j\nY5Km8vSTfPud2LWbBl2ZdzpCYSSdDuAYECLnhJgUgCAgrV2v583sloN3Xe95Drh5MCpcDuem\nO6XEo/HeKJEIQa1XqivUSVHHbuPCUzxhoChgzOt5AW7dUUub2siSiCuK/vYljILvAyQSCwlZ\nQej17XLWUhCIIep2UKmCi08AkgTWIHm+9BEAACAASURBVIpQhOFt/p1xAmeR9Gh2D8ZqJM4t\nLwhLcStLBL8K/5I0OWMxtbe0X7+EkGIflRnE81ABmi/AdKACIIUrahevh0kUx1TUVQH69jxS\n/RorsP1htFEwZXME4mXJswMG7EwGdxzbgiydt9/9jsQx1Wqv2vDm+zQ55U4ck5MnxNqXHz/g\n+iFZJp4nWtNGpl3xbWsPni/WYnVZ9uxTg7rtA7aOcmJC04697kha104HJsg4d+Q2FqD0Y+f6\nzSMuqmbHAoKTfpW39cqLIFaFPa+Iur5Qi9cLoBRYkXNULpOI+D6cg7NwhjwfAEURlBazniHU\nV3Vl8Kb7UmuRpohjNNawsoyiRVXcAYjrQ7K4IGurVz3gNIPnF6rxJfDrGD0Mr4zxw/CGN1zL\nYKzn2xYHxuvl6wAQuDAK+tAlkAdQ3yFbZEgAAyk34MZjYLHbeqTZcD/4PiYmyA9efvQVUYrG\nJ+XMKfJ8zO4Z+GR3LnEPvYSjMtJUkl5Rfh+eh3IVWSILi/ymN6m3vPV6z3LATcVULD2vF7gR\nAJ5Vlpx2+qKI/yKXk8Std4ilfr3evlmOmGQ92K7fdkKK5AEBCExgBXHQHggIQhgDBqCkWqdu\nDGtdlnMQiCIyDr6HPO97Kns90Qr1OjbPp5B0ut9QQsKQqlUxOZY6qCZUqVKtJidfpEoFV7pm\nul5Xzc5evv1ySuMYuxdLP8DoG5A10D4DpWEzmBgiYG9d9tJ6s4mN5hsaxGAfeRcw4PU+ZwPT\nx82AtYi7MEaCkG6NprcDYbfVpImcO4uRkdeu6gqIaHTcPf8shyFNTG7R5AZsMfQjP0KVils8\ny6UIzJxnAiAqUZqKNfqBB9W7/gsG/vQBW4plLmfBTGccJInOAuvTuvOQBA603oTiQvYEgZyg\niAaljVxZKfyzAmIqbHXFAxCJAzOUgu/DOgDCGp7HfhlaS7tFQs4YioGhGphhHUwueYoook6G\nsIQk6dvYejF6CZSCBUyKapWGhou8ImgPaeoaDdq1G6WSNJs0PnHp0TpHSUK12is8OeEIph5C\nvIBmhuoskhWIIBhB3oVkgFrvTlGcAAdnwbqfZhFNIGshXYPLAR/IAXvB8nc15PIyeAN2DNJu\ny9l5OXMampHlfOddtHvPy1p/b3QGwm6LcefOupVlHh7Zgn0pRWPjWFnG0NAVb2QHXHd4fJJ+\n7K38w6dkfk6SHpxQ0SR9ZoZGR/mt7xhUKByw5TgQCzsQkbCw59RG7maR5urgWEBFc1MBAEtQ\ntNGhdd1aVSTQOrh+JWL0+zYwww/gHAhIUonKCDwuV0UppKkYQxPT0mpwr+dsTs0mfA9DI1Sr\noRQhTYo6xtJpEwG9HjodaI08FWNQKpHnwW2SQp5PRGi3qVpDksDaS26EpN2iA4dQehWGFq+M\n2m0IhpAso/k80i7SBphAAfIUJgNR/8BJAwLHCCpgDV3C0B0QxvnvIlkElwADm6/3012vdbxR\nQaaAB7+iO5Y8x/xpt7BAUQkChJE7/jxpzTO7r/fMtpfBktxKpN2S55/j6Zkt26Pvu+UlrlRp\nds+W7XPAlsLj43LgEA7env/7N9NOm/2wdPc92L2bgpCiQZWTAVuPEFlWOaeVvExFpwiBFMXW\nQMK26aeBUUo0s2bHpKAdnAMTi2IBURCQ70meI8mELDlHBKF+frdon5yQWPEjBIGa3g3N7txZ\nKI/qQ4jKWFkWa2lomNIEgOSGlEK5Sp6WboeKOp2ej0YDxggrGEvlEgUh/ADOorkm5QpKUT/r\nVnvS68HzEXcwOnrRX42xaLfpjfe+2nAUYoSjCEZQ3o28g3QN8TJsA1kPJoFJwQQiqAC6DK+G\nsAavhtIYvBpYY/QuzP0LOi+gtwovghFQDnEXYvX6zW09aFUYNAfsRGTpvPnhU5QbeB6IYHKp\n1WlxAVMzN7fRbiDsthJZOo9qbWtdbzQ05I7+UI2ND2oX70xofFLW1uT4sdw4m1urbDB/hpzj\nh34MevD3NWDrYYdY61xbnTJBrZfWLbIDxAo6XtLTUAi7I0FcGSn53vSphYqrUSkSP+A8l7Qn\nnbhIrnDOEJic9Kv1CNgaBIHUx1iz8z3bbZO1pHwM10kpOCujo1yvkh8iy0kcDQ/J/Lx02uh2\nZW0FUYQ0l7gLa0gclSvwfARBX5wpDaURxxCHcrXvKQ4CWVmi0THk5sJxOpGVJb77Hiq/xhsk\nInhleGVEkxjGBRubyHo+7FUqD3sRbnsY+TJO/RviOZjzcAqk+g3SmOEsnIPN4AT1g69tdgO2\nGefc3BmK44tqh3U77pkjfOddKN3Md92DH54tQ3o9OfY87dq1xftVGqVImms0EHY7EiqVoJQ0\n1rxOSxFRloGALHvlUUEDBrwqSNDRfLq8XEsqoSGhi/qEGW1YyMJ5wgqU1aKer0eGgpKuq713\nmtXV9NxZU63ZqBq1m8rTnrEOAgbaHbCCVqI9vv8hiko0d5qckDgaG5fVFUzOIPQpLNHiApwD\nK+SpC6J4fFzK1Wx03GcJn3pS+7401nh6SjodWVkmLwAESQ/YqAJHYEKnK0pT4WNlBitY028i\nCwBwqyu8dx/NbN0VdaP4C1367xXxxnDwZ5Hn6JxA+ySSNZgEzkILyENYhZrEyEFEgxDoHYk0\nG3J+oW+r20D78JxtNNVA2A14RXTaEgbE2xApH0XSatPVKxwPuJ6YHKdOklI0MuI5JwDCEqJQ\nujG9msCgAQNeIY2oF5k9C5XTw2ltT2cMNtdOM8hBMpXn5ADRUJnnSKjHq612ZzrPF+uVNY3R\nZnt1Yrrm+9V2q61V5tykVrUsBWsKyrAGQ0M0MkL1uiQ9esNhxQoisrbGQSiliGpViAixONMF\ndZut5Yno6UZrLMnOOvirzcNrjbJiDXLGBsSh53nNBntaAZSmFiLKs9wvjWyzrDcxxVp5RCHB\n5vlqbrpxLzfWW11Jpqez2kgp7k363rjvXy/Pmedh+A4M33GdPn7AayZJii6dbnWVKxUQS56j\n08bkNKfp9Z7c9nJzCTuTSxwjTWGMWEuKhZiCgEolFHXStxPpdincFqMahYG8eBz7bhtE4u9E\nivqnSuWzexvdOPS8umICv/JkubyL3nn4NYSj2znPATcLsWeH82AtKM9VzpfzoGx8LV5gPMs2\nJ+TKsOPABT3PzdeTrl3N8iDN3Ipbaq4c1QhWxYV5HDZWMj8Y7nY6oFIYenmOXtd5fr660ilV\n9LHnoje+KSxuU4loeFhOvijtNsRSqZwHwer86XM27dUrmde+zTQ56ZyLPbva7Ckv6jSNc9yL\nLcFmxjFRnnt53i8tYlPjBSCws2STtNeNw1LHWu71TG7PdeKKtbV2e37P3vMT01mWxb1ew9oH\nqtXdgb83DGNnEyepcwIETCFzXalokHh+jRFBnotztOFh34kImGl8gpWSuTkCaHwCtx0AX1TY\n8abk5hF2srrqnn9Ozp6F74EVFItzcM4ZQ1lG+26j+hCNjG6jNkoT6O3ZOSsojSwdCLudiNY4\neDuOPdvrdF9U3hShniRSil555kTWQvskKrNbKexMD7YHvw4AaRM6hB5YD28WEkq+PZ289dxw\nJ2h0/TxX+Vgy1PNScpwrm6rckdZijw2np1XiuxHtxPmuVx1J2ucCoyqmtNxdHkqV2CocdZVf\njWNPK5umMStJkm6j0ahGveapGc9VWIiUwHncTvfvWosXl5YWeH5plRanbMnmvjqnXii5F4bM\nRPPpcb0rj6SyljSiqspNrdtmwPi+ZSYizxpHTAwNZ5UGgZzTSS/1de4wDDLGpK1mrzLbu3MW\no2NDkouk0GoPwmfj+KsrqxO+v5bl04HnsyIgF8mcS5x71/DQdODvCwP/po6I3wmICJbOy+qK\nnD5JAtmzl4aGaGJqJxZ18gNJUxoapokpGh2HtYVbVlaWcbN3Y7+JhF3cgbE0c2lGar9JYqct\niws0PkFDwzQ+sfVR7SKwlrztOp+ileT5jr0zuqUh4pnd7oVjlSg6kNsSE5crfO+P4hVHfAfD\nqB+6rI3S66Mzh84cRt8AEFaeQmU3hg4OaujfJOjyvd26/220dnfUcpnGuzRfWQ0MBTaw5DwJ\nMnbPjp49EVUMSt3EzvRwYqgrJlFmnhPn2DdUV+xZaYZZ6nHQ1EqXZ5wtGeek6s3vai3QnDm7\nctqvjfuBJ+28d7bS8o+E+hS5TJbNjKeZQjubG9XhUNeq5bBkG47PrbZMt4tO2EmqRoJcYs+5\nPLHkMcAKECErIqm4HCyUG9frerIwoqrVrnlq/8zp3cHJcnM4PTd1PuPek0QUW9vx3zCfuygY\nWzUTK64+Gwa3hYFaNxRlIseT5Fut9psq5YNRaX8pHIi7beTsvHvmKI2M0K5ZAZAm7tlnVJLQ\nvv07zXRH9SGenkGriUoVSvWlZ5IgjmlL6pHtYG4eYUd+AH2VmwYiKkUoRUhT+9wz3GzQrt1U\n3dLYduewqT/0lkPMGLQX26lQtare/q5weWmq26EgoKFheTUXDh2istVllXSIygx0CBAqM1DB\nQNXdPNwxeliGDi26r1rvxabu7NfeG5Z3p9p0VY8dn44W1qJsKdrnSzqSZjqnb0yujUj53sbx\nzGusVXYZUyZOm14jMtW1sg8Rz/GcPrsyuXsq7vbc4rf9eZNX72nTindqQWvl7ZpIdx0ZXlrM\nn2E9mXtTZcrFxUn+rcz1hEfjdgWxd5wPdAP70CoFTpbLnTylcVti0ZagAC1CzikIRKwFQRkw\nWCWev1QJvNws1N3Xa0+2O4+lyZ7zwexxnrkzuj3w/BMmrubdSTLonRXbLEdvfGZtOrW3312p\nFNrOJ5rwvAnPO5/n31/svG2o9kCtGg5Md9tBHLsjT9P0NJRGcUXxA5qYdC+8wEPDO04tac27\nZ90c3OI5CksAIcsQd/nNDyIcWOxuJnyfJyal1ZRTJ/lH30xj41u7e9q2H09xL5m+NeB6IwTJ\nM4hImsEJXe8vqzILcf1qW/VD662TBtwU7C1F77795788v/f08tcMfbERLZyqnZqIR3fFY7FK\nY8Ww3mRPB7Z8sn7q5FTUpNVlF06X8ijhVklPLCELuKMSa72a8ZVNWpFmEb/9fIfl+1P5C34U\nyMmg0rl7aZLYKCw+MZZntZy8XXFyrkyrIM5QbvCU8qClA9LnTVDFkbVo8T8mDxAmlQSNQMqZ\nyVlFRmnrPOuEiqZjgJJE5cqxdhyTaSt/Vxw/MzkfRyz6vtAllJ1QnP1jOh351QnPc9Ad0ppK\npWAqNMtT2dOn0jlPHrijNr35z2xY61pZfa/VSZ08UKvWr3afP+C1Ip0WSmGh6i5AhHIZ7TZ2\nmrADUKnygYM0MoqkJ04oDDE0tE2h8DuKW0zYAQCoUhXPcz/4Hh++lya2KNe0uEF0Ftieq4m7\ntCD7gB2EiJw5I8vnqVqVPJf/+C6/5a1Ufa2+VUHWAml4ry8ff0PMDVTdTQYBh6vVkX1v+RRN\nhl5Flj7dqMTN6uK8S2wSecY4UmmptlrepV0XOKr9e6zpzgULP9a6/ZQXdCYRdfIqzJBLl30h\nyitS1qGfZqeP1Fa/E5XrvWPKxYtlWQrXIoQdsqTExyyyZc92QGTU0JJVuVgHCEqROT8kDedN\n9bw9S9VesrSSerXhuNzSqBjjC0GcZacdFwX3WMg31ijT0yib5NBK+6nJtecmh3KtASQU5jxl\n8vN1ytJsao3GK0ppIiuymMuwjmr+oVF79vmlf4vo/j3VfZvPjCI6FJWej3sA3jZUG9jtthhr\nL1V1BVrBmCts3wl4ftGT85ayi9yKwg4ABaGMT7onfsBvfoiGh7dijwTfl7hL25PfQMZs054H\nbAFp6p49ugYkJ08qzx8tR367hdcq7PIuzn8fItj1E2Bvayc64OZhNgz+j9sO/Efjf/m+O4/G\n3zEFvXy1U0oz8smlvXBXxCueHm8O/dczeVBKn+GSaoxUp9pBtprNl2zFnj9TqVuvqkznDn9k\n2KRnJlZ/WKHQvshIhJWB73vUs01ADE+Z5KwS63sBnMuSOcO7WVUUiKUbyppzlJkULLHWRyfc\n3tW1c6Gr96qjmYLLtSiS4r5UGAQHYk7Y5pyxo67XawSrKVvBeOzQs1aAnOoV9Ibz767yg8t2\neCrwI+YISKxbM2bCG6ujtXj2syN7f7kSXhrHsCcMftiNA6Yfr9fU9bad31QoDXslAWesaLVD\nTrSsrcrqCvk+Tc3Au0UvoLeosANAvi/DI7J4jkqlLfG4U1hy6cK2dJHKc5rdc9Mn8ty4yLmz\n2emT3W6clULO8ijw9dgYj45R8Fo6/LKHyi6QAl3dRGti5DF0BJdBLII66Nb9U751mfL9/zQ2\nWXH/9fvPnVjNVmATH124FN6YCfZEyqj6T0XlB8O4N1KaOJSfJrtSGzmYVHT1fGvP6kjOJZV6\nLOM07r2g5xZkydJMOT8HVRIqB0QsmYNlQe7aJbvmuAwrucBKOurOdGifJVWyZw0iUdp3q85V\noEoL5Vxn/lCyvBZ4u7oBOy5qS1BRGUiECIEgE9fWvfnawmqUzzbHz1RfWPIbmd6nSXWt9Ygt\nSqLGVH428IOG4RIzQAIh4EyaDqkooplK48lDo5H2LnUC7g/Df220pn3/UHTz+92uGVStopfA\nmIuyD0XQ7WxxzPprJsvk378tw8Ou12MQ36qtOG/pXwMql2V5SaKI9uzbgt2Vy9tU9lCSHtfq\nA1fszkRWlt0Lx3r7bsuefmpVEDjnaiPluEtzZ2j/gdcQGakC1A8BV3eh5l0sfBs6QG8FugQd\norwL9f23mLNhAAAgZH7b5H1T9Gv/9Mzvk78f5Jy//wxN++LO0Z5c3dWNe/vC8L2TbzzE/9uR\ns1968fy/6HI13z/9g6Fzoagkfb5a3TNSyerRHZVG4iUZSy6oMzEgVowVsIhv1kBEFBBpIyIU\nCnTk5lNUHYUCDcDBNyYOVKnruROV7KfX6rU8aes8zKsACkEG9FcpCTW87MjY3JpvNGW+Tsq9\nsINjcL1VvcehnIkjIiDUtoH89JI7UFI6ZEWAECCYz1JNgTROdC1PjbxdK6WJfOaQWQGxczWt\nv9FsiUhN61BxTSkeWO9eJ6WI7nmDHD1Cw8MoItWy1DUafOAQhrbC8fW6EWOEiMoViNAVjYu3\nBre0sANA9bp75hkeGaNK5fXuqlaX6RmkW19tTrpd1Otbu88BW0ZjLa/VjoTR6bHJ/Y3VU9Ua\nj40N1UdGXzgmMzOvrfnES0fF2Ri6hHAEySp0iNI4Wi+iOgse+OpvSYj49smfGq8cOtv4DyvZ\n+eaRQ+U3rkjN5DOWyz8xVDtYCu+MSorueMuBj8yOPNBOzp9Ze2KuWi+VDuwJ3z5bHg+9qBmf\nacQn6+mpjh42EIFTrkM2Vi4nVtaB2XdMFi4XENgSsYiPtZRGUNR7JfbRJqfhKPb5ibHeW7Nw\nKp6xDIIjIQgxIH1lJm2/txrmADt4Pe5pEyY84pmzdduyan+PRy2RdUJSGc7PDPNwUyYq1Ldi\n5xBN3DH2B3l5Mf7mWreSefsixT3nhrXnMcXWDnu6a93ROK4rnTv3lqH6hOfNBP6Uf4u657YE\nntktfiDLS/L8s6IUHzjEUzM8ObVTcvtKJbr9Dnn+OZrdsxOTOa4Vt7qwg9KoVbB8Hq9b2EEp\nqg+548/R2MRWzKyPxDHv2k0DYbdTkSw76SReXZ0tV/y4O5HnWZqejntVrcIkxTZ0FVMlmB6S\nNYiDTZGsoLZvEI13qzNc3jdc3mdddsfkf/F0mcDvdg6gSPHGT66vq/vH3yni7px+90LjiTNr\n3x2tHCxy+Rk68Icib6XTyz2XGqesTYS0RcAOmlOCg1kFyBMFEFEosLkEAEiMliSQLsMQW3FK\nw3giS6WJ4aReyX0nADkWFpAADgC5jFMrxIAltuj5hsl1AdGut9t+d03N9rgq5KeIuiizXSYa\nSR1bSOxc21gUxUOBeRntdV84oYaEPAA+EwE+c1mpXYHfNe5wOawpXs6y43FvxZh3DNXuiqLx\ngbx7rdDYOILQnDyhZnbzwUPQO+hMEhHtvQ2T0/A0ROT8ItIEAoQhDQ3Bfy2xMTcit7ywA6hc\nc88/q2Z2v35LG01MUKspcUxbWCan2aB9t105F2nAdUfEtporzz/3hsaqy01bMMSIFuYWmmtx\nvRa6bfEF2AQ2Q3kC1d0gBdIIhgZ+2AEAoNhX65bb8lWCN4g40NWZ4fuNy+bXvlsOxhX5me00\nu6cOjtwPri91X9TpHJQGfBGnpMMCcT0QA6RB5BzDkaTCdZFeIC0HJaLAlqAyhD5lDomDLERL\no72K7xSJJJ4oy55jRy7hbClMAAAWzpA4T6ySHsSW0E6oPG6fz2wFRDmFCVW0sSagJbcvhycC\nIjiBAE6kR5VJ89x52t9SE0QQIGAGqG3tk+3O8bh3rJv+57HabBDsDYPd4j8bJ/+01vz58dF7\nytHAOfva+GGaLzVaSS1+mHjHnUEilErSbsn8nFtYoDAAgCyj8QmanqGd4TLebnauXDhz5syf\n//mfP/fcc0qpe+6555d/+ZfHx7e47FwfreD50mnTyOvu6KQ9Gh6Rs/PYItO0tJo0u2cLJnYr\n4Zx77LHHHn/8cWvtW9/61ve9733q4l+4b37zm4888sgl73rnO9/5G7/xG1/4whc++9nPbmxU\nSv3t3/7tS3yWrK7YkyfHV5ZdVEq0F+eZsA48f3xlyeu23fw8G0vjE3jFZRfEvlTOREEegxXW\njqE0Dpth+segBk7Y6821XHVbgqdK+8Z+olbaFWcruYnr5dlWb64ezUz25mGizD/U6Z4kskqU\ntT7EEDOcAWmWwu5mhTxPep70AA6kAWEyjiQOMcTEThktqu23lkurQ2m5ZAN2ygFdbbpeHnud\n5VLMkmnJLZhd4Gg5kJYTVnABeg4+weSIFEzZrfjS3Z/+P0PqtibPLun9sdScgEiYSICYqxWs\n9WjSQaxI5pwAHlOkdMfab7dbp9JeTas7y6UZz69qHTD/9/nF/zw6/J9G6qUbuSTK9Vp1zwN/\nsv/Oh4bqP6N2SjLsRZhc5uak3eSJC94z6XZx7ixKJboF0hB3qLDLsuz3fu/3Dhw48IlPfKLd\nbn/xi1/8wz/8w0996lPb9XlhiDjGVugnGp+g2/a7M6d5dOx1ajtJelQf4t2zr1wWDADw2GOP\nfe1rX/vwhz+stf70pz9tjPnABz6wecDdd9/9yU9+cuOpMeZP//RP77//fgDz8/P333//u9/9\n7uKll60zLHGs9+4dO3HcmLyS9IrkVBeGic1l712w1h15im47wLN7Xt5hIejMYe051PahMgt1\ndadBNAmbonsOpXEkq8g70IPMv+vNtVx1W4Vib7x658bTUNePnv1b49KQlbI9p7TvjWQ2abmM\nCSJGARYs5AClJXPwlWRADsBSIKw1jCMvcGsk0lVeaNVqmMxXlnq6p61XzysWqhEkBHe2vGS4\nrcUISMFUTHDOb0McCYiccimTDcQ6agEkUI6oZFu77JMzeGqWR5f1/kV1xyrvZuQeDDszKsdi\nlEE6R2g4yuAbERFHRE7cmTStG7WUZ3XlBcx7wsBn/Onc/HO93tuH6rdHYeXGTE27Xqtut+8d\nqFTuqFSK9yTONYwd9/QOKS4jjYZbPMsXF6mlKJLl8zQyisktKl67g9mhwu7FF19cXFz84z/+\n41qtBiAIgk984hNJkoTb0wmEPA/ZFiW0MvPsXjLGnV98XS1WshTLy/TmB7Ed9VNuXnq93le/\n+tWPfOQjDzzwAIAPfvCDjzzyyHvf+976piDFoaGh++67b+PpF77whQcffPDHf/zHAczPz7/t\nbW/b/OpLQ0FAUeTVa1hctMwZWEj8TrsWhlFUpiDA5LQ7fQrW8f4DmwW6y9FbhliEI9ARAGQd\nNI+jPIV4CTpC+dKmxxdQAcpT6JxB2oBNoG/++8+dzjVeddvEePWu6aGTp1a+2csbzuVKBwA0\nkSYyoggOcBADYoKw5MIkEBYr8FkMCOQsWCw8LbFRcnxobqYztBo2gHw4rS3rGPDI8WJ5uRWu\neCAIEUzgvJXSctPvaDhHzLDoO/hYQIAiGBLFIgmXNeUlae3PvrcP323xBJMkVHFQFsGYO5ki\nEmBR32Go1KaRjprKJPSJPEjHWs3aikSKY+uWc7s3DP6l0VxM858Yru32/TvKpRuroPF1XHWH\nSqX3TU9OeBqNNen1jq41vtVLf3LP7nu2yav2akmTK5ea8gMkvWs+m+vADhV2t99++5e//GVm\nzvN8bW3tn//5n++4445LVN0jjzzinANw6tSpkZHXlf8izMjzLbvX0Jpm9zBgz519bXY76Xaw\nusr33b/juu/teObm5uI4vvfee4unhw8fzvP85MmThw8fvuL4hYWFv//7v/+zP/uzjbc/8cQT\nX/rSl9I0vfvuu9///vdPT09vHn+FVddqhcaqSrVFFOcm1KpEFMKh3cTUFIh4bNydOUVBQJuK\nKnXOonkMHCDvYuggSIEYInAOsC/fK8KrYuJHkcfwInivshCyOORtsIYe3DJsEddh1W0DWoW3\njb3j9Mq/PT33JU+F/dbXxB4pJ8YBxBrwnLCW1IlP4gAAJCAiJpcCDHjoS0BZDleijCIbnI9W\nW0HXc+yAVJlUZ4AjQaEMh5L6s6MvGE5YwCCQkGhLGuQ0jMAJWMGARCNRzvmuA2YSO+pOE9Ci\n8YSrgSyLFQd9Tt9ZsqsATdGzS25fi6eaataxL4KVLLcecsjuwJ/0veU8bxrzQ+sOReEz3Xgh\nz3+0Wh67cUraXsdVN+Lpd1TL7tRJ9+ILKJWqeb6v0RpbOCP33U/TV78lvXYUNROBJJFOG8ZC\n637F+J1hU9xudqiwI6LCMvyJT3zi6NGjtVptYzlu8OUvf9mstzGpvL6cVmIF515igHPGwUKg\nWNPLxkABCEt02wFWSk6ewNDwq2hOZ61rNnh0nO64i2qDTNhXzfLysta6XO5rFs/zyuXyysrK\n1cY/+uij73nPe4p7hmaz2el0Y+rliQAAIABJREFUtNYf/ehHjTF//dd//Tu/8zuPPvpoqXTh\n67vCqut2xDkvDIc77ZITz1gvKpNiWVnBnhRhCCIeG3NHj6iREZQrAMSicxrxeTDDNFG/DaSg\nIwzfhayFYDfCVxAU4Nfhv/oFIoL2KbRPQRxG34DSzrjBvtG5Dqtueyj5QyOVQ/XSzFp8Ak7g\nESACo1lZZ0UIrOCcJU3iqLDSFZfr4v0EcqkCizAIqZcsllfuW7yzEXa6XgxAwAQLFEkOUE6P\nJPUT9TPLpcZ6q20BiGA1rEAB5MACIhCL8ZEAAIPgLHkEEaDiVjzkOXnaZQq9XflRwJ3x3rhG\newLE+/Nvrbrzy2p/qich1LLWOsmcMw4zgRcyn0myJzqdnxoePtVLjHP3VMp7wxsjd/I6rrrY\nurnjx8bn5+pTM2C6TWRqdCxy1h15isPwOtojZHUFWYYgkF4C1ZLjx/rNbY3B6ZM0PoHb9l+v\nuV1Ldqiw2+DjH/94o9H4u7/7u9/6rd969NFHo+hC8Yi//Mu/FBEAX//613/913/9dX2MyOVx\nbKlpt5NzcbaS5i3rsrm1f989/AAArcJA1yJvpIZhnZNkGawBiDwN7SGK+jJOa963X8oVWV1x\n5xe4PvwyWbfOSbuNdotvv5OnprekGcaNzpvf/OZf+qVfes973rN796Vdg66GiFweLGKu0sfw\n8ccfX1lZeec731k8rVarn//85yuVSrGHAwcOvO997/vOd77z9re/feMtl64659BsIDewpuV5\nzdyEisdEuN0m1q7d5uJ7VBq1qpw/T7dVAJgEeQus4HKEE/1KJUQoT6M8jW3FZWieQGUGpoe8\nPRB2W8O1XnXbhnVZIz552/hPVtqTy51nMpMyoDgMVeB51ThZDSlo2JxsTEhARDAAAwSxBCci\ngAIEIkwkYpp+9/tTRyfjscl4OPaShI1lx4588Ut5WDLhM6MvnI9WBVLUwoMw4ECEIvNVCCQM\nxzAkDiQOSoqPgDgwQYS5JA2NcEntd/AUssD19uY/KKvmor5zRe0ru9XD5shzeGdD71NEGaQs\n9Fyvpwljvrcn9L/RaI17/r3V8qqxf35u4VdmpmZfU8+Y18MNcK3bxKlWc/HJJ1t7Zu9lIoCJ\nykpBKanVsbpyvQrISZ67730HzOpNP8pTU+65Z6le3+gqJsaAmeqDrNjrx9LSUrfb3bdvX61W\nq9VqH/zgB3/xF3/xqaeeeuihhzbG3HlnP+z32LFjWZa9rs9zdnNfhzhbWWo/e3zh/wu8WuDV\nPBVpFcwM3WddLmKzeLW3enS13ZpcK0kQaK/GWsNaEacsaQM6cDAc2a3Gd5HWNDlFQ8MURbK2\nKidfRFRCKYIfkNLo3wwbyVKkKSUJHbyd7nnj5c3jxVoCwHyLmJE3+N73vve9733vox/96Nve\n9rb3vve9v/ALv/CymdEjIyN5nsdxXNwDWGu73e7Y2NgVB3/xi198+OGHNy6OzFzddPIrlcrk\n5OQld8CXrDrptqXZpHotb661jQOQWtdLs/LIGLJMXjwuQUC1GgCqVKQXI8vI95WP2kH0zsMk\nqOy5ppVKWKM6i2QFLkdpK+st3tJc41W3LccAADAuUeRXw5nl7vOhHh2rjDsRJ1kjPh2quhcF\nnfR8xbmMLZxYZwEtZAED5IAUPyiKnGMnxdWK0PbiuD7XCBq1rKqtt6szAchc9dxSqbEWNBOV\n9H2+QGHxKx4VqoXIseRMTkBgcmCCK5y/DNc35om15GlJRuzpZbXfwo/Z95DuNk/6Lp733xRz\n3VJwMHv8NN6y4t0lIqm4CvOZNCXCmOeNe/6/t9rjvjcb+AdK0VPtbsRq1LumP447/1q3+aVJ\ncRxFOgguuXRRWJI0JeeuS8IfeR7f+SPIUqnVyRocySRN4PsESJ7T6KhkCawBbgyL7Othh8aK\nPvvss7/7u79b3C4AyPPcGKP1dv2lSZ4X5rTcxnOr//6t4392rvHE1NDhseod1XA69OqaQyat\nRJVW86EFW53v6Th7Tj15NPvGE+2/O5U9taQXV/2VxdL5udLcqRf+36XvfeX8d7/Uefob7sRx\nd+K4e/aoLC7AWVlekRPHafm8NBtIEyiFcpmmZvjue/gn38EHDvZVnbVoNNz8GXnhmDxzRJ57\nxj33jHvmiHvuGTn5opxflF68TadiR/Frv/ZrBw4cEJGvf/3rH/7wh2dmZn7mZ37mr/7qr5rN\n5tXesmfPnjAMn3766eLpkSNHmHn//iuY348ePTo/P/+Od7xjY8sTTzzxsY99rNPpFE97vd7S\n0tLs7OxLTTFJMTTcS9NmalSW1Xo9L8/jLG2nqQwN08ionDiGuAsASrtz59DtAGAP1d2o78f4\nmxBdW3VFCpVZVPdi+M5ttw7eOlzrVbediEg1nJ4deotWgXGZpwJfVSvBRG66mkv10u4oHAM8\nxWXFnmLtUCLJIJB+YJNzYoV8EhEwgYjEklkuNU7Uz74wdPKbu77/jdnvHq+dOlc+n+jk4rsa\n6YvBCzmawmQFRBARWheA671nUahAJnE5wrpbrLmF4m05ghZPjrsXZrPvB9LLKVzhffuyb1bM\nKUXUMoYZqWAhM2vGRIojpf610WwYO6zVmTT7frudv2RwzpZzA1zrNjGi9cEovG3nuZVoz146\neDv5PsISRkbpwEFMTcvkFO0/SNO7aFCg+Ppy+PDhJEkeffTRhx9+OM/zv/mbvxkfH7/nnnu2\n6eMkSbkUddPls43vL7WPTtbfqC9rz0SpCRaadG6xGbQb/z97bxpk11WejT7vWmtPZ5+hT/fp\nWWrNlgfi2RgDBjOkuNTHvcANuTGBpAiOPyhCYidxQRJwhYRrDCYEuLj4cXECGYoKCQHiEAL+\nQvDNl2Dwh7GxsCVLsjW0uls9n3lPa633/jjdrZbUktu2ppb0lKvcZ5+9dy+dXufdz3rX8z6v\nGHcRFvxBImlsFmXzUroFf0hJDy7IZ9tomb07W3M/Toc2+xsu99eNLC5fwdZykiJq8dwcbSxR\n/wAVS0dScVrb6SlU53n8EPwcux45ClIAAIOjmKvzSGJEMW3dRr39nWzQ+Yr7778fwN69e7/7\n3e9+97vf/cEPfvC9733ve9/7nud5b37zm3/1V3/17W9/+zF0P5fLvfGNb/zyl7/c09MjhPjS\nl750yy23dHV1Afj+97+fpumb3/zmzpk//OEPt2/fvlw7cvnll3dqsd/2trcppb72ta8NDQ1d\nd911Jxui47RAjVbTcZ0mO8000UJ2u66t1+bDfMXzmC1X5ynIgYhcF1nWuU6FZ612QQUX7VFO\nMc70rDttUMIHseGsJ791XXb9nsMPiWBYqTBwuy2bVDcdmSs4xczjKJ0U5DCnnhDWqI5PCQtJ\nTEJIy4aJCMwEsF3KyBlhDQwWmuYtrNtBAC/I7hbX8h1wR3HXydJ17sGdlrMEBpaSdhIacDLy\nu/hwhK4MAQCGrIv+it1nM+c59xWWnFm5/tL4e7vCX9LUnRq4BIdoIkk3+P5kklwahgfjuBTm\nhj33p43miO9fmjtz35M1EOuWgUFoNdFsIgiOamIeR+ipnAv+XJQLxfB6xBHlFxKT3GrT+g1Y\nvd59LeMcJXbFYvFjH/vY1772tT/+4z8WQlxxxRUf//jHT5PXCZgpTeqyenh+Tz0a7w63Hn+K\nbKXuZDWZHj1M+12bz7t9S1RMCifweqJ0zrIpeAO+8Z16JGsR5/JmqJA2pmb2HuzZ9Iqu4tbO\nIpSEQOAj8FHu5vl5PrBPbNtO60YgJc9M88wMT05QqYsGh48fBikF+CgUYS1PT/Fze2nrdjEw\niOB8nqxbt2794Ac/eOutt37jG9/4kz/5k/Hx8SRJvvWtb33rW9/asmXLf/zHfwwNHVWHddtt\ntwkhPvGJT1hrb7zxxttvv71z/OGHH261WkvB7qc//Wmn7H8Jrut+5jOfeeCBB/7sz/5MCHHN\nNdfcdddd8uQGV9ZG1XkhnVaW9DZbDltDFGdeFubM1FRWLDpBjqenUShRoQApWJ+64utFpDW0\npwAgHFi5QlZHaI3DagSVVZVlXMSLwBmddacNUrhbet8419oben3D5RuMSWrtQ61k0hE5zymA\nbaKbjgxyTtDQobAtAGwNSangMgRDaLLMKcAExTBgCwiCtVBEC8SNOvyMFkgcAUwL+7EMWrYv\ny0y0yP+WWODiChlH3urc1MBxbauIyVm5YfEmVBP9Q+bnTd07o7ZpBA3R3509G7ldCVtfSs0c\nStnQBkQFKXa2owHX7XOd9b73XBRv8LxAnlGOck7Hug6Y7cQ47/iZzTQ9+QT19oIIQQ7d3aQc\nrlXltu2n5rN4iXAc6uu3P/lfKOSZmUBoNuiS7ecC6TwDID56ibQW8eCDD771rW/9v+/4nT/8\n77e/iMu52Ujy8hH8S0+4xXdXUFaKVDsHp6P50XlM+G63OEFVbKpb+TTXP5NDmDPeImNmplZc\n95r5Db9Qym1gWMsGYAEhpOcInxg8O0MDg0zA6EGUu19ACa01XKtTd/d53CnlmWeeefDBB//5\nn//5hz/8oTGmc/Dyyy+/5ZZb/umf/mlsbOzuu+/+0z/90zM/sM6s+9P/47994NWvaoInjYEx\nXdrERJHnlaXMJ3FXqeQKgUKR1q2j/kGenxVbt9MptcdkRm0P0gZg4XWhtG2Fcxr70Z6E8JDM\nYug1F7vKrmF0Zt0999zzR3/0R6fpV8y1nnty9Kv9xSuJqNYePTj3I98tpVlTm6gWHUp0I85q\nodebUjgXjTmUGh2DNYRgq8GwMGADOIYTcAYmgImIoYgtk8YCLVvYuO0UwXZycwAW1EHMnW3Z\nTsYPAIh4sTwWbC3JTnWFXaycBZBRQLAAJuW2lI7kw320pE2fCt6SIC/AvXbfc/4b5sW6Qddt\nGzPsuy1jAyFeUSpa5j7XuSofAtjVit7S233ZGUzaneOxrjPreG7WPvYT9PWRlHZyknc+hTRG\nb5/I5ZHPi6uuoYFzSOHBzYbZtRN799All8rLLjsdnbvPTZyjGbszB2ZTnZns0iVn/Yqsjiyr\nidlkal/Vreack5nS5dMgP95q5BG44dJJTJT5yNXt5HP/3958lvf7iRSBGMay6clvc2WY8/PB\nricpZdp2Cb2gfrVCUrmMdsv++Efi6mtOLWM4F3DJJZfs2bNn6eXw8PCtt9767ne/++qrrwZw\n++23X3PNNTt37jx7AwQ8L3PdJE1JSENUlY4lKKKI2ZPCBjk4jm02xcQEcnnoo2p0Tgk6BhFC\nLmxjMa8wQ5kBASGO3+q6iIs4FuXcxpHuV041nu7KbSgGQ32FS+fa+0q5dQyUw03MJjXtRDem\n6k8Vne5qe58jQ2trsCkzBHmKlLUZhGADgjIwBAkGyDJZwkI/sqOVdcs3ZRf+v0DoaGm+MsEC\ncsFcb5HudZJ9DAewtOB4l/rcWE7sUoRF1Lqz/Yedl1lQU/aWzViNBi2YiAzDFyKxlsEFKZ9q\ntTcHfkHKXtc5FCdnjNitgVgHAOD5OXSVyHEAiP5+bjV5dpqDHNatF79wFeVeGHNiRmsMViO/\nDuI0kBHKF0RfH8/Pir7eC4fV4SKx40ZjpicZp/Eef9OKJ8jZph3dP+vOBk7PSaoXVcZdEyYJ\ng4hrSJ2c18NApttp1minM8rxyjU3cTMnzBWChQUNM4yJG8lca2q+Ky16TsGbnRT9w/RCc8VB\nDv3KPvm4uPYG6lm5JGqNohPpSqXSL/3SL73rXe+65ZZbxLIP57LLLvvgBz94IjfOMwMCkRSZ\nNYGUkRUGVpEAOmkEVlJCCApyyFLs3Q0/OPXBheD3IpoCCEHfyuuOoBc2A1v0vOxiV9mLeB4Q\nieHuGwynM429XbmRSuESw1kjPpxzK0IoQPnCY9itfW9iiKen/2M+aTpuWcf7JUkQLJtOxSox\nAyyhIB3DBGRkDQAwg8SRJcbyklgcoXF8FPs7slIGiKnTkQIABGwGr5PJI7aWFDG73O6QvKVr\nEir0mj1VNdKiYoJCf7ZzTGy0PCwAy/AFTWptGIKQI1HXpiBllxT/s1p7RalQOCPb4ud+rFuA\n1rQk9RMCfX0wmnp6xPZLXyirA2AzzO8CCfjdcE+PXFwMr0d5wUD0wsEFTew4TeO50ecGnu4J\nr1nxBKFNOnOgKg7nvMpJWB0x/LrNPFgFh8NGPKGkl+l2K512ZBC4XQAZ3xbTrunmXiX9wC0D\nIIIQXqFlnKSQ5LieHSqN1j2ZBH0riPxODnJdrvTanzwqXnnz8W4paxdvf/vb3/Wud73lLW/x\nVrKV8jzvC1/4wpkf1THwPT+o11Kpek3mJIkVlPg5nWWqUOqInQkWrs++jyRFbR7hKS6a8Lvh\nlxcflyvByaO0DTjxCQCsRnsCOoZbQNB/ofnqXMRR8FRhpOdVUrgHZx4pBoM9+S1EYr69P+/0\nZTaK0vme/Jae/NZq++CWrium4/m5ZLolStJMp/EoYAhSCF9IR2cN6YbGpJKkYQFowIKEAAFW\ngDqVrqYjkTsy5ZaScR1Gd9RcXM7YALZQiy8XqysgXLQV0gxHNNmaPAEO7WxLFkGUiGIXz1ms\nW+KXEpRaBhAo2TQGgBQiJ8V8ps8MsVsTsQ4Aua6tzo8yzWRZIMWWQlElidx26YvTAkkXlath\nMzgvnXfVqnbyMEmJgSFaHmOlROF8LjFcERcwsbMGM9NzmzzfX7diMwkGmtOz0ejuVrlYS7UF\nM1iAJEgKeCQ8IRxBzBBNo+qmnSNiSwCRqEVjgkTgdC1FJe2KfNNGQTFK53ynKDREqlU18qYb\nNnC8JnlUyHTc2PfDosulrq0v1NyMPJ+7yjw+Rlu3nfL9vrOFf/iHfyAicXQKs6M+OVvq8uMR\nBkGzVObZ2WIas4DUrIyJyj3dpcVoojWFkpOY1o/Yp3YIzz/1iVV6HipG9DwTKp5FfT+cAlqH\n4IQvuE3ZRZxn8FRhY+W1XbkN1fbB56b/nS2DeXT+keHy9et7XlH0B4lkZhJH5dbl84GkmWgq\ndTdn8JFOCkEgwSaWssBWCBFYGxETQTBLQUfaVXQsiSXD0pKIoJOqW9DX8ZFZ29l1PcLqOqWy\nBvJIxo87jiukOBGcgXwsvmcgDahXPzMj1gm4KYW+rbcX76vB3Y7TIZQOUWwWjE58sUDyzgDW\nRKwDQJXe9q5dPwZRkIutCZvNkYEBvISAFpySWGitHR9Ds2G1Jsu07ZKF48ZcgP6vuHCJnbU8\nPZ1srOzJvj3oHZuuY2AyTQ8nWTS2p+QIa0nAdr5bGmxhjeGUOTK2pKQLEVZ1TZBOAYA5cfUc\nyOb99cSsFv2YCDCKAu3V69Ooe2FNspIySolBmSVmMLuWgrZTs4/YwWrXwC/QC6wCpkLBjo1S\nuYv6zhOxnVLqzjvv/OxnP7v84HXXXTc7Ozs6Onq2RnU8+kqlWQExPl4jqcjIIDfY06MW/vLM\nWcaWRU8fdXWzH2JqkvN58s4tCyi2IAXhAgQ+owZeF3GOQpDsDrd0h1vWd79Cm8iyaSVTtejQ\n+PxjBPJVF0MLKCmdSjjiCjXfPuQ6qs15bWJmTsh3KBOw1iauUEIqY9nCsGHDmgBrF8ojiLhD\nZwxIgHhBLLogsjtmQUILOlJrlxJ1x4A6ueljGjCIvJ1tiD6fW4CXkS+RKWQaShBSw6EUhhmA\nArLFHWGXKD1TotS1EutQKM5dde2+nz+15dAoMR+87IoN60demDT8dKAzZRwXRGQNz86gXuc4\n6kwWcl3kC9Tdg7XTCPgl4oIkdmnKM9O0bXstPxPM9xzTlWU204fiZFc7GrDxFYcnq33FY3aw\nmEkzW0bGdiazfoL1kUpC4QmwzbJ0BiKwnNayaE6jS8mcFE7HtYngzZoeGzS8WiPoVYnJxVnm\nO4KtJFIkHEVQQWiD1sQub6oWbL6Kyt0vKP1GpTLPzVF3D9QansHf+c53lvYdvvnNb+7atWvp\nrUajsWPHjhV3K84OFneNwrAwUypXGrVWWJDlkrM0qeJE5AsodVGlAiIKfDMzLfMFrB85i6M+\nHn43dAv1/ejadrrELhexRuHIwJEBgNDrrRS2V/KXNJLDjfjw/pn/KPrrpHQBNka70pdECjp0\nNztO0IgOx1lVStfoONENQSoF4rQmpBCWmElIIhYMy52yWYaEXayHJTB4hTwLM4QFdaooBCwf\nu4MLdBJ+zMs5oQWBYEh5FKeAZtlvdo3zDW0oAhJwj5C645pCsItkjgBzmondWop1S+jufqRv\n6Fu5woZc7v9aN0jngnxNSuqp2CceE30DttXE2CjlC+x6EALWcrOBQ6PU10+9fVS5ILooXmDE\njpnrddTrnRrS1tTuwCktf3N/kvyo1uhx1CbfU3OHrSNx9C5tbG1kuGW0RyIvJYCSRVPYzLIH\nkGmR8IgkWYdgfSnaxlS1KTsylwkTGb9uxvrI2HkvC7tilSqpmZlh2RpmA5SVLLS02989pccH\nxnJesoF6e5+nyewyUODbw+PUU6HeNdwuanR09Lvf/W7n5wMHDhw4cOCYE66//vozPqiVwVna\nUQI5JFAu7wtDzbzZXxQRpylLQeUeqvRiMUBTqcs+/XNRqdCZqtJijawFAE7hhDI76aG0BYUN\nIHVGW5xdxNqCIFUON5XDTcYm1sZRWicSkqQgR0p3vPpkIx41VjeiMWZDBAklnYKUnjaJYe1I\nRcKH4Ey3QMSd5DAvqOksAIIAMYgJ6PgSLxE2ZoAMKSz4nrDB8sm6XEPKILF8FgswGJpcl7Ok\nY7JHkmALQmSW+x3lENSiabJYXJVZQJ7mXbw1FOuWwAwSVMiHh4w9d4rsqX9AXH+j/V8/RrlM\n/YNYnrBxXYR5RJF9/CfiZVfR4NBJ7nN+4DwidicXQ1jDrRYaddq0pdOPVdt4/8z/HOq6duFq\n5t3taEerPeL7TmdGZHVWR62WWsbOZplPIlyWRQs0IMkw19JmDpErfQBEAjAEuFIIi2YzC+ow\nnoBDAUQqPV+n+djGvlILO7UEkGW0jI2tkbFxc+WmipzGnGCLSu/qt2UpzHO9tqaJ3fbt29/z\nnvcA+MpXvnL55Ze//OUvX/7uwMDAb//2b5+dkR0PAlfnqRDGhqN6XRorBaVCcBCg3YIQtG6E\nhoaxbNlNSnGQQ71+ZsrvdYTmKFoTACM/jPwI5IlSAHTR4u4iVgspvMDtMdb4y9bGjnRDr88R\nfjEYqEfj1dYBV4UMEAlBkogn67skXCFlM5kSLDXFmYmWUnSSFIM0A2CC6djKLmsjJixJQCzT\n2y1/fnU6mLGBI6EtHXmLAQnbFBUmR8BasAJJYs2QghK2ReVFxnRCsWY4i2QxZfZOM7FbS7Fu\nEZZ4NksHfR+cdYjdbKans6xLyYGzuCcbRTw1Sb0ntjXxfeobsD//mfB9Knef2cGdaZxHxE5K\nnhiD58NxSUmGIAIbDZ1xkogkFVsvwfbLqNTVkVKmuiVICVIAmHlPFO1stUc8TywoeDXplJes\ndRgNY6pa54RYvobrmGYaAQkjbCMmX1hWgkDCmIaUxdQiScxIQ7RcJgFDncWmlFlqhXOMqFMQ\nPCIpRTXTZLx2NuUVi2G7RbNA/yBW1yqX/ABJAq1Xef45iFtuueWWW24BsGvXrl/5lV+58847\nz/aITggaWkcbNtoD+2h2ukgqESQsc62aeZ53yXYMDFK5+3jpLgU5rtfOjO9gNIlkHrl+AIhm\nIAPk152BX3sR5z9cGWYmWk7sBClmAyJHhnm/r5lM57xl2njmojdYiw+5KHoqz2wVBxJOaptg\ngpRgK0CS2Fgs7qQuClUhiJY87ECAOVIPCyxq7wDLpDR7ltUyUzwQtCWvs+VrAUUswQmksnaj\n73tEDUZHPpFYu9RtIjI2f5oLF9ZQrFtCTojri8Vex6n5ethz57X+f0bHuhynpvV/HxoY8s4O\nt+PJCZ6bo/JJi3Mdh8rdPDVJxdJ5U2K4Ik727J+cnATQ29srVnJWi+P4r/7qrwC8733vO02D\ne0GgSq+4/ka025zEyDRZC0EkJbkegoDyeRwtVzc2W+ohcSBJftY8wuoAsI2T5BBhIWfbtLaq\ndU4e219GMLqbmM8DJiJSiqhlbJ6kJAAUW5MYVFKROiBJhq22ZIBOis+IE6wFhQiIahZz2pXR\n3IZiWdZrEJIGBldV3SMljx3CyAbk13xl4yOPPHK2h/A8IMcRW7ZGUv5MqLa1RUZCHENc7Tv9\nm7acsNVb4PNze7FxE5zTHgRNCrk48ZUPk5zuX7iGYa19/PHHe3p6Nm7c2Dny6KOPlkql7dvP\njS5J5xhCry/Ofljwj6xPlPCMTTs/OyLM+73WmiOteog8t+hmoTYxQVqrhRBK+poThmW2YAsh\nBQNExnRoW4fMiQUut8jzCJZxJL1Mi74nEhmzjEVB08I3iwEBKKRtqkhiw8IBBZTNuVeA/CHP\n7VLSAKm1eSUBxHaBzGnLMdvuMyW3P/dj3RJ6Hef/7O35H/PVW7pK23O5uUwXpNoa+PvjeCbL\nXiKxSxsA4L7QZ1cS8zO7aHgVa9ZcyGOj3NtH3edzd8WTEbuBgQEAhw8f7u/vB/Abv/EbAD7/\n+c8Xi0UArVbr/e9/P84ZYgchqNSFUteqU+cdEyXMa/1IrTHie3LZlZY1pJQMAJGx81kWHMfq\nAIhOvT5bmAZkIEBKcMYsibTlyJhK5gQZEgcAJJFlRGwdEsgaLE80eYkYroBQ/qHWYd8pD4f9\nPDWJfGG1HnWOw2m6RoVSn//85wHcfvvt//qv/3ro0KETnXbHHXecwUGdDGwMzc5MFopppmeT\n2LXsS2GF4KhNJyJ2QpAQyPQZIHbSRTK/sP2qY/gXhHT4xeCnP/3pu9/97p07dz7wwAO33XZb\n5+CXvvSlBx544Lrrrvu7v/u7rVtfsMHk+Y28P5BmDcOZpAX246hQ27jzsxBKkpeaWVcdEdc7\nIidIulLFusEwgCKSSgSZaRNJIhDIUiZIGJYC1gKdJmOdaGYhABawGi7jSB9ZwDK5BGvIEzAZ\n+ctZIcAOUiMC10ZCuCBrmedNAAAgAElEQVT43J62XX2e3+M4AGJjNwS+Q2SYI2u7lAIwr/Ut\nXaXwdPaKXXOxrgNJ9Oqu4lX5XCClQySAhtF7o7imdWU1PNhars5zq0Vaw3UpzKOrq/OOiTH1\nKBgYetWR5eiKYHOU+p0bDet5K2agVkAQcrNx4RK7Y/CVr3wFwCc/+ckOsVvrEKRAlplH46Tb\nUc7RyTBmayWEgWXEln0hVszbGoG5PIRJLXXWlJBEmbUAMrZ5Ft0ttBclTcRQQBsmMCAmppWF\np8Sd1tdQAnkV7mnOFrxKIReiXkMYrqqHsZDQ+vlPOyfR2Yy49dZb77///ocffvhEp507wY6I\nFFF3mrhT07GUmRAFKQux5elpBLkTcTuWknV2Bsh30A+TojUBZhSGEVwkdithfHz8jW984/z8\n/IrvPvbYYzfddNOePXu6Fp9AFwHAU4WtA2+aqD7eldvQOeI7xY5mrhMMA7er1j7gyHBJyK6k\nVwiGWvG0J/PNrCGES0RKuNpEDCOFTyC2VpKCFMakAmwXbmcZkgCC1XCYxFIjMgIzpIWQrFPy\nmBFTAYuDIILLUZ36Nbwc1Q15imB1vZi/us9zRcev1JoNygNQN+YXwjAnBYCpLHt11+l90q25\nWLcEAoqLUp+yo35n/fBqNXbG2AP7sO9ZzoWsFLRGsyW2XUIbNoCEUMivBwA6KTFJ5jH1GLov\nQzi8cITTlFa/SHYUsmy1J69NrFUZ1kuHIwNrs6k03dmONvnH6smJRNsxc10iTU3ENjjBtqkl\nMEDGsDiS7xOEtjGeDAMtM0l2cVUpLWoBCSn0YlfPFe9JbHmRvDnSC9lOJlGQy6uZaZRKtJoN\n1jWarAMArFu3DoAQore3t/PzuQ4p40rv1id/Nsq2nGhLIM9rVyo5azA7w4NDtJKYg7GYjzjN\nUAGKmxe6jZ2kKvYCx7333js/P18sFj/3uc/deuutS8c/85nPvOY1r7njjjtmZmY+8YlP3Hff\nfWdxkOcg+oqX7518KO/1K+kD8FShN39pO53znAIAVxVLuZF2OuupI1HLU8UGJjy3YJG2kzlH\nhkIooTyjI4IkgMHMkJBWKOZUgsyCqR0RrCWXIdApi2UQQbDR5AnoTPgSaVNWUg5AC4k+CXYR\nVcWIAhSnKYUBca+Istxw56vQMnbY88pKWcacNjcUXADzWl+dDzce91w4tVh7se4E6HFUj7Mq\nLsETY3zwAPUPHalaLhTtc88Kz6PBIVIobQHoecKUSSAkdAwAbNE+DDnHzqqfeiTOUOw9i7iQ\niV1uXfeNP52f7F7J8o1IWWnaipNa5gYnm2WZQhAbsyyRZhhgy+woAy2OpOUci5ZiASRsrAgJ\nK6fshGWzlP8nIbLJ6aS/4Hj9nocoWpVyju2qEnvnJJbcOP/+7//+7I5klWgYM87co7MrGo2O\nbNNvNaaEnC2Ve+ZmqVRasaENWU1nSr4jFLyLmaaT4jvf+Q6Ae++9tyM4WUKxWPy1X/s1rfV7\n3/vef//3fz9Lozt3ETjly4beunfyoUrhMkECoNDrm23tdZ1Cp8whdHurrf1KeFIsJFSkcLqC\n9bVozHcqljnJagQpmaTMaU4VeUo4xlpBQkJqSwBLsIEAORYSJCQ6GbyODsYYcgAYeBaCIJpU\nOdJmheFwvSaGrQhz3KqqzaEKfTPJhZulKgHIgLrR23IBgLlMXxnmuh3V2cZ5ZanonuYouuZi\n3UuFMdxooFzG8kSJEOgqcbXaEZGv1ATqWPgV9Fy50IUsa6D6DGRdVUpmldSOMyPWss/rarBW\nH/8vHURCU/HpxmRhJRWFEB6zmXazUkqSNdsYNmIbs83AR/mqxIodLZY+ScOcMQuyyqp8jKW5\nJpiURaIAQIEzdFybjgNz4jtaLZ/dqqBsQ+tUOYhjrMYwU+sLx2L7rONwq+3vezYftaUULkGA\nIET/7JSzZ5euVXlqamUjHmtZvoBllc2QNU5x6YNJkTVgz/NNiVVhfHwcwE033bTiuzfffDOA\n3bt3n9ExrREMFK9c33NTtb2v8zLvD/aE25Ks1nnpqFxf8ReibM4uC5ueU8r7/dpGgdPliIAg\nPKekVCDIyUwMJsBYtgQWQoINIJT0lXAECQI65mmCSBJ3lFaGHEOOx+26GIbwVOc8god2KnvY\n7fUIIZqO20PQ/aIuvC0ADDCXZpfnwrwUbWurOhvxPQAHk/SmUvH4bZyLeKlIEhw6ePyeKXk+\nH9zHabrK2wgFv2dBN6xCFDejeEVO6NU9HAFOIuTOkIfo2cKFm7EDkMgez9bFiqWmJDx3aFIf\ncAp+pRk1Xd3xmuOO5MPpAXkgH8KJHevoRLC0BACGIQFpjWZlxZFdUVfbuZC0BADBukV+aaVZ\nqDQnvuTlVJNIgue0yUs5MDOFgYHnERNYRqbPtY5Vq0dHUPy8OEd0JxzH8sC+7pkZFgQmYhYE\nYisYTr3KWca1qm3UUekT/f1H2HaS0MjG1ffhiecQT6M5ARiUL0N4Kvw121OY2wFIwKDnygtd\nezcyMrJ79+49e/Zcc82xDQYBPPfcc1gsJruIYyCEWle+wdh0svbz7nCzIFEMhmaau1wZCqEA\n5Lzubt4219wTuD1y0SkxcMqWbSuZlsJV0gu9fstZYtpxOpdkDQFFggGHWFoSxliGEiQcQLMl\nggUEWxDAnIjAsuOhWZdDiexetLkj3zaNLFnVD0gf7bazXqneER7XudcaVc6AuSzbHAS9rsoY\nE2n6+u6ugpQzWbYl8K8vhKfbmhhrLdYdg7oxs2nW4zrFlaUmGE8Swxj2llUlEjp/tJXxoj5w\noVDYANi8NRu5XqdwBcbG1lLUZhIUBGwMRRGVzvMtjOcndm94wxucZemfpZd6zcrzl5DKvnzh\nZUY35DIJCJizbCZNJxrtva14phoOb6p2xV5mRKdzIcAMG7FtEGdw+rUIJorNnrjc9KxlZMwK\n2sqco50lW25p4RhqO0d+g3E9blvC0V8JhsxMWjqek1FOiJa1IIKxOGkyjpNEbNyEc7ARzeqw\nSiencyTY8fycardi18k1Y8MAIK2xIC0lQLAWlQraLfv0DrQaNDRMhSIAjtpU6V3ldrnNMPM4\n/ArCAbBG9Rm4BTjHbchnTUTTsBmkh1z/89SU6QhzOxD0QTiwKWZ3YPCVz3PJ+Y1rrrlm9+7d\nd9999+tf//pK5ai25K1W66Mf/SiAa6+99iyN7lyHI3Mbel6thHdg5r/K4ebQ6xsuXz9RfaIY\nrO+UTeS9ASnUVP1p3ykqGRJAJFwZJqhKp+jIXGbargpzwvNUPkrn4nQ+MW0Jo4QLUiRlbNqW\nHUAoEoDVnIGEoQDSU4YdbjXlYKoGwR1zFOvZRqIqkejNWLnEnq0Kf3tI89q7NPG2t62taXNZ\nLuh1nJR5LE5vLBYGXHc6zQ7E8Zu7y8UzYgK6tmLdcsxl2edHx0MlW9rcsX7oeFOYJxqtb8/M\nSaKbu4o3L9WgeD6NjCCOjrEe46gtNm15SdIUIai3lw8egO8da1BnLe95hqenwIyNm9jz5RUv\nwwvsw77m8PzT96mnnjrJyzWNGE4u2JhUH8qpBZ8qa9M4ejZqP6OcsvS3UDzX8M3u3mjLTDBT\nSBc7CBLgknTBFnoO5NZ94+m2ykQirGQITozKCYYlBkgwchkmizCd+caGZDEjX5gEiwWwHTiZ\nbhc97RzzvGfBwtd6ttlOs9SdOsyuR4JYSHIcuC55/lEUIWphoP/FrX7OBawtETEFQUqUWpJC\nKAtpjZUqI2JrpYADgAE/IMfhQ4e4XhWXXIZiCe32ap1rAJOA5MK+AykIDzo+ltiZBK1xpDUI\nD/EsTIbSJpxEraJjCHehz4RwIRRMckETuzvuuOMf//Efd+/evW3btttuu+3aa6/t7++fm5vb\nsWPHAw88MDExIaX8rd/6rbM9zHMXjgxGel7lO121aHS2uSfv9FcKl861ni14AyAiQs6tDJau\naaXTjXhCCs+aNPT71/XcBLLtdGa+ua+tq4EsSXICtwdAFiUkRIbUlwVByiMYk2ibMoHZSspb\nGRgmwSkTtdVIU3QbBrFVaLscN+SGtigrkg7Qgzmbu8xTUqvBuvuyqpb9ntzk+0Ul28ZOZNlN\npfyI543GyabAf3NPeeBMWeyurVi3HFOZ7lLOtsDbX29OpdnxxG4yTbcEvi/pobn5G4p5v/OE\nEgLlbn7qSfT20xJ1zjLMV7Fpy4t/ZsUx16owRmzYYEZHRV/fcm7HM1O87zn0lDk19Mwu8erX\n0uDwSW52fuBkxO5tb3vbGRvHWUHGHAQboV+WppOO021MK4meS5JDrr+eQAxr3BG2s1Ml4Woa\nqnnzoT7iUcIGnIAzNrOZjRqqvL7Rm7mpREokpakKa8n6gp0gpdmQYnfhQrYROb0Qci7v9qU2\ncxemoNScek7iH/UXkYZVpMOs5bdnATLWoNGEmzAY1kIb6Iy7KxwEotgF14ExaLepaw33S1kS\nFK8NEDWMlcS9DGUNwMKwhLWAIQFJSBI4DkmFQh4QvGc3Nm4SIxuoWHr+mwMAhAM2YA1SAMMk\nkMc9d7IWoin4FQCQLlqjCAcWxMUrQjpgDbYgAWth9YXeTOymm2761Kc+ddddd1Wr1c985jPH\nn3DPPfd0lHYXcSIIkgOlK7vDzaHb10omdStxZTjd2FUIhpVwrDXMxlU5T+U9VYzSeUf6JCDg\nFv1BSc74/BO19KAkJ/T7S8H6YjBcbx/MTOw5RYKQ1tHUFjqSKtBWg5Th1BEus2ucQYXA5TTV\nsVbdlvprKBlyCkJ4Qvi2lqlhC7dJPXNiW5/btV3KslIGOJzqIc95Q5hzBT3Vim4sFW4o5Ivq\nzDUkWGOxbhn2t1v79j2nsviymanK5VfYSoX6B5aX/xeV3NFqKRKv6Sp5y/IOon+AjbE/f5ID\nH9LhLBNJLK665kW7ynG7xQf28/Q0BKHdEutGeGwUxS4KQwjBzIgTVCpotymO0NcvSud5z4kO\nTkbsvvnNb56xcZwVWLAQjh9e2mz8RJBI4gNpOu24/QvvMpPqpviQlbnxcsICG+b8uTDTwsJE\n4AwcgxSJAqAaqjqZD3qa+aqbWVFgSJCROnUtz+bR8txFp8yMVAEyD1Dbk6qVaUcwkTDspLpZ\nCJfqYYVlP9KluRbIh9uGEK4xpFNKEoYlx4XvQ0gArFOaqvPoAYxsYjbikksRhmfpE73wwFZq\n3ZNlhpkFEWSnJtkKAjMbRqvJ7Tbyefg+pSl7Hk2M0ZVXr75sWXooX4b5XZAeTIbixhX2YYHj\ndMMnXf06IYpbUH8OwoVN0HUJ5AmslC8c/N7v/d4tt9zyB3/wBw8//HC2aHOllLr55pvvvffe\nG2+88ewOb63AVfl13TcYmw52XV2PJyaqTxyc/aH0enJeRQjHEf6G7pt9p5joVpJVU9s2JjM2\nC9xKJb+9GU+30ql6PN6KpxhcKVyemmY7niZS5dwgwKmOjI0FuQaZNUnoD/lOXolACkWkIrgN\n601kVgAtY6bTzNNzcLoCIbLc9p7cNeudYiAoYZ7Jsrox1+XzBSXmtH5ZmLuxWNgc+GrNbnSc\nSYyl6T888eRrpg7v8bxLhteXrbHP7BTG0PqRpXOuKxRKShnmzYF/1GdKRMPrRLkb7RayDI5D\nhQJeiiJ8doar81SpAGAvgFLi5Tdheoqf3cuuC0HcqMEY0dfHhSLlQlar90VZw1iVkqDVaoWL\nXOFHP/rRww8/7LruK1/5yle84hWnc2ynHRJkmVxvuLvnvx0e+38JUN5R+mgmV3ubbHKwJUu1\nop4n/bLDQdttt5wGC0Xkyo7ojhSEnHcnbZD1xH11QSD4WkHIqXwUywg2DxECBBtBlgEBZi1F\no+Tn2qkRwkl1rTvU7sLD3ol1oR4HccbGsOMLa8EsrdWuZ9MYSQStEebZdUWYJ+Wg4CDI8dgo\njBGXvewsfJSnDmvLjZ2jKIjbrJSTJE3ldjbWJdhl01ReIAQ8H1IiSbjZRJhHo4ahdS+04UQ4\nBLcIHUE48EorkDa3gFw/knlIF1mE/Aick1d9EQoj8EowKaQHt7C2vQ9PFa699tqHHnrIGHPg\nwIHR0dGhoaFNmzapNdtz+SxCCrcYrCsG64bL12/pe/1sc89E7WdFf8hTxY7qzneKvnOsDRCz\naSXTh+s/LwbD863nfFUUwmknM81kKvT7BYSxUT2ezEwzr/pLuXW+U17OxIpAL6OsdV2bg3Fz\nRM2kMsykOOi/Fs5WGG/GpgnbDZ5blGrQcxvWXBWEw567JfC9s+EPtbZi3RJmWq3XjB10hoaL\nxpIgcl2qVPjpn6Ovb4mieYKuWKmOoQPK5U5ZXWp2xDeKHEXGdBpQYd16ESesM+6b5gPPUaUX\nllGbFxeGx/jzxKzvfOc7d9555+joaBRFAD796U9/6EMfWnr3Ax/4wP33309rbZVjOWsl06lu\nZdFso91w3VyajgHWmFTajBY3pWJra8bM2aJPvX42a2VxPFdvDB/ub8nN1b5IZi1HZ8qSYEXK\nwmrwbE5b4k3VwAjeU263Mi0FQfgwbYYlAKoXcoEiEyj1HL+dgmm+ku9I64iRb8SVyUbmyNRR\nRqSO41khAQhjjHLI9QDAA6ylWhVpykFAYR5CgC36+81PfyKuvZ56+87KZ/vSsbbc2NnYquvX\n3EymqW+0gRBAKmVTOIatdn2vQwscB0pyow7PRZBDu/VCs6pOfuWtVZshrcMa+D1QAUwKvw9B\n76qImrva3eALC1LKzZs3b968+WwP5HwAgbrDLV25kVJupNo+MD7/mOeUPJV3ZCBIgQRbYzjN\ndBRn9VTXNva+dtvAm4v+sOGklcykuhGn9enm0wdm/sv3ewv+ZRtUQQjZjCbHaj/JTFOJnJSe\nEi6RYAbD5pHATI2YycG+N4WF69zg0gaFzUxrwBGUE6KsZE7KgpLdyilKcRafX2sr1i1hK2E+\nH/6PJNPgzZ0qBKmgFJLkJeXeloGzbLW1FEHAzSb5AYi42aBF3kZ+AD8ggHp6uLubG3USkrZd\ngvO9HraDkxG7Z5999h3veEcURb7vA5ienv7IRz4CQClVKpVmZ2e/+MUv/uIv/uIakuLVo7Fq\ndLCdzB6uPaFk0Ey5GWdSZHG0x+iWcrtbzSc9b711equZnsyy1HBRKZL9yKxjZomzWCX7u9yZ\ncCKfuPnU2VAtaAJIS9tNrA4WmpPFmadLQcHIvqZvCLlMRJ6FdMnUWZZJFQGAWRibtzYwmOst\nFOfbhlNjNGlTbGTled3yyEoARoiAxEJ9KxmD5baKQiDIsTWYmWat2bIYHKLefk5T+8Rj4urr\n1ii3W1tu7C1my5YYDd9vaZPLEgIJsLJWu96cENJaXwiAKc1QKMBaqs1zvXZK/jomQeMA2pMg\nCROj9xrkyy/9rhciBgcHVzwupdy0adOrXvWqu+++O7yocHixEOT0FS7vzW8fKF7ZSqbjrJqa\nFrM9NP/ouvLLPVHwcoXA7c57/YG7oA9W5JeCha//+p4bLx3436caO5+b/ndmzrndPfmtXbkN\nUVZNsrq2kbYJs9U2zkwzM/HmymsHy9f1F69wzm2FwdqKdUsIPe8VYbiu0lt2nbJSAJgZ1rBU\nL50j88w0Zmfs/udoZCOVuqh/4OSSOOrrF0lsd+8Cg7ZsxeAQOgOq1zlqo1FDmlFPj9iybe0W\nFL4InIzYfexjH4uiaNOmTZ/61KcA/Mu//EuWZUKIxx577Morr/zwhz983333ffGLXzxNxC6O\n47/8y7984okn6vX6tm3b3vve927atOlF362VzEw3nn5u+gd5vz9Q5YHSVQCQZrt0jXgCgB9u\nY5s6bl892lttHhDuSNnpamvDYEAZp1fpGalnjSgDaDpZw8kM867CjG81cwEicHGorbyY2lZ2\nM8tyPt3UDF49WpJGMxJ2eqyFNFUhisrweOgg77ZzFIlGTbV6xxvGp3wiy3Oy7cESw7I1iZA+\nkRDCk3Darue6xy1ihESQ48nDNDCArm4Qkedxpc/+9Cfypldh1Qr9cwcv3Y3dWvvVr3714Ycf\nNsa8+tWvfs973iOPCw1f//rX//qv/3rppZSyoyhdzbXLkVrb73nVdrueccDWY1gwAZKQk4JI\nTKbpoJSu1sjnKZeD1ogjnpzE+g1YtY/diRDPIppdsKAzMaIZeBeJ3YvC4cOHT/TW2NjYf/7n\nfz744IOPP/64d2IXoTM569YoiGQxGC4GCzWJxmbb+v83IiFW0W0g51U2ejdXCtvr0aFWMn1g\n9j+V8ASpzn9KcGriLb2vzwcDXcH6nNu7JvaR1lasO4IgJ7Zu7R4bcxdZOGpV2rSFXsLuatYE\nCcjmpH3yCZTLGFqHJLa7d1KWiZENJ+NkUtLGzWJwiJjZ84kIWWoPHOB9z0Ipu28vSUWuJ15+\nE23Z+qKHt+ZwMmL3s5/9DMBf/MVfvO51rwPwyCOPAHjd61535ZVXArjjjjvuu+++0+fG/vGP\nf3x+fv5973tfoVD45je/effdd3/hC18ol1/Mg2u2ufeJA3+TDwYGilcJcWTulhy10XOrzapU\nRQAk3FQNHValIo2rdDeb7pLoTdmXpMi0GVY7AzKbscLXkNpmCplV+QbljQgNSJpyYOcUsxZh\nQrlqTu/MNayMhmuy5Zc8EbiA5Wnj57VbmBLZkKhzvE/KQLuBHsoPTduuOicBWHR8N41UBSly\n1qbGtqT24lyhfHzLWmM4alH/ADOoOofe/gVu191tx8ZEELxQOdc5iL179z700EMHDhzYuHHj\nG97whksuueTk53/1q1/9t3/7tw984ANKqS9+8Yta6/e9733HnDM2Nnb99de/5S1v6bxcegys\n5trlcIxpJYkGlXQmLDeVAwFmSKJclgXMBSlaKud2dZHrg8BEkAq1qh09KDa/hAp/AIDVkItU\nXyiwAZuTuZxcxInwm7/5myseN8bs2rXrkUce2blz5+c+97kPf/jDJ7rDmZx15wfkC6/Eznt9\nea8PwMbKq+OsYWxi2QiSUnieyjsyXBN87iQ4l2PdElJr/6vQdUhMDzyz67rucomZNmykdetf\ndDRrjqK6B2RMT37G7aksmLBKSZVe7N7JPRXKn7jCHwBg4Sd1eCVIl+3oQZ4Yp6Fhbrcol6dy\nmatV+/hjcnAQuQsl6f48W7EArrvuus7LRx99FMBb3/rWzssOx5qcnDwdwxobG9uxY8e99957\nxRVXALjrrrt+/dd//dFHH33Tm970gu7DzBO1J3ZNPFgpbl/eiLoDAUhuzzZ2l3ObwUisOZAk\nReUpZzNML9KDLtmmFSW7T+pqp+7QqjzrpgUEcpkoW+kzJAAJsMy3bepxlTix7Lkik7Y9Whxe\n1+xreM4EmVDJAAFEwwhVNLNkG8qtdHrjuZqDhI2A1ICCJstWCycPgiAltIwdjjGttTSiW5ID\nAGw5SShNqX8AYV4AOHyYHY/KZQAU5u30JBULNLz+FPw9zh7uvPPO+++/3yx25ZJS/s7v/M6f\n//mfn+j8KIq+/e1vf/CDH3z5y18O4P3vf/8nP/nJW2+9tVQ6Knk5NjZ28803H2M5u8prl2NG\nqpYQxSQup2nDcTxYWBCgLDxCorUOik3X85QbdoKesTCWBgaw71nu7qEXtVBZgvShI6gcQMha\n8CsXWd2LxJe+9KWTvPvRj370nnvu+da3vnUiYneGZ91FODJ05Pn2kD7HY90SZrX+r3Zy9aWX\nTgz0jzqq3F2mfOFFdydng/lnEA7AtlOz9wCuPlJaCyGt48o00e18awJs4XbBtKFjuEWEy9QT\n7cOYeRxeBaW+lrd/jxheByLyAvT08NwstEZfn52ZESPn25w5EU5G7MrlcrvdfuaZZ2644Yb9\n+/c/8cQTAK6//vrOu08//TSA4eHT4vXnOM473/nObdu2dV5qrdM0tfao7qrvec97Ot+BarV6\nom4/M81dzxz+dn/xCrmoVGOgrs28zqqZSdnubExNZ3KyHTFBEoFhmAWRlAXyt3vpRKTWOQhz\nvNOoMhhtpkjAp0TZyDMTZFImB5BMBIYVHqxSZqogNan1qVgfifzjQ+LKCdgCtbSBdHLpAcP7\nAm9QqQUVp9IYnOZMQechLJwMTmLgFgDJgLCwiiLXKTlBputta31RUFYg09RToXyIpaq9fAHN\nBgr5jhSPSl325ztEd4WCc1pochJ8+tOf7hSOVSqVzZs379+/f2pq6rOf/ezw8PDv//7vr3jJ\noUOH2u32UmOoq666Ksuy/fv3X3XVVcec9sQTT3zjG99IkuTyyy+/7bbbBgcHV3PtMbPOel6z\nVK5pa4w2RJ5laS0AI0TmuVKqOjOTiNiEnW7CSYKhIXI9FIs8PUVdXS8laRdUYCJU90IQ8iOn\nptXYRRyPX/7lX77nnnueeeaZE51whmfdqf8XXsTZxrkf65aO54TI2I6l2bzrX1fpXrGL1+pB\nEoUNiGfBqcxJhrXLOaKwBlK2JxHPQLpI5mBiuGVUd8HNHzF+sgbWgAQaz8YSrtsJqkpS/xB1\ndbPjwBrE8UsZ59rCyVj2pZdeCuC+++7bsWPHPffcA6BQKHSI3aFDhzoVPZ2M2ilHX1/fO9/5\nTtd1ASRJ8rnPfa5QKBxjEDo+Pj42NjY2NlatVle0JKhHYztG/66S37bE6qra/LzZ/s7s3I5m\na05rzdjsyk1B6ErhkWgZmzBPZ7qqddtaS0o6/euyp4yez1TFiFKbCnXOSRlq0R2r4bp3ZeRs\nTuRAJstGFI0sGtVXlZsThMYZydSQEQWAJkN+po/7m5QHxbqe6XlP5nLqCNnKR8wEJyMrKJO2\n5bTb+XzmeBpwIu22U2tBmQ4TGyQOmo2IGqaYo5ER6iph+T/cUVyvcbXaeUWOgzDkmelT/cc5\nc/ibv/kbAB/60IcmJiZ+/OMfj42N/eEf/iGAL3/5yye6ZGZmRim1pHN3HCcMw9nZ2eXn1Gq1\nZrOplLrrrrs+/OEPt1qtj3zkI1EUrebaY2adBCLLNSFrygmMcYzWgjIhBCEXx8pql0izTTtr\nEmMRRaLSCyLkC5G9Kp0AACAASURBVHxwH5qNl/L5kEBhA4Zehb4bUdwMtVYJ/LmOjq2de2JN\n5Bmedaf4n3cR5wDO/Vi3dLyk1O3Dg1flw7f0dG/PnYKgk1+H/DoUt7retZfx/BwvenJyo4Hh\nEcoXwIAAKbQmAAEhwARelufxSggq8EogYvCypbKSCENyXTABKzRnP19xshjxu7/7u9///ve/\n/vWvf/3rX+8cecc73uE4zuzs7Pr1Cxt8J1pMnBIw8w9+8IO//du/7e/v/+xnP5s/eqP9oYce\n6vzw4IMPLm0QL8Gynmo81ZXb0MnYG+Zno/ixRrNLqU2+v6RVyzgNpRtasT9OSkoRACLDPJvp\nUIqCdFxZKsU/b/tXW+a6Np44kmBhuFocG+tjYV1Krer39aSW3SCHCQdLnBFfNaFrzuysKlRg\nBbjjSCEtKrNsgUwi04kVrlU9VasEwc84DZ3IkW2JspLCdT0pPaeQ/P/svWmspFd19/tfe3im\nmuvMp09Pbk8Y22AbDGGKSSBcQO8FRSjJlZILCooSLCFQRBIpgwKJEgmIwwcHQpTwhugNugJk\nEHoTQibEBW6IwQYb47F9eu4z9DlVdWp45r33uh/q9Dy47W5wd3N+Oh/qPPVMVfXUrv+z9lr/\nhdSoQUM2zv7wKIyQJnB27F2MahWjIVtLV2cu9tLSkhDiIx/5yHhYUUp9+MMf/vjHP36BVHdm\nPjvP5oy+xrVa7XOf+1y1Wh2vuWfPnve85z0PPvig53nPue3ZV13BDloxgZiZyAJMMCQySUFZ\nIoxS61LLsMb1+2J+DsbysVUIwca6zrqonenm9XyRAa7KT/cqwRhz//33A7jpppvOt85P/qrb\n4hrjqhjrTrDgewu+h6Lg9TXUG5fYmlyFqI5rgosFB8MHD7DnwRjato22bYOUwSScATtMvxI2\nw2A/mjee5tPuN1Hdgf4i6gu+OlqA+cyZkCKj4LQe0Nc2FxJ2b3/728f5wnmeA3jlK185Lo8d\no7X+y7/8yze84Q0/pjPr9/sf/ehHV1dX3/3ud7/hDW94vlmx3XjxaO/h6dpLAWTO7U2yvWm6\nKwjk6bshgiT4QjjAMqtNg1mKBOWOmW2FtM9Jv+yXsqkFiQv6gxXMkZAgvS63T8umnz/mRM2J\nBpNcruZ2dn1y6G1LIos++1NEGoCXMzP81PR1VooqUSigagy/cIPISwNVKiqsLZReJc5dOeV0\nXYZp2RWpakY7z3xXtOJuB+0JqlQBkOfzkSO0sB2XrB5eFH7u537ui1/84uOPP34i0fPJJ580\nxvzsz/7s+TZpt9tlWSZJEkURAGttHMdntHUXQtROadVarVZnZmY6nc5LX/rS59z2bByjJ1Sk\n/JYocqGaZV6SsIJZUJAXXSl9diqJwQGyjIsS3Q4EwTGyhP/r2ywkZufokitkt7gUztcuzFq7\nb9++cSbxe97znvNt/pO/6ra4xrgqxroTcBLz0hIfW0a3R7fcKq7b83xf77nxPHH9DZiecWme\ndJVBXQ9VVIFXh1cDO5AEGLWdEKcrF5Ko70Z1O4SsOf86Xlul5inpy0WJ4ZBaV3GnzefLc0T1\nP/CBD7zvfe974oknoii6/vrrhRAAoij6/Oc///rXv/585k+XDjN/5CMfabfb999/f/T8i6iZ\nXS/e3wgXiKhkfjpJD2XZwrlbOytH1jrMaN0zNhA40VXGI7LgTplPi6hG8UFbrYgLOl8we0KE\nAlKQFNGAmlFlSpsVL3vCiUC4UVcPhhOtssLVYTrTHZSaclFQwpnRjBqJekihNpDWDEJ/UNGZ\nliDkjhtK1aQAIQJG1qwVPO1V8uRwoBuhd6bdIns+pRkqx6Obvo80vYqE3Ve+8pUTj9/+9rf/\n67/+69ve9rb3v//9119//eLi4v3331+pVO69997zbb5jx44gCB577LFxD6jHH39cCHGG2ewj\njzzyuc997o//+I/HMeA0TdfW1rZv334x256NA/esDcJwPksI3Nfad853LnCu0F4tidOgylJx\nlon5BUxM0PFwMQc+QHb/PpEmtH3nZbNi3+L58+1vf/vCK7zlLW9597vffb5nf/JX3RbXAFfd\nWHcCXl7iYyucZehviOeTw+2YC+bgApUWRGg0ihKDdeg6RstQIfwmQMcrw+hMVXcCoQCQ2LHD\nseOjRxCEEISiQJKIO+66in4EL53nTtfwPO/lL3/5qUvCMPylX/qlH9spAcAPf/jDxcXFd7zj\nHXv37j2xcNu2bRd5S5EU64c6D25r3cHA3iTdn2bz51Z1EEIba9ZN2VbKFzSyLmf2j2s7CZLC\nFdaTZrWu2glLn/jsoJ0DSuZAilAIyYXzduVOVD3KXMjedUbNK3PEG/2XEIFn962r4XrD9V1v\nSuykQrtRsFBERjnGIBc29nThVUulxwfJmUMhqlKeOGYghE/oGVOloJetB7pOdNqXhJSCPSWi\nrjXn+VXkAXC2LeJgMPijP/qjU5f84z/+48///M+fc/Moit70pjf9/d///cTEhBDib//2b++5\n555mswngP//zP4uieOtb33rLLbesrq7ed99973znO5VSn//85+fn5++66y4p5fm2PR/SWknk\nmDva31etN4ucQZEtPcvK5KXvG6kmBr3Q92lunhpNnGJYQ0Lw+rpY2M7drgPE7j0X5WxXlpyl\nKM3mp6w0tKIw+mnobP3j401vetM5lwshdu3a9drXvvbXfu3XLjBp8BO+6ra4Nri6xrrTsA5a\ni0oF03M0PXORG+1PsyeT5Dv94RuajZdVK9Nn27Iehx1IQPogcVou3UXhB2LP9dyeRJbAOvg+\n6o0LeOyxRRlD6GsqR/k5qmIvZhe9Xu8yncxJ9u/fz8z33XffqQt/8zd/8+1vf/vFbD7MViKv\nCdCRLH88TrafPwNAyErpUokaAb4Qgii2dmCdT3R8WlYY5yy8CmVKRgNjNUEd11kMGLBhVKUI\nhCBAcOZEXQtZFeqIKUBCiaA0caAqoRsJb45olzG9jfCmRLeOleUdUh6UVCmQehpswDHsGmgG\nspaDfKKqlGdPH1elSK23khzVujUVTpz+kgSMPbmyFGRPy5y4vLhyM/sBgFCQ3ovfdfS9732v\nEOLP//zPnXOvetWrfuM3fmO8/Bvf+EYcx29961s9z7vvvvv+7u/+7i/+4i+EEHfccceHPvSh\nsTnn+bY9H0aqZp4TkBOtBwETzaXxZJFlJFcrtUol9JKkboxPAkXBZUmEk/UuRBDEzlKjwceO\nuSAUO3ed90jWcq/Lgz6yjJeWoAQLSQBbS9bQ/AIqFao3qNn6qTJYv1z8+7//+yXu4Sd51W2x\nxZgX66qjqSmyxh06IF5+10XeUvaM+ezKsT1hcGeteiDLDPPPt5re2c6sAACviWgWgwOoXwf/\nBdj+SEUXFwOyOTo/wvoP4DUx+zOo/lhMPl4E6EQFyjmeu7hfiAvs4SfDOLXzz/7sz37/939/\nvORg59vrw6e1N/XDUTwyNpLnDfwy22P9R46m/drxrukMpM5lzsXWaSKPR17+7IbTgW6TN2+Y\nc8epc2OxZRiRFCdUIABlVvPgtg5N7vR9B6wURZEd3pN/XalIyupY9bBZX5E3eTJqKXXDKub6\nqOTIT9y9sGOXFKIW6Imq8vX5g9Z5GYd+e09jT3hqZLs00Jq2H3cDSmK0JsRlNd1mi2KIcgST\nYHgIRJtijh2qCxAevAp0/QXeAI0TOi+MlPJFrw0cX3X/56/93//XW/8Pf211RchYKTBuiAe1\nsiQhGtbOmqJa5GkUhZ4XKo0ohLGi3eYwIt8HgdfW6faXk1KwlpeOije88RzeNMzcWefOOi8d\nRVShMII6ayQtCk5TSmLs2EXTM7RlgXZZcc595zvf+dKXvnTGreZPnrPHui2uXq6use7Uq25o\n7cH+wKXxjsGgVhSiUacduy5sZbc3Sf93p3dDuNlM9tFR/BvzMzPnn6NghishLyH3mB2SFdgM\nugISyIeQHqLZ02Zy02NY+S9Awyao7cL0XaAX6Md3ZXFRV8yePXvuvvvumZmLjbi+6BibSfKW\n8vxwmm8LLnRpEEmImnBHgU1hR0AkhC9EIFzh3KggOMPwc1fCORyvt9AkCnaREAHRiUpZYkMu\nK2TDGO4aI4hu1D2bPpzDOaqO51MZXFizwdwW6BmzVnKlFMpSIiEAScQgR1GINMKGRBs4b7za\nk+GoTJfydEdQ0SeLdfnULxhbFufXtc8Xdsi7SDtIlqEjCB/R/MmjMcMZlAmSFbgS286b9Xsh\nzuja5Jzbu3fvaDQ6saTX6+3du/d973vfC38Zl4/I9x6p1DylRBzrvLghHVwXDx0wacuh5xda\nF0oPA7/u+0hT8gOEipMUGxtotlCt0tQUjRM3pUQYYDjAGcLOGHfoIO9/lpotmjm/gZnnkeeh\nXufuOh/YRzffIhYWrpEh6sXDGPPNb37zgQce+PKXv7y8vAzgRRd2W1xLXF1j3Qn2b2z8P489\nHg/6ty8f5VF/vlZvlQW98lVi93XUap9vxkALURx3onUMw6wvGDkiuiRVByDvYWMv/Dr6i3Al\nojmYGEIhOnUclWCGS+AySO/aGTIvJOza7Xa32wWwuLi4uLi4a9euu++++1WvetXdd9995513\nvoCahp8YxuVC6m5uW95zK1cdzFL/IaB1qoSSQCREJERN1jOasWZk4HwpAhJEUEQCMMy5g4Eb\nlnZ8NXu229MvCSkIJO8Kghk5SPo/EMFEDJuRGFlDIOuyPqYsqZKdICG00IKEAzNyZguWQF3J\nUFbYphYbQk+c73IjIXSxtppOh8Kb9zdPnq2hU+/wnMFluuErBkhWES/Ba5z+3ThxPgRSEAo6\nev6JEefi2Weffctb3rJv374zlr/nPe+5QgY7AWTMfa17lZoJXNfXNw4H7aIg5mqeVzkH8bQQ\n0tOgsT0B4HnwtBsMKM9pZvZk1p3nc5adNtRlGR8+yCtLNDt/UROsRFSrI6zw3me4LLF9B+nn\n3bVpi6Io/uM//uOBBx74yle+cqqz1wV87LbY4hK58se6McWx1eG3vjlp3c5WS83MrNvyaFi5\npxJ5/Z77/kN03fVi+w6ca9iZ8/SdteqPRnFNya4xP99qNn/MkUhnWJYbXr9r13K27Cmiwuf1\nGibbJ34TgxYm70C8AuWhcZlKe68ELvTOdjqdxcXF7x3n+9///he+8IVxu2Kl1K233joWeb/+\n67/+kzrbi4UghsY8nRR7jgd+L4CW1Sy4LSr3QZ8jJClJSBEq7goZKdCps7qSyBcAREMpZjBn\noG0U7lbCWy3KaVVydkTraWN6gfIUZOlc31pr0lw2CGQdl8SJYMOSHQjwBSkiQVQyr5dlQ3ow\nA0Bob+LsE9t8paRr0u1NkpaubU7IWnuakjMlvEsyGQIARrKG7mPwWwinT2oMtnAl2G3KOJIQ\nEkIDdHluff7wD//w7JEOwJWTWn40L25Q6kBaSqIG3MsGg6o1TlJX+pLIOdtwzityTghE4mQy\nClEUob/hGg1p7WaSihAYdxNKUx4NkaXu8GEe9Gl6+vmdk5I0M+MOHyQC7dz9glv9/LSRJMnX\nvva1Bx544J/+6Z8Gg8EZz37sYx+7gN3JFltcIlf+WAeAj60WP3jo+2F1jZA4eNrjenNnv5vt\n3BVMTkNKPnqInaXde85OvPOFeF2zPuvpxLqmVnvCQPxYs4Hj2Hv2h419i6Y3qs3PsPbSwzV/\n2vOW9js5TzMz1J4AQAL1Xajv+jGeyIvCc0jmPXv27Nmz51d+5VcAOOeeeOKJsch76KGHHnnk\nkUceeeRv/uZvrkBhp2TQL9LwLPfgc+IRcjXPIiezDnmObxGLSNoEcqI8j3W1BADD5RpVXwER\nWMACrlgy5ZrWk6ZYK5xI2DigJWloCui6YNnQUpFASDVCHbBSiOP794gU0dDaSAZeuS5kJOV5\nEtZICHaRlJ2yXBgH9osC4clgKuX5qf++AJgRH0F/EeE0xGaXWpQxbApXIl0HaLNewjHAqExD\nhpenwuhb3/oWgH/7t3/70z/9U2vtP/zDP3zyk5/87Gc/+6EPfegy7P1ysC3wh9ZkzILolkH/\n9kEXJJxzzLDgQAjr2LFVaULt9hlql4koiV23I6amAcA5lAXvW3R7n4YfoL9hN3oyiri7jqlZ\nNBtUeY5O2Cchoskp3r+PPe9qbxb8k+Fd73rXv/zLvyRJcurCu+66601vetPYvPN3fud3XqRT\n2+Kngit/rENRuEe+703P+hvDld7aFK2C3XI0tdG6/sb52XE0gSam3NEjwvdp+86zdxAJcXv1\nJ9KtdTiw3/8eHzigZ9tqvknkUKZeuS4mrqfmLNLYPfxdcdvLaPaabcL4PO7mjTFpmiZJEsfx\nYDA4w6X6ikLJIOP8wlP4J/BJLASVTO+AbMGNzl5ByFohJyRyMM4zwei4PEbRrdCzAHLndug8\njx9TqjX+d7UsS8e+IONGpd6uZCSJKlJWpXAVOjYhhlUK7GmqUQC+EKnlHL61o/N1QyEQ4CpS\nPpvmhWMuDbUnKDgep8xzLOygyiV9l5IVbCwimILQYEYxRLyM/iJGS0jXAaDoIx8g76McgIDk\nGAYH0TtvX83nQafTaTQab37zm3/xF3/xhz/84Z49e+677z5r7bid3ZVAXYptnhcIMZmn1w02\nhkL2pexrXXEmYJaAx1wa55QaN/A9ARe5aLXQbOPIIc5SANztuscfc6vL2LYA3+ckltOzqNdp\nYhLJiPc+7Trrz6NQiQiTU+7xH2F0jkt6izN44IEHTqi617zmNZ/4xCcOHDjw0EMPbem5LX4y\nXPljHY+GqNWs9paLomaP5sm+ND1s8oM31iq1U+aIqD3hnnwCSfyinWhRuKee5AP7xcws+Z7Q\nREpSGFKrgWEfxiAIaXrWPfYodzvPvberk+eI2D377LMPPvjgd7/73QcffPCRRx45UcUTRdHr\nXve6V73qVWN7wyuNyJtI8w0lLs6QkFCRcr0MQ2878sMwXajTfF4UCUseuSwxeUOrM7WwK2FW\nKbwZ/mYhamrdDHWVahDJwnHsXEikBMGVzqbstx1QlUISATACI81k4ZUoTo9ejydnUycp70hZ\nk/Ic08oMBkgRPEJsbTNN0Jg7MfvGo6GYnr6Uybi8h96TCKchJJxBto74KEAw6WY6HQl4teMW\nJwznwPakAcolMjMzc+jQoe9973svf/nLR6PR17/+9TvuuCPLsm984xuXYe+XBYYBJrSaGOQD\nrZolgWgkVUkiAFel8Iw1WgulJAjWgpmdAzukqWu2hCCEAUaxLfJs/+LgtpdTGLUd6+EAlerJ\n264gIE/zkUMQAhftn05Kca3ujhyiySnOcyoLNpbALARpD76PKKKosjVXewaLi4tPP/30M888\nMz9/zd7Qb3GlceWPddxZp1o9Y2fAO33hZIuFLtNDIX7mtPWk5CjkjQ2Kzh9QcA7OnnGve7lw\nqyu8dASN5hnGW6S12+jJZgu1GrRGawJrx9BoXpMOoM9RPHHCo46Ibr755rGSe/WrX33bbbfJ\nK/jtqPmzxo4g7EWW/TakTB3XdFOFPrL9KI9AtnF8JlfCVZQc6Lt0+jTcDpzsP+FgBrAbqNwF\nb9tY3ZSMSS1Cm5CsMKNXmsxRIBhsret15J5AeAVzVZycluvUsZxhrgdlYc56Uz0hUiM9m0Tn\nEnZgNy6r9ElkRUH1Bhqbs8lcFDQxSZNTz+ONOx1XIjmGoA2hYHOkS4jXwCWED10BCMxgA+cw\nDiQJAVIQ3qVWM53gzW9+82c+85lXv/rVnU5Ha/22t72tUqmUZVmvX0Ee4o5RKfKX9zoDqarW\nhs5YEk7QCNRQyg8CL02InUtiiocYl1CXJXs+rSxzs0kkys76aprun5zdgDD9wY1FvquzVmmf\nYU8oqdHkg/spjBA8d+YoAB4NORm5H3yPdt9AjTorDUEMgJmN5SKnohA7dqLRoMnpy1Vhc5Xy\nyU9+8gtf+MK3vvUt59zq6uqnP/3pT3/6081m83zesFtscXm5CsY6pSAEWSsAVq0o+0FY+lxc\n31rrMASd4npLQchxfN75snjkDh/ig/vFS2+jbdsvs+9mWfITjyEIkWcAHKNk1rTZ7oekZGNM\nDFtAiEgdOiInp2jiGmzfd6HR/ISqu+666+6+++5xFuejjz766KOPnrraX//1X//4zu+F4ev6\nTPsNa+uPQ1+U4WAgxY1heCjP2ipCeBNUA6aLcpVFhWTIdkjBTX3s9Dw7bQ5Dz4AtXMJ2iPAG\n0ndAnvzu9U25W+cmO+AHO4fGdk3ZlD7KLnPs9G7nmgQy7PQp3clyhfUqtq/DLzE4Kx1OAEp6\nmc3Cs/oaA2AuCRqAB/BggLnZkyWx/Q3ccCP8ixIB5yRbR7aGYBKuxOgQhkegq5BVEMAMLmBz\nlDGINieKieEYfgPSP97+5dL4kz/5k4ceeujRRx+t1Wrvfe97P/3pTxdFAeC3f/u3L8PeLwc6\nS3d2VtvHVqeSOFQeA9q42SIRYAUOpFQMURZkSgJQqZLSgEMYUVQhU/JgyKYsux3XaDVuuS3Q\nCkR5r7smlMfQZ3zcUiIIXX9DBOf3PWHmsuBuF9117nSgNbSHeAitICWTADtyFtYROzDss89w\nlou5OZrfhoUd4qdV3t1777333nvv6urqAw888MUvfvGb3/ymc25jY+OBBx4Yr/C+973vt37r\nt172spe9uOe5xbXKlT/WjVOEQyFaSnvpvjJ5spnviUTQNqXbvyhrd5zw1ySlYcrz7catrXGv\ni7lt7onH5cTkJaaAnwEPh6x9Irg0Wc3LkTVLebEt8OtSTmlNzhUjOdqA9MEGvo6C7lD9tAm7\nE+zbt++cBTtjrkBhB2CydnO58nXmGaKLeo2Tno6dja2NhIK3ADUF1YYZIH8GphdUmm234pQn\nUEHyKIe3wV+AbJE6bV5sZN205zVEkgmfmWPrIiHZMpl1Cl9mREuwNcw1KfzT57/Wanh6Drce\npaDg7Kxwl4Qsim6qp6IzYtfOMayQATOqabwxNT3f2Lxt4tFIzM7R9PkVwHNhcxQDeA2wxeAA\nRkfg1zF+L5lRDlEOAAFnwIAAGJvyLu9BBtAXneh/Aebn5x966KGHH35YCPHJT37yta997bPP\nPvua17zmF37hFy7D3i8HXhI3rVkJI5RlR3t9JRfA03mmrQWRzgsBtkq5IFBSoSw5HlG9CT9A\nErs0ISFcaXLn6kms9u3tLews6rWp7vqhqNo0tqnPUsdBQEWOE4W0Z5Am3Ou5A/toNOQgoiBg\nQQThkliWxqUj7g9ErebCiqhVoD04BjMpiYMHzI8ekzfe6F7yUpqevUhn8muPmZmZcyo8AOMY\n3otuxr7FtcqVP9aN0USvr5oHj/5nZNPGaLTLHK2lJc1Mge1mGSEA8IUaEAmCMWQMM19+47iy\nIKWMUZ3e6EAUNLWa973C8bN5Ct9MFYWxgVffjDsUXV/28mvyXvZCL2p29oXLghed6Wibrb62\nyJ7yw3OU55yNJsx63sODofK0RwThw1sgDxCa/N0yvMlmcae0s7UmlSs0/DbIh6ydugcu7Y7M\n7nTWz0dl7DvhqCx8MSq9yRHtiEWLiQDkzI2zLIOZcLiNwPD1K+wbOvNaI5Ikcmej082KLee+\nv1NA+qPBsNnOmi2XpaIokMRYW3cvvZUOH4SU5PkUhqhUn9d0W9FHtg5/AqNDGB2C1wJJMMAF\n8iHKPoQHAojgCrhxoFuBBEAwMWx68Yc6L9ZaIcQ4j5OIfvVXf3W80Fp7hWQCsJC59Aobj3/w\nLSgRsq9UBRQZE2tPaulIBuOUwzyD53GeolcSg6SEsVyv56bUlcgfDltHDybZlCMSQrhz1cuQ\nlNztYHL6zAYVZeFWV7F8lEtDRY5GC0odbx1jcewYV6oIIzE1DecQD5ndOMGOSEJrBAG12m5p\niVaWxUtvx/U3kOdxWaI0cBZCkqeh9DWZjHJOLqDwttjix8GVP9ZtmjEBEzJr6HBy1J4qG14g\nKQqQFW4wEMdDX1yWonbe5jdiZs6VBqYU218B5/joYWq1cYGEvOcDO5cPRG9QWx62F4pePkEs\npSZMEZbX1prXvyRd8fwT0RgFNtfmrdqFfunHZutXKTOevn3yFesbZVksae+inMCqUtxarTwe\nJy0lAyEAsFmj8CWovhKi4lDuqopj1s1UdyK8hbO9NPh/WdZI1SMTVnt2YWUwEfoi1IVLuShj\n9Bom871dKqn7abCe9tf9sOe7RqCCc6WrW4lnZ0CO7tpPnSqnp3vPCRIjW9Y4UKfcCDmbeGou\nGPSH7Unj+dX1NR71HQsqUuy6TqQpJzGcc6akoqDZeUSRmJhE7aKSNvIBZIhigMFBeHUICXaw\nCfIRbA4VggFXgi1IH6+dYLgC0oPwLk/xhFLqgx/84Cc+8YlTF951112dTufw4cOX4QCXzEhI\n0+sys2Am5kZZKOsykj651SDUAiFJCYRCwRSo1SkK2TJ11nlqkjwfSsnS1LMsJyHANByoonQC\nRVjxz1fTIAScBTCO/3EcYzTk/ga6XSaiPHdKIUkorDgthXWcjOAYYUhq0y2PwojzFN11brSo\nXofWpJQgwsQEx4l77BGsrIhWizvrEAJSwVpyFjt2UhhRvY5646cnIe9UhfelL33pxT6dLa5Z\nrvyxDs7CWChZU7PXu5sbxzaUrvgi5DSFUrI8OffKaYrjVgyOOWM+tT8TfP9Ei0t3YD+efQZ7\nrqfdz9sduIxhRiAB3TiZ1U1Sucy6GjZoIrKaRBxt9OA4nphcmtt23UQrciiGkCHgYFMnw5ND\nmUlQDEECXh3ykr1fX1yuqQGamRHHyFJYq8tye5yM4nk3+C4a5KKLqiGY0Opl1WitKAfWVbFB\nxTIab4GoWObMmjc0W4ey/FhZtlWNKnci2KPylenVgzsPrRq95pp6KIlhpa47VAuaKkXkhAdg\nGDRVsrQQewgUNVS3Bj5XoNoIPDWPXPP2DiZH2IhgT/64OwIZ59TxYB+VWcM0Xa2ZRFpaF6yu\nIPRRqdJo/Myd0wAAIABJREFURLfcSvUGjkfDNw9lDS8vu71P4/qbxMwsLtg4xBYYHUQ4jcFh\nQEB4YAuTwGQQAtAAgQ3YnpZLRwRI2BwkLynE/tWvfvX+++8fP/7yl7/81FNPnXhqOBw+9thj\nZ7TieRHZK1Wj1Z4ZDqbL3Gc3UeQFyaYpUyGUc8LBwZVa59rz6zUwYxRTtYIwEiThB7DWZamU\naqi0T0RKcZFTUd5cb4bnE3bOcV7wYBkHFmGtSxIqS7CDdSQIlYikAjOcpX5s00REERHYWYKC\nc8gzLg2nCRFhZZl7Xfg+mk0E4bhWl3s9jGK37NPNt1D1lJh0Ubh+H/sXaXaeJidpYvKnqpx2\nZmbmimoAsMW1wVU01tHUNA82UKttLB0L+zNWpIKC3HnSlWJlmWdmqSxZSjJGTk1Tq20ZTybJ\nwSz/7/7gFbVqIMS0p2FsDW7O9ytBACGo2eQdO9n3kaXkBxdfSJF20HlkU5+F06jtPC7FopBc\nxikhV/HUBLfq8eQUExmlNtLME+RNgIH0GADU2qk3s5mFn/ew9gPIELAIplDbDnXlttZ6bq4d\nYceDPj/zFB86AM9nKUjqXcYudTpzdCeWji5NLJrGbFwP7XM1Tm0opQXJ+NARO1Vpvb4qWwR0\nS3NHrTqptSeIEywXxZRS2obtjl9fryXbd00EwicLkCBtivXB6NFOWW0e7zbMsjrQ877sVU24\ncwX7DZabp4q2U14FYd801qu4eRWzPdKWUw0nYBlOSGGcsixNKU3Z8/u2dYPmpNpLyrCehGG7\nyGSzRTt2IjiXO7BUqDWMrLpnjuAHT7kb78LkjFAQGrp6Zq2DzQGJYoi8BxWAHUwCW4AUyuFm\nuTDzZmHspucKjjsVi+dIsXhODh8+/LWvfW38+ODBgwcPHjxjhVe84hUvfO+XFUfUj6r7tG+B\nlwwHUtrpIh8JZQUcCERWCCdkBihGJBV8IMsh4EYDEUUwBWWprlanpMyc6zpX973QcTgaUrW6\n2TfWuU2fFCmJCEmMvU8xM0xJfkDWghgOXIyIFfICHqAUrOWyEGHElilPKU2YCFmOLIWnN2dy\nAx9ZDikxinlpCVqjVqdmi9OEhaRel5U+6YmoNWmNep3T2D12VCxsFzt2XWR97hZbbHFOrqKx\njqo1mpjs7Fvcu7pqtD+jdWSkSRIBhKZ0+xep06HJKTvYwPYd8uiRvab8usFke+LWSuX7/eHh\npaW72WwX1FheMp5q7t4z3WxQpUpCuMceY7C48SbasevC+R4MHMry5axwh2RYVfN17RFlHegq\nKvMAQJWq/9JdU8v9Xkse0dmEUEZ7mXMreX5rtRoKAQ/RLMJJEFy5EnfCkMuyJmXeEX5zU8zl\nPXjVLWF3ZcDdLo9izC/Q8dzvBrArDPYl2eTETKv/NC/uDScnk9bkqB4QszBOWkcEJ4SVdELw\nGbOBfGlP48654Nb1wj/S7VSshS1nJyYzKUiped8bWvt0t397d/W6PNPbttXPmJkSgeUSx1NJ\nS+bY2slgSqfLqfZXtFzoQFkcnmArzi1/BhEe3ompNk/3MTNAIawTLYMyptRIk2M9DVIp6+14\nSSe2q5zXscP6Nt2aKibr/lmqjhk2RRnDFsi6Uqo2XEU//Eg+9RJTX7ClqMxDBfBb0LXNWyZX\ngghlH66A9GESmBzSAxuQADHYwZVwxz2qCXAA8WYzMS6BS7Aouummm8btmz772c/ecsstd999\n96nPzs7Ovv/973/he7+sZM7FzubAsh++dNDPpep6SI6XPAvAk6QhSubVopzxEEkFk3FewPfZ\nGDjAMaLAz3OfqDHuLVEU8HyORwymwYDyzI5GNK49DgIUBVotKg0qNTfYwGBE1rAt4RxrSabk\nPKdKxElCWoOIJLFSSDNOM5ISpwUACL6P/gb7AaRkU4qy2FTonTWX5zQcYHIaWkNpCnwoDSIK\nQpoJuLPunKP5bePY8BZbbPECuIrGOijFU9O9/+9bG4yRHU06kSD1CjUQWrfbXr2JZpOPrQqC\ne/xH+XrnWePuIazf/NJHGK2jR+7e6BxUnmq3hvPzG5ZHq8feNuxXlo4iimjXbpCwBw/IIKS5\nC5lHPjQc/WunNy313MFqtznqCu+WSiSUOPFLxKDSn3bdQ9ua08753x0OUudWy3LW04UdSsJ1\nQRBJYRUvLS//a625ujGijbhw7n8MZnYqfxyQkB7seYt6rw6uHWFHUUSndoa1BtbtdNaW2Sgr\nKnp3MdHW8cr0kSeTWiWtVKc7BszETIATtDQXjrxs5I30xO21+t2RnN/R7epR11tZOmjdNt9v\nbXRcWXZ3XWenpl/SqotB12dzqNHwSfDpRiRKN7S3QOUwdzJ3rq7Ugq9qUlp5SzZ4vBDNbk3P\n9WEkHW2de04WgBVYaaBbxUoDtVF/oR9a0UlEwMp3OmrYycqo5uVcAhuRn1aw7CWT9tkDR37U\nrt8xWdkx4dfrShJgc+QbSFahQkgPwWbVrI/abNh/xtXYzu5wJSUDDPahtgvhNHQF7FBmKDYg\nNbiASTcD3ezAFiYHBDBuEeuOx+3GcbpxApgAXUKO3T333HPPPfcAeOqpp375l3/5yvFePxvD\nrIUk61b8MFNyxzDuHG8VPxZI1oHhIiVBOFaWC0IoP0CeU54TM+CcVhRGGA1BAr6mvGCArHWr\nq3COrXF+kJMw1sHZ8PAh+KHyPFGtcppgbY2CAEqSKaE8a40tjNUej2IS5I3jsAxyYDAVBTwf\n1uHUoLWznGdgUBCQs27tmPA8KA1nkYw4TTkvKAhgDMoSs3OoVKlWgxDUaPJoiKNHSEpcfK+z\nLbbY4hSuorEOQBFGPyA5EPktG4ulEJ5TqY66CEJj9dEjNBrSxKSTApPTZjiIoMrpqalHHha1\nxk7wWntyOUl7cRIJYRkVKffp4LZ6A70uH1ul6VlbqYg0ucBMz9E8/5f13q3VUJPwdvJ81z+a\n5RWS29PwRAfLbB3dw+2wfbNe2VtDq17XM03cXqtEQjjmg2leOL4lCg4fO/aoH03t2j3v+QBy\nx4+sD8sN3DzrE6iMUb0on7Qrl2tH2AEAMw8H6PXcaITOMTbGc25PlneYKYp861QySqqTGOWJ\nn7q8Y7RgIjB71m0Ts6wqQs/0/N2lq4aDw5XOel6rH2xN3FSJbo5CAmAtj4b40RLm5niwUSws\ntIuiV9onkyQgoQUkBAALjp1OytGkF7a1qko5DsxJ1QrCPWm8CN3uVcSuNco01msXekGFMMei\nY6t4aql1M8zstNA1k+XpRtvOdIQcTXpWC6ulA3wRBp5mZ5aHzxzsPWTCO25u3TBbBOqI0iH5\nrU3pyRamAEo4J2Gn5JNPFXO+a8xKH7qJdA39fZh6GQhwOYSEBWwBoQEGG9gcrgQJOANXgMd5\ndSc0HIEdwHAFLq5P74WI4/g73/nO+PF///d/f+Mb3/A87zWvec2rX/3qS9315SMQIiASoJFS\nBZF30g6DcTzP0AG5swwERBvGtJQSlQp6PR4OHENUqhgN0GojS2Esj3Pghn0i4rKwtfrQucQ6\nLWWYpoXvOzi5sRE4piwhrSEUyhxS5HAZsxIEa2RZZkE4LMuW1podeZqLAp4Ha1BkCMLjgVmH\nvCCtOc84z8jTJCVLCc8DEdIEzSYxUxTBlJwKXj6KPKdKBZUqVassJQ/6bK284SZcMZlAW2xx\n1XFVjHUAYmAVtOGv36SWhGkbQjvz+lKlLmgoaSqVdGWlMz1TUsbK0xu90FMuy+ZJjKame6Xp\nG7sjCAJBANZK82yW3iKlqFQ2lpdX8nKxNJNhpRknN0WhOleyXac0bU9pEgDKKaOdmjnqp32q\n3cbBxOb6xQB+kyjYXnrsnvnhjX7NaH/sRyGIprRa2xgdOLb2cKMxsXuP9TZHLV/Q7IJ89MCw\ncVg1pGregOCC3nY9Y9ZLE1sbCjGh1aT+sbTQuBSuIWFX5O7pJ93+RShNUiLLUeTwtCdFU+le\nlpG1ELKSFCLPvaiSTNyU1CoOTEI4KL90XpbGUbu9tFRdW43bU72pmaWiuDGKrg+DzatGSmo0\nIYX9r2+L214WCLErCHYGuD4KYmsLxyUzAZqo9HdWy0XfU/L0C7Tmz6bWmWyfUs2NiteK0Y9Q\nnpFUwA6cMhvYFHadTJf0bOLXYs/l6ER2Ynp0YzURWagqhXGlcGSGQBTqUGsrtS+n2bWK4snO\nUa/f3TnZ9id9VSXhCpQJ8h6EBBSEAEg6f0of/EEy/9pM1G2OcAoqwPoPEU0jX4dXhzWbha42\nh82AsTVxCWaQBNwp6XS8+UcKDLB94Z/kV7/61Q9+8IOHDx9O0xTAxz/+8d/93d898ey99977\nV3/1V1eI15phHhorAcUuFWpN+1VrYqkcaNPGmViALCN31kIwSgIqUgaex1rL6/YgjChJAHZH\njyBPEUQEgrNgpqJMrE0dR4AscklgqUiKERGyNOx2UGuALcrSSJlZpwUBUuc5C6HZQajMWemY\nhCApAIJUKEsICd8HM/KMspTLkgBWapwdyWlKngcIJoEk5V6Pel0uchISUkAIZCmvrnKjTvUG\nGWv3LfKgL2+/g6pbcbsttnh+XEVjHQCh/e9PtH9h5du+qW348WxSW6r21/TM9UGIZnM5iNTK\nKsVDpydS59a9YProwdBy3GyDcSDNFwI/OJ59VJXySFYM61UsHT1k3eS+vTO33nGo1vjmWuct\nE+07a+dwPxl3zdl8HHCxvcymuKJFbS447Q0igESvMf/oDO+U/drqPudpJwQZ6J4VdtuS3MEz\nDadPEz/kg3aYLMxfUlMXKIll4Iej+EvH1utKBUIUzBum/B+TE3fWqldUKdm1I+zc0lE+eAC+\nj40NpCmYicC5Zk8H5WBWyEG1lpdlWOaQ0stz2e2x8rNqfaxLrI/M86ONDkDW82WnExt3+8LC\ndZXwDHHmRjG127x/HypV8jwCalLWTk/5ZH/bcnxzL1mK/NPazkpBlWB2hWWt2FuIYDKuDkOx\nMk5SYoBHsCO4DGaNnGMXE0pwAUgR9yeSWr1cuG0QzMamV5UyNw4kYBxzndGMS+9Y3J2opBWv\n9HTF3uCvwjWWe2768EDc5Cr+hpCalA8IkDhRLeFxteGVK5io6SqZDFkHfgsbz8AakAAMhIDN\nYHMQweQA4Ma+kuNA3Yn3ZvxAAACJF253sri4+K53vStN0yAIAKytrf3BH/wBAKVUo9HodDqf\n+tSn3vzmN7/zne98gQe4rJTHLWs180yRxVKOhJrJ00SoUggQwHBgQaRJlOxylrnjMosnpVRz\n82Jhx2ayMLOYneNDB9HrMhh5DiktkGRZRQjhnPF9P47zKCTnPJB1DkLCWnZERBYsTljOEFkp\nlTFO69xy6JxUAoaNK5xzllnmGzYIidkbDgmAEEREec7OEgQbAzEECTIZj/0JHVNUGb8WsGNm\nMPjYKooS7QkxO4ennrCdjrj95WLbwk+PE8oWW1wiV9dYB6Dtqcm5qdl9lZxGgXG5yC2074pK\nWQ6D8FBZ7pycrG30bKUmPG/O02AqTbkMQUUeSXFqZKtwHAgx8sMHZxd2w8VxbJrNsFK90fFX\n1jt7wqChzqyimNCqV9ptnmOiDWNG1h7Kixtl8INhHAqa8byWVl4No8PwmygLcnaic1OrG04l\ncZyUmVjzvbyyMalTJ2ZXfdlgM2EBMPOGsX1rjhalEEPnuZZVc75XPVcZx7Np9r/XujdXK4mx\nA2tLdp4Qn11e7RXmnnZDXzES/BoahcsSztFohDxjonFnLbKG44I8n6yt9TeMUgOpJJF2zh8O\n68ylH9jjSVFMMFLVlo4emZqut1qvSOPGaEDV02tjioKWj6I94ZKE+huYOsUhz1oQxp1kiWgq\n2r7UfyzUVRKnxWmrUtb9iUxGvusNyyPVOJAN3zrDpoviKIkQMoBsgrsgyZAkatWiXksmZvph\nURE5jq6HU5n2jzebpcTalta5FIXj6ihvdZN+o8mDyaKWGXPML/1aMtdNbBUUKaJxPpyDroIU\npAfn13R/v61M2qA9tqAzKfIBij50FSaBCDZV3bgqlksAm7/yoOMzvKcIPHaX1P3vwx/+cJqm\nu3fv/uhHPwrgn//5n8uyFEI8/PDDt99+++/93u997GMf+9SnPnWFDHaWuWD2BOnSxUpbBd+5\nI2GlZkzDFAXJXBADlsHM2kHYUtlSCtmbnZ8uCmTpZoIaEff7MIZm55Cm3OlQHDMJo3QehU5p\nfzTarO9hFpJiYyLtCWvG2zrerEgmxzAGnh7Lbu2sE5KK0hZFDiJBwlk2VhQFOS6ZhacFKUkA\nmIQEgRyQpszjNsQCYYSxQqXxESQAKJDvIY55FGNmmipVCj23+Az1N3h2loKQlIL2oDWfqw/e\nFltsgattrBtz9+zCUJPP3Zn+JLHwHMqKL2cXcq1964pqtba+7o+GRbU2mWWtJNaN5qvbjRXH\nxjnDmz8UOXNszZTvAbCeb0M/CUOXZwezbGTd4TT/+kb/1ii8LgxOjaps87zXNetfXOtIYLko\n2PF84JfsHhwMU+v61ry+Wb+hGk7e4psBQktLOilr4lDOP7Jc1dEuRGVE3aTIrdtbpjvX/R1N\nmYMPptnjcaKJVvJipSieTBLLUMCvzk3PeZ5hNsyBEG2tfCEW09QX9HSc7EuzihCKCESxsf9r\ndbVg95JKdF14RRgFXEPCTkoWAnlGzhEJlMeDKQRmJq1EnntOtDxZEKygmLVOEuqsj6ZmGLCA\ncRwmSa1auY4o0oL8SnnskJBOTs3RcVs2TlN4GkKIIESWwVrOM8QxsmyzpRYJBAEq1Zlo2kR3\nFvmP/GDbqW4igtBQar1UQs2i2pjr9Vbi9ZF5kGSVRY1JCFc60yU3IhAxGuXOG1YmDwXqYF1V\nC942Ep2gZ7NMqyaJwIFrUkVjO2VBeaAKrZrHykwfs66i0+2qFypZSt8foBRKVaVku1kn6yxk\nBOWT8BsyXrN+a9yrQIXwGkjXkXc31wTB5Jv9xOAgxGYuHcYRohOqjjcjdudqmnCxjDsRf+Yz\nn3njG98IYJx68sY3vvH2228H8IEPfOBjH/vYM88888IPcFmRRB4RgyNwIUwhzfyoWPWqqe8P\nlfat1c62y2IsihI/zIX0paBmS1ZrcJazjMbCLk2wfJRabQhBlQqEYFOgWjFCC8+X1qoihSBm\nVs5mKoiYhVIwBqaA0kRjpU3EzinJYwlmnSoK63vWshFCkiBrpXUsBDELGBbEpXGKIaQEYCyP\np4CVhjWQEsawsbCWgnNZTAUBihyrqw6glSXRmrCHDiIMUW8wdDm0ZnIPgoqYaIY7a/KKGO62\n2OIK4uoa68bcSkcO+fExr+eBo8LPvKjV8quRP3Rkma3Wo4nJuNV2Sjkp/YnJ2SC4uxI9Y9xD\ng+EgjifhhONMiLlK9a56tamVBQNIhTgcJ4/0RxWtBs4eSLMn4+R1jfrPNGonwmCH8iK2ti1V\nIGje0xUl5z3vhJH7uDbivzYGP9dq3rm7KooyWzX/c3ljR+Dv9n0mGsKajBJtZjwvTvk7o8F/\nr1njeN73JpT6/nCUM+8WgQClzj2TpX+2/3BdqeujwBdUOL69WomU+l8rxyxj3vN2Bifna5tK\nLqbZgSx/eBi/fbJ1Z636ot/MXkPCjohMibKEY8AwmByDBDPYlE4IgFCWSgrlhxBUCoksm++s\nVVtNU6krgmQXpIaocIPDQ9Xp8xo58p98wpU3jPyolHWIWtRNlCVkuSSqrq/51gT9DfZDeJqk\nBBi2dMMBHT5UX9h+e+P6fUOL/GlPz5I4+VaHgnb4/pE8r4sgUzJKRsPWXQQGl8TW2QFxCdUy\nJm0Xt80fqz8VuVJSVVDbUOmRlJFCScWhRM05UYkkp86FYtPZm4xvXN0rRs2OV5a+0TLHMQ+z\nIetBbIUUsjwuUxm2i1LB1CqVdF9Z28beZpqUlPBqSDuAga7CZBDqeG0Ebcbr2GwG7QDwOJrD\ngAMcmHFxHXrPweLiIoC77rpr/O93v/tdAO94xzvG/7ZaLQCrq6svcO8/HhwgOI9cl+T6yBdz\n+dy6rsZSjqQE85ofSICJJKFli4N+lCuvUZQVQdUsH7/jbjhCEJ6w/KUgRHtSHT3assWo0NIa\nCEku16Y0flBAREUOPyAwSjjjpJYlM7FjEiwBZiZSaTLSnlSabKEZgg05y1LCOWkME0FIArO1\nzoEEk7WkNKxlW5LQIEBKcoYB2BLitMQTdo6MgbGcJuT7Ls/RmhCz85wmIJ3RbKFID4fUPWaf\niZOV66NbZ+TERbU82WKLnxKuurHOsZHlcltxXr09LoeRqwShUnbF2e01VZ/QqjAW7Eb/P3vv\nGmvbdZUJfmPMOddrP8773Kd9/cZ2knIcEhuqkhRq0Ujp0ApVjZAitUREQOHxAyHyCxoQQiCB\nEhERwR8QtKqTiLTcRIpolUokIkDRTQJUOXHsxI7te31f557nfq7nnHOM/rH3ub52nJDOwy/u\nJ8s6d++19ll7rXHmGmuM8X3f1nYwdjw6Otk0BOTMbyrc2fHR/GhvyjyJcsZQwthe6Tlj/k2W\n7ZTlXPRIcTZ1+yIP9Hq3ZykB/89kumbNm/o9AF+p6k/u7t+Z5w8Ne7UooDnz9enDKkolElVX\nrfm/9g7+78PRyIevlNU4+rKSSqIjmls8VK6e6CWrakPEl/vTx8fze4r8XJ5+tapa1TvyHErT\nEA59mIc41VhYy6C783wu8Yl59YXZ/L9OJg/2B6RtbszQ2sGimwEAWHP2ZJr858NxxvyG3iss\ngvcaSOz+7u/+7uGHH06Sb4Fm6aNGgSoxqWpUECJAErX1HUMBNl2nYEqcJTbWgnlzXmJ1tfbj\npj3qDi7HfurIwod+/7RXKbWrrh3tmJ3Ctyxt2m2zZKHqByIZH42abnBie83aFWuPh8wMO4ei\n0N1rt66ufZnPbBeuKb/o3Bab5y/20JjTaXptvjPQq86cWNg9EQANpJ2YlYZWnd+4fb9/vheV\nOWdKFGlAZxZD+S5yvxd3rL1V1Rx53zO2b4wjMl2iLoofpnMx5qh0aywpWm9CmjJNJGwklheS\nc4qlGcERmTSjan49sVOCsWAg0pIGi+MergpIoQQY0DFDgm4kg35nxoZra2tVVT355JNve9vb\nLly48Oijj+IGlc4nnngCwJkzryIyugKdKMivyLijdpyku86l3m51TWNMIBbAQNMQU5HL/aHp\n9SdREpFLQaaj8a3rG7cnzrQN0mWE1yLzKD7NsyJvyOwwn/HesJW8B9HaJj5GR0xMsA5oSCIH\ndc61Cgvm6I0IeV9nKWwSRRNjOLSkpGwAsKouWqsAQGQMfIQorFMFVGHs8zVYZgpB2w7G0XWr\nieDRefUdjKMsh+84yzE60jynwdDvjaMYd/oUkGue02AtXNnvDp9OH/w+PnsW7jvmS9/ETbwu\n8Jpb60Jsnz38u/VQJqEMaRl8O2iH6mpFSJm2mOfl+Kmtk3PR2jcP9vurowOokjF8cLA+HW+c\nPi2AV3HE1LU0GiHKnePRyrWdv2EbTp19tvN3FvmdWbrowJ5NkyudfyNwqW0/ubt/T54dBv90\nHZ6qagD35NmKdZYwjfHxeemYLYiZLtfNFR8S4FSanMnSoDKPoRJsrbn9QX3Kp0ccyn7odXSq\nS662rQJX23bLuZHv9jt/EP28C0yUGrPTNWMfDr2/2nWO6Lm23bbuq1W9nRhHplU5lyW3pqko\nzqVpSpwQ3V1k/+fewS/dcnr1FZ02frUndteuXfvQhz70sY997FtJ7EQCE4t4DYuaxcJlDKya\nxg4KIYVa1KENydy6PrFLUkyP5ofVlI8S7vdtz5h0Ibw7Cn6vala9nDqqB6tZXayoUeMnGvcy\n2c4byso5DYbjprlMfDZNtxPXM8eadsy0slKMRj8wXP3n4sz2sPDdta69at06LYRACEOUUZ+b\n01ACH8+oqXZXtb1QuTu02/j3u8XlQiKTIzgiF2izooNCAQRowsZRn2UOdoVxrYpGHWiaVwUg\ntkki2UQm0nJDpzR4cZ5hnXDXwhHkWICRHcigrbLwXG0cXA+4LkdngA6xhcmhCkSIBwRqwIDy\nse2EvGDSjug78hO79957r1y58nu/93u//uu//gd/8AcABoPBYrG7fPnyQufpDW94wzcNA/nE\nJz7xuc99Lsb49re//X3ve9/Xu2g3TfOnf/qnjz766HQ6vfvuu3/qp37q9ttvB/DII4/8p//0\nn65vZoz51Kc+9U1+V1D1KkSYc1HRekQnoH3XC7aYJEkegxNVqBC1aT6ytmPTiyGI7rbcQgtj\n/vPh0RuseejabnFiuxXZ7/z5pk2JHWHoknkxQDU7SjNhW5AOppOUac0mpm0AgBlZBu9NlsGY\nINL4LgvRSKiT1CRpGzUzxFGWuTgAESzN7I+TcRGCioJUWSKYrg8eLDdZ5nYtsgxE2rUoK3Lu\n+RTNGMSIPNeDfRgj6cDu76DpSzYAACIMhrHo6eWL0nV8yy3fLcPvVxVezqi7idcHXltrnWjc\nmXxxKkeT4tmiciHBUeF9WW0cVIf1P0l+24pdz++40w1WIlAYswrFeKALJfadK1jbAIGBdLG8\npKnOZ2ibNITB1ok3Xzh/4a5iuLqy4Z6XkugZ8/fjycPD/mOz8vYsu9J1X57XW87emaVR9ULT\nXe7mBeHOPL+jyEjRKQ58mEk85dzTdb3mnIRQxXgqSS7U5RuKbNCjOToAe52/3LStSlBtRDZd\nYggjH3Y6r0BhDKmWIXwthCrGL5XzW7K0jvJc0xIg0Enph9YQ6Erb/hOVDjiXp5bJMq8YkzA9\nWdYPr3xTJbPvMV69id2zzz77D//wD5/5zGdUv7WJrRClaRCDqBCWQv1QYoICLFCCUVaVSEhC\nZyWKYE7RShfnTb6xSYQ4UAoiwMjHaYgnvWQ+tr0kGBMck6JoTZ0NQ3eALvNJmrTdiepab31r\nEuzuob8zyftsFpkNJzC94Zmj/ap37mlsbPc3rFsL/qhpLxkuiJLO7xbJSo8SVV+11apv89ix\nuFoPOgXZAAAgAElEQVTe6OPKnXM7SzDmCLBjZsBFBEIERDVjdkwMhtSIBnYjIQqq40Z7oonP\nlEAcvfZ79Si61Y4zCsqRmVBD2LJxx7mYQgUKp007u4TBGbgB2CJ6mBTSQQJYltLEC7uwhfOE\n6pL6en34asmoiDiekf128Eu/9Euf/exnH3nkkUceeWTxyo//+I875w4PD2+55ZbFK7/8y7/8\nTT7hE5/4xGc+85mf//mft9b+0R/9UQjhAx/4wIu2+a3f+q3RaPSBD3xgMBh86lOf+rVf+7WP\nfvSja2trV65ceetb3/qjP/qjy2/0L03+L7rfCh1z+mRvcHt75cBuNNQjokNO2CUgxBvi1wB1\nlIzYMlbZdEQ9Y748nhVN0ysrrxIVJ50jAqnkRPMzpwfT2eozT87YuKxYWaG0beAc0vR6Qk2s\nIBiivjF5ZEhAmvSLnlcxpCxivI/WGh+UhRdDkc830ZWigEhVSSKIXkKBUAHn0LYwrKJoakqT\nF1xgY9E2CCkNV/XaDlbPIemjnCDtX0/5yRja2NTRoUikM2dff34VL2fU3cTrA6+ttW5cPffl\ny3/eyqRN5hvTM/t8SEDdq8ueDKzZSWb3nHvP2mDr5PUdRKQsqSy1rrWqqOgtPRKvwzmIqPdO\nkQc/2N6m5AVEw6D60HDwTFV/tao3nHt0Vt2Wpww0IlfbbrfzA2vGPnSqZedL0ctNW8ZYRrkm\n3ocw3N+9W8IQOGD+okm+kia3pRqgc4mX6vYo+DXn6igXmzZhWqzSlcjQGEOwxF2MASqgURcm\nPhBh3TpHNI1xomEc1BILNJCsW7fm3J4PPearTXvkgyptJu72LOVX6G/5VaW98gIcHBxMJpO7\n7777W9xemypqG0iwYH4SGMAiveNFuWJBGwSBSUSoUwpox533PIvcBRCpM+JD6b334eykcVG8\nM96yEYWoQutewqBE86hNk8Qucz4tBjtHm3vduk8ult3Ex4U3Q3OEbkqC4vYQzqbpNY8kPVf0\n3zQc/ts8ux0I4ifeT4adua3L/9186wd3zt2+e+etV5Nz++v//or9dxc4b/TszJzuyClFQEQ6\n0oSob/g6JxacajhSqQFYolxcDKwK7hKASWxEkca59Sk8lFQXHdTFJByWA3NkQI4JUQNml9CM\noASbL08kE2KzzCKIQYDEZWKHRVZ3w3/Xczt8u3In7373uz/ykY9ct75+29vetqCMLeCc++hH\nP/rOd77zG+1e1/Vf/uVf/vRP//RDDz30lre85Wd/9mf/6q/+ajKZ3LjNlStXHnvssV/4hV/4\n/u///nvuueeDH/ygqi4GXK5cufLggw++5RgPPvjgv3jABERVC0+8txrrNblc6EyPq11Rnz89\nFrDMjsgwTUI89N1hlFEXLGMcwhNV9ZWynsbYqQIwnZ9vbockLdfXmxOniqLP5awkIE3QHisK\nYsGXWPjJerQNKzhNTd4zRKJgVY5RmJWNGMtRKUYQK6CqoqqqIOii0a5KN4ytPM+AcQ7EZJ02\nDdU1JelLpO1sEAKMQZqYUHY+1f29OK5jB1X4GiYHABquaDnXq1dQ1//iiX0N4eWPupt4HeC1\ntdbNmp3crQdNd5KjK8W1vDU+1m2sZjQb89GRXLk6/WLrp8tj60az8/+99aM2EakqdJ1eu6rz\n+Y0fSMTU6/HpMynz5G0/uDN48cPeKMSCedeHM2kyCWFgmYF5lMttNwpx09mEqFP5/HR2qWnr\nEE45u2HtwHCf+e3To7fsXPSTyXQ+2zg6+KnzT+4dHT1V1xfb5olZdeQDQLutPwwBwCzGgTEJ\nUc4cVOdRZlGmIo3AMUBIDHvFoQ/7ITCw6qwhGlizZm3BplW91vony2oe43bi7s2zL0yn//vO\n7t+Mp7PwHQi6fgd49VbsHnrooYceeujpp5++Lsn9IkynxzFU18wc1Hds0igEXVQgjo0RltK5\ni9EiJbBEIYawqu9YE4lxXk4Pa6w7hklqz2rWO1sX1hMHUQHyeesTlsQWICMxa7zCVlRSO+Ww\nHsnkfi8WJ2HTy76znPSsYQsNqMskS7r7tzJH9NWq2k5c5jbhVkOcrbj7+pWsj6rZSk9dHk8b\nX+8elcwW+QzPFdFH2RDuVTTtY55Swtyz9HWpOBGnkAqcA8jaPGkKKJEyR4awwOZ+1JmtyCmp\ngohBGhGhZGmR1QEAmEiNESVu90EKX2HZVnaAXzJeVSC67LnSSxZSF68zvsUy60viF3/xF3/u\n537uiSeeKIrirrvuYmYARVF88pOffMc73nHq1Klvsu/ly5erqrq+SD3wwAPe+wsXLjzwwAPX\nt3HOvfe9773+zBBC6LpORBa7P/roo3/xF3/Rtu3999///ve//0W/7kVRJ4CoGsQ13U34UhYM\n21jIUTCZRxKXpwREYBATMcCgVjRAZyFMkqxVsUwqMo8xJz5fN/MQTqXpya4tNzYBKHOX5cKG\nesXVqkoY/dZrM6cosFZjVO8py8AMx9Qb6GS0uDYCMMDeB7aqGgyzslPRGEmxkKSzMQoRNCqg\ntBwJOO7lCKwFFHxsWlLVKF7sRLwEH5PCk0wPD6F5U6cxVG1SuAKrd+F4ehNU9HR0JGzo9jvo\n1afY/u3hZY66l+Mr3cTLgtfQWpe7taNu8vToMTXZpd6h1xOr3Ubpxl2ooLo9/L796RO9dPvc\nxtuPymcOLn8+79KwlnS6e+ZoWIhDMdTda5TfjuNmsfqOslS7zt53f//kmWdH07sLKo7fHYVw\npW0fGvT/8uDogUHPS2eJ6iiHVWXa5qRzM3IHMYx8OJUm1wfaLCEotqrybQe7jw9WG9WUeTfh\nk4U+UM3+sddbtEQmMbYiXsURlVEYOAzBqBomUUxDyA1nRF60U3XLehA6Uaeq1jhQZE6ZHAhA\no3qlbe/Is6fK2hKtWJsZvrtXPF5VjchbBr0T3wpD4LuKV29i9y/iR37kR0JYev+ePXs2EhMp\niyqElEFQ0kXpTgXCIAUpooECHAVAY0NQz8KNc8mYjwrxHFMbNpuszFGpnUfPIAaYtAxxBjEq\na144atVLNCm0rEFdTBKNmWvLaJM+8zTEhNkRwcANrL+0ZzdP3ZXkaelHB7OpbxzNstn5zdqU\nmY4N7SdxrBK8zLraGGcUaaQ0ZQElzJ6wFnBmjDKFeckyGCUaDsmsOF/0ygGAQJFJjVgSQyCh\n1FIZyIF1UTryUMO0pK8KyAIkYBJhkyPWaEYgWVbpRAALCCQeM2LpuJzzjcrM+m13YpdIkuTN\nb37zja/kef4TP/ET/+KOBwcH1tpebznF5Zzr9XqHh4c3brO9vf3e97538XPbth/5yEcGg8E7\n3vGOyWQyn8+ttR/84AdDCH/+53/+q7/6q3/4h3+Y39BBeFHUsWpPd4t4eDp8KcGzkzw/0fUI\nTw5l94DPVbxOnCxM15gWkUgAogqIVjReYtOIGDIqkopmjlPmaZBxPWlWhjbvRREBMhCraJqS\nS/aI+htbXJV6+SKShKJgPKLBEFkG59B1cBZRsHAKjtErWhUoVMGiQgTiNAbQov9KSgxCJBjV\nKAoSJVjiZV4vCmZVoRBhnYrQS2vTEdoWMYRofZuarA62R1InuQLEL+zc0nBFd66g16MzZ7+V\nYHj142WOupflO93Ey4TXylpHxFfmO0HrRouYdHE41Zk5NV+fJmVnkCVbDNmffHmAtfHOF/sn\nzqX7dbO+4lR38NzZ8XY6myug3pMxAKQscfWyEPHmNrZP3hP9j21vXKibx8oyIa5F3zIofmj1\nxDzGgWUAzlATJT/au308un98BJF/2DhxOFzNXZLcsLgUxgysZl13ZF0j6ggpUVSMk2RLYvBh\nDrLAPEYmBNVWlRTOciexjWqIlJRADgxogDqQJw3QKOqYomIe48KHXY4njhLCROS5uulZ87fj\nydCaqY93ZFnKfKnpLjXtD2+s3pG9rIJPr+HE7qGHHlqE3f7+/qVLlyCBvBciVgIpLVS9SKFg\nIlVRosXtbaHNQQhCnskySDgKeDKJyPJNb4MIzTtNKc/SRfGDQRlIjGlZTd1VhtrUcUhMcNHV\nJiQKlzRVcKmm/TKKC+F5lW3SwwvjznSdXuoZa1R5fJjP8jkPJrLXi5O93vk5n6hpUIcqVaxF\nrFWY95SPayWdhTdYK1F4lCni1xftYCCt82tiIotx0QrUBrswPhCkVpvFyOFyByx7rEsFkwCQ\nwNnYweQg87yyiQTAwBgoQ7pjbuzS5OCbXp5XaExIVb9+WOT68vSiLf/6r//6Yx/72IkTJ37/\n93+/3++LyMc//vF+v7/4hDvvvPN973vf5z//+YVL9wIviroeRmf8E06nm3IhoWqedCeqtaEZ\nOWpM9BXWp3S2xtAwMUggURVEhkhEE4kxScchemOeWV1fryt1TgBHGLbNX8fB+OAoYRLg1Ly8\ndT5Dr8jJCHRtZbixsgpVLUsYpizXNF1Wv0RhHbraG6lFJEbDRIBXZZBdDiRQNLwsYy+oPqKg\nJaKiU40sVtUAcG7ZZo2RjNHOq3P09VdXZEGhUG+FHVrvemSa3To53c5MO0Jyo9QJEa2t0XSi\nGxuUfYMS4GsKL3PUfQ+/yU28dvDyR92upyARJCKY2q5c2Z+6fLUzZ2a35axO1XDxXP9qdW5z\nuL5+8mgHqkRkhhtlv5+PM718SfZ2KUnRtTqZmFvP4dxt1B+gbfWf//ENP/Bv71hbfXDQr0V6\nzJuJc0T/bTZfmMOuGRsPDr7vwlPz1Y0L65vTGNcmowck/N3GiewGCzBLVBB1hOAjp2gVrJox\nD0l2Q+w7WzB3IikzQI6ikohSSsiNmVKsgzpiSzCEWRQDJtIgsKQpswJMqkAjAqAV8gRRBNUy\nihrMO8mYDdGp1F1rPREYeKaq/mY8/anTJ75/0HvZqLKv4cRuQSMC8OlPf/qRRx6h+28j1cWN\nihaNQAXRkgzISpEUBFZSQEhZGQBEACfUptGcneVxPpj0M8Cz6ua8brw0OaIBAy5ITzXvok9s\nJ9KKDoMlOC7BRoidxNSxD45yw7ud7xuTEdezeRPy+bTkNd8brANIxyN05ahgonaz26rygct6\nK5gnfm8ItjZZE2MsvEirCogjMkRKmOVYrzCsMO0hvujPmSxFzau+sAIgJY6WhEFKBFULCFEE\nlrnmjXvTookmnbh0cdLYQiPIIpaAWQ7VMcNmEH9sAstAfKm63fVXXqF+0fr6uve+qqqiKADE\nGMuy3Nx8saXzZDL53d/93d3d3Z/8yZ985zvfuVjdmHkweJ7K1O/3T5w48aIn4BdF3X1xEsmc\njU+lcc4UKxuv9q+dKLeQlIGdC81qvLrr7i1xVkCQkCIaCDTkUS/3+61DolTFWOb5rdPRVPJZ\nCBtN/eV+/2mb+LZbdTZhHifpPf7oYucjvKi2Krdn2d1Z1j86gmHNBzKZoOeMWYiVmDbL5nXF\nbJIYvLFeYY6HIZdQMACRyGyAyAwQQUiELBsijRIMqygxiwg1tTrHqkRg0ZdQtBFFYlUhHuLZ\n0Ux0oABTtKkJ5ddtb1083Df9Pm459+1e6lcRXuao+95+mZt4jeBljjpVPcJAeXs1XhCwlSYS\nHeQHh7l5aiB3DnuD/l0XMBQ7Z0pkNn3Hen5u3vEgJTIhszh5C52+1a6sIESZz3Q6put/+1mG\nlRU9PCiGK4V5QddSgAX/wBGdmI2v5X01RlWPfMh7vdNHh28arNSD512qg6IWCb3eevQzlQlz\njzlj2pxWj62sVwrvAxH12Uxi6FREkRhe6n+BHYmqClBGaVX6TIuJ54S404UXvHpFJ2qZFvWi\nVhRAUAlBU6ZGpIyxZ3jPe1FtRU4mbh7jI3uHfzsa/8ftzTf1ey/DvfE1nNi9CJFglwocyzF+\nfT69W/606CFGRANWKCsrpLUewq5DZXsXeyJUG1ZnuHSu3+XQGLlRoE540kur1AzqcOveLHZC\nVUYgdT5Sa4xVa9Nq6pOh75mMuYwhNLUfz6WXuTyDWEFVTUbJ4UE0GIoSxUSmGja2m2w6vDYW\n15dr1O1xOAcumOCAoGhFCsOOKDCOChQdhDEtXlgvo0WeBWVZzMmz2MV3XyjO5XGv5q0bG7mq\nLxiDo9D4mMPcUI0TqMIwRCERZMALlRMBAI3foCZ3/cVXZmYUt956a5Zljz322MMPPwzg8ccf\nZ+Y77rjjxm1U9Td/8zfX19c/+tGPLtbEBR599NGPf/zjv/Ebv9Hv9wHUdb2/v3+dnvaSMNqd\n9f89j0esssicxunUiF1rVig5aJELzG3tF8bmamU2kzgmsiAWxYkGF3PdKL9UJXfvSXHVrBWD\nlfWq7IleyvKvFYOEGCwgFEyapgeDwXqMrTGt6jjGL0xmXyP3ZpOerLvKDzOBG9VEWZpQjLIP\nHVhnYxQlr3pdlW45Bqfq2bCqgy7Su2CtA6BQZhBBQaqdIhjqRJPYOUBEVMFRTIxMsMT2erVA\nIrIUzIAuWbBsWMLCM1j1eALvhQHDwxWdzyl42Nf8pN3LHHU3cRN4JaKu1NRw1knmZMaIrADQ\nwbWuesrIqivupPXhZMzN7OSILq2kecAtnW91nCcDun2NztxCi2zyufPEL1wObALvv/43pkRe\nBcBO05yxJu31xkwX60bEJ8blaboJXK9gK3Dk/TxKLy/+6213/Q/PPVOlqbNu4Nt/XN14enX9\n0PsN6xxjJiEhaoiOFffVi4rIQkyDiRyjjhREGiAjakWDarcQ3FjI8KtWISxEXReJWsKkQFAV\nhWMeLhwgYTrVRuSrVbU+7H/64GgW4tuGg5Rf8vb5XcPrJ7FTIlYw+PmMZaFFsXBbWnRmEQNL\n4JiICaxQIajXWskJ5Y31g2CSGEdFJkBNHNwsj2emvXHkZm8tr1ILJSUmxbBjClbTDmCJNXNG\nZNQwN0KJdU7L6ggTk5tQJ32QatT68Gh1/6pTDdQpYERJyfnRynxYpsNh4doklgnYH4oQkC5i\niIiqKLnhhChYkqCDBt6iesGDDSmFJiuzusfRkLCS4HqdXgExy+l2BQmMEmg5KkcASFnath0g\nR2yx8MhYJHPRY8Evvk6VIAMwyEAj9HrR7vkDWb77Sik2FEXxwz/8w3/2Z3+2sbHBzH/8x3/8\nQz/0Q6urqwA++9nPdl33rne960tf+tIzzzzznve852tf+9r1Hc+cOXP//ffv7u5++MMf/rEf\n+zFr7Sc/+cnTp09f14V/SfRlr5Dh4ssKFkmRjrKxAdbqtVlSRpCAN+SZXI5Kux3hPPUywfmB\n2evljtTG0Xq8OMbmoe29aV5/eWX9uf5QjSXAEdVRcmYmnqT5idHBTlEEkfWQuJZXG+6a5hm7\nqfnYO3OymiHRbKYMb/MUZLiaklChxrMoL21BCGBox8bE6KAQ9cZEIAGgUEJUJRHP5A13bC0o\ngbJhJgKBwV6kjhRV+pZTYkOEEJBliAIQZ+A5JBrECJHQsk3hei/1GGCd7uzoyVO0tv5dDoKX\nHS9z1N3ETeCViDov7Wb3NCMosUhggsAYdHPaGMXs1Gh0x26UlS2frO+vXB4qTct2t6+bK2f6\nq/fAJagrZClcAuvwwpax+vYQ/avzsowxZ7Ph7C1ZykBOsfLThub77fysHvXaI2ND4+t1uRj4\ntk3vL4dR0pyMbiuatTLqgQ+r1gDYX1v7E/N953x3wvDY2v9mXBU1sxygGdiAiOBV+sYY4irG\nJkZm9iIZ8YInYYG5CBPVogtR14Uk/6KIEpY3vqUIBAOdCIiiKkOfKmvuYd1aJnJEa9aMJf7z\nvPzB4eBvx5NO5R2rK+57eY98/SR2LmrgkIilZd1Al7NkCwonQSGRVAgENMZH0sBg5Wg1CRoY\nedCtJpBSZFenQhSFqJO282BRCr1B3TNiAOwPsnPVkWqrC1MGYkUkGCECReqI/byrPEdqsxXv\nCgptrI/6zTUiLhMXY0iCZi2guZqEIaverMxta083tphlDWNCThXZogi0uMGT4c7QSkTrNPXU\nOMgLWqoIzqOBiZaUhY7f1OPkLiQLTgkAIVh6vjlntazcndEUrBCPWMNkS9U6Edh0aVDBx1W6\n5cnl41MsywN4XtDulWvFAnj/+9/PzL/zO78jIg8//PDP/MzPLF7/3Oc+V5blu971rvPnz6vq\nhz/84Rv3+sAHPvDud7/7wx/+8J/8yZ986EMfYuYHH3zwgx/84NcLft4IQiREgrAu0yYFAsW9\n7Mgjnqq2Sts2NogYw8FoTHSUkB92vfODRIEIblGUSAddc67a//vtM3dOyc/zqbWzJE4T71Xb\nKJmla0WRNEXSNInkG2XS2s5i8szgrPPriVzbDLPZYHs42x8hLVofYRCdT/N+PWFiK9xBPCsD\nTqVlEwmWoRGBORIBCIARCcQShYjEWrXOEAUR6rwe60strrtjckAdZa6yRnBpCusQmuihADlo\nJKgQw/VNVCTHOgaq8FOEFhBwgsSlKEu89hM7vLxRdxM3scDLGXVXyp20gdPKaqtEDFUFw4O4\nH/bKbiObxvNWT/TvyfNbtBnGMBVp1kZ+0ww5l3bnWTk/k+2teiOpteztfZXiZn/tdmcKadsr\nBwd/0V9LxtPMcBvlqKvfko3usrNpN96/+F9GOtlrjmzd297buNbvix3UfGajaXd6mLlR0Vzi\nclplD4xxtkfbi6N1ICp616i/CxLS0HbWkAWqGAtmBirR7SS1QCfaYwbgiDLmVgRALaqqstDC\nPTbI1GPqIaBxOaO+hAKdglQdE0SnMX5hOl+zpm/MduISYsc8i+F83ZzL0r8dTwtjHh4OvneZ\nHX2r8r+vYnz6059+z3ve8ys/9NYP/sBbspgsxsywdC9VJdJFrUohJnYcAa1NBwDgzsqSZxFP\nqAws/MLo9KDXnye1ks+61YPenF1h9Q7v2mgjABv4lt32xPxKmSdknMIzF8yp82VNa15sx4fO\nE1G/s2smq4rq8pRtX8djmxBR0ZZrja94UDpDBAOYkLXpKItNxUilEB5dGl6buxWiZV1uUeMd\nGF6vKevUKh0VqK7PjEoLMyCznlW9E1fP2eiWFbtlIhZTORzxA2CnBgAEsIRj7UTN49VD95DH\nGhiuBxVAEGq4DL4BGbADO8Qai9O4FCumb1iWUwEZvO1/+15c7VcLFlH3H/7jyf/5f1ph9Vhy\nX5eeuUqgiFzzYdtfb1Ya03kTSurVZmutTZ7u87VekRslib3WDuv86by3n2/cOrsv1wt3zP3c\nFYr84vDqUbqv4NRmpaR9n3z/QRl0ezV2vTA73++TbCYhyYOVZO9WbRicl1NhyZswcWwhW+2c\nNQYwKVdWEgms0rIDNI8hEicQIV5MK6QxeGYl6lzSWadERLAirm1dstCzBscYkkSu3wBExHu3\nstJzSZxUPtmixEIQq06R6NoJPzw7OEfp6pJ20+yj2ofNAIJ0cK7q3b1m7v1WtSpvYhF1v/3b\nv/0rv/Irr/Sx3MS/Fiyi7u3vf8Od7+mf9v8kMALmpRQqGLGlXh5OvnX/7qdXVk6c+A+39k+n\nRMb74ZNP9Iv+ypq5lu8fls8kti+z8R4uFfc+uI4zfDDaHOWrvXM7m2f/C9vt06dCe0WkDeEg\n+Mml6RO39k7EWD82udy2OzUNC3ZnJs0bdzAxfaXeM5ubB2tZnS2eOTX4cdVeafMH58l9gQcC\njEO8NUtbiU/X7SSEnJmARmRgTY9NB2xZC9IqyrXOQ2EIEahjbGWp4i6Le+XxE/tSnnVhx6nL\nLOOF1RUQYAiOaGhtFWPGvJW4hNirzII8MOxtGHsqSwj4X09s3f2N1KO+Y7x+KnY3TPEQoAyO\nCl6U7JadMmUxiXJt60jqxHiORri2IQuFS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8ruBLdfSAFjE50CGim3WkdA0UvC0MW+F/FuryvOXxmssPRTXydagmNL6rmrnGf9Su5vW2se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\nW9g4RG9Tp04F0NnZWShFuGPHjiMbS3+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c2M7tfQ+iyyPTDDSHfATYI9KH95UBQt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\nTK69qLX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UTUQ1SrlxhIJc/jsNeX5GCD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\nsccA4I477qA1Bord1LZ69WrlLYZhAKBjx46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i/PjxAFBYWFhrzfHx8SzL1qwqNTUVAHr06FG1sPIyLS3twoULANC7d++q7/bs2dPb/l0l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\naURyYlUo94oAAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figS1_combined <- ggarrange(ind_cts_mse_fnr, ind_cts_stab_fnr, ind_bin_ROC_fnr, ind_bin_stab_fnr, \n",
+    "                            toe_cts_mse_fnr, toe_cts_stab_fnr, toe_bin_ROC_fnr, toe_bin_stab_fnr, \n",
+    "                            block_cts_mse_fnr, block_cts_stab_fnr, block_bin_ROC_fnr, block_bin_stab_fnr,       \n",
+    "                            nrow=3, ncol = 4, align = \"hv\", labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "figS1_combined\n",
+    "\n",
+    "ggsave(\"../figures_combined/figS1_combined.png\", figS1_combined, dpi = 300, width = 8, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 2: best method chosen by Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe5_cts_stab <- ggplot(toe_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe9_cts_stab <- ggplot(toe_cts_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.9\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0,1) \n",
+    "\n",
+    "block5_cts_stab <- ggplot(block_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block1_cts_stab <- ggplot(block_cts_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.1\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe5_cts_stab, toe9_cts_stab, block5_cts_stab, block1_cts_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe5_bin_stab <- ggplot(toe_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe9_bin_stab <- ggplot(toe_bin_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.9 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0,1) \n",
+    "\n",
+    "block5_bin_stab <- ggplot(block_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block1_bin_stab <- ggplot(block_bin_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.1 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe5_bin_stab, toe9_bin_stab, block5_bin_stab, block1_bin_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 11 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig2_combined <- ggarrange(toe5_cts_stab + rremove(\"x.title\"), toe9_cts_stab + rremove(\"x.title\"),  \n",
+    "          block5_cts_stab + rremove(\"x.title\"), block1_cts_stab + rremove(\"x.title\"),       \n",
+    "          toe5_bin_stab + rremove(\"x.title\"), toe9_bin_stab + rremove(\"x.title\"), \n",
+    "          block5_bin_stab, block1_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "\n",
+    "ggsave(\"../figures_combined/fig2_combined.png\", fig2_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S2: best method chosen by Stability in easy Toeplitz correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe1_cts_stab <- ggplot(toe_cts_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.1\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe3_cts_stab <- ggplot(toe_cts_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.3\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.3, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe5_cts_stab <- ggplot(toe_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe7_cts_stab <- ggplot(toe_cts_sub7, aes(fill=method, y=Stab, x=N)) + ylim(0, 1) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.7\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe1_cts_stab, toe3_cts_stab, toe5_cts_stab, toe7_cts_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe1_bin_stab <- ggplot(toe_bin_sub1, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.1 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe3_bin_stab <- ggplot(toe_bin_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.3 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.3, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe5_bin_stab <- ggplot(toe_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "toe7_bin_stab <- ggplot(toe_bin_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz Correlation 0.7 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\", \n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "# ggarrange(toe1_bin_stab, toe3_bin_stab, toe5_bin_stab, toe7_bin_stab,       \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS2_combined <- ggarrange(toe1_cts_stab + rremove(\"x.title\"), toe3_cts_stab + rremove(\"x.title\"),  \n",
+    "          toe5_cts_stab + rremove(\"x.title\"), toe7_cts_stab + rremove(\"x.title\"),       \n",
+    "          toe1_bin_stab + rremove(\"x.title\"), toe3_bin_stab + rremove(\"x.title\"), \n",
+    "          toe5_bin_stab, toe7_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/figS2_combined.png\", figS2_combined, dpi = 300, width = 6.5, height = 8)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S3: best method chosen by Stability in easy Block correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block3_cts_stab <- ggplot(block_cts_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.3\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block5_cts_stab <- ggplot(block_cts_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block7_cts_stab <- ggplot(block_cts_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.7\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block9_cts_stab <- ggplot(block_cts_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.9\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "# ggarrange(block9_cts_stab, block7_cts_stab, block5_cts_stab, block3_cts_stab,    \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block3_bin_stab <- ggplot(block_bin_sub3, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.3 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block5_bin_stab <- ggplot(block_bin_sub5, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.5 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block7_bin_stab <- ggplot(block_bin_sub7, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.7 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "block9_bin_stab <- ggplot(block_bin_sub9, aes(fill=method, y=Stab, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Block Correlation 0.9 (Binary)\") + xlab(\"Number of Samples\") + ylab(\"Stability\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "# ggarrange(block9_bin_stab, block7_bin_stab, block5_bin_stab, block3_bin_stab,    \n",
+    "#   nrow=2, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS3_combined <- ggarrange(block9_cts_stab + rremove(\"x.title\"), block7_cts_stab + rremove(\"x.title\"),  \n",
+    "          block5_cts_stab + rremove(\"x.title\"), block3_cts_stab + rremove(\"x.title\"),       \n",
+    "          block9_bin_stab + rremove(\"x.title\"), block7_bin_stab + rremove(\"x.title\"), \n",
+    "          block5_bin_stab, block3_bin_stab, \n",
+    "          nrow=4, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/figS3_combined.png\", figS3_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure 3: best method based on MSE/AUC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe9_cts_mse <- ggplot(toe_cts_sub9, aes(fill=method, y=MSE_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz 0.9\") + xlab('') + ylab(\"MSE\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block1_cts_mse <- ggplot(block_cts_sub1, aes(fill=method, y=MSE_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ggtitle(\"Block 0.1\") + ylab(\"MSE\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "toe9_cts_FNR <- ggplot(toe_cts_sub9, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_cts_FNR <- ggplot(block_cts_sub1, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "toe9_cts_FPR <- ggplot(toe_cts_sub9, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_cts_FPR <- ggplot(block_cts_sub1, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe9_cts_mse, block1_cts_mse, toe9_cts_FNR, block1_cts_FNR, toe9_cts_FPR, block1_cts_FPR,         \n",
+    "#   nrow=3, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe9_bin_ROC <- ggplot(toe_bin_sub9, aes(fill=method, y=ROC_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                ggtitle(\"Toeplitz 0.9 (Binary)\") + xlab('') + ylab(\"AUC\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1)\n",
+    "\n",
+    "block1_bin_ROC <- ggplot(block_bin_sub1, aes(fill=method, y=ROC_mean, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ggtitle(\"Block 0.1 (Binary)\") + ylab(\"AUC\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) + ylim(0, 1) \n",
+    "\n",
+    "toe9_bin_FNR <- ggplot(toe_bin_sub9, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_bin_FNR <- ggplot(block_bin_sub1, aes(fill=method, y=FN_mean/6, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab('') + ylab(\"FNR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "toe9_bin_FPR <- ggplot(toe_bin_sub9, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both) \n",
+    "\n",
+    "block1_bin_FPR <- ggplot(block_bin_sub1, aes(fill=method, y=FP_mean/num_select, x=N)) + \n",
+    "                geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "                xlab(\"Number of Samples\") + ylab(\"FPR\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                      legend.position=\"top\",\n",
+    "                      axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                      axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                     axis.text.x = element_text(angle = 55)) + \n",
+    "                facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "# ggarrange(toe9_bin_ROC, block1_bin_ROC, toe9_bin_FNR, block1_bin_FNR, toe9_bin_FPR, block1_bin_FPR,         \n",
+    "#   nrow=3, ncol = 2, labels = \"AUTO\", common.legend = T, legend = \"bottom\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 1 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 1 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 4 rows containing missing values (geom_bar).”\n",
+      "Warning message:\n",
+      "“Removed 11 rows containing missing values (geom_bar).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig3_combined <- ggarrange(toe9_cts_mse, block1_cts_mse, toe9_bin_ROC, block1_bin_ROC,\n",
+    "                           toe9_cts_FNR, block1_cts_FNR, toe9_bin_FNR, block1_bin_FNR, \n",
+    "                           toe9_cts_FPR, block1_cts_FPR, toe9_bin_FPR, block1_bin_FPR,\n",
+    "                           nrow=3, ncol = 4, labels = \"AUTO\", common.legend = T, legend = \"bottom\") \n",
+    "ggsave(\"../figures_combined/fig3_combined.png\", fig3_combined, dpi = 300, width = 10, height = 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Figure S4: check feature correlation impact (continuous, selected case)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"lasso: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"elnet: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0\"\n",
+      "[1] \"rf: n: 100 p: 1000 rou: 0\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>6</li><li>7</li><li>8</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 7\n",
+       "\\item 8\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 1\n",
+       "2. 2\n",
+       "3. 3\n",
+       "4. 6\n",
+       "5. 7\n",
+       "6. 8\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 1 2 3 6 7 8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load results for 4 methods\n",
+    "load('../sim_data/independent_Lasso.RData')\n",
+    "load('../sim_data/independent_Elnet.RData')\n",
+    "load('../sim_data/independent_compLasso.RData')\n",
+    "load('../sim_data/independent_RF.RData')\n",
+    "\n",
+    "# check index\n",
+    "b = 14\n",
+    "print(paste0('lasso: ', 'n: ', results_ind_lasso[[b]]$n, ' p: ', results_ind_lasso[[b]]$p, \n",
+    "            ' rou: ', results_ind_lasso[[b]]$rou))\n",
+    "print(paste0('elnet: ', 'n: ', results_ind_elnet[[b]]$n, ' p: ', results_ind_elnet[[b]]$p, \n",
+    "            ' rou: ', results_ind_elnet[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_ind_compLasso[[b]]$n, ' p: ', results_ind_compLasso[[b]]$p, \n",
+    "            ' rou: ', results_ind_compLasso[[b]]$rou))\n",
+    "print(paste0('rf: ', 'n: ', results_ind_rf[[b]]$n, ' p: ', results_ind_rf[[b]]$p, \n",
+    "            ' rou: ', results_ind_rf[[b]]$rou))\n",
+    "\n",
+    "# load simulated data\n",
+    "load('../sim_data/sim_independent_P_1000_N_100.RData', dat <- new.env())\n",
+    "i = 1\n",
+    "sub = dat$sim_array[[i]]\n",
+    "coef = sub$beta\n",
+    "coef.true = which(coef != 0)\n",
+    "coef.true\n",
+    "\n",
+    "# summarize results\n",
+    "table_ind = data.frame(matrix(0, nrow=1000, ncol=6))\n",
+    "colnames(table_ind) = c('FeatureIndex', 'FeatureTrue', 'Lasso', 'Elnet', 'compLasso', 'RF')\n",
+    "table_ind$FeatureIndex = seq(1, 1000, 1)\n",
+    "table_ind$FeatureTrue[coef.true] =  1\n",
+    "\n",
+    "table_ind$Lasso = colMeans(results_ind_lasso[[b]]$Stab.table)\n",
+    "table_ind$Elnet = colMeans(results_ind_elnet[[b]]$Stab.table)\n",
+    "table_ind$compLasso = colMeans(results_ind_compLasso[[b]]$Stab.table)\n",
+    "table_ind$RF = colMeans(results_ind_rf[[b]]$Stab.table)\n",
+    "\n",
+    "# zoom in first 15 features\n",
+    "ind_cts_Lasso <- ggplot(table_ind, aes(x=FeatureIndex, y=Lasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") + \n",
+    "             labs(title='Lasso (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_Elnet <- ggplot(table_ind, aes(x=FeatureIndex, y=Elnet, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Elastic Net (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_compLasso <- ggplot(table_ind, aes(x=FeatureIndex, y=compLasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Compositional Lasso (Ind)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "ind_cts_RF <- ggplot(table_ind, aes(x=FeatureIndex, y=RF, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Random Forests (Independent)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"lasso: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"elnet: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0.9\"\n",
+      "[1] \"complasso: n: 100 p: 1000 rou: 0.9\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       ".list-inline {list-style: none; margin:0; padding: 0}\n",
+       ".list-inline>li {display: inline-block}\n",
+       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
+       "</style>\n",
+       "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>6</li><li>7</li><li>8</li></ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 1\n",
+       "\\item 2\n",
+       "\\item 3\n",
+       "\\item 6\n",
+       "\\item 7\n",
+       "\\item 8\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 1\n",
+       "2. 2\n",
+       "3. 3\n",
+       "4. 6\n",
+       "5. 7\n",
+       "6. 8\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 1 2 3 6 7 8"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# load results for 4 methods\n",
+    "load('../sim_data/toe_Lasso.RData')\n",
+    "load('../sim_data/toe_Elnet.RData')\n",
+    "load('../sim_data/toe_compLasso.RData')\n",
+    "load('../sim_data/toe_RF.RData')\n",
+    "\n",
+    "# check index\n",
+    "b = 78\n",
+    "print(paste0('lasso: ', 'n: ', results_toe_lasso[[b]]$n, ' p: ', results_toe_lasso[[b]]$p, \n",
+    "            ' rou: ', results_toe_lasso[[b]]$rou))\n",
+    "print(paste0('elnet: ', 'n: ', results_toe_elnet[[b]]$n, ' p: ', results_toe_elnet[[b]]$p, \n",
+    "            ' rou: ', results_toe_elnet[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_toe_compLasso[[b]]$n, ' p: ', results_toe_compLasso[[b]]$p, \n",
+    "            ' rou: ', results_toe_compLasso[[b]]$rou))\n",
+    "print(paste0('complasso: ', 'n: ', results_toe_rf[[b]]$n, ' p: ', results_toe_rf[[b]]$p, \n",
+    "            ' rou: ', results_toe_rf[[b]]$rou))\n",
+    "\n",
+    "# load simulated data\n",
+    "load('../sim_data/sim_toeplitz_corr0.9P_1000_N_100.RData', dat <- new.env())\n",
+    "i = 1\n",
+    "sub = dat$sim_array[[i]]\n",
+    "coef = sub$beta\n",
+    "coef.true = which(coef != 0)\n",
+    "coef.true\n",
+    "\n",
+    "# summarize results\n",
+    "table_toe = data.frame(matrix(0, nrow=1000, ncol=6))\n",
+    "colnames(table_toe) = c('FeatureIndex', 'FeatureTrue', 'Lasso', 'Elnet', 'compLasso', 'RF')\n",
+    "table_toe$FeatureIndex = seq(1, 1000, 1)\n",
+    "table_toe$FeatureTrue[coef.true] =  1\n",
+    "\n",
+    "table_toe$Lasso = colMeans(results_toe_lasso[[b]]$Stab.table)\n",
+    "table_toe$Elnet = colMeans(results_toe_elnet[[b]]$Stab.table)\n",
+    "table_toe$compLasso = colMeans(results_toe_compLasso[[b]]$Stab.table)\n",
+    "table_toe$RF = colMeans(results_toe_rf[[b]]$Stab.table)\n",
+    "\n",
+    "# zoom in first 15 features\n",
+    "toe_cts_Lasso <- ggplot(table_toe, aes(x=FeatureIndex, y=Lasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") + \n",
+    "             labs(title='Lasso (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_Elnet <- ggplot(table_toe, aes(x=FeatureIndex, y=Elnet, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Elastic Net (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_compLasso <- ggplot(table_toe, aes(x=FeatureIndex, y=compLasso, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Compositional Lasso (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    "\n",
+    "\n",
+    "toe_cts_RF <- ggplot(table_toe, aes(x=FeatureIndex, y=RF, color=as.factor(FeatureTrue))) + \n",
+    "             geom_point() + scale_color_manual(values=c('black', 'red')) + \n",
+    "             theme(legend.position=\"top\") + labs(color =\"True Feature\") +\n",
+    "             labs(title='Random Forests (Toeplitz 0.9)', x='Feature Index', y='Selection Prob') +\n",
+    "             theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                   axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                   axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                   panel.background = element_blank(), axis.line = element_line(colour = \"black\"),\n",
+    "                   legend.text = element_text(size = 8)) + xlim(1, 15) + ylim(0, 1)\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n",
+      "Warning message:\n",
+      "“Removed 985 rows containing missing values (geom_point).”\n"
+     ]
+    }
+   ],
+   "source": [
+    "figS4_combined <- ggarrange(ind_cts_Lasso, toe_cts_Lasso, \n",
+    "                       ind_cts_Elnet, toe_cts_Elnet,\n",
+    "                       ind_cts_compLasso, toe_cts_compLasso,\n",
+    "                       ind_cts_RF, toe_cts_RF,      \n",
+    "                       nrow=4, ncol = 2, align = \"hv\", labels = \"AUTO\", \n",
+    "                       common.legend = T, legend = \"bottom\")\n",
+    "\n",
+    "\n",
+    "ggsave(\"../figures_combined/figS4_combined.png\", figS4_combined, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Supplementary Figure 5: number of features (continuous, selected case)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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axZM1//cjgcZrOZIGswGDiOq66u9tfZqdVqo9Fot9stFotfggghvV7P8/z169f9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dWFhoNpudSshxXFhYWEVFRUlJichuIhVCCLl793EC4t4AACAASURBVK7IszqdTqvV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\n19NK/+qtmpcnKtNmQgqn5o5xpUKIO944tn4Mc5ldf7Hia94/hBUil8uZjyB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w4cM/+OAD608gP4vFMnv27Pnz52u1Wuvt6enps2fPrvWC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\n",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind_num_select <- ggplot(ind_cts, aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Independent\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe1_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.1,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.1\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe5_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.5,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.5\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "toe9_num_select <- ggplot(toe_cts[toe_cts$Corr %in% 0.9,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Toeplitz 0.9\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "block9_num_select <- ggplot(block_cts[block_cts$Corr %in% 0.9,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Block 0.9\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "block1_num_select <- ggplot(block_cts[block_cts$Corr %in% 0.1,], aes(fill=method, y=num_select, x=as.factor(N))) + \n",
+    "            geom_bar(position=\"dodge\", stat=\"identity\") + \n",
+    "            ggtitle(\"Block 0.1\") + xlab(\"Number of Samples\") + ylab(\"# Selected Features\") + \n",
+    "                theme(plot.title = element_text(hjust = 0.5, size=12, face=\"bold.italic\"), \n",
+    "                  legend.position=\"top\", \n",
+    "                    axis.title.x = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.title.y = element_text(size=10, face=\"bold\"),\n",
+    "                    axis.text.x = element_text(angle = 55)) + \n",
+    "                    facet_grid(~P, labeller = label_both)\n",
+    "\n",
+    "fig_cts_num_select <- ggarrange(ind_num_select, toe1_num_select, \n",
+    "                       toe5_num_select, toe9_num_select, \n",
+    "                       block9_num_select, block1_num_select,    \n",
+    "                       nrow=3, ncol = 2, align = \"hv\", labels = \"AUTO\", \n",
+    "                       common.legend = T, legend = \"bottom\") \n",
+    "fig_cts_num_select\n",
+    "ggsave(\"../figures_combined/fig_cts_num_select.png\", fig_cts_num_select, dpi = 300, width = 6.5, height = 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.1_sim_indepdent_lasso-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.1_sim_indepdent_lasso-checkpoint.ipynb
new file mode 100755
index 0000000..84c4b66
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.1_sim_indepdent_lasso-checkpoint.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_lasso))\n",
+    "for (i in 1:length(results_ind_lasso)){\n",
+    "    table_ind = rbind(table_ind, results_ind_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>8.36 ( 0.45 )</td><td>0.02 ( 0.01 )</td><td>0.54 ( 0.03 )</td><td>0.37         </td><td>13.34        </td><td>0.51         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>5.5 ( 0.42 ) </td><td>0 ( 0 )      </td><td>0.36 ( 0.01 )</td><td>0.51         </td><td>10.50        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>2.33 ( 0.14 )</td><td>0 ( 0 )      </td><td>0.28 ( 0 )   </td><td>0.79         </td><td> 7.33        </td><td>0.15         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>1.82 ( 0.13 )</td><td>0 ( 0 )      </td><td>0.26 ( 0 )   </td><td>0.86         </td><td> 6.82        </td><td>0.10         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>11.1 ( 0.38 )</td><td>0.08 ( 0.03 )</td><td>0.66 ( 0.04 )</td><td>0.32         </td><td>16.02        </td><td>0.61         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>7.23 ( 0.4 ) </td><td>0 ( 0 )      </td><td>0.41 ( 0.01 )</td><td>0.46         </td><td>12.23        </td><td>0.46         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 8.36 ( 0.45 ) & 0.02 ( 0.01 ) & 0.54 ( 0.03 ) & 0.37          & 13.34         & 0.51         \\\\\n",
+       "\t 100           & 50            & 0             & 5.5 ( 0.42 )  & 0 ( 0 )       & 0.36 ( 0.01 ) & 0.51          & 10.50         & 0.37         \\\\\n",
+       "\t 500           & 50            & 0             & 2.33 ( 0.14 ) & 0 ( 0 )       & 0.28 ( 0 )    & 0.79          &  7.33         & 0.15         \\\\\n",
+       "\t 1000          & 50            & 0             & 1.82 ( 0.13 ) & 0 ( 0 )       & 0.26 ( 0 )    & 0.86          &  6.82         & 0.10         \\\\\n",
+       "\t 50            & 100           & 0             & 11.1 ( 0.38 ) & 0.08 ( 0.03 ) & 0.66 ( 0.04 ) & 0.32          & 16.02         & 0.61         \\\\\n",
+       "\t 100           & 100           & 0             & 7.23 ( 0.4 )  & 0 ( 0 )       & 0.41 ( 0.01 ) & 0.46          & 12.23         & 0.46         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 8.36 ( 0.45 ) | 0.02 ( 0.01 ) | 0.54 ( 0.03 ) | 0.37          | 13.34         | 0.51          |\n",
+       "| 100           | 50            | 0             | 5.5 ( 0.42 )  | 0 ( 0 )       | 0.36 ( 0.01 ) | 0.51          | 10.50         | 0.37          |\n",
+       "| 500           | 50            | 0             | 2.33 ( 0.14 ) | 0 ( 0 )       | 0.28 ( 0 )    | 0.79          |  7.33         | 0.15          |\n",
+       "| 1000          | 50            | 0             | 1.82 ( 0.13 ) | 0 ( 0 )       | 0.26 ( 0 )    | 0.86          |  6.82         | 0.10          |\n",
+       "| 50            | 100           | 0             | 11.1 ( 0.38 ) | 0.08 ( 0.03 ) | 0.66 ( 0.04 ) | 0.32          | 16.02         | 0.61          |\n",
+       "| 100           | 100           | 0             | 7.23 ( 0.4 )  | 0 ( 0 )       | 0.41 ( 0.01 ) | 0.46          | 12.23         | 0.46          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   8.36 ( 0.45 ) 0.02 ( 0.01 ) 0.54 ( 0.03 ) 0.37 13.34      0.51\n",
+       "2 100  50  0   5.5 ( 0.42 )  0 ( 0 )       0.36 ( 0.01 ) 0.51 10.50      0.37\n",
+       "3 500  50  0   2.33 ( 0.14 ) 0 ( 0 )       0.28 ( 0 )    0.79  7.33      0.15\n",
+       "4 1000 50  0   1.82 ( 0.13 ) 0 ( 0 )       0.26 ( 0 )    0.86  6.82      0.10\n",
+       "5 50   100 0   11.1 ( 0.38 ) 0.08 ( 0.03 ) 0.66 ( 0.04 ) 0.32 16.02      0.61\n",
+       "6 100  100 0   7.23 ( 0.4 )  0 ( 0 )       0.41 ( 0.01 ) 0.46 12.23      0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.37          </td><td>0.54 ( 0.03 ) </td><td>8.36 ( 0.45 ) </td><td>0.02 ( 0.01 ) </td><td>13.34         </td><td>0.51          </td><td>0.54          </td><td> 8.36         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.51          </td><td>0.36 ( 0.01 ) </td><td>5.5 ( 0.42 )  </td><td>0 ( 0 )       </td><td>10.50         </td><td>0.37          </td><td>0.36          </td><td> 5.50         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.79          </td><td>0.28 ( 0 )    </td><td>2.33 ( 0.14 ) </td><td>0 ( 0 )       </td><td> 7.33         </td><td>0.15          </td><td>0.28          </td><td> 2.33         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.86          </td><td>0.26 ( 0 )    </td><td>1.82 ( 0.13 ) </td><td>0 ( 0 )       </td><td> 6.82         </td><td>0.1           </td><td>0.26          </td><td> 1.82         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.32          </td><td>0.66 ( 0.04 ) </td><td>11.1 ( 0.38 ) </td><td>0.08 ( 0.03 ) </td><td>16.02         </td><td>0.61          </td><td>0.66          </td><td>11.10         </td><td>0.08          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.46          </td><td>0.41 ( 0.01 ) </td><td>7.23 ( 0.4 )  </td><td>0 ( 0 )       </td><td>12.23         </td><td>0.46          </td><td>0.41          </td><td> 7.23         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.71          </td><td>0.29 ( 0 )    </td><td>3.26 ( 0.25 ) </td><td>0 ( 0 )       </td><td> 8.26         </td><td>0.22          </td><td>0.29          </td><td> 3.26         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.82          </td><td>0.27 ( 0 )    </td><td>2.25 ( 0.15 ) </td><td>0 ( 0 )       </td><td> 7.25         </td><td>0.15          </td><td>0.27          </td><td> 2.25         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.19          </td><td>1.41 ( 0.11 ) </td><td>19.89 ( 0.38 )</td><td>0.64 ( 0.08 ) </td><td>24.25         </td><td>0.77          </td><td>1.41          </td><td>19.80         </td><td>0.64          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.30          </td><td>0.48 ( 0.02 ) </td><td>14.71 ( 0.7 ) </td><td>0 ( 0 )       </td><td>19.71         </td><td>0.66          </td><td>0.48          </td><td>14.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.61          </td><td>0.28 ( 0 )    </td><td>4.82 ( 0.34 ) </td><td>0 ( 0 )       </td><td> 9.82         </td><td>0.32          </td><td>0.28          </td><td> 4.82         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.72          </td><td>0.27 ( 0 )    </td><td>3.35 ( 0.32 ) </td><td>0 ( 0 )       </td><td> 8.35         </td><td>0.21          </td><td>0.27          </td><td> 3.35         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.14          </td><td>2.24 ( 0.19 ) </td><td>23.95 ( 0.37 )</td><td>1.19 ( 0.09 ) </td><td>27.76         </td><td>0.82          </td><td>2.24          </td><td>23.90         </td><td>1.19          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.25          </td><td>0.53 ( 0.02 ) </td><td>18.53 ( 0.59 )</td><td>0 ( 0 )       </td><td>23.53         </td><td>0.73          </td><td>0.53          </td><td>18.50         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.52          </td><td>0.29 ( 0 )    </td><td>6.51 ( 0.45 ) </td><td>0 ( 0 )       </td><td>11.51         </td><td>0.4           </td><td>0.29          </td><td> 6.51         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.64          </td><td>0.27 ( 0 )    </td><td>4.39 ( 0.56 ) </td><td>0 ( 0 )       </td><td> 9.39         </td><td>0.26          </td><td>0.27          </td><td> 4.39         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.37           & 0.54 ( 0.03 )  & 8.36 ( 0.45 )  & 0.02 ( 0.01 )  & 13.34          & 0.51           & 0.54           &  8.36          & 0.02          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.51           & 0.36 ( 0.01 )  & 5.5 ( 0.42 )   & 0 ( 0 )        & 10.50          & 0.37           & 0.36           &  5.50          & 0.00          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.79           & 0.28 ( 0 )     & 2.33 ( 0.14 )  & 0 ( 0 )        &  7.33          & 0.15           & 0.28           &  2.33          & 0.00          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.86           & 0.26 ( 0 )     & 1.82 ( 0.13 )  & 0 ( 0 )        &  6.82          & 0.1            & 0.26           &  1.82          & 0.00          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.32           & 0.66 ( 0.04 )  & 11.1 ( 0.38 )  & 0.08 ( 0.03 )  & 16.02          & 0.61           & 0.66           & 11.10          & 0.08          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.46           & 0.41 ( 0.01 )  & 7.23 ( 0.4 )   & 0 ( 0 )        & 12.23          & 0.46           & 0.41           &  7.23          & 0.00          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.71           & 0.29 ( 0 )     & 3.26 ( 0.25 )  & 0 ( 0 )        &  8.26          & 0.22           & 0.29           &  3.26          & 0.00          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.82           & 0.27 ( 0 )     & 2.25 ( 0.15 )  & 0 ( 0 )        &  7.25          & 0.15           & 0.27           &  2.25          & 0.00          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.19           & 1.41 ( 0.11 )  & 19.89 ( 0.38 ) & 0.64 ( 0.08 )  & 24.25          & 0.77           & 1.41           & 19.80          & 0.64          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.30           & 0.48 ( 0.02 )  & 14.71 ( 0.7 )  & 0 ( 0 )        & 19.71          & 0.66           & 0.48           & 14.70          & 0.00          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.61           & 0.28 ( 0 )     & 4.82 ( 0.34 )  & 0 ( 0 )        &  9.82          & 0.32           & 0.28           &  4.82          & 0.00          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.72           & 0.27 ( 0 )     & 3.35 ( 0.32 )  & 0 ( 0 )        &  8.35          & 0.21           & 0.27           &  3.35          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.14           & 2.24 ( 0.19 )  & 23.95 ( 0.37 ) & 1.19 ( 0.09 )  & 27.76          & 0.82           & 2.24           & 23.90          & 1.19          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.25           & 0.53 ( 0.02 )  & 18.53 ( 0.59 ) & 0 ( 0 )        & 23.53          & 0.73           & 0.53           & 18.50          & 0.00          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.52           & 0.29 ( 0 )     & 6.51 ( 0.45 )  & 0 ( 0 )        & 11.51          & 0.4            & 0.29           &  6.51          & 0.00          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.64           & 0.27 ( 0 )     & 4.39 ( 0.56 )  & 0 ( 0 )        &  9.39          & 0.26           & 0.27           &  4.39          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.37           | 0.54 ( 0.03 )  | 8.36 ( 0.45 )  | 0.02 ( 0.01 )  | 13.34          | 0.51           | 0.54           |  8.36          | 0.02           |\n",
+       "|  100           |   50           |  0.50          | 0.51           | 0.36 ( 0.01 )  | 5.5 ( 0.42 )   | 0 ( 0 )        | 10.50          | 0.37           | 0.36           |  5.50          | 0.00           |\n",
+       "|  500           |   50           |  0.10          | 0.79           | 0.28 ( 0 )     | 2.33 ( 0.14 )  | 0 ( 0 )        |  7.33          | 0.15           | 0.28           |  2.33          | 0.00           |\n",
+       "| 1000           |   50           |  0.05          | 0.86           | 0.26 ( 0 )     | 1.82 ( 0.13 )  | 0 ( 0 )        |  6.82          | 0.1            | 0.26           |  1.82          | 0.00           |\n",
+       "|   50           |  100           |  2.00          | 0.32           | 0.66 ( 0.04 )  | 11.1 ( 0.38 )  | 0.08 ( 0.03 )  | 16.02          | 0.61           | 0.66           | 11.10          | 0.08           |\n",
+       "|  100           |  100           |  1.00          | 0.46           | 0.41 ( 0.01 )  | 7.23 ( 0.4 )   | 0 ( 0 )        | 12.23          | 0.46           | 0.41           |  7.23          | 0.00           |\n",
+       "|  500           |  100           |  0.20          | 0.71           | 0.29 ( 0 )     | 3.26 ( 0.25 )  | 0 ( 0 )        |  8.26          | 0.22           | 0.29           |  3.26          | 0.00           |\n",
+       "| 1000           |  100           |  0.10          | 0.82           | 0.27 ( 0 )     | 2.25 ( 0.15 )  | 0 ( 0 )        |  7.25          | 0.15           | 0.27           |  2.25          | 0.00           |\n",
+       "|   50           |  500           | 10.00          | 0.19           | 1.41 ( 0.11 )  | 19.89 ( 0.38 ) | 0.64 ( 0.08 )  | 24.25          | 0.77           | 1.41           | 19.80          | 0.64           |\n",
+       "|  100           |  500           |  5.00          | 0.30           | 0.48 ( 0.02 )  | 14.71 ( 0.7 )  | 0 ( 0 )        | 19.71          | 0.66           | 0.48           | 14.70          | 0.00           |\n",
+       "|  500           |  500           |  1.00          | 0.61           | 0.28 ( 0 )     | 4.82 ( 0.34 )  | 0 ( 0 )        |  9.82          | 0.32           | 0.28           |  4.82          | 0.00           |\n",
+       "| 1000           |  500           |  0.50          | 0.72           | 0.27 ( 0 )     | 3.35 ( 0.32 )  | 0 ( 0 )        |  8.35          | 0.21           | 0.27           |  3.35          | 0.00           |\n",
+       "|   50           | 1000           | 20.00          | 0.14           | 2.24 ( 0.19 )  | 23.95 ( 0.37 ) | 1.19 ( 0.09 )  | 27.76          | 0.82           | 2.24           | 23.90          | 1.19           |\n",
+       "|  100           | 1000           | 10.00          | 0.25           | 0.53 ( 0.02 )  | 18.53 ( 0.59 ) | 0 ( 0 )        | 23.53          | 0.73           | 0.53           | 18.50          | 0.00           |\n",
+       "|  500           | 1000           |  2.00          | 0.52           | 0.29 ( 0 )     | 6.51 ( 0.45 )  | 0 ( 0 )        | 11.51          | 0.4            | 0.29           |  6.51          | 0.00           |\n",
+       "| 1000           | 1000           |  1.00          | 0.64           | 0.27 ( 0 )     | 4.39 ( 0.56 )  | 0 ( 0 )        |  9.39          | 0.26           | 0.27           |  4.39          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN            num_select\n",
+       "1    50   50  1.00 0.37 0.54 ( 0.03 ) 8.36 ( 0.45 )  0.02 ( 0.01 ) 13.34     \n",
+       "2   100   50  0.50 0.51 0.36 ( 0.01 ) 5.5 ( 0.42 )   0 ( 0 )       10.50     \n",
+       "3   500   50  0.10 0.79 0.28 ( 0 )    2.33 ( 0.14 )  0 ( 0 )        7.33     \n",
+       "4  1000   50  0.05 0.86 0.26 ( 0 )    1.82 ( 0.13 )  0 ( 0 )        6.82     \n",
+       "5    50  100  2.00 0.32 0.66 ( 0.04 ) 11.1 ( 0.38 )  0.08 ( 0.03 ) 16.02     \n",
+       "6   100  100  1.00 0.46 0.41 ( 0.01 ) 7.23 ( 0.4 )   0 ( 0 )       12.23     \n",
+       "7   500  100  0.20 0.71 0.29 ( 0 )    3.26 ( 0.25 )  0 ( 0 )        8.26     \n",
+       "8  1000  100  0.10 0.82 0.27 ( 0 )    2.25 ( 0.15 )  0 ( 0 )        7.25     \n",
+       "9    50  500 10.00 0.19 1.41 ( 0.11 ) 19.89 ( 0.38 ) 0.64 ( 0.08 ) 24.25     \n",
+       "10  100  500  5.00 0.30 0.48 ( 0.02 ) 14.71 ( 0.7 )  0 ( 0 )       19.71     \n",
+       "11  500  500  1.00 0.61 0.28 ( 0 )    4.82 ( 0.34 )  0 ( 0 )        9.82     \n",
+       "12 1000  500  0.50 0.72 0.27 ( 0 )    3.35 ( 0.32 )  0 ( 0 )        8.35     \n",
+       "13   50 1000 20.00 0.14 2.24 ( 0.19 ) 23.95 ( 0.37 ) 1.19 ( 0.09 ) 27.76     \n",
+       "14  100 1000 10.00 0.25 0.53 ( 0.02 ) 18.53 ( 0.59 ) 0 ( 0 )       23.53     \n",
+       "15  500 1000  2.00 0.52 0.29 ( 0 )    6.51 ( 0.45 )  0 ( 0 )       11.51     \n",
+       "16 1000 1000  1.00 0.64 0.27 ( 0 )    4.39 ( 0.56 )  0 ( 0 )        9.39     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.51 0.54      8.36   0.02   \n",
+       "2  0.37 0.36      5.50   0.00   \n",
+       "3  0.15 0.28      2.33   0.00   \n",
+       "4  0.1  0.26      1.82   0.00   \n",
+       "5  0.61 0.66     11.10   0.08   \n",
+       "6  0.46 0.41      7.23   0.00   \n",
+       "7  0.22 0.29      3.26   0.00   \n",
+       "8  0.15 0.27      2.25   0.00   \n",
+       "9  0.77 1.41     19.80   0.64   \n",
+       "10 0.66 0.48     14.70   0.00   \n",
+       "11 0.32 0.28      4.82   0.00   \n",
+       "12 0.21 0.27      3.35   0.00   \n",
+       "13 0.82 2.24     23.90   1.19   \n",
+       "14 0.73 0.53     18.50   0.00   \n",
+       "15 0.4  0.29      6.51   0.00   \n",
+       "16 0.26 0.27      4.39   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.2_sim_indepdent_elnet_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.2_sim_indepdent_elnet_cts-checkpoint.ipynb
new file mode 100755
index 0000000..0db1fc6
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.2_sim_indepdent_elnet_cts-checkpoint.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_elnet))\n",
+    "for (i in 1:length(results_ind_elnet)){\n",
+    "    table_ind = rbind(table_ind, results_ind_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50            </td><td>50            </td><td>0             </td><td>14.99 ( 0.59 )</td><td>0 ( 0 )       </td><td>0.53 ( 0.03 ) </td><td>0.2           </td><td>19.99         </td><td>0.67          </td></tr>\n",
+       "\t<tr><td>100           </td><td>50            </td><td>0             </td><td>13.46 ( 0.55 )</td><td>0 ( 0 )       </td><td>0.34 ( 0.01 ) </td><td>0.23          </td><td>18.46         </td><td>0.65          </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0             </td><td>13.65 ( 0.56 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.23          </td><td>18.65         </td><td>0.64          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0             </td><td>11.76 ( 0.53 )</td><td>0 ( 0 )       </td><td>0.25 ( 0 )    </td><td>0.27          </td><td>16.76         </td><td>0.60          </td></tr>\n",
+       "\t<tr><td>50            </td><td>100           </td><td>0             </td><td>19.45 ( 0.66 )</td><td>0.04 ( 0.02 ) </td><td>0.74 ( 0.06 ) </td><td>0.19          </td><td>24.41         </td><td>0.74          </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>19.2 ( 1.02 )</td><td>0 ( 0 )      </td><td>0.38 ( 0.01 )</td><td>0.2          </td><td>24.20        </td><td>0.71         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50             & 50             & 0              & 14.99 ( 0.59 ) & 0 ( 0 )        & 0.53 ( 0.03 )  & 0.2            & 19.99          & 0.67          \\\\\n",
+       "\t 100            & 50             & 0              & 13.46 ( 0.55 ) & 0 ( 0 )        & 0.34 ( 0.01 )  & 0.23           & 18.46          & 0.65          \\\\\n",
+       "\t 500            & 50             & 0              & 13.65 ( 0.56 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.23           & 18.65          & 0.64          \\\\\n",
+       "\t 1000           & 50             & 0              & 11.76 ( 0.53 ) & 0 ( 0 )        & 0.25 ( 0 )     & 0.27           & 16.76          & 0.60          \\\\\n",
+       "\t 50             & 100            & 0              & 19.45 ( 0.66 ) & 0.04 ( 0.02 )  & 0.74 ( 0.06 )  & 0.19           & 24.41          & 0.74          \\\\\n",
+       "\t 100           & 100           & 0             & 19.2 ( 1.02 ) & 0 ( 0 )       & 0.38 ( 0.01 ) & 0.2           & 24.20         & 0.71         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50             | 50             | 0              | 14.99 ( 0.59 ) | 0 ( 0 )        | 0.53 ( 0.03 )  | 0.2            | 19.99          | 0.67           |\n",
+       "| 100            | 50             | 0              | 13.46 ( 0.55 ) | 0 ( 0 )        | 0.34 ( 0.01 )  | 0.23           | 18.46          | 0.65           |\n",
+       "| 500            | 50             | 0              | 13.65 ( 0.56 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.23           | 18.65          | 0.64           |\n",
+       "| 1000           | 50             | 0              | 11.76 ( 0.53 ) | 0 ( 0 )        | 0.25 ( 0 )     | 0.27           | 16.76          | 0.60           |\n",
+       "| 50             | 100            | 0              | 19.45 ( 0.66 ) | 0.04 ( 0.02 )  | 0.74 ( 0.06 )  | 0.19           | 24.41          | 0.74           |\n",
+       "| 100           | 100           | 0             | 19.2 ( 1.02 ) | 0 ( 0 )       | 0.38 ( 0.01 ) | 0.2           | 24.20         | 0.71          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   14.99 ( 0.59 ) 0 ( 0 )       0.53 ( 0.03 ) 0.2  19.99      0.67\n",
+       "2 100  50  0   13.46 ( 0.55 ) 0 ( 0 )       0.34 ( 0.01 ) 0.23 18.46      0.65\n",
+       "3 500  50  0   13.65 ( 0.56 ) 0 ( 0 )       0.26 ( 0 )    0.23 18.65      0.64\n",
+       "4 1000 50  0   11.76 ( 0.53 ) 0 ( 0 )       0.25 ( 0 )    0.27 16.76      0.60\n",
+       "5 50   100 0   19.45 ( 0.66 ) 0.04 ( 0.02 ) 0.74 ( 0.06 ) 0.19 24.41      0.74\n",
+       "6 100  100 0   19.2 ( 1.02 )  0 ( 0 )       0.38 ( 0.01 ) 0.2  24.20      0.71"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.20          </td><td>0.53 ( 0.03 ) </td><td>14.99 ( 0.59 )</td><td>0 ( 0 )       </td><td>19.99         </td><td>0.67          </td><td>0.53          </td><td>14.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.23          </td><td>0.34 ( 0.01 ) </td><td>13.46 ( 0.55 )</td><td>0 ( 0 )       </td><td>18.46         </td><td>0.65          </td><td>0.34          </td><td>13.4          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>13.65 ( 0.56 )</td><td>0 ( 0 )       </td><td>18.65         </td><td>0.64          </td><td>0.26          </td><td>13.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.27          </td><td>0.25 ( 0 )    </td><td>11.76 ( 0.53 )</td><td>0 ( 0 )       </td><td>16.76         </td><td>0.6           </td><td>0.25          </td><td>11.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.19          </td><td>0.74 ( 0.06 ) </td><td>19.45 ( 0.66 )</td><td>0.04 ( 0.02 ) </td><td>24.41         </td><td>0.74          </td><td>0.74          </td><td>19.4          </td><td>0.04          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.20          </td><td>0.38 ( 0.01 ) </td><td>19.2 ( 1.02 ) </td><td>0 ( 0 )       </td><td>24.20         </td><td>0.71          </td><td>0.38          </td><td>19.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.23          </td><td>0.27 ( 0 )    </td><td>16.28 ( 0.78 )</td><td>0 ( 0 )       </td><td>21.28         </td><td>0.67          </td><td>0.27          </td><td>16.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>17.05 ( 0.76 )</td><td>0 ( 0 )       </td><td>22.05         </td><td>0.69          </td><td>0.26          </td><td>17.0          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.13          </td><td>1.65 ( 0.13 ) </td><td>30.38 ( 1.24 )</td><td>0.58 ( 0.08 ) </td><td>34.80         </td><td>0.83          </td><td>1.65          </td><td>30.3          </td><td>0.58          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.15          </td><td>0.5 ( 0.02 )  </td><td>32.18 ( 1.44 )</td><td>0 ( 0 )       </td><td>37.18         </td><td>0.81          </td><td>0.50          </td><td>32.1          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.16          </td><td>0.28 ( 0 )    </td><td>29.79 ( 1.73 )</td><td>0 ( 0 )       </td><td>34.79         </td><td>0.78          </td><td>0.28          </td><td>29.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.18          </td><td>0.26 ( 0 )    </td><td>27.21 ( 1.58 )</td><td>0 ( 0 )       </td><td>32.21         </td><td>0.77          </td><td>0.26          </td><td>27.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.10          </td><td>2.5 ( 0.22 )  </td><td>36.6 ( 2.03 ) </td><td>1.12 ( 0.09 ) </td><td>40.48         </td><td>0.86          </td><td>2.50          </td><td>36.6          </td><td>1.12          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.13          </td><td>0.56 ( 0.02 ) </td><td>40.61 ( 1.71 )</td><td>0 ( 0 )       </td><td>45.61         </td><td>0.85          </td><td>0.56          </td><td>40.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.14          </td><td>0.27 ( 0 )    </td><td>35.98 ( 2.2 ) </td><td>0 ( 0 )       </td><td>40.98         </td><td>0.8           </td><td>0.27          </td><td>35.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.15          </td><td>0.27 ( 0 )    </td><td>34.65 ( 2.02 )</td><td>0 ( 0 )       </td><td>39.65         </td><td>0.79          </td><td>0.27          </td><td>34.6          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.20           & 0.53 ( 0.03 )  & 14.99 ( 0.59 ) & 0 ( 0 )        & 19.99          & 0.67           & 0.53           & 14.9           & 0.00          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.23           & 0.34 ( 0.01 )  & 13.46 ( 0.55 ) & 0 ( 0 )        & 18.46          & 0.65           & 0.34           & 13.4           & 0.00          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.23           & 0.26 ( 0 )     & 13.65 ( 0.56 ) & 0 ( 0 )        & 18.65          & 0.64           & 0.26           & 13.6           & 0.00          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.27           & 0.25 ( 0 )     & 11.76 ( 0.53 ) & 0 ( 0 )        & 16.76          & 0.6            & 0.25           & 11.7           & 0.00          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.19           & 0.74 ( 0.06 )  & 19.45 ( 0.66 ) & 0.04 ( 0.02 )  & 24.41          & 0.74           & 0.74           & 19.4           & 0.04          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.20           & 0.38 ( 0.01 )  & 19.2 ( 1.02 )  & 0 ( 0 )        & 24.20          & 0.71           & 0.38           & 19.2           & 0.00          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.23           & 0.27 ( 0 )     & 16.28 ( 0.78 ) & 0 ( 0 )        & 21.28          & 0.67           & 0.27           & 16.2           & 0.00          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.23           & 0.26 ( 0 )     & 17.05 ( 0.76 ) & 0 ( 0 )        & 22.05          & 0.69           & 0.26           & 17.0           & 0.00          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.13           & 1.65 ( 0.13 )  & 30.38 ( 1.24 ) & 0.58 ( 0.08 )  & 34.80          & 0.83           & 1.65           & 30.3           & 0.58          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.15           & 0.5 ( 0.02 )   & 32.18 ( 1.44 ) & 0 ( 0 )        & 37.18          & 0.81           & 0.50           & 32.1           & 0.00          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.16           & 0.28 ( 0 )     & 29.79 ( 1.73 ) & 0 ( 0 )        & 34.79          & 0.78           & 0.28           & 29.7           & 0.00          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.18           & 0.26 ( 0 )     & 27.21 ( 1.58 ) & 0 ( 0 )        & 32.21          & 0.77           & 0.26           & 27.2           & 0.00          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.10           & 2.5 ( 0.22 )   & 36.6 ( 2.03 )  & 1.12 ( 0.09 )  & 40.48          & 0.86           & 2.50           & 36.6           & 1.12          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.13           & 0.56 ( 0.02 )  & 40.61 ( 1.71 ) & 0 ( 0 )        & 45.61          & 0.85           & 0.56           & 40.6           & 0.00          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.14           & 0.27 ( 0 )     & 35.98 ( 2.2 )  & 0 ( 0 )        & 40.98          & 0.8            & 0.27           & 35.9           & 0.00          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.15           & 0.27 ( 0 )     & 34.65 ( 2.02 ) & 0 ( 0 )        & 39.65          & 0.79           & 0.27           & 34.6           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.20           | 0.53 ( 0.03 )  | 14.99 ( 0.59 ) | 0 ( 0 )        | 19.99          | 0.67           | 0.53           | 14.9           | 0.00           |\n",
+       "|  100           |   50           |  0.50          | 0.23           | 0.34 ( 0.01 )  | 13.46 ( 0.55 ) | 0 ( 0 )        | 18.46          | 0.65           | 0.34           | 13.4           | 0.00           |\n",
+       "|  500           |   50           |  0.10          | 0.23           | 0.26 ( 0 )     | 13.65 ( 0.56 ) | 0 ( 0 )        | 18.65          | 0.64           | 0.26           | 13.6           | 0.00           |\n",
+       "| 1000           |   50           |  0.05          | 0.27           | 0.25 ( 0 )     | 11.76 ( 0.53 ) | 0 ( 0 )        | 16.76          | 0.6            | 0.25           | 11.7           | 0.00           |\n",
+       "|   50           |  100           |  2.00          | 0.19           | 0.74 ( 0.06 )  | 19.45 ( 0.66 ) | 0.04 ( 0.02 )  | 24.41          | 0.74           | 0.74           | 19.4           | 0.04           |\n",
+       "|  100           |  100           |  1.00          | 0.20           | 0.38 ( 0.01 )  | 19.2 ( 1.02 )  | 0 ( 0 )        | 24.20          | 0.71           | 0.38           | 19.2           | 0.00           |\n",
+       "|  500           |  100           |  0.20          | 0.23           | 0.27 ( 0 )     | 16.28 ( 0.78 ) | 0 ( 0 )        | 21.28          | 0.67           | 0.27           | 16.2           | 0.00           |\n",
+       "| 1000           |  100           |  0.10          | 0.23           | 0.26 ( 0 )     | 17.05 ( 0.76 ) | 0 ( 0 )        | 22.05          | 0.69           | 0.26           | 17.0           | 0.00           |\n",
+       "|   50           |  500           | 10.00          | 0.13           | 1.65 ( 0.13 )  | 30.38 ( 1.24 ) | 0.58 ( 0.08 )  | 34.80          | 0.83           | 1.65           | 30.3           | 0.58           |\n",
+       "|  100           |  500           |  5.00          | 0.15           | 0.5 ( 0.02 )   | 32.18 ( 1.44 ) | 0 ( 0 )        | 37.18          | 0.81           | 0.50           | 32.1           | 0.00           |\n",
+       "|  500           |  500           |  1.00          | 0.16           | 0.28 ( 0 )     | 29.79 ( 1.73 ) | 0 ( 0 )        | 34.79          | 0.78           | 0.28           | 29.7           | 0.00           |\n",
+       "| 1000           |  500           |  0.50          | 0.18           | 0.26 ( 0 )     | 27.21 ( 1.58 ) | 0 ( 0 )        | 32.21          | 0.77           | 0.26           | 27.2           | 0.00           |\n",
+       "|   50           | 1000           | 20.00          | 0.10           | 2.5 ( 0.22 )   | 36.6 ( 2.03 )  | 1.12 ( 0.09 )  | 40.48          | 0.86           | 2.50           | 36.6           | 1.12           |\n",
+       "|  100           | 1000           | 10.00          | 0.13           | 0.56 ( 0.02 )  | 40.61 ( 1.71 ) | 0 ( 0 )        | 45.61          | 0.85           | 0.56           | 40.6           | 0.00           |\n",
+       "|  500           | 1000           |  2.00          | 0.14           | 0.27 ( 0 )     | 35.98 ( 2.2 )  | 0 ( 0 )        | 40.98          | 0.8            | 0.27           | 35.9           | 0.00           |\n",
+       "| 1000           | 1000           |  1.00          | 0.15           | 0.27 ( 0 )     | 34.65 ( 2.02 ) | 0 ( 0 )        | 39.65          | 0.79           | 0.27           | 34.6           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN            num_select\n",
+       "1    50   50  1.00 0.20 0.53 ( 0.03 ) 14.99 ( 0.59 ) 0 ( 0 )       19.99     \n",
+       "2   100   50  0.50 0.23 0.34 ( 0.01 ) 13.46 ( 0.55 ) 0 ( 0 )       18.46     \n",
+       "3   500   50  0.10 0.23 0.26 ( 0 )    13.65 ( 0.56 ) 0 ( 0 )       18.65     \n",
+       "4  1000   50  0.05 0.27 0.25 ( 0 )    11.76 ( 0.53 ) 0 ( 0 )       16.76     \n",
+       "5    50  100  2.00 0.19 0.74 ( 0.06 ) 19.45 ( 0.66 ) 0.04 ( 0.02 ) 24.41     \n",
+       "6   100  100  1.00 0.20 0.38 ( 0.01 ) 19.2 ( 1.02 )  0 ( 0 )       24.20     \n",
+       "7   500  100  0.20 0.23 0.27 ( 0 )    16.28 ( 0.78 ) 0 ( 0 )       21.28     \n",
+       "8  1000  100  0.10 0.23 0.26 ( 0 )    17.05 ( 0.76 ) 0 ( 0 )       22.05     \n",
+       "9    50  500 10.00 0.13 1.65 ( 0.13 ) 30.38 ( 1.24 ) 0.58 ( 0.08 ) 34.80     \n",
+       "10  100  500  5.00 0.15 0.5 ( 0.02 )  32.18 ( 1.44 ) 0 ( 0 )       37.18     \n",
+       "11  500  500  1.00 0.16 0.28 ( 0 )    29.79 ( 1.73 ) 0 ( 0 )       34.79     \n",
+       "12 1000  500  0.50 0.18 0.26 ( 0 )    27.21 ( 1.58 ) 0 ( 0 )       32.21     \n",
+       "13   50 1000 20.00 0.10 2.5 ( 0.22 )  36.6 ( 2.03 )  1.12 ( 0.09 ) 40.48     \n",
+       "14  100 1000 10.00 0.13 0.56 ( 0.02 ) 40.61 ( 1.71 ) 0 ( 0 )       45.61     \n",
+       "15  500 1000  2.00 0.14 0.27 ( 0 )    35.98 ( 2.2 )  0 ( 0 )       40.98     \n",
+       "16 1000 1000  1.00 0.15 0.27 ( 0 )    34.65 ( 2.02 ) 0 ( 0 )       39.65     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.67 0.53     14.9    0.00   \n",
+       "2  0.65 0.34     13.4    0.00   \n",
+       "3  0.64 0.26     13.6    0.00   \n",
+       "4  0.6  0.25     11.7    0.00   \n",
+       "5  0.74 0.74     19.4    0.04   \n",
+       "6  0.71 0.38     19.2    0.00   \n",
+       "7  0.67 0.27     16.2    0.00   \n",
+       "8  0.69 0.26     17.0    0.00   \n",
+       "9  0.83 1.65     30.3    0.58   \n",
+       "10 0.81 0.50     32.1    0.00   \n",
+       "11 0.78 0.28     29.7    0.00   \n",
+       "12 0.77 0.26     27.2    0.00   \n",
+       "13 0.86 2.50     36.6    1.12   \n",
+       "14 0.85 0.56     40.6    0.00   \n",
+       "15 0.8  0.27     35.9    0.00   \n",
+       "16 0.79 0.27     34.6    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.3_sim_indepdent_rf_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.3_sim_indepdent_rf_cts-checkpoint.ipynb
new file mode 100755
index 0000000..337fe47
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.3_sim_indepdent_rf_cts-checkpoint.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_rf))\n",
+    "for (i in 1:length(results_ind_rf)){\n",
+    "    results_ind_rf[[i]]$OOB = paste(round(mean(results_ind_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_ind_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_ind = rbind(table_ind, results_ind_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.31 ( 0.06 )</td><td>NaN          </td><td>2.09 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>1.85 ( 0.11 )</td><td>4.11 ( 0.1 ) </td><td>0.85 ( 0.02 )</td><td>0.18         </td><td>1.79 ( 0.01 )</td><td>3.74         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>0.3 ( 0.05 ) </td><td>1.61 ( 0.08 )</td><td>0.47 ( 0.01 )</td><td>0.8          </td><td>1.31 ( 0 )   </td><td>4.69         </td><td>0.05         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>0.07 ( 0.03 )</td><td>0.78 ( 0.07 )</td><td>0.35 ( 0 )   </td><td>0.88         </td><td>1.17 ( 0 )   </td><td>5.29         </td><td>0.01         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.32 ( 0.05 )</td><td>NaN          </td><td>2.23 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>4.33 ( 0.22 )</td><td>4.45 ( 0.09 )</td><td>0.91 ( 0.03 )</td><td>0.08         </td><td>2.02 ( 0.01 )</td><td>5.88         </td><td>0.72         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 1 ( 0 )       & 6 ( 0 )       & 1.31 ( 0.06 ) & NaN           & 2.09 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0             & 1.85 ( 0.11 ) & 4.11 ( 0.1 )  & 0.85 ( 0.02 ) & 0.18          & 1.79 ( 0.01 ) & 3.74          & 0.48         \\\\\n",
+       "\t 500           & 50            & 0             & 0.3 ( 0.05 )  & 1.61 ( 0.08 ) & 0.47 ( 0.01 ) & 0.8           & 1.31 ( 0 )    & 4.69          & 0.05         \\\\\n",
+       "\t 1000          & 50            & 0             & 0.07 ( 0.03 ) & 0.78 ( 0.07 ) & 0.35 ( 0 )    & 0.88          & 1.17 ( 0 )    & 5.29          & 0.01         \\\\\n",
+       "\t 50            & 100           & 0             & 1 ( 0 )       & 6 ( 0 )       & 1.32 ( 0.05 ) & NaN           & 2.23 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 100           & 0             & 4.33 ( 0.22 ) & 4.45 ( 0.09 ) & 0.91 ( 0.03 ) & 0.08          & 2.02 ( 0.01 ) & 5.88          & 0.72         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 1 ( 0 )       | 6 ( 0 )       | 1.31 ( 0.06 ) | NaN           | 2.09 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0             | 1.85 ( 0.11 ) | 4.11 ( 0.1 )  | 0.85 ( 0.02 ) | 0.18          | 1.79 ( 0.01 ) | 3.74          | 0.48          |\n",
+       "| 500           | 50            | 0             | 0.3 ( 0.05 )  | 1.61 ( 0.08 ) | 0.47 ( 0.01 ) | 0.8           | 1.31 ( 0 )    | 4.69          | 0.05          |\n",
+       "| 1000          | 50            | 0             | 0.07 ( 0.03 ) | 0.78 ( 0.07 ) | 0.35 ( 0 )    | 0.88          | 1.17 ( 0 )    | 5.29          | 0.01          |\n",
+       "| 50            | 100           | 0             | 1 ( 0 )       | 6 ( 0 )       | 1.32 ( 0.05 ) | NaN           | 2.23 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 100           | 0             | 4.33 ( 0.22 ) | 4.45 ( 0.09 ) | 0.91 ( 0.03 ) | 0.08          | 2.02 ( 0.01 ) | 5.88          | 0.72          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab OOB          \n",
+       "1 50   50  0   1 ( 0 )       6 ( 0 )       1.31 ( 0.06 ) NaN  2.09 ( 0.02 )\n",
+       "2 100  50  0   1.85 ( 0.11 ) 4.11 ( 0.1 )  0.85 ( 0.02 ) 0.18 1.79 ( 0.01 )\n",
+       "3 500  50  0   0.3 ( 0.05 )  1.61 ( 0.08 ) 0.47 ( 0.01 ) 0.8  1.31 ( 0 )   \n",
+       "4 1000 50  0   0.07 ( 0.03 ) 0.78 ( 0.07 ) 0.35 ( 0 )    0.88 1.17 ( 0 )   \n",
+       "5 50   100 0   1 ( 0 )       6 ( 0 )       1.32 ( 0.05 ) NaN  2.23 ( 0.02 )\n",
+       "6 100  100 0   4.33 ( 0.22 ) 4.45 ( 0.09 ) 0.91 ( 0.03 ) 0.08 2.02 ( 0.01 )\n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.74       0.48\n",
+       "3 4.69       0.05\n",
+       "4 5.29       0.01\n",
+       "5 0.00        NaN\n",
+       "6 5.88       0.72"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')\n",
+    "result.table_ind$Index = seq(1, length(results_ind_rf), 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$OOB_mean = as.numeric(substr(result.table_ind$OOB, start=1, stop=4))\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))\n",
+    "#result.table_ind$FDR = as.numeric(as.character(result.table_ind$FDR))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>Index</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td> NaN          </td><td>1.31 ( 0.06 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.09 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 1            </td><td>1.31          </td><td>   NA         </td><td>  NA          </td><td>2.09          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.18          </td><td>0.85 ( 0.02 ) </td><td>1.85 ( 0.11 ) </td><td>4.11 ( 0.1 )  </td><td>1.79 ( 0.01 ) </td><td> 3.74         </td><td>0.48          </td><td> 2            </td><td>0.85          </td><td> 1.85         </td><td>4.11          </td><td>1.79          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.80          </td><td>0.47 ( 0.01 ) </td><td>0.3 ( 0.05 )  </td><td>1.61 ( 0.08 ) </td><td>1.31 ( 0 )    </td><td> 4.69         </td><td>0.05          </td><td> 3            </td><td>0.47          </td><td> 0.30         </td><td>1.61          </td><td>1.31          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.88          </td><td>0.35 ( 0 )    </td><td>0.07 ( 0.03 ) </td><td>0.78 ( 0.07 ) </td><td>1.17 ( 0 )    </td><td> 5.29         </td><td>0.01          </td><td> 4            </td><td>0.35          </td><td> 0.07         </td><td>0.78          </td><td>1.17          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td> NaN          </td><td>1.32 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.23 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 5            </td><td>1.32          </td><td>   NA         </td><td>  NA          </td><td>2.23          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.08          </td><td>0.91 ( 0.03 ) </td><td>4.33 ( 0.22 ) </td><td>4.45 ( 0.09 ) </td><td>2.02 ( 0.01 ) </td><td> 5.88         </td><td>0.72          </td><td> 6            </td><td>0.91          </td><td> 4.33         </td><td>4.45          </td><td>2.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.60          </td><td>0.59 ( 0.01 ) </td><td>1.91 ( 0.15 ) </td><td>1.41 ( 0.07 ) </td><td>1.54 ( 0 )    </td><td> 6.50         </td><td>0.27          </td><td> 7            </td><td>0.59          </td><td> 1.91         </td><td>1.41          </td><td>1.54          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.81          </td><td>0.47 ( 0 )    </td><td>0.71 ( 0.09 ) </td><td>0.53 ( 0.06 ) </td><td>1.39 ( 0 )    </td><td> 6.18         </td><td>0.1           </td><td> 8            </td><td>0.47          </td><td> 0.71         </td><td>0.53          </td><td>1.39          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td> NaN          </td><td>1.33 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td> 9            </td><td>1.33          </td><td>   NA         </td><td>  NA          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.02          </td><td>1.07 ( 0.03 ) </td><td>23.8 ( 0.52 ) </td><td>4.26 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>25.54         </td><td>0.93          </td><td>10            </td><td>1.07          </td><td>23.80         </td><td>4.26          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.14          </td><td>0.84 ( 0.01 ) </td><td>19.92 ( 0.41 )</td><td>1.71 ( 0.08 ) </td><td>2.14 ( 0.01 ) </td><td>24.21         </td><td>0.82          </td><td>11            </td><td>0.84          </td><td>19.90         </td><td>1.71          </td><td>2.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.19          </td><td>0.78 ( 0.01 ) </td><td>18.73 ( 0.42 )</td><td>0.84 ( 0.06 ) </td><td>2.06 ( 0 )    </td><td>23.89         </td><td>0.78          </td><td>12            </td><td>0.78          </td><td>18.70         </td><td>0.84          </td><td>2.06          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td> NaN          </td><td>1.31 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td>13            </td><td>1.31          </td><td>   NA         </td><td>  NA          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.01          </td><td>1.06 ( 0.03 ) </td><td>48.48 ( 0.63 )</td><td>4.33 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>50.15         </td><td>0.97          </td><td>14            </td><td>1.06          </td><td>48.40         </td><td>4.33          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.06          </td><td>0.89 ( 0.01 ) </td><td>44.77 ( 0.71 )</td><td>1.81 ( 0.1 )  </td><td>2.3 ( 0.01 )  </td><td>48.96         </td><td>0.91          </td><td>15            </td><td>0.89          </td><td>44.70         </td><td>1.81          </td><td>2.30          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.09          </td><td>0.85 ( 0.01 ) </td><td>41.77 ( 0.68 )</td><td>0.99 ( 0.07 ) </td><td>2.25 ( 0.01 ) </td><td>46.78         </td><td>0.89          </td><td>16            </td><td>0.85          </td><td>41.70         </td><td>0.99          </td><td>2.25          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & Index & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          &  NaN           & 1.31 ( 0.06 )  & 1 ( 0 )        & 6 ( 0 )        & 2.09 ( 0.02 )  &  0.00          & NaN            &  1             & 1.31           &    NA          &   NA           & 2.09          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.18           & 0.85 ( 0.02 )  & 1.85 ( 0.11 )  & 4.11 ( 0.1 )   & 1.79 ( 0.01 )  &  3.74          & 0.48           &  2             & 0.85           &  1.85          & 4.11           & 1.79          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.80           & 0.47 ( 0.01 )  & 0.3 ( 0.05 )   & 1.61 ( 0.08 )  & 1.31 ( 0 )     &  4.69          & 0.05           &  3             & 0.47           &  0.30          & 1.61           & 1.31          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.88           & 0.35 ( 0 )     & 0.07 ( 0.03 )  & 0.78 ( 0.07 )  & 1.17 ( 0 )     &  5.29          & 0.01           &  4             & 0.35           &  0.07          & 0.78           & 1.17          \\\\\n",
+       "\t   50           &  100           &  2.00          &  NaN           & 1.32 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.23 ( 0.02 )  &  0.00          & NaN            &  5             & 1.32           &    NA          &   NA           & 2.23          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.08           & 0.91 ( 0.03 )  & 4.33 ( 0.22 )  & 4.45 ( 0.09 )  & 2.02 ( 0.01 )  &  5.88          & 0.72           &  6             & 0.91           &  4.33          & 4.45           & 2.02          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.60           & 0.59 ( 0.01 )  & 1.91 ( 0.15 )  & 1.41 ( 0.07 )  & 1.54 ( 0 )     &  6.50          & 0.27           &  7             & 0.59           &  1.91          & 1.41           & 1.54          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.81           & 0.47 ( 0 )     & 0.71 ( 0.09 )  & 0.53 ( 0.06 )  & 1.39 ( 0 )     &  6.18          & 0.1            &  8             & 0.47           &  0.71          & 0.53           & 1.39          \\\\\n",
+       "\t   50           &  500           & 10.00          &  NaN           & 1.33 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            &  9             & 1.33           &    NA          &   NA           & 2.48          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.02           & 1.07 ( 0.03 )  & 23.8 ( 0.52 )  & 4.26 ( 0.1 )   & 2.43 ( 0.02 )  & 25.54          & 0.93           & 10             & 1.07           & 23.80          & 4.26           & 2.43          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.14           & 0.84 ( 0.01 )  & 19.92 ( 0.41 ) & 1.71 ( 0.08 )  & 2.14 ( 0.01 )  & 24.21          & 0.82           & 11             & 0.84           & 19.90          & 1.71           & 2.14          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.19           & 0.78 ( 0.01 )  & 18.73 ( 0.42 ) & 0.84 ( 0.06 )  & 2.06 ( 0 )     & 23.89          & 0.78           & 12             & 0.78           & 18.70          & 0.84           & 2.06          \\\\\n",
+       "\t   50           & 1000           & 20.00          &  NaN           & 1.31 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            & 13             & 1.31           &    NA          &   NA           & 2.48          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.01           & 1.06 ( 0.03 )  & 48.48 ( 0.63 ) & 4.33 ( 0.1 )   & 2.43 ( 0.02 )  & 50.15          & 0.97           & 14             & 1.06           & 48.40          & 4.33           & 2.43          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.06           & 0.89 ( 0.01 )  & 44.77 ( 0.71 ) & 1.81 ( 0.1 )   & 2.3 ( 0.01 )   & 48.96          & 0.91           & 15             & 0.89           & 44.70          & 1.81           & 2.30          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.09           & 0.85 ( 0.01 )  & 41.77 ( 0.68 ) & 0.99 ( 0.07 )  & 2.25 ( 0.01 )  & 46.78          & 0.89           & 16             & 0.85           & 41.70          & 0.99           & 2.25          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | Index | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          |  NaN           | 1.31 ( 0.06 )  | 1 ( 0 )        | 6 ( 0 )        | 2.09 ( 0.02 )  |  0.00          | NaN            |  1             | 1.31           |    NA          |   NA           | 2.09           |\n",
+       "|  100           |   50           |  0.50          | 0.18           | 0.85 ( 0.02 )  | 1.85 ( 0.11 )  | 4.11 ( 0.1 )   | 1.79 ( 0.01 )  |  3.74          | 0.48           |  2             | 0.85           |  1.85          | 4.11           | 1.79           |\n",
+       "|  500           |   50           |  0.10          | 0.80           | 0.47 ( 0.01 )  | 0.3 ( 0.05 )   | 1.61 ( 0.08 )  | 1.31 ( 0 )     |  4.69          | 0.05           |  3             | 0.47           |  0.30          | 1.61           | 1.31           |\n",
+       "| 1000           |   50           |  0.05          | 0.88           | 0.35 ( 0 )     | 0.07 ( 0.03 )  | 0.78 ( 0.07 )  | 1.17 ( 0 )     |  5.29          | 0.01           |  4             | 0.35           |  0.07          | 0.78           | 1.17           |\n",
+       "|   50           |  100           |  2.00          |  NaN           | 1.32 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.23 ( 0.02 )  |  0.00          | NaN            |  5             | 1.32           |    NA          |   NA           | 2.23           |\n",
+       "|  100           |  100           |  1.00          | 0.08           | 0.91 ( 0.03 )  | 4.33 ( 0.22 )  | 4.45 ( 0.09 )  | 2.02 ( 0.01 )  |  5.88          | 0.72           |  6             | 0.91           |  4.33          | 4.45           | 2.02           |\n",
+       "|  500           |  100           |  0.20          | 0.60           | 0.59 ( 0.01 )  | 1.91 ( 0.15 )  | 1.41 ( 0.07 )  | 1.54 ( 0 )     |  6.50          | 0.27           |  7             | 0.59           |  1.91          | 1.41           | 1.54           |\n",
+       "| 1000           |  100           |  0.10          | 0.81           | 0.47 ( 0 )     | 0.71 ( 0.09 )  | 0.53 ( 0.06 )  | 1.39 ( 0 )     |  6.18          | 0.1            |  8             | 0.47           |  0.71          | 0.53           | 1.39           |\n",
+       "|   50           |  500           | 10.00          |  NaN           | 1.33 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            |  9             | 1.33           |    NA          |   NA           | 2.48           |\n",
+       "|  100           |  500           |  5.00          | 0.02           | 1.07 ( 0.03 )  | 23.8 ( 0.52 )  | 4.26 ( 0.1 )   | 2.43 ( 0.02 )  | 25.54          | 0.93           | 10             | 1.07           | 23.80          | 4.26           | 2.43           |\n",
+       "|  500           |  500           |  1.00          | 0.14           | 0.84 ( 0.01 )  | 19.92 ( 0.41 ) | 1.71 ( 0.08 )  | 2.14 ( 0.01 )  | 24.21          | 0.82           | 11             | 0.84           | 19.90          | 1.71           | 2.14           |\n",
+       "| 1000           |  500           |  0.50          | 0.19           | 0.78 ( 0.01 )  | 18.73 ( 0.42 ) | 0.84 ( 0.06 )  | 2.06 ( 0 )     | 23.89          | 0.78           | 12             | 0.78           | 18.70          | 0.84           | 2.06           |\n",
+       "|   50           | 1000           | 20.00          |  NaN           | 1.31 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            | 13             | 1.31           |    NA          |   NA           | 2.48           |\n",
+       "|  100           | 1000           | 10.00          | 0.01           | 1.06 ( 0.03 )  | 48.48 ( 0.63 ) | 4.33 ( 0.1 )   | 2.43 ( 0.02 )  | 50.15          | 0.97           | 14             | 1.06           | 48.40          | 4.33           | 2.43           |\n",
+       "|  500           | 1000           |  2.00          | 0.06           | 0.89 ( 0.01 )  | 44.77 ( 0.71 ) | 1.81 ( 0.1 )   | 2.3 ( 0.01 )   | 48.96          | 0.91           | 15             | 0.89           | 44.70          | 1.81           | 2.30           |\n",
+       "| 1000           | 1000           |  1.00          | 0.09           | 0.85 ( 0.01 )  | 41.77 ( 0.68 ) | 0.99 ( 0.07 )  | 2.25 ( 0.01 )  | 46.78          | 0.89           | 16             | 0.85           | 41.70          | 0.99           | 2.25           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN           \n",
+       "1    50   50  1.00  NaN 1.31 ( 0.06 ) 1 ( 0 )        6 ( 0 )      \n",
+       "2   100   50  0.50 0.18 0.85 ( 0.02 ) 1.85 ( 0.11 )  4.11 ( 0.1 ) \n",
+       "3   500   50  0.10 0.80 0.47 ( 0.01 ) 0.3 ( 0.05 )   1.61 ( 0.08 )\n",
+       "4  1000   50  0.05 0.88 0.35 ( 0 )    0.07 ( 0.03 )  0.78 ( 0.07 )\n",
+       "5    50  100  2.00  NaN 1.32 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "6   100  100  1.00 0.08 0.91 ( 0.03 ) 4.33 ( 0.22 )  4.45 ( 0.09 )\n",
+       "7   500  100  0.20 0.60 0.59 ( 0.01 ) 1.91 ( 0.15 )  1.41 ( 0.07 )\n",
+       "8  1000  100  0.10 0.81 0.47 ( 0 )    0.71 ( 0.09 )  0.53 ( 0.06 )\n",
+       "9    50  500 10.00  NaN 1.33 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "10  100  500  5.00 0.02 1.07 ( 0.03 ) 23.8 ( 0.52 )  4.26 ( 0.1 ) \n",
+       "11  500  500  1.00 0.14 0.84 ( 0.01 ) 19.92 ( 0.41 ) 1.71 ( 0.08 )\n",
+       "12 1000  500  0.50 0.19 0.78 ( 0.01 ) 18.73 ( 0.42 ) 0.84 ( 0.06 )\n",
+       "13   50 1000 20.00  NaN 1.31 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "14  100 1000 10.00 0.01 1.06 ( 0.03 ) 48.48 ( 0.63 ) 4.33 ( 0.1 ) \n",
+       "15  500 1000  2.00 0.06 0.89 ( 0.01 ) 44.77 ( 0.71 ) 1.81 ( 0.1 ) \n",
+       "16 1000 1000  1.00 0.09 0.85 ( 0.01 ) 41.77 ( 0.68 ) 0.99 ( 0.07 )\n",
+       "   OOB           num_select FDR  Index MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  2.09 ( 0.02 )  0.00      NaN   1    1.31        NA     NA    2.09    \n",
+       "2  1.79 ( 0.01 )  3.74      0.48  2    0.85      1.85   4.11    1.79    \n",
+       "3  1.31 ( 0 )     4.69      0.05  3    0.47      0.30   1.61    1.31    \n",
+       "4  1.17 ( 0 )     5.29      0.01  4    0.35      0.07   0.78    1.17    \n",
+       "5  2.23 ( 0.02 )  0.00      NaN   5    1.32        NA     NA    2.23    \n",
+       "6  2.02 ( 0.01 )  5.88      0.72  6    0.91      4.33   4.45    2.02    \n",
+       "7  1.54 ( 0 )     6.50      0.27  7    0.59      1.91   1.41    1.54    \n",
+       "8  1.39 ( 0 )     6.18      0.1   8    0.47      0.71   0.53    1.39    \n",
+       "9  2.48 ( 0.03 )  0.00      NaN   9    1.33        NA     NA    2.48    \n",
+       "10 2.43 ( 0.02 ) 25.54      0.93 10    1.07     23.80   4.26    2.43    \n",
+       "11 2.14 ( 0.01 ) 24.21      0.82 11    0.84     19.90   1.71    2.14    \n",
+       "12 2.06 ( 0 )    23.89      0.78 12    0.78     18.70   0.84    2.06    \n",
+       "13 2.48 ( 0.03 )  0.00      NaN  13    1.31        NA     NA    2.48    \n",
+       "14 2.43 ( 0.02 ) 50.15      0.97 14    1.06     48.40   4.33    2.43    \n",
+       "15 2.3 ( 0.01 )  48.96      0.91 15    0.89     44.70   1.81    2.30    \n",
+       "16 2.25 ( 0.01 ) 46.78      0.89 16    0.85     41.70   0.99    2.25    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0\n",
+      "[1] 0\n",
+      "[1] 0\n",
+      "[1] 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## nothing was selected when Stab is NaN\n",
+    "for (i in c(1,5,9,13)){\n",
+    "    print(sum(results_ind_rf[[i]]$Stab.table))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>Index</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.00          </td><td>1.31 ( 0.06 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.09 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 1            </td><td>1.31          </td><td> 1.00         </td><td>6.00          </td><td>2.09          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.18          </td><td>0.85 ( 0.02 ) </td><td>1.85 ( 0.11 ) </td><td>4.11 ( 0.1 )  </td><td>1.79 ( 0.01 ) </td><td> 3.74         </td><td>0.48          </td><td> 2            </td><td>0.85          </td><td> 1.85         </td><td>4.11          </td><td>1.79          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.80          </td><td>0.47 ( 0.01 ) </td><td>0.3 ( 0.05 )  </td><td>1.61 ( 0.08 ) </td><td>1.31 ( 0 )    </td><td> 4.69         </td><td>0.05          </td><td> 3            </td><td>0.47          </td><td> 0.30         </td><td>1.61          </td><td>1.31          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.88          </td><td>0.35 ( 0 )    </td><td>0.07 ( 0.03 ) </td><td>0.78 ( 0.07 ) </td><td>1.17 ( 0 )    </td><td> 5.29         </td><td>0.01          </td><td> 4            </td><td>0.35          </td><td> 0.07         </td><td>0.78          </td><td>1.17          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.00          </td><td>1.32 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.23 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 5            </td><td>1.32          </td><td> 1.00         </td><td>6.00          </td><td>2.23          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.08          </td><td>0.91 ( 0.03 ) </td><td>4.33 ( 0.22 ) </td><td>4.45 ( 0.09 ) </td><td>2.02 ( 0.01 ) </td><td> 5.88         </td><td>0.72          </td><td> 6            </td><td>0.91          </td><td> 4.33         </td><td>4.45          </td><td>2.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.60          </td><td>0.59 ( 0.01 ) </td><td>1.91 ( 0.15 ) </td><td>1.41 ( 0.07 ) </td><td>1.54 ( 0 )    </td><td> 6.50         </td><td>0.27          </td><td> 7            </td><td>0.59          </td><td> 1.91         </td><td>1.41          </td><td>1.54          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.81          </td><td>0.47 ( 0 )    </td><td>0.71 ( 0.09 ) </td><td>0.53 ( 0.06 ) </td><td>1.39 ( 0 )    </td><td> 6.18         </td><td>0.1           </td><td> 8            </td><td>0.47          </td><td> 0.71         </td><td>0.53          </td><td>1.39          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.00          </td><td>1.33 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td> 9            </td><td>1.33          </td><td> 1.00         </td><td>6.00          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.02          </td><td>1.07 ( 0.03 ) </td><td>23.8 ( 0.52 ) </td><td>4.26 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>25.54         </td><td>0.93          </td><td>10            </td><td>1.07          </td><td>23.80         </td><td>4.26          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.14          </td><td>0.84 ( 0.01 ) </td><td>19.92 ( 0.41 )</td><td>1.71 ( 0.08 ) </td><td>2.14 ( 0.01 ) </td><td>24.21         </td><td>0.82          </td><td>11            </td><td>0.84          </td><td>19.90         </td><td>1.71          </td><td>2.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.19          </td><td>0.78 ( 0.01 ) </td><td>18.73 ( 0.42 )</td><td>0.84 ( 0.06 ) </td><td>2.06 ( 0 )    </td><td>23.89         </td><td>0.78          </td><td>12            </td><td>0.78          </td><td>18.70         </td><td>0.84          </td><td>2.06          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.00          </td><td>1.31 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td>13            </td><td>1.31          </td><td> 1.00         </td><td>6.00          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.01          </td><td>1.06 ( 0.03 ) </td><td>48.48 ( 0.63 )</td><td>4.33 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>50.15         </td><td>0.97          </td><td>14            </td><td>1.06          </td><td>48.40         </td><td>4.33          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.06          </td><td>0.89 ( 0.01 ) </td><td>44.77 ( 0.71 )</td><td>1.81 ( 0.1 )  </td><td>2.3 ( 0.01 )  </td><td>48.96         </td><td>0.91          </td><td>15            </td><td>0.89          </td><td>44.70         </td><td>1.81          </td><td>2.30          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.09          </td><td>0.85 ( 0.01 ) </td><td>41.77 ( 0.68 )</td><td>0.99 ( 0.07 ) </td><td>2.25 ( 0.01 ) </td><td>46.78         </td><td>0.89          </td><td>16            </td><td>0.85          </td><td>41.70         </td><td>0.99          </td><td>2.25          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & Index & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.00           & 1.31 ( 0.06 )  & 1 ( 0 )        & 6 ( 0 )        & 2.09 ( 0.02 )  &  0.00          & NaN            &  1             & 1.31           &  1.00          & 6.00           & 2.09          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.18           & 0.85 ( 0.02 )  & 1.85 ( 0.11 )  & 4.11 ( 0.1 )   & 1.79 ( 0.01 )  &  3.74          & 0.48           &  2             & 0.85           &  1.85          & 4.11           & 1.79          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.80           & 0.47 ( 0.01 )  & 0.3 ( 0.05 )   & 1.61 ( 0.08 )  & 1.31 ( 0 )     &  4.69          & 0.05           &  3             & 0.47           &  0.30          & 1.61           & 1.31          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.88           & 0.35 ( 0 )     & 0.07 ( 0.03 )  & 0.78 ( 0.07 )  & 1.17 ( 0 )     &  5.29          & 0.01           &  4             & 0.35           &  0.07          & 0.78           & 1.17          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.00           & 1.32 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.23 ( 0.02 )  &  0.00          & NaN            &  5             & 1.32           &  1.00          & 6.00           & 2.23          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.08           & 0.91 ( 0.03 )  & 4.33 ( 0.22 )  & 4.45 ( 0.09 )  & 2.02 ( 0.01 )  &  5.88          & 0.72           &  6             & 0.91           &  4.33          & 4.45           & 2.02          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.60           & 0.59 ( 0.01 )  & 1.91 ( 0.15 )  & 1.41 ( 0.07 )  & 1.54 ( 0 )     &  6.50          & 0.27           &  7             & 0.59           &  1.91          & 1.41           & 1.54          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.81           & 0.47 ( 0 )     & 0.71 ( 0.09 )  & 0.53 ( 0.06 )  & 1.39 ( 0 )     &  6.18          & 0.1            &  8             & 0.47           &  0.71          & 0.53           & 1.39          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.00           & 1.33 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            &  9             & 1.33           &  1.00          & 6.00           & 2.48          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.02           & 1.07 ( 0.03 )  & 23.8 ( 0.52 )  & 4.26 ( 0.1 )   & 2.43 ( 0.02 )  & 25.54          & 0.93           & 10             & 1.07           & 23.80          & 4.26           & 2.43          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.14           & 0.84 ( 0.01 )  & 19.92 ( 0.41 ) & 1.71 ( 0.08 )  & 2.14 ( 0.01 )  & 24.21          & 0.82           & 11             & 0.84           & 19.90          & 1.71           & 2.14          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.19           & 0.78 ( 0.01 )  & 18.73 ( 0.42 ) & 0.84 ( 0.06 )  & 2.06 ( 0 )     & 23.89          & 0.78           & 12             & 0.78           & 18.70          & 0.84           & 2.06          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.00           & 1.31 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            & 13             & 1.31           &  1.00          & 6.00           & 2.48          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.01           & 1.06 ( 0.03 )  & 48.48 ( 0.63 ) & 4.33 ( 0.1 )   & 2.43 ( 0.02 )  & 50.15          & 0.97           & 14             & 1.06           & 48.40          & 4.33           & 2.43          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.06           & 0.89 ( 0.01 )  & 44.77 ( 0.71 ) & 1.81 ( 0.1 )   & 2.3 ( 0.01 )   & 48.96          & 0.91           & 15             & 0.89           & 44.70          & 1.81           & 2.30          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.09           & 0.85 ( 0.01 )  & 41.77 ( 0.68 ) & 0.99 ( 0.07 )  & 2.25 ( 0.01 )  & 46.78          & 0.89           & 16             & 0.85           & 41.70          & 0.99           & 2.25          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | Index | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.00           | 1.31 ( 0.06 )  | 1 ( 0 )        | 6 ( 0 )        | 2.09 ( 0.02 )  |  0.00          | NaN            |  1             | 1.31           |  1.00          | 6.00           | 2.09           |\n",
+       "|  100           |   50           |  0.50          | 0.18           | 0.85 ( 0.02 )  | 1.85 ( 0.11 )  | 4.11 ( 0.1 )   | 1.79 ( 0.01 )  |  3.74          | 0.48           |  2             | 0.85           |  1.85          | 4.11           | 1.79           |\n",
+       "|  500           |   50           |  0.10          | 0.80           | 0.47 ( 0.01 )  | 0.3 ( 0.05 )   | 1.61 ( 0.08 )  | 1.31 ( 0 )     |  4.69          | 0.05           |  3             | 0.47           |  0.30          | 1.61           | 1.31           |\n",
+       "| 1000           |   50           |  0.05          | 0.88           | 0.35 ( 0 )     | 0.07 ( 0.03 )  | 0.78 ( 0.07 )  | 1.17 ( 0 )     |  5.29          | 0.01           |  4             | 0.35           |  0.07          | 0.78           | 1.17           |\n",
+       "|   50           |  100           |  2.00          | 0.00           | 1.32 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.23 ( 0.02 )  |  0.00          | NaN            |  5             | 1.32           |  1.00          | 6.00           | 2.23           |\n",
+       "|  100           |  100           |  1.00          | 0.08           | 0.91 ( 0.03 )  | 4.33 ( 0.22 )  | 4.45 ( 0.09 )  | 2.02 ( 0.01 )  |  5.88          | 0.72           |  6             | 0.91           |  4.33          | 4.45           | 2.02           |\n",
+       "|  500           |  100           |  0.20          | 0.60           | 0.59 ( 0.01 )  | 1.91 ( 0.15 )  | 1.41 ( 0.07 )  | 1.54 ( 0 )     |  6.50          | 0.27           |  7             | 0.59           |  1.91          | 1.41           | 1.54           |\n",
+       "| 1000           |  100           |  0.10          | 0.81           | 0.47 ( 0 )     | 0.71 ( 0.09 )  | 0.53 ( 0.06 )  | 1.39 ( 0 )     |  6.18          | 0.1            |  8             | 0.47           |  0.71          | 0.53           | 1.39           |\n",
+       "|   50           |  500           | 10.00          | 0.00           | 1.33 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            |  9             | 1.33           |  1.00          | 6.00           | 2.48           |\n",
+       "|  100           |  500           |  5.00          | 0.02           | 1.07 ( 0.03 )  | 23.8 ( 0.52 )  | 4.26 ( 0.1 )   | 2.43 ( 0.02 )  | 25.54          | 0.93           | 10             | 1.07           | 23.80          | 4.26           | 2.43           |\n",
+       "|  500           |  500           |  1.00          | 0.14           | 0.84 ( 0.01 )  | 19.92 ( 0.41 ) | 1.71 ( 0.08 )  | 2.14 ( 0.01 )  | 24.21          | 0.82           | 11             | 0.84           | 19.90          | 1.71           | 2.14           |\n",
+       "| 1000           |  500           |  0.50          | 0.19           | 0.78 ( 0.01 )  | 18.73 ( 0.42 ) | 0.84 ( 0.06 )  | 2.06 ( 0 )     | 23.89          | 0.78           | 12             | 0.78           | 18.70          | 0.84           | 2.06           |\n",
+       "|   50           | 1000           | 20.00          | 0.00           | 1.31 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            | 13             | 1.31           |  1.00          | 6.00           | 2.48           |\n",
+       "|  100           | 1000           | 10.00          | 0.01           | 1.06 ( 0.03 )  | 48.48 ( 0.63 ) | 4.33 ( 0.1 )   | 2.43 ( 0.02 )  | 50.15          | 0.97           | 14             | 1.06           | 48.40          | 4.33           | 2.43           |\n",
+       "|  500           | 1000           |  2.00          | 0.06           | 0.89 ( 0.01 )  | 44.77 ( 0.71 ) | 1.81 ( 0.1 )   | 2.3 ( 0.01 )   | 48.96          | 0.91           | 15             | 0.89           | 44.70          | 1.81           | 2.30           |\n",
+       "| 1000           | 1000           |  1.00          | 0.09           | 0.85 ( 0.01 )  | 41.77 ( 0.68 ) | 0.99 ( 0.07 )  | 2.25 ( 0.01 )  | 46.78          | 0.89           | 16             | 0.85           | 41.70          | 0.99           | 2.25           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN           \n",
+       "1    50   50  1.00 0.00 1.31 ( 0.06 ) 1 ( 0 )        6 ( 0 )      \n",
+       "2   100   50  0.50 0.18 0.85 ( 0.02 ) 1.85 ( 0.11 )  4.11 ( 0.1 ) \n",
+       "3   500   50  0.10 0.80 0.47 ( 0.01 ) 0.3 ( 0.05 )   1.61 ( 0.08 )\n",
+       "4  1000   50  0.05 0.88 0.35 ( 0 )    0.07 ( 0.03 )  0.78 ( 0.07 )\n",
+       "5    50  100  2.00 0.00 1.32 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "6   100  100  1.00 0.08 0.91 ( 0.03 ) 4.33 ( 0.22 )  4.45 ( 0.09 )\n",
+       "7   500  100  0.20 0.60 0.59 ( 0.01 ) 1.91 ( 0.15 )  1.41 ( 0.07 )\n",
+       "8  1000  100  0.10 0.81 0.47 ( 0 )    0.71 ( 0.09 )  0.53 ( 0.06 )\n",
+       "9    50  500 10.00 0.00 1.33 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "10  100  500  5.00 0.02 1.07 ( 0.03 ) 23.8 ( 0.52 )  4.26 ( 0.1 ) \n",
+       "11  500  500  1.00 0.14 0.84 ( 0.01 ) 19.92 ( 0.41 ) 1.71 ( 0.08 )\n",
+       "12 1000  500  0.50 0.19 0.78 ( 0.01 ) 18.73 ( 0.42 ) 0.84 ( 0.06 )\n",
+       "13   50 1000 20.00 0.00 1.31 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "14  100 1000 10.00 0.01 1.06 ( 0.03 ) 48.48 ( 0.63 ) 4.33 ( 0.1 ) \n",
+       "15  500 1000  2.00 0.06 0.89 ( 0.01 ) 44.77 ( 0.71 ) 1.81 ( 0.1 ) \n",
+       "16 1000 1000  1.00 0.09 0.85 ( 0.01 ) 41.77 ( 0.68 ) 0.99 ( 0.07 )\n",
+       "   OOB           num_select FDR  Index MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  2.09 ( 0.02 )  0.00      NaN   1    1.31      1.00   6.00    2.09    \n",
+       "2  1.79 ( 0.01 )  3.74      0.48  2    0.85      1.85   4.11    1.79    \n",
+       "3  1.31 ( 0 )     4.69      0.05  3    0.47      0.30   1.61    1.31    \n",
+       "4  1.17 ( 0 )     5.29      0.01  4    0.35      0.07   0.78    1.17    \n",
+       "5  2.23 ( 0.02 )  0.00      NaN   5    1.32      1.00   6.00    2.23    \n",
+       "6  2.02 ( 0.01 )  5.88      0.72  6    0.91      4.33   4.45    2.02    \n",
+       "7  1.54 ( 0 )     6.50      0.27  7    0.59      1.91   1.41    1.54    \n",
+       "8  1.39 ( 0 )     6.18      0.1   8    0.47      0.71   0.53    1.39    \n",
+       "9  2.48 ( 0.03 )  0.00      NaN   9    1.33      1.00   6.00    2.48    \n",
+       "10 2.43 ( 0.02 ) 25.54      0.93 10    1.07     23.80   4.26    2.43    \n",
+       "11 2.14 ( 0.01 ) 24.21      0.82 11    0.84     19.90   1.71    2.14    \n",
+       "12 2.06 ( 0 )    23.89      0.78 12    0.78     18.70   0.84    2.06    \n",
+       "13 2.48 ( 0.03 )  0.00      NaN  13    1.31      1.00   6.00    2.48    \n",
+       "14 2.43 ( 0.02 ) 50.15      0.97 14    1.06     48.40   4.33    2.43    \n",
+       "15 2.3 ( 0.01 )  48.96      0.91 15    0.89     44.70   1.81    2.30    \n",
+       "16 2.25 ( 0.01 ) 46.78      0.89 16    0.85     41.70   0.99    2.25    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## assign zero values to NA Stab; also recover missing FP values (1 in this case); missing FN values (6 in this case)\n",
+    "result.table_ind$Stab[is.na(result.table_ind$Stab)] = 0\n",
+    "result.table_ind$FP_mean[is.na(result.table_ind$FP_mean)] = 1\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 6\n",
+    "#result.table_ind$FDR[is.na(result.table_ind$FDR)] = 0\n",
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.4_sim_indepdent_compLasso_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.4_sim_indepdent_compLasso_cts-checkpoint.ipynb
new file mode 100755
index 0000000..9304d4e
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/0.4_sim_indepdent_compLasso_cts-checkpoint.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_compLasso))\n",
+    "for (i in 1:length(results_ind_compLasso)){\n",
+    "    table_ind = rbind(table_ind, results_ind_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>2.99 ( 0.27 )</td><td>0.05 ( 0.02 )</td><td>0.8 ( 0.05 ) </td><td>0.61         </td><td> 8.94        </td><td>0.28         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>0.79 ( 0.15 )</td><td>0 ( 0 )      </td><td>0.98 ( 0.06 )</td><td>0.87         </td><td> 6.79        </td><td>0.09         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>0.51 ( 0.11 )</td><td>0 ( 0 )      </td><td>1.08 ( 0.05 )</td><td>0.91         </td><td> 6.51        </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>0.39 ( 0.15 )</td><td>0 ( 0 )      </td><td>1.18 ( 0.04 )</td><td>0.93         </td><td> 6.39        </td><td>0.04         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>4.3 ( 0.31 ) </td><td>0.24 ( 0.05 )</td><td>1.05 ( 0.07 )</td><td>0.52         </td><td>10.06        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>1.02 ( 0.19 )</td><td>0 ( 0 )      </td><td>0.94 ( 0.04 )</td><td>0.85         </td><td> 7.02        </td><td>0.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 2.99 ( 0.27 ) & 0.05 ( 0.02 ) & 0.8 ( 0.05 )  & 0.61          &  8.94         & 0.28         \\\\\n",
+       "\t 100           & 50            & 0             & 0.79 ( 0.15 ) & 0 ( 0 )       & 0.98 ( 0.06 ) & 0.87          &  6.79         & 0.09         \\\\\n",
+       "\t 500           & 50            & 0             & 0.51 ( 0.11 ) & 0 ( 0 )       & 1.08 ( 0.05 ) & 0.91          &  6.51         & 0.06         \\\\\n",
+       "\t 1000          & 50            & 0             & 0.39 ( 0.15 ) & 0 ( 0 )       & 1.18 ( 0.04 ) & 0.93          &  6.39         & 0.04         \\\\\n",
+       "\t 50            & 100           & 0             & 4.3 ( 0.31 )  & 0.24 ( 0.05 ) & 1.05 ( 0.07 ) & 0.52          & 10.06         & 0.37         \\\\\n",
+       "\t 100           & 100           & 0             & 1.02 ( 0.19 ) & 0 ( 0 )       & 0.94 ( 0.04 ) & 0.85          &  7.02         & 0.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 2.99 ( 0.27 ) | 0.05 ( 0.02 ) | 0.8 ( 0.05 )  | 0.61          |  8.94         | 0.28          |\n",
+       "| 100           | 50            | 0             | 0.79 ( 0.15 ) | 0 ( 0 )       | 0.98 ( 0.06 ) | 0.87          |  6.79         | 0.09          |\n",
+       "| 500           | 50            | 0             | 0.51 ( 0.11 ) | 0 ( 0 )       | 1.08 ( 0.05 ) | 0.91          |  6.51         | 0.06          |\n",
+       "| 1000          | 50            | 0             | 0.39 ( 0.15 ) | 0 ( 0 )       | 1.18 ( 0.04 ) | 0.93          |  6.39         | 0.04          |\n",
+       "| 50            | 100           | 0             | 4.3 ( 0.31 )  | 0.24 ( 0.05 ) | 1.05 ( 0.07 ) | 0.52          | 10.06         | 0.37          |\n",
+       "| 100           | 100           | 0             | 1.02 ( 0.19 ) | 0 ( 0 )       | 0.94 ( 0.04 ) | 0.85          |  7.02         | 0.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   2.99 ( 0.27 ) 0.05 ( 0.02 ) 0.8 ( 0.05 )  0.61  8.94      0.28\n",
+       "2 100  50  0   0.79 ( 0.15 ) 0 ( 0 )       0.98 ( 0.06 ) 0.87  6.79      0.09\n",
+       "3 500  50  0   0.51 ( 0.11 ) 0 ( 0 )       1.08 ( 0.05 ) 0.91  6.51      0.06\n",
+       "4 1000 50  0   0.39 ( 0.15 ) 0 ( 0 )       1.18 ( 0.04 ) 0.93  6.39      0.04\n",
+       "5 50   100 0   4.3 ( 0.31 )  0.24 ( 0.05 ) 1.05 ( 0.07 ) 0.52 10.06      0.37\n",
+       "6 100  100 0   1.02 ( 0.19 ) 0 ( 0 )       0.94 ( 0.04 ) 0.85  7.02      0.11"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td>  50         </td><td> 1.00        </td><td>0.61         </td><td>0.8 ( 0.05 ) </td><td>2.99 ( 0.27 )</td><td>0.05 ( 0.02 )</td><td> 8.94        </td><td>0.28         </td><td>0.80         </td><td>2.99         </td><td>0.05         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>  50         </td><td> 0.50        </td><td>0.87         </td><td>0.98 ( 0.06 )</td><td>0.79 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.79        </td><td>0.09         </td><td>0.98         </td><td>0.79         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td>  50         </td><td> 0.10        </td><td>0.91         </td><td>1.08 ( 0.05 )</td><td>0.51 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.51        </td><td>0.06         </td><td>1.08         </td><td>0.51         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>  50         </td><td> 0.05        </td><td>0.93         </td><td>1.18 ( 0.04 )</td><td>0.39 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.39        </td><td>0.04         </td><td>1.18         </td><td>0.39         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td> 100         </td><td> 2.00        </td><td>0.52         </td><td>1.05 ( 0.07 )</td><td>4.3 ( 0.31 ) </td><td>0.24 ( 0.05 )</td><td>10.06        </td><td>0.37         </td><td>1.05         </td><td>4.30         </td><td>0.24         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 100         </td><td> 1.00        </td><td>0.85         </td><td>0.94 ( 0.04 )</td><td>1.02 ( 0.19 )</td><td>0 ( 0 )      </td><td> 7.02        </td><td>0.11         </td><td>0.94         </td><td>1.02         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 100         </td><td> 0.20        </td><td>0.96         </td><td>1.16 ( 0.04 )</td><td>0.25 ( 0.09 )</td><td>0 ( 0 )      </td><td> 6.25        </td><td>0.03         </td><td>1.16         </td><td>0.25         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 100         </td><td> 0.10        </td><td>0.89         </td><td>1.04 ( 0.05 )</td><td>0.71 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.71        </td><td>0.08         </td><td>1.04         </td><td>0.71         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td> 500         </td><td>10.00        </td><td>0.38         </td><td>2.18 ( 0.13 )</td><td>5.53 ( 0.36 )</td><td>1.27 ( 0.11 )</td><td>10.26        </td><td>0.48         </td><td>2.18         </td><td>5.53         </td><td>1.27         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 500         </td><td> 5.00        </td><td>0.75         </td><td>1 ( 0.05 )   </td><td>1.9 ( 0.29 ) </td><td>0.03 ( 0.02 )</td><td> 7.87        </td><td>0.18         </td><td>1.00         </td><td>1.90         </td><td>0.03         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 500         </td><td> 1.00        </td><td>0.96         </td><td>1.18 ( 0.04 )</td><td>0.27 ( 0.13 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.18         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 500         </td><td> 0.50        </td><td>0.96         </td><td>1.24 ( 0.04 )</td><td>0.25 ( 0.1 ) </td><td>0 ( 0 )      </td><td> 6.25        </td><td>0.03         </td><td>1.24         </td><td>0.25         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>1000         </td><td>20.00        </td><td>0.32         </td><td>3.09 ( 0.23 )</td><td>5.56 ( 0.36 )</td><td>1.95 ( 0.14 )</td><td> 9.61        </td><td>0.51         </td><td>3.09         </td><td>5.56         </td><td>1.95         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>1000         </td><td>10.00        </td><td>0.67         </td><td>1.1 ( 0.07 ) </td><td>2.8 ( 0.37 ) </td><td>0.04 ( 0.02 )</td><td> 8.76        </td><td>0.24         </td><td>1.10         </td><td>2.80         </td><td>0.04         </td></tr>\n",
+       "\t<tr><td> 500         </td><td>1000         </td><td> 2.00        </td><td>0.96         </td><td>1.12 ( 0.04 )</td><td>0.27 ( 0.08 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.12         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>1000         </td><td> 1.00        </td><td>0.96         </td><td>1.16 ( 0.04 )</td><td>0.24 ( 0.07 )</td><td>0 ( 0 )      </td><td> 6.24        </td><td>0.03         </td><td>1.16         </td><td>0.24         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &   50          &  1.00         & 0.61          & 0.8 ( 0.05 )  & 2.99 ( 0.27 ) & 0.05 ( 0.02 ) &  8.94         & 0.28          & 0.80          & 2.99          & 0.05         \\\\\n",
+       "\t  100          &   50          &  0.50         & 0.87          & 0.98 ( 0.06 ) & 0.79 ( 0.15 ) & 0 ( 0 )       &  6.79         & 0.09          & 0.98          & 0.79          & 0.00         \\\\\n",
+       "\t  500          &   50          &  0.10         & 0.91          & 1.08 ( 0.05 ) & 0.51 ( 0.11 ) & 0 ( 0 )       &  6.51         & 0.06          & 1.08          & 0.51          & 0.00         \\\\\n",
+       "\t 1000          &   50          &  0.05         & 0.93          & 1.18 ( 0.04 ) & 0.39 ( 0.15 ) & 0 ( 0 )       &  6.39         & 0.04          & 1.18          & 0.39          & 0.00         \\\\\n",
+       "\t   50          &  100          &  2.00         & 0.52          & 1.05 ( 0.07 ) & 4.3 ( 0.31 )  & 0.24 ( 0.05 ) & 10.06         & 0.37          & 1.05          & 4.30          & 0.24         \\\\\n",
+       "\t  100          &  100          &  1.00         & 0.85          & 0.94 ( 0.04 ) & 1.02 ( 0.19 ) & 0 ( 0 )       &  7.02         & 0.11          & 0.94          & 1.02          & 0.00         \\\\\n",
+       "\t  500          &  100          &  0.20         & 0.96          & 1.16 ( 0.04 ) & 0.25 ( 0.09 ) & 0 ( 0 )       &  6.25         & 0.03          & 1.16          & 0.25          & 0.00         \\\\\n",
+       "\t 1000          &  100          &  0.10         & 0.89          & 1.04 ( 0.05 ) & 0.71 ( 0.15 ) & 0 ( 0 )       &  6.71         & 0.08          & 1.04          & 0.71          & 0.00         \\\\\n",
+       "\t   50          &  500          & 10.00         & 0.38          & 2.18 ( 0.13 ) & 5.53 ( 0.36 ) & 1.27 ( 0.11 ) & 10.26         & 0.48          & 2.18          & 5.53          & 1.27         \\\\\n",
+       "\t  100          &  500          &  5.00         & 0.75          & 1 ( 0.05 )    & 1.9 ( 0.29 )  & 0.03 ( 0.02 ) &  7.87         & 0.18          & 1.00          & 1.90          & 0.03         \\\\\n",
+       "\t  500          &  500          &  1.00         & 0.96          & 1.18 ( 0.04 ) & 0.27 ( 0.13 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.18          & 0.27          & 0.00         \\\\\n",
+       "\t 1000          &  500          &  0.50         & 0.96          & 1.24 ( 0.04 ) & 0.25 ( 0.1 )  & 0 ( 0 )       &  6.25         & 0.03          & 1.24          & 0.25          & 0.00         \\\\\n",
+       "\t   50          & 1000          & 20.00         & 0.32          & 3.09 ( 0.23 ) & 5.56 ( 0.36 ) & 1.95 ( 0.14 ) &  9.61         & 0.51          & 3.09          & 5.56          & 1.95         \\\\\n",
+       "\t  100          & 1000          & 10.00         & 0.67          & 1.1 ( 0.07 )  & 2.8 ( 0.37 )  & 0.04 ( 0.02 ) &  8.76         & 0.24          & 1.10          & 2.80          & 0.04         \\\\\n",
+       "\t  500          & 1000          &  2.00         & 0.96          & 1.12 ( 0.04 ) & 0.27 ( 0.08 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.12          & 0.27          & 0.00         \\\\\n",
+       "\t 1000          & 1000          &  1.00         & 0.96          & 1.16 ( 0.04 ) & 0.24 ( 0.07 ) & 0 ( 0 )       &  6.24         & 0.03          & 1.16          & 0.24          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |   50          |  1.00         | 0.61          | 0.8 ( 0.05 )  | 2.99 ( 0.27 ) | 0.05 ( 0.02 ) |  8.94         | 0.28          | 0.80          | 2.99          | 0.05          |\n",
+       "|  100          |   50          |  0.50         | 0.87          | 0.98 ( 0.06 ) | 0.79 ( 0.15 ) | 0 ( 0 )       |  6.79         | 0.09          | 0.98          | 0.79          | 0.00          |\n",
+       "|  500          |   50          |  0.10         | 0.91          | 1.08 ( 0.05 ) | 0.51 ( 0.11 ) | 0 ( 0 )       |  6.51         | 0.06          | 1.08          | 0.51          | 0.00          |\n",
+       "| 1000          |   50          |  0.05         | 0.93          | 1.18 ( 0.04 ) | 0.39 ( 0.15 ) | 0 ( 0 )       |  6.39         | 0.04          | 1.18          | 0.39          | 0.00          |\n",
+       "|   50          |  100          |  2.00         | 0.52          | 1.05 ( 0.07 ) | 4.3 ( 0.31 )  | 0.24 ( 0.05 ) | 10.06         | 0.37          | 1.05          | 4.30          | 0.24          |\n",
+       "|  100          |  100          |  1.00         | 0.85          | 0.94 ( 0.04 ) | 1.02 ( 0.19 ) | 0 ( 0 )       |  7.02         | 0.11          | 0.94          | 1.02          | 0.00          |\n",
+       "|  500          |  100          |  0.20         | 0.96          | 1.16 ( 0.04 ) | 0.25 ( 0.09 ) | 0 ( 0 )       |  6.25         | 0.03          | 1.16          | 0.25          | 0.00          |\n",
+       "| 1000          |  100          |  0.10         | 0.89          | 1.04 ( 0.05 ) | 0.71 ( 0.15 ) | 0 ( 0 )       |  6.71         | 0.08          | 1.04          | 0.71          | 0.00          |\n",
+       "|   50          |  500          | 10.00         | 0.38          | 2.18 ( 0.13 ) | 5.53 ( 0.36 ) | 1.27 ( 0.11 ) | 10.26         | 0.48          | 2.18          | 5.53          | 1.27          |\n",
+       "|  100          |  500          |  5.00         | 0.75          | 1 ( 0.05 )    | 1.9 ( 0.29 )  | 0.03 ( 0.02 ) |  7.87         | 0.18          | 1.00          | 1.90          | 0.03          |\n",
+       "|  500          |  500          |  1.00         | 0.96          | 1.18 ( 0.04 ) | 0.27 ( 0.13 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.18          | 0.27          | 0.00          |\n",
+       "| 1000          |  500          |  0.50         | 0.96          | 1.24 ( 0.04 ) | 0.25 ( 0.1 )  | 0 ( 0 )       |  6.25         | 0.03          | 1.24          | 0.25          | 0.00          |\n",
+       "|   50          | 1000          | 20.00         | 0.32          | 3.09 ( 0.23 ) | 5.56 ( 0.36 ) | 1.95 ( 0.14 ) |  9.61         | 0.51          | 3.09          | 5.56          | 1.95          |\n",
+       "|  100          | 1000          | 10.00         | 0.67          | 1.1 ( 0.07 )  | 2.8 ( 0.37 )  | 0.04 ( 0.02 ) |  8.76         | 0.24          | 1.10          | 2.80          | 0.04          |\n",
+       "|  500          | 1000          |  2.00         | 0.96          | 1.12 ( 0.04 ) | 0.27 ( 0.08 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.12          | 0.27          | 0.00          |\n",
+       "| 1000          | 1000          |  1.00         | 0.96          | 1.16 ( 0.04 ) | 0.24 ( 0.07 ) | 0 ( 0 )       |  6.24         | 0.03          | 1.16          | 0.24          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP            FN            num_select\n",
+       "1    50   50  1.00 0.61 0.8 ( 0.05 )  2.99 ( 0.27 ) 0.05 ( 0.02 )  8.94     \n",
+       "2   100   50  0.50 0.87 0.98 ( 0.06 ) 0.79 ( 0.15 ) 0 ( 0 )        6.79     \n",
+       "3   500   50  0.10 0.91 1.08 ( 0.05 ) 0.51 ( 0.11 ) 0 ( 0 )        6.51     \n",
+       "4  1000   50  0.05 0.93 1.18 ( 0.04 ) 0.39 ( 0.15 ) 0 ( 0 )        6.39     \n",
+       "5    50  100  2.00 0.52 1.05 ( 0.07 ) 4.3 ( 0.31 )  0.24 ( 0.05 ) 10.06     \n",
+       "6   100  100  1.00 0.85 0.94 ( 0.04 ) 1.02 ( 0.19 ) 0 ( 0 )        7.02     \n",
+       "7   500  100  0.20 0.96 1.16 ( 0.04 ) 0.25 ( 0.09 ) 0 ( 0 )        6.25     \n",
+       "8  1000  100  0.10 0.89 1.04 ( 0.05 ) 0.71 ( 0.15 ) 0 ( 0 )        6.71     \n",
+       "9    50  500 10.00 0.38 2.18 ( 0.13 ) 5.53 ( 0.36 ) 1.27 ( 0.11 ) 10.26     \n",
+       "10  100  500  5.00 0.75 1 ( 0.05 )    1.9 ( 0.29 )  0.03 ( 0.02 )  7.87     \n",
+       "11  500  500  1.00 0.96 1.18 ( 0.04 ) 0.27 ( 0.13 ) 0 ( 0 )        6.27     \n",
+       "12 1000  500  0.50 0.96 1.24 ( 0.04 ) 0.25 ( 0.1 )  0 ( 0 )        6.25     \n",
+       "13   50 1000 20.00 0.32 3.09 ( 0.23 ) 5.56 ( 0.36 ) 1.95 ( 0.14 )  9.61     \n",
+       "14  100 1000 10.00 0.67 1.1 ( 0.07 )  2.8 ( 0.37 )  0.04 ( 0.02 )  8.76     \n",
+       "15  500 1000  2.00 0.96 1.12 ( 0.04 ) 0.27 ( 0.08 ) 0 ( 0 )        6.27     \n",
+       "16 1000 1000  1.00 0.96 1.16 ( 0.04 ) 0.24 ( 0.07 ) 0 ( 0 )        6.24     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.28 0.80     2.99    0.05   \n",
+       "2  0.09 0.98     0.79    0.00   \n",
+       "3  0.06 1.08     0.51    0.00   \n",
+       "4  0.04 1.18     0.39    0.00   \n",
+       "5  0.37 1.05     4.30    0.24   \n",
+       "6  0.11 0.94     1.02    0.00   \n",
+       "7  0.03 1.16     0.25    0.00   \n",
+       "8  0.08 1.04     0.71    0.00   \n",
+       "9  0.48 2.18     5.53    1.27   \n",
+       "10 0.18 1.00     1.90    0.03   \n",
+       "11 0.03 1.18     0.27    0.00   \n",
+       "12 0.03 1.24     0.25    0.00   \n",
+       "13 0.51 3.09     5.56    1.95   \n",
+       "14 0.24 1.10     2.80    0.04   \n",
+       "15 0.03 1.12     0.27    0.00   \n",
+       "16 0.03 1.16     0.24    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fix missing values\n",
+    "result.table_ind$MSE_mean[is.na(result.table_ind$MSE_mean)] = 1\n",
+    "result.table_ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.1_sim_toe_lasso_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.1_sim_toe_lasso_cts-checkpoint.ipynb
new file mode 100755
index 0000000..231c1a8
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.1_sim_toe_lasso_cts-checkpoint.ipynb
@@ -0,0 +1,819 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_lasso))\n",
+    "for (i in 1:length(results_toe_lasso)){\n",
+    "    table_toe = rbind(table_toe, results_toe_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>8.59 ( 0.46 )</td><td>0.02 ( 0.01 )</td><td>0.62 ( 0.04 )</td><td>0.36         </td><td>13.57        </td><td>0.51         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>6.3 ( 0.43 ) </td><td>0 ( 0 )      </td><td>0.37 ( 0.01 )</td><td>0.47         </td><td>11.30        </td><td>0.40         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>2.88 ( 0.21 )</td><td>0 ( 0 )      </td><td>0.28 ( 0 )   </td><td>0.73         </td><td> 7.88        </td><td>0.20         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>1.66 ( 0.11 )</td><td>0 ( 0 )      </td><td>0.27 ( 0 )   </td><td>0.89         </td><td> 6.66        </td><td>0.08         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>11.84 ( 0.4 )</td><td>0 ( 0 )      </td><td>0.67 ( 0.05 )</td><td>0.32         </td><td>16.84        </td><td>0.62         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>7.71 ( 0.56 )</td><td>0 ( 0 )      </td><td>0.4 ( 0.01 ) </td><td>0.44         </td><td>12.71        </td><td>0.46         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 8.59 ( 0.46 ) & 0.02 ( 0.01 ) & 0.62 ( 0.04 ) & 0.36          & 13.57         & 0.51         \\\\\n",
+       "\t 100           & 50            & 0.1           & 6.3 ( 0.43 )  & 0 ( 0 )       & 0.37 ( 0.01 ) & 0.47          & 11.30         & 0.40         \\\\\n",
+       "\t 500           & 50            & 0.1           & 2.88 ( 0.21 ) & 0 ( 0 )       & 0.28 ( 0 )    & 0.73          &  7.88         & 0.20         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 1.66 ( 0.11 ) & 0 ( 0 )       & 0.27 ( 0 )    & 0.89          &  6.66         & 0.08         \\\\\n",
+       "\t 50            & 100           & 0.1           & 11.84 ( 0.4 ) & 0 ( 0 )       & 0.67 ( 0.05 ) & 0.32          & 16.84         & 0.62         \\\\\n",
+       "\t 100           & 100           & 0.1           & 7.71 ( 0.56 ) & 0 ( 0 )       & 0.4 ( 0.01 )  & 0.44          & 12.71         & 0.46         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 8.59 ( 0.46 ) | 0.02 ( 0.01 ) | 0.62 ( 0.04 ) | 0.36          | 13.57         | 0.51          |\n",
+       "| 100           | 50            | 0.1           | 6.3 ( 0.43 )  | 0 ( 0 )       | 0.37 ( 0.01 ) | 0.47          | 11.30         | 0.40          |\n",
+       "| 500           | 50            | 0.1           | 2.88 ( 0.21 ) | 0 ( 0 )       | 0.28 ( 0 )    | 0.73          |  7.88         | 0.20          |\n",
+       "| 1000          | 50            | 0.1           | 1.66 ( 0.11 ) | 0 ( 0 )       | 0.27 ( 0 )    | 0.89          |  6.66         | 0.08          |\n",
+       "| 50            | 100           | 0.1           | 11.84 ( 0.4 ) | 0 ( 0 )       | 0.67 ( 0.05 ) | 0.32          | 16.84         | 0.62          |\n",
+       "| 100           | 100           | 0.1           | 7.71 ( 0.56 ) | 0 ( 0 )       | 0.4 ( 0.01 )  | 0.44          | 12.71         | 0.46          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 8.59 ( 0.46 ) 0.02 ( 0.01 ) 0.62 ( 0.04 ) 0.36 13.57      0.51\n",
+       "2 100  50  0.1 6.3 ( 0.43 )  0 ( 0 )       0.37 ( 0.01 ) 0.47 11.30      0.40\n",
+       "3 500  50  0.1 2.88 ( 0.21 ) 0 ( 0 )       0.28 ( 0 )    0.73  7.88      0.20\n",
+       "4 1000 50  0.1 1.66 ( 0.11 ) 0 ( 0 )       0.27 ( 0 )    0.89  6.66      0.08\n",
+       "5 50   100 0.1 11.84 ( 0.4 ) 0 ( 0 )       0.67 ( 0.05 ) 0.32 16.84      0.62\n",
+       "6 100  100 0.1 7.71 ( 0.56 ) 0 ( 0 )       0.4 ( 0.01 )  0.44 12.71      0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>38</th><td>100          </td><td>100          </td><td>0.5          </td><td>1            </td><td>0.36         </td><td>0.44 ( 0.02 )</td><td>10 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>14.98        </td><td>0.56         </td><td>0.44         </td><td>NA           </td><td>0.02         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t38 & 100           & 100           & 0.5           & 1             & 0.36          & 0.44 ( 0.02 ) & 10 ( 0.48 )   & 0.02 ( 0.01 ) & 14.98         & 0.56          & 0.44          & NA            & 0.02         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 38 | 100           | 100           | 0.5           | 1             | 0.36          | 0.44 ( 0.02 ) | 10 ( 0.48 )   | 0.02 ( 0.01 ) | 14.98         | 0.56          | 0.44          | NA            | 0.02          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "38 100 100 0.5  1     0.36 0.44 ( 0.02 ) 10 ( 0.48 ) 0.02 ( 0.01 ) 14.98     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "38 0.56 0.44     NA      0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$FP_mean[is.na(result.table_toe$FP_mean)] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>38</th><td>100          </td><td>100          </td><td>0.5          </td><td>1            </td><td>0.36         </td><td>0.44 ( 0.02 )</td><td>10 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>14.98        </td><td>0.56         </td><td>0.44         </td><td>10           </td><td>0.02         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t38 & 100           & 100           & 0.5           & 1             & 0.36          & 0.44 ( 0.02 ) & 10 ( 0.48 )   & 0.02 ( 0.01 ) & 14.98         & 0.56          & 0.44          & 10            & 0.02         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 38 | 100           | 100           | 0.5           | 1             | 0.36          | 0.44 ( 0.02 ) | 10 ( 0.48 )   | 0.02 ( 0.01 ) | 14.98         | 0.56          | 0.44          | 10            | 0.02          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "38 100 100 0.5  1     0.36 0.44 ( 0.02 ) 10 ( 0.48 ) 0.02 ( 0.01 ) 14.98     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "38 0.56 0.44     10      0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[38, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.36         </td><td>0.62 ( 0.04 )</td><td>8.59 ( 0.46 )</td><td>0.02 ( 0.01 )</td><td>13.57        </td><td>0.51         </td><td>0.62         </td><td> 8.59        </td><td>0.02         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.47         </td><td>0.37 ( 0.01 )</td><td>6.3 ( 0.43 ) </td><td>0 ( 0 )      </td><td>11.30        </td><td>0.4          </td><td>0.37         </td><td> 6.30        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.73         </td><td>0.28 ( 0 )   </td><td>2.88 ( 0.21 )</td><td>0 ( 0 )      </td><td> 7.88        </td><td>0.2          </td><td>0.28         </td><td> 2.88        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.89         </td><td>0.27 ( 0 )   </td><td>1.66 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.66        </td><td>0.08         </td><td>0.27         </td><td> 1.66        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.32         </td><td>0.67 ( 0.05 )</td><td>11.84 ( 0.4 )</td><td>0 ( 0 )      </td><td>16.84        </td><td>0.62         </td><td>0.67         </td><td>11.80        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.44         </td><td>0.4 ( 0.01 ) </td><td>7.71 ( 0.56 )</td><td>0 ( 0 )      </td><td>12.71        </td><td>0.46         </td><td>0.40         </td><td> 7.71        </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.36          & 0.62 ( 0.04 ) & 8.59 ( 0.46 ) & 0.02 ( 0.01 ) & 13.57         & 0.51          & 0.62          &  8.59         & 0.02         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.47          & 0.37 ( 0.01 ) & 6.3 ( 0.43 )  & 0 ( 0 )       & 11.30         & 0.4           & 0.37          &  6.30         & 0.00         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.73          & 0.28 ( 0 )    & 2.88 ( 0.21 ) & 0 ( 0 )       &  7.88         & 0.2           & 0.28          &  2.88         & 0.00         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.89          & 0.27 ( 0 )    & 1.66 ( 0.11 ) & 0 ( 0 )       &  6.66         & 0.08          & 0.27          &  1.66         & 0.00         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.32          & 0.67 ( 0.05 ) & 11.84 ( 0.4 ) & 0 ( 0 )       & 16.84         & 0.62          & 0.67          & 11.80         & 0.00         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.44          & 0.4 ( 0.01 )  & 7.71 ( 0.56 ) & 0 ( 0 )       & 12.71         & 0.46          & 0.40          &  7.71         & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.36          | 0.62 ( 0.04 ) | 8.59 ( 0.46 ) | 0.02 ( 0.01 ) | 13.57         | 0.51          | 0.62          |  8.59         | 0.02          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.47          | 0.37 ( 0.01 ) | 6.3 ( 0.43 )  | 0 ( 0 )       | 11.30         | 0.4           | 0.37          |  6.30         | 0.00          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.73          | 0.28 ( 0 )    | 2.88 ( 0.21 ) | 0 ( 0 )       |  7.88         | 0.2           | 0.28          |  2.88         | 0.00          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.89          | 0.27 ( 0 )    | 1.66 ( 0.11 ) | 0 ( 0 )       |  6.66         | 0.08          | 0.27          |  1.66         | 0.00          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.32          | 0.67 ( 0.05 ) | 11.84 ( 0.4 ) | 0 ( 0 )       | 16.84         | 0.62          | 0.67          | 11.80         | 0.00          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.44          | 0.4 ( 0.01 )  | 7.71 ( 0.56 ) | 0 ( 0 )       | 12.71         | 0.46          | 0.40          |  7.71         | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.36 0.62 ( 0.04 ) 8.59 ( 0.46 ) 0.02 ( 0.01 ) 13.57     \n",
+       "2  100  50 0.1  0.50  0.47 0.37 ( 0.01 ) 6.3 ( 0.43 )  0 ( 0 )       11.30     \n",
+       "3  500  50 0.1  0.10  0.73 0.28 ( 0 )    2.88 ( 0.21 ) 0 ( 0 )        7.88     \n",
+       "4 1000  50 0.1  0.05  0.89 0.27 ( 0 )    1.66 ( 0.11 ) 0 ( 0 )        6.66     \n",
+       "5   50 100 0.1  2.00  0.32 0.67 ( 0.05 ) 11.84 ( 0.4 ) 0 ( 0 )       16.84     \n",
+       "6  100 100 0.1  1.00  0.44 0.4 ( 0.01 )  7.71 ( 0.56 ) 0 ( 0 )       12.71     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.51 0.62      8.59   0.02   \n",
+       "2 0.4  0.37      6.30   0.00   \n",
+       "3 0.2  0.28      2.88   0.00   \n",
+       "4 0.08 0.27      1.66   0.00   \n",
+       "5 0.62 0.67     11.80   0.00   \n",
+       "6 0.46 0.40      7.71   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.25          </td><td>0.3 ( 0 )     </td><td>18.18 ( 0.69 )</td><td>0.03 ( 0.02 ) </td><td>23.15         </td><td>0.72          </td><td>0.30          </td><td>18.1          </td><td>0.03          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.33          </td><td>0.28 ( 0 )    </td><td>12.68 ( 0.57 )</td><td>0 ( 0 )       </td><td>17.68         </td><td>0.63          </td><td>0.28          </td><td>12.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.08          </td><td>0.9 ( 0.05 )  </td><td>18.15 ( 0.34 )</td><td>3.92 ( 0.08 ) </td><td>19.23         </td><td>0.89          </td><td>0.90          </td><td>18.1          </td><td>3.92          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.16          </td><td>0.59 ( 0.02 ) </td><td>15.99 ( 0.66 )</td><td>2.88 ( 0.06 ) </td><td>18.11         </td><td>0.81          </td><td>0.59          </td><td>15.9          </td><td>2.88          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.20          </td><td>0.31 ( 0 )    </td><td>24.62 ( 1.01 )</td><td>0.05 ( 0.02 ) </td><td>29.57         </td><td>0.77          </td><td>0.31          </td><td>24.6          </td><td>0.05          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.25          </td><td>0.28 ( 0 )    </td><td>18.51 ( 0.86 )</td><td>0 ( 0 )       </td><td>23.51         </td><td>0.71          </td><td>0.28          </td><td>18.5          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.25           & 0.3 ( 0 )      & 18.18 ( 0.69 ) & 0.03 ( 0.02 )  & 23.15          & 0.72           & 0.30           & 18.1           & 0.03          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.33           & 0.28 ( 0 )     & 12.68 ( 0.57 ) & 0 ( 0 )        & 17.68          & 0.63           & 0.28           & 12.6           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.08           & 0.9 ( 0.05 )   & 18.15 ( 0.34 ) & 3.92 ( 0.08 )  & 19.23          & 0.89           & 0.90           & 18.1           & 3.92          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.16           & 0.59 ( 0.02 )  & 15.99 ( 0.66 ) & 2.88 ( 0.06 )  & 18.11          & 0.81           & 0.59           & 15.9           & 2.88          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.20           & 0.31 ( 0 )     & 24.62 ( 1.01 ) & 0.05 ( 0.02 )  & 29.57          & 0.77           & 0.31           & 24.6           & 0.05          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.25           & 0.28 ( 0 )     & 18.51 ( 0.86 ) & 0 ( 0 )        & 23.51          & 0.71           & 0.28           & 18.5           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.25           | 0.3 ( 0 )      | 18.18 ( 0.69 ) | 0.03 ( 0.02 )  | 23.15          | 0.72           | 0.30           | 18.1           | 0.03           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.33           | 0.28 ( 0 )     | 12.68 ( 0.57 ) | 0 ( 0 )        | 17.68          | 0.63           | 0.28           | 12.6           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.08           | 0.9 ( 0.05 )   | 18.15 ( 0.34 ) | 3.92 ( 0.08 )  | 19.23          | 0.89           | 0.90           | 18.1           | 3.92           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.16           | 0.59 ( 0.02 )  | 15.99 ( 0.66 ) | 2.88 ( 0.06 )  | 18.11          | 0.81           | 0.59           | 15.9           | 2.88           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.20           | 0.31 ( 0 )     | 24.62 ( 1.01 ) | 0.05 ( 0.02 )  | 29.57          | 0.77           | 0.31           | 24.6           | 0.05           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.25           | 0.28 ( 0 )     | 18.51 ( 0.86 ) | 0 ( 0 )        | 23.51          | 0.71           | 0.28           | 18.5           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.25 0.3 ( 0 )     18.18 ( 0.69 ) 0.03 ( 0.02 )\n",
+       "76 1000  500 0.9   0.5  0.33 0.28 ( 0 )    12.68 ( 0.57 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.08 0.9 ( 0.05 )  18.15 ( 0.34 ) 3.92 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.16 0.59 ( 0.02 ) 15.99 ( 0.66 ) 2.88 ( 0.06 )\n",
+       "79  500 1000 0.9   2.0  0.20 0.31 ( 0 )    24.62 ( 1.01 ) 0.05 ( 0.02 )\n",
+       "80 1000 1000 0.9   1.0  0.25 0.28 ( 0 )    18.51 ( 0.86 ) 0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 23.15      0.72 0.30     18.1    0.03   \n",
+       "76 17.68      0.63 0.28     12.6    0.00   \n",
+       "77 19.23      0.89 0.90     18.1    3.92   \n",
+       "78 18.11      0.81 0.59     15.9    2.88   \n",
+       "79 29.57      0.77 0.31     24.6    0.05   \n",
+       "80 23.51      0.71 0.28     18.5    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.1           </td><td> 1.00         </td><td>0.36          </td><td>0.62 ( 0.04 ) </td><td>8.59 ( 0.46 ) </td><td>0.02 ( 0.01 ) </td><td>13.57         </td><td>0.51          </td><td>0.62          </td><td> 8.59         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.1           </td><td> 0.50         </td><td>0.47          </td><td>0.37 ( 0.01 ) </td><td>6.3 ( 0.43 )  </td><td>0 ( 0 )       </td><td>11.30         </td><td>0.4           </td><td>0.37          </td><td> 6.30         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.1           </td><td> 0.10         </td><td>0.73          </td><td>0.28 ( 0 )    </td><td>2.88 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 7.88         </td><td>0.2           </td><td>0.28          </td><td> 2.88         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.1           </td><td> 0.05         </td><td>0.89          </td><td>0.27 ( 0 )    </td><td>1.66 ( 0.11 ) </td><td>0 ( 0 )       </td><td> 6.66         </td><td>0.08          </td><td>0.27          </td><td> 1.66         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.1           </td><td> 2.00         </td><td>0.32          </td><td>0.67 ( 0.05 ) </td><td>11.84 ( 0.4 ) </td><td>0 ( 0 )       </td><td>16.84         </td><td>0.62          </td><td>0.67          </td><td>11.80         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.1           </td><td> 1.00         </td><td>0.44          </td><td>0.4 ( 0.01 )  </td><td>7.71 ( 0.56 ) </td><td>0 ( 0 )       </td><td>12.71         </td><td>0.46          </td><td>0.40          </td><td> 7.71         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.1           </td><td> 0.20         </td><td>0.70          </td><td>0.27 ( 0 )    </td><td>3.35 ( 0.22 ) </td><td>0 ( 0 )       </td><td> 8.35         </td><td>0.23          </td><td>0.27          </td><td> 3.35         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.1           </td><td> 0.10         </td><td>0.81          </td><td>0.27 ( 0 )    </td><td>2.29 ( 0.23 ) </td><td>0 ( 0 )       </td><td> 7.29         </td><td>0.13          </td><td>0.27          </td><td> 2.29         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.1           </td><td>10.00         </td><td>0.18          </td><td>1.61 ( 0.1 )  </td><td>20.85 ( 0.38 )</td><td>0.69 ( 0.08 ) </td><td>25.16         </td><td>0.78          </td><td>1.61          </td><td>20.80         </td><td>0.69          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.1           </td><td> 5.00         </td><td>0.30          </td><td>0.53 ( 0.02 ) </td><td>14.69 ( 0.7 ) </td><td>0 ( 0 )       </td><td>19.69         </td><td>0.66          </td><td>0.53          </td><td>14.60         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.1           </td><td> 1.00         </td><td>0.55          </td><td>0.28 ( 0 )    </td><td>5.86 ( 0.51 ) </td><td>0 ( 0 )       </td><td>10.86         </td><td>0.36          </td><td>0.28          </td><td> 5.86         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.1           </td><td> 0.50         </td><td>0.78          </td><td>0.28 ( 0 )    </td><td>2.69 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 7.69         </td><td>0.17          </td><td>0.28          </td><td> 2.69         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.1           </td><td>20.00         </td><td>0.13          </td><td>2.23 ( 0.14 ) </td><td>24.37 ( 0.39 )</td><td>1.36 ( 0.1 )  </td><td>28.01         </td><td>0.83          </td><td>2.23          </td><td>24.30         </td><td>1.36          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.1           </td><td>10.00         </td><td>0.25          </td><td>0.57 ( 0.03 ) </td><td>18.79 ( 0.7 ) </td><td>0 ( 0 )       </td><td>23.79         </td><td>0.73          </td><td>0.57          </td><td>18.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.1           </td><td> 2.00         </td><td>0.49          </td><td>0.28 ( 0 )    </td><td>7.08 ( 0.53 ) </td><td>0 ( 0 )       </td><td>12.08         </td><td>0.41          </td><td>0.28          </td><td> 7.08         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.1           </td><td> 1.00         </td><td>0.70          </td><td>0.27 ( 0 )    </td><td>3.59 ( 0.33 ) </td><td>0 ( 0 )       </td><td> 8.59         </td><td>0.23          </td><td>0.27          </td><td> 3.59         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.3           </td><td> 1.00         </td><td>0.35          </td><td>0.57 ( 0.03 ) </td><td>8.86 ( 0.42 ) </td><td>0.05 ( 0.02 ) </td><td>13.81         </td><td>0.53          </td><td>0.57          </td><td> 8.86         </td><td>0.05          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.3           </td><td> 0.50         </td><td>0.47          </td><td>0.36 ( 0.01 ) </td><td>6.18 ( 0.36 ) </td><td>0 ( 0 )       </td><td>11.18         </td><td>0.41          </td><td>0.36          </td><td> 6.18         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.3           </td><td> 0.10         </td><td>0.70          </td><td>0.28 ( 0 )    </td><td>3.14 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 8.14         </td><td>0.22          </td><td>0.28          </td><td> 3.14         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.3           </td><td> 0.05         </td><td>0.86          </td><td>0.27 ( 0 )    </td><td>1.86 ( 0.11 ) </td><td>0 ( 0 )       </td><td> 6.86         </td><td>0.11          </td><td>0.27          </td><td> 1.86         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.3           </td><td> 2.00         </td><td>0.31          </td><td>0.76 ( 0.04 ) </td><td>11.12 ( 0.37 )</td><td>0.17 ( 0.04 ) </td><td>15.95         </td><td>0.62          </td><td>0.76          </td><td>11.10         </td><td>0.17          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.3           </td><td> 1.00         </td><td>0.38          </td><td>0.41 ( 0.01 ) </td><td>9.43 ( 0.53 ) </td><td>0 ( 0 )       </td><td>14.43         </td><td>0.54          </td><td>0.41          </td><td> 9.43         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.3           </td><td> 0.20         </td><td>0.63          </td><td>0.27 ( 0 )    </td><td>4.16 ( 0.27 ) </td><td>0 ( 0 )       </td><td> 9.16         </td><td>0.29          </td><td>0.27          </td><td> 4.16         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.3           </td><td> 0.10         </td><td>0.80          </td><td>0.27 ( 0 )    </td><td>2.36 ( 0.17 ) </td><td>0 ( 0 )       </td><td> 7.36         </td><td>0.15          </td><td>0.27          </td><td> 2.36         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.3           </td><td>10.00         </td><td>0.14          </td><td>1.78 ( 0.11 ) </td><td>21.4 ( 0.41 ) </td><td>1.39 ( 0.1 )  </td><td>25.01         </td><td>0.81          </td><td>1.78          </td><td>21.40         </td><td>1.39          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.3           </td><td> 5.00         </td><td>0.26          </td><td>0.53 ( 0.02 ) </td><td>16.81 ( 0.77 )</td><td>0.02 ( 0.01 ) </td><td>21.79         </td><td>0.7           </td><td>0.53          </td><td>16.80         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.3           </td><td> 1.00         </td><td>0.55          </td><td>0.29 ( 0 )    </td><td>5.89 ( 0.51 ) </td><td>0 ( 0 )       </td><td>10.89         </td><td>0.36          </td><td>0.29          </td><td> 5.89         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.3           </td><td> 0.50         </td><td>0.69          </td><td>0.27 ( 0 )    </td><td>3.68 ( 0.32 ) </td><td>0 ( 0 )       </td><td> 8.68         </td><td>0.25          </td><td>0.27          </td><td> 3.68         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.3           </td><td>20.00         </td><td>0.11          </td><td>2.21 ( 0.15 ) </td><td>25.14 ( 0.35 )</td><td>1.87 ( 0.1 )  </td><td>28.27         </td><td>0.85          </td><td>2.21          </td><td>25.10         </td><td>1.87          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.3           </td><td>10.00         </td><td>0.22          </td><td>0.61 ( 0.03 ) </td><td>21.11 ( 0.75 )</td><td>0.02 ( 0.01 ) </td><td>26.09         </td><td>0.75          </td><td>0.61          </td><td>21.10         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.7           </td><td> 0.10         </td><td>0.60          </td><td>0.27 ( 0 )    </td><td>4.38 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 9.38         </td><td>0.33          </td><td>0.27          </td><td> 4.38         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.7           </td><td> 0.05         </td><td>0.74          </td><td>0.27 ( 0 )    </td><td>2.84 ( 0.14 ) </td><td>0 ( 0 )       </td><td> 7.84         </td><td>0.21          </td><td>0.27          </td><td> 2.84         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.7           </td><td> 2.00         </td><td>0.23          </td><td>0.8 ( 0.04 )  </td><td>11.57 ( 0.53 )</td><td>1.26 ( 0.1 )  </td><td>15.31         </td><td>0.66          </td><td>0.80          </td><td>11.50         </td><td>1.26          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.7           </td><td> 1.00         </td><td>0.31          </td><td>0.39 ( 0.01 ) </td><td>11.68 ( 0.51 )</td><td>0.1 ( 0.04 )  </td><td>16.58         </td><td>0.61          </td><td>0.39          </td><td>11.60         </td><td>0.10          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.7           </td><td> 0.20         </td><td>0.49          </td><td>0.28 ( 0 )    </td><td>6.56 ( 0.32 ) </td><td>0 ( 0 )       </td><td>11.56         </td><td>0.44          </td><td>0.28          </td><td> 6.56         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.7           </td><td> 0.10         </td><td>0.63          </td><td>0.27 ( 0 )    </td><td>4.24 ( 0.26 ) </td><td>0 ( 0 )       </td><td> 9.24         </td><td>0.3           </td><td>0.27          </td><td> 4.24         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.7           </td><td>10.00         </td><td>0.12          </td><td>1.37 ( 0.08 ) </td><td>19.8 ( 0.33 ) </td><td>2.78 ( 0.08 ) </td><td>22.02         </td><td>0.85          </td><td>1.37          </td><td>19.80         </td><td>2.78          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.7           </td><td> 5.00         </td><td>0.19          </td><td>0.62 ( 0.03 ) </td><td>18.58 ( 0.85 )</td><td>0.88 ( 0.08 ) </td><td>22.70         </td><td>0.75          </td><td>0.62          </td><td>18.50         </td><td>0.88          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.7           </td><td> 1.00         </td><td>0.36          </td><td>0.3 ( 0 )     </td><td>11.31 ( 0.6 ) </td><td>0 ( 0 )       </td><td>16.31         </td><td>0.58          </td><td>0.30          </td><td>11.30         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.7           </td><td> 0.50         </td><td>0.46          </td><td>0.27 ( 0 )    </td><td>7.93 ( 0.49 ) </td><td>0 ( 0 )       </td><td>12.93         </td><td>0.47          </td><td>0.27          </td><td> 7.93         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.7           </td><td>20.00         </td><td>0.09          </td><td>1.5 ( 0.08 )  </td><td>22.99 ( 0.35 )</td><td>3.2 ( 0.09 )  </td><td>24.79         </td><td>0.88          </td><td>1.50          </td><td>22.90         </td><td>3.20          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.7           </td><td>10.00         </td><td>0.14          </td><td>0.77 ( 0.04 ) </td><td>22.43 ( 0.68 )</td><td>1.45 ( 0.1 )  </td><td>25.98         </td><td>0.82          </td><td>0.77          </td><td>22.40         </td><td>1.45          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.7           </td><td> 2.00         </td><td>0.30          </td><td>0.31 ( 0 )    </td><td>14.7 ( 0.84 ) </td><td>0 ( 0 )       </td><td>19.70         </td><td>0.65          </td><td>0.31          </td><td>14.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.7           </td><td> 1.00         </td><td>0.41          </td><td>0.28 ( 0 )    </td><td>9.63 ( 0.54 ) </td><td>0 ( 0 )       </td><td>14.63         </td><td>0.53          </td><td>0.28          </td><td> 9.63         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.9           </td><td> 1.00         </td><td>0.31          </td><td>0.55 ( 0.03 ) </td><td>5.65 ( 0.29 ) </td><td>2.17 ( 0.13 ) </td><td> 8.48         </td><td>0.51          </td><td>0.55          </td><td> 5.65         </td><td>2.17          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.9           </td><td> 0.50         </td><td>0.38          </td><td>0.38 ( 0.02 ) </td><td>5.85 ( 0.3 )  </td><td>1.03 ( 0.11 ) </td><td> 9.82         </td><td>0.45          </td><td>0.38          </td><td> 5.85         </td><td>1.03          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.9           </td><td> 0.10         </td><td>0.61          </td><td>0.28 ( 0 )    </td><td>4.34 ( 0.19 ) </td><td>0.01 ( 0.01 ) </td><td> 9.33         </td><td>0.33          </td><td>0.28          </td><td> 4.34         </td><td>0.01          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.9           </td><td> 0.05         </td><td>0.76          </td><td>0.27 ( 0 )    </td><td>2.71 ( 0.12 ) </td><td>0 ( 0 )       </td><td> 7.71         </td><td>0.2           </td><td>0.27          </td><td> 2.71         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.9           </td><td> 2.00         </td><td>0.26          </td><td>0.6 ( 0.03 )  </td><td>7.26 ( 0.35 ) </td><td>2.69 ( 0.1 )  </td><td> 9.57         </td><td>0.62          </td><td>0.60          </td><td> 7.26         </td><td>2.69          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.9           </td><td> 1.00         </td><td>0.31          </td><td>0.44 ( 0.01 ) </td><td>7.79 ( 0.39 ) </td><td>1.51 ( 0.11 ) </td><td>11.28         </td><td>0.57          </td><td>0.44          </td><td> 7.79         </td><td>1.51          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.9           </td><td> 0.20         </td><td>0.51          </td><td>0.29 ( 0 )    </td><td>6.45 ( 0.27 ) </td><td>0 ( 0 )       </td><td>11.45         </td><td>0.45          </td><td>0.29          </td><td> 6.45         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.9           </td><td> 0.10         </td><td>0.61          </td><td>0.27 ( 0 )    </td><td>4.72 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 9.72         </td><td>0.35          </td><td>0.27          </td><td> 4.72         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.9           </td><td>10.00         </td><td>0.13          </td><td>0.81 ( 0.04 ) </td><td>13.78 ( 0.36 )</td><td>3.54 ( 0.08 ) </td><td>15.24         </td><td>0.83          </td><td>0.81          </td><td>13.70         </td><td>3.54          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.9           </td><td> 5.00         </td><td>0.18          </td><td>0.54 ( 0.02 ) </td><td>13.78 ( 0.72 )</td><td>2.58 ( 0.08 ) </td><td>16.20         </td><td>0.76          </td><td>0.54          </td><td>13.70         </td><td>2.58          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.00         </td><td>0.25          </td><td>0.3 ( 0 )     </td><td>18.18 ( 0.69 )</td><td>0.03 ( 0.02 ) </td><td>23.15         </td><td>0.72          </td><td>0.30          </td><td>18.10         </td><td>0.03          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.50         </td><td>0.33          </td><td>0.28 ( 0 )    </td><td>12.68 ( 0.57 )</td><td>0 ( 0 )       </td><td>17.68         </td><td>0.63          </td><td>0.28          </td><td>12.60         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.00         </td><td>0.08          </td><td>0.9 ( 0.05 )  </td><td>18.15 ( 0.34 )</td><td>3.92 ( 0.08 ) </td><td>19.23         </td><td>0.89          </td><td>0.90          </td><td>18.10         </td><td>3.92          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.00         </td><td>0.16          </td><td>0.59 ( 0.02 ) </td><td>15.99 ( 0.66 )</td><td>2.88 ( 0.06 ) </td><td>18.11         </td><td>0.81          </td><td>0.59          </td><td>15.90         </td><td>2.88          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.00         </td><td>0.20          </td><td>0.31 ( 0 )    </td><td>24.62 ( 1.01 )</td><td>0.05 ( 0.02 ) </td><td>29.57         </td><td>0.77          </td><td>0.31          </td><td>24.60         </td><td>0.05          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.00         </td><td>0.25          </td><td>0.28 ( 0 )    </td><td>18.51 ( 0.86 )</td><td>0 ( 0 )       </td><td>23.51         </td><td>0.71          </td><td>0.28          </td><td>18.50         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           & 0.1            &  1.00          & 0.36           & 0.62 ( 0.04 )  & 8.59 ( 0.46 )  & 0.02 ( 0.01 )  & 13.57          & 0.51           & 0.62           &  8.59          & 0.02          \\\\\n",
+       "\t  100           &   50           & 0.1            &  0.50          & 0.47           & 0.37 ( 0.01 )  & 6.3 ( 0.43 )   & 0 ( 0 )        & 11.30          & 0.4            & 0.37           &  6.30          & 0.00          \\\\\n",
+       "\t  500           &   50           & 0.1            &  0.10          & 0.73           & 0.28 ( 0 )     & 2.88 ( 0.21 )  & 0 ( 0 )        &  7.88          & 0.2            & 0.28           &  2.88          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.1            &  0.05          & 0.89           & 0.27 ( 0 )     & 1.66 ( 0.11 )  & 0 ( 0 )        &  6.66          & 0.08           & 0.27           &  1.66          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.1            &  2.00          & 0.32           & 0.67 ( 0.05 )  & 11.84 ( 0.4 )  & 0 ( 0 )        & 16.84          & 0.62           & 0.67           & 11.80          & 0.00          \\\\\n",
+       "\t  100           &  100           & 0.1            &  1.00          & 0.44           & 0.4 ( 0.01 )   & 7.71 ( 0.56 )  & 0 ( 0 )        & 12.71          & 0.46           & 0.40           &  7.71          & 0.00          \\\\\n",
+       "\t  500           &  100           & 0.1            &  0.20          & 0.70           & 0.27 ( 0 )     & 3.35 ( 0.22 )  & 0 ( 0 )        &  8.35          & 0.23           & 0.27           &  3.35          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.1            &  0.10          & 0.81           & 0.27 ( 0 )     & 2.29 ( 0.23 )  & 0 ( 0 )        &  7.29          & 0.13           & 0.27           &  2.29          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.1            & 10.00          & 0.18           & 1.61 ( 0.1 )   & 20.85 ( 0.38 ) & 0.69 ( 0.08 )  & 25.16          & 0.78           & 1.61           & 20.80          & 0.69          \\\\\n",
+       "\t  100           &  500           & 0.1            &  5.00          & 0.30           & 0.53 ( 0.02 )  & 14.69 ( 0.7 )  & 0 ( 0 )        & 19.69          & 0.66           & 0.53           & 14.60          & 0.00          \\\\\n",
+       "\t  500           &  500           & 0.1            &  1.00          & 0.55           & 0.28 ( 0 )     & 5.86 ( 0.51 )  & 0 ( 0 )        & 10.86          & 0.36           & 0.28           &  5.86          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.1            &  0.50          & 0.78           & 0.28 ( 0 )     & 2.69 ( 0.21 )  & 0 ( 0 )        &  7.69          & 0.17           & 0.28           &  2.69          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.1            & 20.00          & 0.13           & 2.23 ( 0.14 )  & 24.37 ( 0.39 ) & 1.36 ( 0.1 )   & 28.01          & 0.83           & 2.23           & 24.30          & 1.36          \\\\\n",
+       "\t  100           & 1000           & 0.1            & 10.00          & 0.25           & 0.57 ( 0.03 )  & 18.79 ( 0.7 )  & 0 ( 0 )        & 23.79          & 0.73           & 0.57           & 18.70          & 0.00          \\\\\n",
+       "\t  500           & 1000           & 0.1            &  2.00          & 0.49           & 0.28 ( 0 )     & 7.08 ( 0.53 )  & 0 ( 0 )        & 12.08          & 0.41           & 0.28           &  7.08          & 0.00          \\\\\n",
+       "\t 1000           & 1000           & 0.1            &  1.00          & 0.70           & 0.27 ( 0 )     & 3.59 ( 0.33 )  & 0 ( 0 )        &  8.59          & 0.23           & 0.27           &  3.59          & 0.00          \\\\\n",
+       "\t   50           &   50           & 0.3            &  1.00          & 0.35           & 0.57 ( 0.03 )  & 8.86 ( 0.42 )  & 0.05 ( 0.02 )  & 13.81          & 0.53           & 0.57           &  8.86          & 0.05          \\\\\n",
+       "\t  100           &   50           & 0.3            &  0.50          & 0.47           & 0.36 ( 0.01 )  & 6.18 ( 0.36 )  & 0 ( 0 )        & 11.18          & 0.41           & 0.36           &  6.18          & 0.00          \\\\\n",
+       "\t  500           &   50           & 0.3            &  0.10          & 0.70           & 0.28 ( 0 )     & 3.14 ( 0.21 )  & 0 ( 0 )        &  8.14          & 0.22           & 0.28           &  3.14          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.3            &  0.05          & 0.86           & 0.27 ( 0 )     & 1.86 ( 0.11 )  & 0 ( 0 )        &  6.86          & 0.11           & 0.27           &  1.86          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.3            &  2.00          & 0.31           & 0.76 ( 0.04 )  & 11.12 ( 0.37 ) & 0.17 ( 0.04 )  & 15.95          & 0.62           & 0.76           & 11.10          & 0.17          \\\\\n",
+       "\t  100           &  100           & 0.3            &  1.00          & 0.38           & 0.41 ( 0.01 )  & 9.43 ( 0.53 )  & 0 ( 0 )        & 14.43          & 0.54           & 0.41           &  9.43          & 0.00          \\\\\n",
+       "\t  500           &  100           & 0.3            &  0.20          & 0.63           & 0.27 ( 0 )     & 4.16 ( 0.27 )  & 0 ( 0 )        &  9.16          & 0.29           & 0.27           &  4.16          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.3            &  0.10          & 0.80           & 0.27 ( 0 )     & 2.36 ( 0.17 )  & 0 ( 0 )        &  7.36          & 0.15           & 0.27           &  2.36          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.3            & 10.00          & 0.14           & 1.78 ( 0.11 )  & 21.4 ( 0.41 )  & 1.39 ( 0.1 )   & 25.01          & 0.81           & 1.78           & 21.40          & 1.39          \\\\\n",
+       "\t  100           &  500           & 0.3            &  5.00          & 0.26           & 0.53 ( 0.02 )  & 16.81 ( 0.77 ) & 0.02 ( 0.01 )  & 21.79          & 0.7            & 0.53           & 16.80          & 0.02          \\\\\n",
+       "\t  500           &  500           & 0.3            &  1.00          & 0.55           & 0.29 ( 0 )     & 5.89 ( 0.51 )  & 0 ( 0 )        & 10.89          & 0.36           & 0.29           &  5.89          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.3            &  0.50          & 0.69           & 0.27 ( 0 )     & 3.68 ( 0.32 )  & 0 ( 0 )        &  8.68          & 0.25           & 0.27           &  3.68          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.3            & 20.00          & 0.11           & 2.21 ( 0.15 )  & 25.14 ( 0.35 ) & 1.87 ( 0.1 )   & 28.27          & 0.85           & 2.21           & 25.10          & 1.87          \\\\\n",
+       "\t  100           & 1000           & 0.3            & 10.00          & 0.22           & 0.61 ( 0.03 )  & 21.11 ( 0.75 ) & 0.02 ( 0.01 )  & 26.09          & 0.75           & 0.61           & 21.10          & 0.02          \\\\\n",
+       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
+       "\t  500           &   50           & 0.7            &  0.10          & 0.60           & 0.27 ( 0 )     & 4.38 ( 0.21 )  & 0 ( 0 )        &  9.38          & 0.33           & 0.27           &  4.38          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.7            &  0.05          & 0.74           & 0.27 ( 0 )     & 2.84 ( 0.14 )  & 0 ( 0 )        &  7.84          & 0.21           & 0.27           &  2.84          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.7            &  2.00          & 0.23           & 0.8 ( 0.04 )   & 11.57 ( 0.53 ) & 1.26 ( 0.1 )   & 15.31          & 0.66           & 0.80           & 11.50          & 1.26          \\\\\n",
+       "\t  100           &  100           & 0.7            &  1.00          & 0.31           & 0.39 ( 0.01 )  & 11.68 ( 0.51 ) & 0.1 ( 0.04 )   & 16.58          & 0.61           & 0.39           & 11.60          & 0.10          \\\\\n",
+       "\t  500           &  100           & 0.7            &  0.20          & 0.49           & 0.28 ( 0 )     & 6.56 ( 0.32 )  & 0 ( 0 )        & 11.56          & 0.44           & 0.28           &  6.56          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.7            &  0.10          & 0.63           & 0.27 ( 0 )     & 4.24 ( 0.26 )  & 0 ( 0 )        &  9.24          & 0.3            & 0.27           &  4.24          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.7            & 10.00          & 0.12           & 1.37 ( 0.08 )  & 19.8 ( 0.33 )  & 2.78 ( 0.08 )  & 22.02          & 0.85           & 1.37           & 19.80          & 2.78          \\\\\n",
+       "\t  100           &  500           & 0.7            &  5.00          & 0.19           & 0.62 ( 0.03 )  & 18.58 ( 0.85 ) & 0.88 ( 0.08 )  & 22.70          & 0.75           & 0.62           & 18.50          & 0.88          \\\\\n",
+       "\t  500           &  500           & 0.7            &  1.00          & 0.36           & 0.3 ( 0 )      & 11.31 ( 0.6 )  & 0 ( 0 )        & 16.31          & 0.58           & 0.30           & 11.30          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.7            &  0.50          & 0.46           & 0.27 ( 0 )     & 7.93 ( 0.49 )  & 0 ( 0 )        & 12.93          & 0.47           & 0.27           &  7.93          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.7            & 20.00          & 0.09           & 1.5 ( 0.08 )   & 22.99 ( 0.35 ) & 3.2 ( 0.09 )   & 24.79          & 0.88           & 1.50           & 22.90          & 3.20          \\\\\n",
+       "\t  100           & 1000           & 0.7            & 10.00          & 0.14           & 0.77 ( 0.04 )  & 22.43 ( 0.68 ) & 1.45 ( 0.1 )   & 25.98          & 0.82           & 0.77           & 22.40          & 1.45          \\\\\n",
+       "\t  500           & 1000           & 0.7            &  2.00          & 0.30           & 0.31 ( 0 )     & 14.7 ( 0.84 )  & 0 ( 0 )        & 19.70          & 0.65           & 0.31           & 14.70          & 0.00          \\\\\n",
+       "\t 1000           & 1000           & 0.7            &  1.00          & 0.41           & 0.28 ( 0 )     & 9.63 ( 0.54 )  & 0 ( 0 )        & 14.63          & 0.53           & 0.28           &  9.63          & 0.00          \\\\\n",
+       "\t   50           &   50           & 0.9            &  1.00          & 0.31           & 0.55 ( 0.03 )  & 5.65 ( 0.29 )  & 2.17 ( 0.13 )  &  8.48          & 0.51           & 0.55           &  5.65          & 2.17          \\\\\n",
+       "\t  100           &   50           & 0.9            &  0.50          & 0.38           & 0.38 ( 0.02 )  & 5.85 ( 0.3 )   & 1.03 ( 0.11 )  &  9.82          & 0.45           & 0.38           &  5.85          & 1.03          \\\\\n",
+       "\t  500           &   50           & 0.9            &  0.10          & 0.61           & 0.28 ( 0 )     & 4.34 ( 0.19 )  & 0.01 ( 0.01 )  &  9.33          & 0.33           & 0.28           &  4.34          & 0.01          \\\\\n",
+       "\t 1000           &   50           & 0.9            &  0.05          & 0.76           & 0.27 ( 0 )     & 2.71 ( 0.12 )  & 0 ( 0 )        &  7.71          & 0.2            & 0.27           &  2.71          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.9            &  2.00          & 0.26           & 0.6 ( 0.03 )   & 7.26 ( 0.35 )  & 2.69 ( 0.1 )   &  9.57          & 0.62           & 0.60           &  7.26          & 2.69          \\\\\n",
+       "\t  100           &  100           & 0.9            &  1.00          & 0.31           & 0.44 ( 0.01 )  & 7.79 ( 0.39 )  & 1.51 ( 0.11 )  & 11.28          & 0.57           & 0.44           &  7.79          & 1.51          \\\\\n",
+       "\t  500           &  100           & 0.9            &  0.20          & 0.51           & 0.29 ( 0 )     & 6.45 ( 0.27 )  & 0 ( 0 )        & 11.45          & 0.45           & 0.29           &  6.45          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.9            &  0.10          & 0.61           & 0.27 ( 0 )     & 4.72 ( 0.21 )  & 0 ( 0 )        &  9.72          & 0.35           & 0.27           &  4.72          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.9            & 10.00          & 0.13           & 0.81 ( 0.04 )  & 13.78 ( 0.36 ) & 3.54 ( 0.08 )  & 15.24          & 0.83           & 0.81           & 13.70          & 3.54          \\\\\n",
+       "\t  100           &  500           & 0.9            &  5.00          & 0.18           & 0.54 ( 0.02 )  & 13.78 ( 0.72 ) & 2.58 ( 0.08 )  & 16.20          & 0.76           & 0.54           & 13.70          & 2.58          \\\\\n",
+       "\t  500           &  500           & 0.9            &  1.00          & 0.25           & 0.3 ( 0 )      & 18.18 ( 0.69 ) & 0.03 ( 0.02 )  & 23.15          & 0.72           & 0.30           & 18.10          & 0.03          \\\\\n",
+       "\t 1000           &  500           & 0.9            &  0.50          & 0.33           & 0.28 ( 0 )     & 12.68 ( 0.57 ) & 0 ( 0 )        & 17.68          & 0.63           & 0.28           & 12.60          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.9            & 20.00          & 0.08           & 0.9 ( 0.05 )   & 18.15 ( 0.34 ) & 3.92 ( 0.08 )  & 19.23          & 0.89           & 0.90           & 18.10          & 3.92          \\\\\n",
+       "\t  100           & 1000           & 0.9            & 10.00          & 0.16           & 0.59 ( 0.02 )  & 15.99 ( 0.66 ) & 2.88 ( 0.06 )  & 18.11          & 0.81           & 0.59           & 15.90          & 2.88          \\\\\n",
+       "\t  500           & 1000           & 0.9            &  2.00          & 0.20           & 0.31 ( 0 )     & 24.62 ( 1.01 ) & 0.05 ( 0.02 )  & 29.57          & 0.77           & 0.31           & 24.60          & 0.05          \\\\\n",
+       "\t 1000           & 1000           & 0.9            &  1.00          & 0.25           & 0.28 ( 0 )     & 18.51 ( 0.86 ) & 0 ( 0 )        & 23.51          & 0.71           & 0.28           & 18.50          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           | 0.1            |  1.00          | 0.36           | 0.62 ( 0.04 )  | 8.59 ( 0.46 )  | 0.02 ( 0.01 )  | 13.57          | 0.51           | 0.62           |  8.59          | 0.02           |\n",
+       "|  100           |   50           | 0.1            |  0.50          | 0.47           | 0.37 ( 0.01 )  | 6.3 ( 0.43 )   | 0 ( 0 )        | 11.30          | 0.4            | 0.37           |  6.30          | 0.00           |\n",
+       "|  500           |   50           | 0.1            |  0.10          | 0.73           | 0.28 ( 0 )     | 2.88 ( 0.21 )  | 0 ( 0 )        |  7.88          | 0.2            | 0.28           |  2.88          | 0.00           |\n",
+       "| 1000           |   50           | 0.1            |  0.05          | 0.89           | 0.27 ( 0 )     | 1.66 ( 0.11 )  | 0 ( 0 )        |  6.66          | 0.08           | 0.27           |  1.66          | 0.00           |\n",
+       "|   50           |  100           | 0.1            |  2.00          | 0.32           | 0.67 ( 0.05 )  | 11.84 ( 0.4 )  | 0 ( 0 )        | 16.84          | 0.62           | 0.67           | 11.80          | 0.00           |\n",
+       "|  100           |  100           | 0.1            |  1.00          | 0.44           | 0.4 ( 0.01 )   | 7.71 ( 0.56 )  | 0 ( 0 )        | 12.71          | 0.46           | 0.40           |  7.71          | 0.00           |\n",
+       "|  500           |  100           | 0.1            |  0.20          | 0.70           | 0.27 ( 0 )     | 3.35 ( 0.22 )  | 0 ( 0 )        |  8.35          | 0.23           | 0.27           |  3.35          | 0.00           |\n",
+       "| 1000           |  100           | 0.1            |  0.10          | 0.81           | 0.27 ( 0 )     | 2.29 ( 0.23 )  | 0 ( 0 )        |  7.29          | 0.13           | 0.27           |  2.29          | 0.00           |\n",
+       "|   50           |  500           | 0.1            | 10.00          | 0.18           | 1.61 ( 0.1 )   | 20.85 ( 0.38 ) | 0.69 ( 0.08 )  | 25.16          | 0.78           | 1.61           | 20.80          | 0.69           |\n",
+       "|  100           |  500           | 0.1            |  5.00          | 0.30           | 0.53 ( 0.02 )  | 14.69 ( 0.7 )  | 0 ( 0 )        | 19.69          | 0.66           | 0.53           | 14.60          | 0.00           |\n",
+       "|  500           |  500           | 0.1            |  1.00          | 0.55           | 0.28 ( 0 )     | 5.86 ( 0.51 )  | 0 ( 0 )        | 10.86          | 0.36           | 0.28           |  5.86          | 0.00           |\n",
+       "| 1000           |  500           | 0.1            |  0.50          | 0.78           | 0.28 ( 0 )     | 2.69 ( 0.21 )  | 0 ( 0 )        |  7.69          | 0.17           | 0.28           |  2.69          | 0.00           |\n",
+       "|   50           | 1000           | 0.1            | 20.00          | 0.13           | 2.23 ( 0.14 )  | 24.37 ( 0.39 ) | 1.36 ( 0.1 )   | 28.01          | 0.83           | 2.23           | 24.30          | 1.36           |\n",
+       "|  100           | 1000           | 0.1            | 10.00          | 0.25           | 0.57 ( 0.03 )  | 18.79 ( 0.7 )  | 0 ( 0 )        | 23.79          | 0.73           | 0.57           | 18.70          | 0.00           |\n",
+       "|  500           | 1000           | 0.1            |  2.00          | 0.49           | 0.28 ( 0 )     | 7.08 ( 0.53 )  | 0 ( 0 )        | 12.08          | 0.41           | 0.28           |  7.08          | 0.00           |\n",
+       "| 1000           | 1000           | 0.1            |  1.00          | 0.70           | 0.27 ( 0 )     | 3.59 ( 0.33 )  | 0 ( 0 )        |  8.59          | 0.23           | 0.27           |  3.59          | 0.00           |\n",
+       "|   50           |   50           | 0.3            |  1.00          | 0.35           | 0.57 ( 0.03 )  | 8.86 ( 0.42 )  | 0.05 ( 0.02 )  | 13.81          | 0.53           | 0.57           |  8.86          | 0.05           |\n",
+       "|  100           |   50           | 0.3            |  0.50          | 0.47           | 0.36 ( 0.01 )  | 6.18 ( 0.36 )  | 0 ( 0 )        | 11.18          | 0.41           | 0.36           |  6.18          | 0.00           |\n",
+       "|  500           |   50           | 0.3            |  0.10          | 0.70           | 0.28 ( 0 )     | 3.14 ( 0.21 )  | 0 ( 0 )        |  8.14          | 0.22           | 0.28           |  3.14          | 0.00           |\n",
+       "| 1000           |   50           | 0.3            |  0.05          | 0.86           | 0.27 ( 0 )     | 1.86 ( 0.11 )  | 0 ( 0 )        |  6.86          | 0.11           | 0.27           |  1.86          | 0.00           |\n",
+       "|   50           |  100           | 0.3            |  2.00          | 0.31           | 0.76 ( 0.04 )  | 11.12 ( 0.37 ) | 0.17 ( 0.04 )  | 15.95          | 0.62           | 0.76           | 11.10          | 0.17           |\n",
+       "|  100           |  100           | 0.3            |  1.00          | 0.38           | 0.41 ( 0.01 )  | 9.43 ( 0.53 )  | 0 ( 0 )        | 14.43          | 0.54           | 0.41           |  9.43          | 0.00           |\n",
+       "|  500           |  100           | 0.3            |  0.20          | 0.63           | 0.27 ( 0 )     | 4.16 ( 0.27 )  | 0 ( 0 )        |  9.16          | 0.29           | 0.27           |  4.16          | 0.00           |\n",
+       "| 1000           |  100           | 0.3            |  0.10          | 0.80           | 0.27 ( 0 )     | 2.36 ( 0.17 )  | 0 ( 0 )        |  7.36          | 0.15           | 0.27           |  2.36          | 0.00           |\n",
+       "|   50           |  500           | 0.3            | 10.00          | 0.14           | 1.78 ( 0.11 )  | 21.4 ( 0.41 )  | 1.39 ( 0.1 )   | 25.01          | 0.81           | 1.78           | 21.40          | 1.39           |\n",
+       "|  100           |  500           | 0.3            |  5.00          | 0.26           | 0.53 ( 0.02 )  | 16.81 ( 0.77 ) | 0.02 ( 0.01 )  | 21.79          | 0.7            | 0.53           | 16.80          | 0.02           |\n",
+       "|  500           |  500           | 0.3            |  1.00          | 0.55           | 0.29 ( 0 )     | 5.89 ( 0.51 )  | 0 ( 0 )        | 10.89          | 0.36           | 0.29           |  5.89          | 0.00           |\n",
+       "| 1000           |  500           | 0.3            |  0.50          | 0.69           | 0.27 ( 0 )     | 3.68 ( 0.32 )  | 0 ( 0 )        |  8.68          | 0.25           | 0.27           |  3.68          | 0.00           |\n",
+       "|   50           | 1000           | 0.3            | 20.00          | 0.11           | 2.21 ( 0.15 )  | 25.14 ( 0.35 ) | 1.87 ( 0.1 )   | 28.27          | 0.85           | 2.21           | 25.10          | 1.87           |\n",
+       "|  100           | 1000           | 0.3            | 10.00          | 0.22           | 0.61 ( 0.03 )  | 21.11 ( 0.75 ) | 0.02 ( 0.01 )  | 26.09          | 0.75           | 0.61           | 21.10          | 0.02           |\n",
+       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
+       "|  500           |   50           | 0.7            |  0.10          | 0.60           | 0.27 ( 0 )     | 4.38 ( 0.21 )  | 0 ( 0 )        |  9.38          | 0.33           | 0.27           |  4.38          | 0.00           |\n",
+       "| 1000           |   50           | 0.7            |  0.05          | 0.74           | 0.27 ( 0 )     | 2.84 ( 0.14 )  | 0 ( 0 )        |  7.84          | 0.21           | 0.27           |  2.84          | 0.00           |\n",
+       "|   50           |  100           | 0.7            |  2.00          | 0.23           | 0.8 ( 0.04 )   | 11.57 ( 0.53 ) | 1.26 ( 0.1 )   | 15.31          | 0.66           | 0.80           | 11.50          | 1.26           |\n",
+       "|  100           |  100           | 0.7            |  1.00          | 0.31           | 0.39 ( 0.01 )  | 11.68 ( 0.51 ) | 0.1 ( 0.04 )   | 16.58          | 0.61           | 0.39           | 11.60          | 0.10           |\n",
+       "|  500           |  100           | 0.7            |  0.20          | 0.49           | 0.28 ( 0 )     | 6.56 ( 0.32 )  | 0 ( 0 )        | 11.56          | 0.44           | 0.28           |  6.56          | 0.00           |\n",
+       "| 1000           |  100           | 0.7            |  0.10          | 0.63           | 0.27 ( 0 )     | 4.24 ( 0.26 )  | 0 ( 0 )        |  9.24          | 0.3            | 0.27           |  4.24          | 0.00           |\n",
+       "|   50           |  500           | 0.7            | 10.00          | 0.12           | 1.37 ( 0.08 )  | 19.8 ( 0.33 )  | 2.78 ( 0.08 )  | 22.02          | 0.85           | 1.37           | 19.80          | 2.78           |\n",
+       "|  100           |  500           | 0.7            |  5.00          | 0.19           | 0.62 ( 0.03 )  | 18.58 ( 0.85 ) | 0.88 ( 0.08 )  | 22.70          | 0.75           | 0.62           | 18.50          | 0.88           |\n",
+       "|  500           |  500           | 0.7            |  1.00          | 0.36           | 0.3 ( 0 )      | 11.31 ( 0.6 )  | 0 ( 0 )        | 16.31          | 0.58           | 0.30           | 11.30          | 0.00           |\n",
+       "| 1000           |  500           | 0.7            |  0.50          | 0.46           | 0.27 ( 0 )     | 7.93 ( 0.49 )  | 0 ( 0 )        | 12.93          | 0.47           | 0.27           |  7.93          | 0.00           |\n",
+       "|   50           | 1000           | 0.7            | 20.00          | 0.09           | 1.5 ( 0.08 )   | 22.99 ( 0.35 ) | 3.2 ( 0.09 )   | 24.79          | 0.88           | 1.50           | 22.90          | 3.20           |\n",
+       "|  100           | 1000           | 0.7            | 10.00          | 0.14           | 0.77 ( 0.04 )  | 22.43 ( 0.68 ) | 1.45 ( 0.1 )   | 25.98          | 0.82           | 0.77           | 22.40          | 1.45           |\n",
+       "|  500           | 1000           | 0.7            |  2.00          | 0.30           | 0.31 ( 0 )     | 14.7 ( 0.84 )  | 0 ( 0 )        | 19.70          | 0.65           | 0.31           | 14.70          | 0.00           |\n",
+       "| 1000           | 1000           | 0.7            |  1.00          | 0.41           | 0.28 ( 0 )     | 9.63 ( 0.54 )  | 0 ( 0 )        | 14.63          | 0.53           | 0.28           |  9.63          | 0.00           |\n",
+       "|   50           |   50           | 0.9            |  1.00          | 0.31           | 0.55 ( 0.03 )  | 5.65 ( 0.29 )  | 2.17 ( 0.13 )  |  8.48          | 0.51           | 0.55           |  5.65          | 2.17           |\n",
+       "|  100           |   50           | 0.9            |  0.50          | 0.38           | 0.38 ( 0.02 )  | 5.85 ( 0.3 )   | 1.03 ( 0.11 )  |  9.82          | 0.45           | 0.38           |  5.85          | 1.03           |\n",
+       "|  500           |   50           | 0.9            |  0.10          | 0.61           | 0.28 ( 0 )     | 4.34 ( 0.19 )  | 0.01 ( 0.01 )  |  9.33          | 0.33           | 0.28           |  4.34          | 0.01           |\n",
+       "| 1000           |   50           | 0.9            |  0.05          | 0.76           | 0.27 ( 0 )     | 2.71 ( 0.12 )  | 0 ( 0 )        |  7.71          | 0.2            | 0.27           |  2.71          | 0.00           |\n",
+       "|   50           |  100           | 0.9            |  2.00          | 0.26           | 0.6 ( 0.03 )   | 7.26 ( 0.35 )  | 2.69 ( 0.1 )   |  9.57          | 0.62           | 0.60           |  7.26          | 2.69           |\n",
+       "|  100           |  100           | 0.9            |  1.00          | 0.31           | 0.44 ( 0.01 )  | 7.79 ( 0.39 )  | 1.51 ( 0.11 )  | 11.28          | 0.57           | 0.44           |  7.79          | 1.51           |\n",
+       "|  500           |  100           | 0.9            |  0.20          | 0.51           | 0.29 ( 0 )     | 6.45 ( 0.27 )  | 0 ( 0 )        | 11.45          | 0.45           | 0.29           |  6.45          | 0.00           |\n",
+       "| 1000           |  100           | 0.9            |  0.10          | 0.61           | 0.27 ( 0 )     | 4.72 ( 0.21 )  | 0 ( 0 )        |  9.72          | 0.35           | 0.27           |  4.72          | 0.00           |\n",
+       "|   50           |  500           | 0.9            | 10.00          | 0.13           | 0.81 ( 0.04 )  | 13.78 ( 0.36 ) | 3.54 ( 0.08 )  | 15.24          | 0.83           | 0.81           | 13.70          | 3.54           |\n",
+       "|  100           |  500           | 0.9            |  5.00          | 0.18           | 0.54 ( 0.02 )  | 13.78 ( 0.72 ) | 2.58 ( 0.08 )  | 16.20          | 0.76           | 0.54           | 13.70          | 2.58           |\n",
+       "|  500           |  500           | 0.9            |  1.00          | 0.25           | 0.3 ( 0 )      | 18.18 ( 0.69 ) | 0.03 ( 0.02 )  | 23.15          | 0.72           | 0.30           | 18.10          | 0.03           |\n",
+       "| 1000           |  500           | 0.9            |  0.50          | 0.33           | 0.28 ( 0 )     | 12.68 ( 0.57 ) | 0 ( 0 )        | 17.68          | 0.63           | 0.28           | 12.60          | 0.00           |\n",
+       "|   50           | 1000           | 0.9            | 20.00          | 0.08           | 0.9 ( 0.05 )   | 18.15 ( 0.34 ) | 3.92 ( 0.08 )  | 19.23          | 0.89           | 0.90           | 18.10          | 3.92           |\n",
+       "|  100           | 1000           | 0.9            | 10.00          | 0.16           | 0.59 ( 0.02 )  | 15.99 ( 0.66 ) | 2.88 ( 0.06 )  | 18.11          | 0.81           | 0.59           | 15.90          | 2.88           |\n",
+       "|  500           | 1000           | 0.9            |  2.00          | 0.20           | 0.31 ( 0 )     | 24.62 ( 1.01 ) | 0.05 ( 0.02 )  | 29.57          | 0.77           | 0.31           | 24.60          | 0.05           |\n",
+       "| 1000           | 1000           | 0.9            |  1.00          | 0.25           | 0.28 ( 0 )     | 18.51 ( 0.86 ) | 0 ( 0 )        | 23.51          | 0.71           | 0.28           | 18.50          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "1     50   50 0.1   1.00 0.36 0.62 ( 0.04 ) 8.59 ( 0.46 )  0.02 ( 0.01 )\n",
+       "2    100   50 0.1   0.50 0.47 0.37 ( 0.01 ) 6.3 ( 0.43 )   0 ( 0 )      \n",
+       "3    500   50 0.1   0.10 0.73 0.28 ( 0 )    2.88 ( 0.21 )  0 ( 0 )      \n",
+       "4   1000   50 0.1   0.05 0.89 0.27 ( 0 )    1.66 ( 0.11 )  0 ( 0 )      \n",
+       "5     50  100 0.1   2.00 0.32 0.67 ( 0.05 ) 11.84 ( 0.4 )  0 ( 0 )      \n",
+       "6    100  100 0.1   1.00 0.44 0.4 ( 0.01 )  7.71 ( 0.56 )  0 ( 0 )      \n",
+       "7    500  100 0.1   0.20 0.70 0.27 ( 0 )    3.35 ( 0.22 )  0 ( 0 )      \n",
+       "8   1000  100 0.1   0.10 0.81 0.27 ( 0 )    2.29 ( 0.23 )  0 ( 0 )      \n",
+       "9     50  500 0.1  10.00 0.18 1.61 ( 0.1 )  20.85 ( 0.38 ) 0.69 ( 0.08 )\n",
+       "10   100  500 0.1   5.00 0.30 0.53 ( 0.02 ) 14.69 ( 0.7 )  0 ( 0 )      \n",
+       "11   500  500 0.1   1.00 0.55 0.28 ( 0 )    5.86 ( 0.51 )  0 ( 0 )      \n",
+       "12  1000  500 0.1   0.50 0.78 0.28 ( 0 )    2.69 ( 0.21 )  0 ( 0 )      \n",
+       "13    50 1000 0.1  20.00 0.13 2.23 ( 0.14 ) 24.37 ( 0.39 ) 1.36 ( 0.1 ) \n",
+       "14   100 1000 0.1  10.00 0.25 0.57 ( 0.03 ) 18.79 ( 0.7 )  0 ( 0 )      \n",
+       "15   500 1000 0.1   2.00 0.49 0.28 ( 0 )    7.08 ( 0.53 )  0 ( 0 )      \n",
+       "16  1000 1000 0.1   1.00 0.70 0.27 ( 0 )    3.59 ( 0.33 )  0 ( 0 )      \n",
+       "17    50   50 0.3   1.00 0.35 0.57 ( 0.03 ) 8.86 ( 0.42 )  0.05 ( 0.02 )\n",
+       "18   100   50 0.3   0.50 0.47 0.36 ( 0.01 ) 6.18 ( 0.36 )  0 ( 0 )      \n",
+       "19   500   50 0.3   0.10 0.70 0.28 ( 0 )    3.14 ( 0.21 )  0 ( 0 )      \n",
+       "20  1000   50 0.3   0.05 0.86 0.27 ( 0 )    1.86 ( 0.11 )  0 ( 0 )      \n",
+       "21    50  100 0.3   2.00 0.31 0.76 ( 0.04 ) 11.12 ( 0.37 ) 0.17 ( 0.04 )\n",
+       "22   100  100 0.3   1.00 0.38 0.41 ( 0.01 ) 9.43 ( 0.53 )  0 ( 0 )      \n",
+       "23   500  100 0.3   0.20 0.63 0.27 ( 0 )    4.16 ( 0.27 )  0 ( 0 )      \n",
+       "24  1000  100 0.3   0.10 0.80 0.27 ( 0 )    2.36 ( 0.17 )  0 ( 0 )      \n",
+       "25    50  500 0.3  10.00 0.14 1.78 ( 0.11 ) 21.4 ( 0.41 )  1.39 ( 0.1 ) \n",
+       "26   100  500 0.3   5.00 0.26 0.53 ( 0.02 ) 16.81 ( 0.77 ) 0.02 ( 0.01 )\n",
+       "27   500  500 0.3   1.00 0.55 0.29 ( 0 )    5.89 ( 0.51 )  0 ( 0 )      \n",
+       "28  1000  500 0.3   0.50 0.69 0.27 ( 0 )    3.68 ( 0.32 )  0 ( 0 )      \n",
+       "29    50 1000 0.3  20.00 0.11 2.21 ( 0.15 ) 25.14 ( 0.35 ) 1.87 ( 0.1 ) \n",
+       "30   100 1000 0.3  10.00 0.22 0.61 ( 0.03 ) 21.11 ( 0.75 ) 0.02 ( 0.01 )\n",
+       "... ...  ...  ...  ...   ...  ...           ...            ...          \n",
+       "51   500   50 0.7   0.10 0.60 0.27 ( 0 )    4.38 ( 0.21 )  0 ( 0 )      \n",
+       "52  1000   50 0.7   0.05 0.74 0.27 ( 0 )    2.84 ( 0.14 )  0 ( 0 )      \n",
+       "53    50  100 0.7   2.00 0.23 0.8 ( 0.04 )  11.57 ( 0.53 ) 1.26 ( 0.1 ) \n",
+       "54   100  100 0.7   1.00 0.31 0.39 ( 0.01 ) 11.68 ( 0.51 ) 0.1 ( 0.04 ) \n",
+       "55   500  100 0.7   0.20 0.49 0.28 ( 0 )    6.56 ( 0.32 )  0 ( 0 )      \n",
+       "56  1000  100 0.7   0.10 0.63 0.27 ( 0 )    4.24 ( 0.26 )  0 ( 0 )      \n",
+       "57    50  500 0.7  10.00 0.12 1.37 ( 0.08 ) 19.8 ( 0.33 )  2.78 ( 0.08 )\n",
+       "58   100  500 0.7   5.00 0.19 0.62 ( 0.03 ) 18.58 ( 0.85 ) 0.88 ( 0.08 )\n",
+       "59   500  500 0.7   1.00 0.36 0.3 ( 0 )     11.31 ( 0.6 )  0 ( 0 )      \n",
+       "60  1000  500 0.7   0.50 0.46 0.27 ( 0 )    7.93 ( 0.49 )  0 ( 0 )      \n",
+       "61    50 1000 0.7  20.00 0.09 1.5 ( 0.08 )  22.99 ( 0.35 ) 3.2 ( 0.09 ) \n",
+       "62   100 1000 0.7  10.00 0.14 0.77 ( 0.04 ) 22.43 ( 0.68 ) 1.45 ( 0.1 ) \n",
+       "63   500 1000 0.7   2.00 0.30 0.31 ( 0 )    14.7 ( 0.84 )  0 ( 0 )      \n",
+       "64  1000 1000 0.7   1.00 0.41 0.28 ( 0 )    9.63 ( 0.54 )  0 ( 0 )      \n",
+       "65    50   50 0.9   1.00 0.31 0.55 ( 0.03 ) 5.65 ( 0.29 )  2.17 ( 0.13 )\n",
+       "66   100   50 0.9   0.50 0.38 0.38 ( 0.02 ) 5.85 ( 0.3 )   1.03 ( 0.11 )\n",
+       "67   500   50 0.9   0.10 0.61 0.28 ( 0 )    4.34 ( 0.19 )  0.01 ( 0.01 )\n",
+       "68  1000   50 0.9   0.05 0.76 0.27 ( 0 )    2.71 ( 0.12 )  0 ( 0 )      \n",
+       "69    50  100 0.9   2.00 0.26 0.6 ( 0.03 )  7.26 ( 0.35 )  2.69 ( 0.1 ) \n",
+       "70   100  100 0.9   1.00 0.31 0.44 ( 0.01 ) 7.79 ( 0.39 )  1.51 ( 0.11 )\n",
+       "71   500  100 0.9   0.20 0.51 0.29 ( 0 )    6.45 ( 0.27 )  0 ( 0 )      \n",
+       "72  1000  100 0.9   0.10 0.61 0.27 ( 0 )    4.72 ( 0.21 )  0 ( 0 )      \n",
+       "73    50  500 0.9  10.00 0.13 0.81 ( 0.04 ) 13.78 ( 0.36 ) 3.54 ( 0.08 )\n",
+       "74   100  500 0.9   5.00 0.18 0.54 ( 0.02 ) 13.78 ( 0.72 ) 2.58 ( 0.08 )\n",
+       "75   500  500 0.9   1.00 0.25 0.3 ( 0 )     18.18 ( 0.69 ) 0.03 ( 0.02 )\n",
+       "76  1000  500 0.9   0.50 0.33 0.28 ( 0 )    12.68 ( 0.57 ) 0 ( 0 )      \n",
+       "77    50 1000 0.9  20.00 0.08 0.9 ( 0.05 )  18.15 ( 0.34 ) 3.92 ( 0.08 )\n",
+       "78   100 1000 0.9  10.00 0.16 0.59 ( 0.02 ) 15.99 ( 0.66 ) 2.88 ( 0.06 )\n",
+       "79   500 1000 0.9   2.00 0.20 0.31 ( 0 )    24.62 ( 1.01 ) 0.05 ( 0.02 )\n",
+       "80  1000 1000 0.9   1.00 0.25 0.28 ( 0 )    18.51 ( 0.86 ) 0 ( 0 )      \n",
+       "    num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1   13.57      0.51 0.62      8.59   0.02   \n",
+       "2   11.30      0.4  0.37      6.30   0.00   \n",
+       "3    7.88      0.2  0.28      2.88   0.00   \n",
+       "4    6.66      0.08 0.27      1.66   0.00   \n",
+       "5   16.84      0.62 0.67     11.80   0.00   \n",
+       "6   12.71      0.46 0.40      7.71   0.00   \n",
+       "7    8.35      0.23 0.27      3.35   0.00   \n",
+       "8    7.29      0.13 0.27      2.29   0.00   \n",
+       "9   25.16      0.78 1.61     20.80   0.69   \n",
+       "10  19.69      0.66 0.53     14.60   0.00   \n",
+       "11  10.86      0.36 0.28      5.86   0.00   \n",
+       "12   7.69      0.17 0.28      2.69   0.00   \n",
+       "13  28.01      0.83 2.23     24.30   1.36   \n",
+       "14  23.79      0.73 0.57     18.70   0.00   \n",
+       "15  12.08      0.41 0.28      7.08   0.00   \n",
+       "16   8.59      0.23 0.27      3.59   0.00   \n",
+       "17  13.81      0.53 0.57      8.86   0.05   \n",
+       "18  11.18      0.41 0.36      6.18   0.00   \n",
+       "19   8.14      0.22 0.28      3.14   0.00   \n",
+       "20   6.86      0.11 0.27      1.86   0.00   \n",
+       "21  15.95      0.62 0.76     11.10   0.17   \n",
+       "22  14.43      0.54 0.41      9.43   0.00   \n",
+       "23   9.16      0.29 0.27      4.16   0.00   \n",
+       "24   7.36      0.15 0.27      2.36   0.00   \n",
+       "25  25.01      0.81 1.78     21.40   1.39   \n",
+       "26  21.79      0.7  0.53     16.80   0.02   \n",
+       "27  10.89      0.36 0.29      5.89   0.00   \n",
+       "28   8.68      0.25 0.27      3.68   0.00   \n",
+       "29  28.27      0.85 2.21     25.10   1.87   \n",
+       "30  26.09      0.75 0.61     21.10   0.02   \n",
+       "... ...        ...  ...      ...     ...    \n",
+       "51   9.38      0.33 0.27      4.38   0.00   \n",
+       "52   7.84      0.21 0.27      2.84   0.00   \n",
+       "53  15.31      0.66 0.80     11.50   1.26   \n",
+       "54  16.58      0.61 0.39     11.60   0.10   \n",
+       "55  11.56      0.44 0.28      6.56   0.00   \n",
+       "56   9.24      0.3  0.27      4.24   0.00   \n",
+       "57  22.02      0.85 1.37     19.80   2.78   \n",
+       "58  22.70      0.75 0.62     18.50   0.88   \n",
+       "59  16.31      0.58 0.30     11.30   0.00   \n",
+       "60  12.93      0.47 0.27      7.93   0.00   \n",
+       "61  24.79      0.88 1.50     22.90   3.20   \n",
+       "62  25.98      0.82 0.77     22.40   1.45   \n",
+       "63  19.70      0.65 0.31     14.70   0.00   \n",
+       "64  14.63      0.53 0.28      9.63   0.00   \n",
+       "65   8.48      0.51 0.55      5.65   2.17   \n",
+       "66   9.82      0.45 0.38      5.85   1.03   \n",
+       "67   9.33      0.33 0.28      4.34   0.01   \n",
+       "68   7.71      0.2  0.27      2.71   0.00   \n",
+       "69   9.57      0.62 0.60      7.26   2.69   \n",
+       "70  11.28      0.57 0.44      7.79   1.51   \n",
+       "71  11.45      0.45 0.29      6.45   0.00   \n",
+       "72   9.72      0.35 0.27      4.72   0.00   \n",
+       "73  15.24      0.83 0.81     13.70   3.54   \n",
+       "74  16.20      0.76 0.54     13.70   2.58   \n",
+       "75  23.15      0.72 0.30     18.10   0.03   \n",
+       "76  17.68      0.63 0.28     12.60   0.00   \n",
+       "77  19.23      0.89 0.90     18.10   3.92   \n",
+       "78  18.11      0.81 0.59     15.90   2.88   \n",
+       "79  29.57      0.77 0.31     24.60   0.05   \n",
+       "80  23.51      0.71 0.28     18.50   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.2_sim_toe_elnet_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.2_sim_toe_elnet_cts-checkpoint.ipynb
new file mode 100755
index 0000000..445c6b6
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.2_sim_toe_elnet_cts-checkpoint.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_elnet))\n",
+    "for (i in 1:length(results_toe_elnet)){\n",
+    "    table_toe = rbind(table_toe, results_toe_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50            </td><td>50            </td><td>0.1           </td><td>14.72 ( 0.57 )</td><td>0.01 ( 0.01 ) </td><td>0.6 ( 0.03 )  </td><td>0.21          </td><td>19.71         </td><td>0.67          </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>13.7 ( 0.66 )</td><td>0 ( 0 )      </td><td>0.34 ( 0.01 )</td><td>0.23         </td><td>18.70        </td><td>0.64         </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0.1           </td><td>13.59 ( 0.55 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.23          </td><td>18.59         </td><td>0.65          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0.1           </td><td>12.64 ( 0.54 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.25          </td><td>17.64         </td><td>0.63          </td></tr>\n",
+       "\t<tr><td>50            </td><td>100           </td><td>0.1           </td><td>19.33 ( 0.67 )</td><td>0 ( 0 )       </td><td>0.69 ( 0.05 ) </td><td>0.2           </td><td>24.33         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>17.77 ( 0.8 )</td><td>0 ( 0 )      </td><td>0.38 ( 0.01 )</td><td>0.22         </td><td>22.77        </td><td>0.70         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50             & 50             & 0.1            & 14.72 ( 0.57 ) & 0.01 ( 0.01 )  & 0.6 ( 0.03 )   & 0.21           & 19.71          & 0.67          \\\\\n",
+       "\t 100           & 50            & 0.1           & 13.7 ( 0.66 ) & 0 ( 0 )       & 0.34 ( 0.01 ) & 0.23          & 18.70         & 0.64         \\\\\n",
+       "\t 500            & 50             & 0.1            & 13.59 ( 0.55 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.23           & 18.59          & 0.65          \\\\\n",
+       "\t 1000           & 50             & 0.1            & 12.64 ( 0.54 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.25           & 17.64          & 0.63          \\\\\n",
+       "\t 50             & 100            & 0.1            & 19.33 ( 0.67 ) & 0 ( 0 )        & 0.69 ( 0.05 )  & 0.2            & 24.33          & 0.73          \\\\\n",
+       "\t 100           & 100           & 0.1           & 17.77 ( 0.8 ) & 0 ( 0 )       & 0.38 ( 0.01 ) & 0.22          & 22.77         & 0.70         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50             | 50             | 0.1            | 14.72 ( 0.57 ) | 0.01 ( 0.01 )  | 0.6 ( 0.03 )   | 0.21           | 19.71          | 0.67           |\n",
+       "| 100           | 50            | 0.1           | 13.7 ( 0.66 ) | 0 ( 0 )       | 0.34 ( 0.01 ) | 0.23          | 18.70         | 0.64          |\n",
+       "| 500            | 50             | 0.1            | 13.59 ( 0.55 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.23           | 18.59          | 0.65           |\n",
+       "| 1000           | 50             | 0.1            | 12.64 ( 0.54 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.25           | 17.64          | 0.63           |\n",
+       "| 50             | 100            | 0.1            | 19.33 ( 0.67 ) | 0 ( 0 )        | 0.69 ( 0.05 )  | 0.2            | 24.33          | 0.73           |\n",
+       "| 100           | 100           | 0.1           | 17.77 ( 0.8 ) | 0 ( 0 )       | 0.38 ( 0.01 ) | 0.22          | 22.77         | 0.70          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 14.72 ( 0.57 ) 0.01 ( 0.01 ) 0.6 ( 0.03 )  0.21 19.71      0.67\n",
+       "2 100  50  0.1 13.7 ( 0.66 )  0 ( 0 )       0.34 ( 0.01 ) 0.23 18.70      0.64\n",
+       "3 500  50  0.1 13.59 ( 0.55 ) 0 ( 0 )       0.26 ( 0 )    0.23 18.59      0.65\n",
+       "4 1000 50  0.1 12.64 ( 0.54 ) 0 ( 0 )       0.26 ( 0 )    0.25 17.64      0.63\n",
+       "5 50   100 0.1 19.33 ( 0.67 ) 0 ( 0 )       0.69 ( 0.05 ) 0.2  24.33      0.73\n",
+       "6 100  100 0.1 17.77 ( 0.8 )  0 ( 0 )       0.38 ( 0.01 ) 0.22 22.77      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>24</th><td>1000         </td><td>100          </td><td>0.3          </td><td>0.1          </td><td>0.20         </td><td>0.26 ( 0 )   </td><td>19 ( 0.86 )  </td><td>0 ( 0 )      </td><td>24.00        </td><td>0.71         </td><td>0.26         </td><td>NA           </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100         </td><td> 50          </td><td>0.9          </td><td>0.5          </td><td>0.21         </td><td>0.35 ( 0.01 )</td><td>14 ( 0.69 )  </td><td>0.27 ( 0.06 )</td><td>18.73        </td><td>0.66         </td><td>0.35         </td><td>NA           </td><td>0.27         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t24 & 1000          & 100           & 0.3           & 0.1           & 0.20          & 0.26 ( 0 )    & 19 ( 0.86 )   & 0 ( 0 )       & 24.00         & 0.71          & 0.26          & NA            & 0.00         \\\\\n",
+       "\t66 &  100          &  50           & 0.9           & 0.5           & 0.21          & 0.35 ( 0.01 ) & 14 ( 0.69 )   & 0.27 ( 0.06 ) & 18.73         & 0.66          & 0.35          & NA            & 0.27         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 24 | 1000          | 100           | 0.3           | 0.1           | 0.20          | 0.26 ( 0 )    | 19 ( 0.86 )   | 0 ( 0 )       | 24.00         | 0.71          | 0.26          | NA            | 0.00          |\n",
+       "| 66 |  100          |  50           | 0.9           | 0.5           | 0.21          | 0.35 ( 0.01 ) | 14 ( 0.69 )   | 0.27 ( 0.06 ) | 18.73         | 0.66          | 0.35          | NA            | 0.27          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "24 1000 100 0.3  0.1   0.20 0.26 ( 0 )    19 ( 0.86 ) 0 ( 0 )       24.00     \n",
+       "66  100  50 0.9  0.5   0.21 0.35 ( 0.01 ) 14 ( 0.69 ) 0.27 ( 0.06 ) 18.73     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "24 0.71 0.26     NA      0.00   \n",
+       "66 0.66 0.35     NA      0.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$FP_mean[24] = 19\n",
+    "result.table_toe$FP_mean[66] = 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>24</th><td>1000         </td><td>100          </td><td>0.3          </td><td>0.1          </td><td>0.20         </td><td>0.26 ( 0 )   </td><td>19 ( 0.86 )  </td><td>0 ( 0 )      </td><td>24.00        </td><td>0.71         </td><td>0.26         </td><td>19           </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100         </td><td> 50          </td><td>0.9          </td><td>0.5          </td><td>0.21         </td><td>0.35 ( 0.01 )</td><td>14 ( 0.69 )  </td><td>0.27 ( 0.06 )</td><td>18.73        </td><td>0.66         </td><td>0.35         </td><td>14           </td><td>0.27         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t24 & 1000          & 100           & 0.3           & 0.1           & 0.20          & 0.26 ( 0 )    & 19 ( 0.86 )   & 0 ( 0 )       & 24.00         & 0.71          & 0.26          & 19            & 0.00         \\\\\n",
+       "\t66 &  100          &  50           & 0.9           & 0.5           & 0.21          & 0.35 ( 0.01 ) & 14 ( 0.69 )   & 0.27 ( 0.06 ) & 18.73         & 0.66          & 0.35          & 14            & 0.27         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 24 | 1000          | 100           | 0.3           | 0.1           | 0.20          | 0.26 ( 0 )    | 19 ( 0.86 )   | 0 ( 0 )       | 24.00         | 0.71          | 0.26          | 19            | 0.00          |\n",
+       "| 66 |  100          |  50           | 0.9           | 0.5           | 0.21          | 0.35 ( 0.01 ) | 14 ( 0.69 )   | 0.27 ( 0.06 ) | 18.73         | 0.66          | 0.35          | 14            | 0.27          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "24 1000 100 0.3  0.1   0.20 0.26 ( 0 )    19 ( 0.86 ) 0 ( 0 )       24.00     \n",
+       "66  100  50 0.9  0.5   0.21 0.35 ( 0.01 ) 14 ( 0.69 ) 0.27 ( 0.06 ) 18.73     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "24 0.71 0.26     19      0.00   \n",
+       "66 0.66 0.35     14      0.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[c(24, 66), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td> 50           </td><td>0.1           </td><td>1.00          </td><td>0.21          </td><td>0.6 ( 0.03 )  </td><td>14.72 ( 0.57 )</td><td>0.01 ( 0.01 ) </td><td>19.71         </td><td>0.67          </td><td>0.60          </td><td>14.7          </td><td>0.01          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 50           </td><td>0.1           </td><td>0.50          </td><td>0.23          </td><td>0.34 ( 0.01 ) </td><td>13.7 ( 0.66 ) </td><td>0 ( 0 )       </td><td>18.70         </td><td>0.64          </td><td>0.34          </td><td>13.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 50           </td><td>0.1           </td><td>0.10          </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>13.59 ( 0.55 )</td><td>0 ( 0 )       </td><td>18.59         </td><td>0.65          </td><td>0.26          </td><td>13.5          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 50           </td><td>0.1           </td><td>0.05          </td><td>0.25          </td><td>0.26 ( 0 )    </td><td>12.64 ( 0.54 )</td><td>0 ( 0 )       </td><td>17.64         </td><td>0.63          </td><td>0.26          </td><td>12.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>100           </td><td>0.1           </td><td>2.00          </td><td>0.20          </td><td>0.69 ( 0.05 ) </td><td>19.33 ( 0.67 )</td><td>0 ( 0 )       </td><td>24.33         </td><td>0.73          </td><td>0.69          </td><td>19.3          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>100           </td><td>0.1           </td><td>1.00          </td><td>0.22          </td><td>0.38 ( 0.01 ) </td><td>17.77 ( 0.8 ) </td><td>0 ( 0 )       </td><td>22.77         </td><td>0.7           </td><td>0.38          </td><td>17.7          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &  50            & 0.1            & 1.00           & 0.21           & 0.6 ( 0.03 )   & 14.72 ( 0.57 ) & 0.01 ( 0.01 )  & 19.71          & 0.67           & 0.60           & 14.7           & 0.01          \\\\\n",
+       "\t  100           &  50            & 0.1            & 0.50           & 0.23           & 0.34 ( 0.01 )  & 13.7 ( 0.66 )  & 0 ( 0 )        & 18.70          & 0.64           & 0.34           & 13.7           & 0.00          \\\\\n",
+       "\t  500           &  50            & 0.1            & 0.10           & 0.23           & 0.26 ( 0 )     & 13.59 ( 0.55 ) & 0 ( 0 )        & 18.59          & 0.65           & 0.26           & 13.5           & 0.00          \\\\\n",
+       "\t 1000           &  50            & 0.1            & 0.05           & 0.25           & 0.26 ( 0 )     & 12.64 ( 0.54 ) & 0 ( 0 )        & 17.64          & 0.63           & 0.26           & 12.6           & 0.00          \\\\\n",
+       "\t   50           & 100            & 0.1            & 2.00           & 0.20           & 0.69 ( 0.05 )  & 19.33 ( 0.67 ) & 0 ( 0 )        & 24.33          & 0.73           & 0.69           & 19.3           & 0.00          \\\\\n",
+       "\t  100           & 100            & 0.1            & 1.00           & 0.22           & 0.38 ( 0.01 )  & 17.77 ( 0.8 )  & 0 ( 0 )        & 22.77          & 0.7            & 0.38           & 17.7           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |  50            | 0.1            | 1.00           | 0.21           | 0.6 ( 0.03 )   | 14.72 ( 0.57 ) | 0.01 ( 0.01 )  | 19.71          | 0.67           | 0.60           | 14.7           | 0.01           |\n",
+       "|  100           |  50            | 0.1            | 0.50           | 0.23           | 0.34 ( 0.01 )  | 13.7 ( 0.66 )  | 0 ( 0 )        | 18.70          | 0.64           | 0.34           | 13.7           | 0.00           |\n",
+       "|  500           |  50            | 0.1            | 0.10           | 0.23           | 0.26 ( 0 )     | 13.59 ( 0.55 ) | 0 ( 0 )        | 18.59          | 0.65           | 0.26           | 13.5           | 0.00           |\n",
+       "| 1000           |  50            | 0.1            | 0.05           | 0.25           | 0.26 ( 0 )     | 12.64 ( 0.54 ) | 0 ( 0 )        | 17.64          | 0.63           | 0.26           | 12.6           | 0.00           |\n",
+       "|   50           | 100            | 0.1            | 2.00           | 0.20           | 0.69 ( 0.05 )  | 19.33 ( 0.67 ) | 0 ( 0 )        | 24.33          | 0.73           | 0.69           | 19.3           | 0.00           |\n",
+       "|  100           | 100            | 0.1            | 1.00           | 0.22           | 0.38 ( 0.01 )  | 17.77 ( 0.8 )  | 0 ( 0 )        | 22.77          | 0.7            | 0.38           | 17.7           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.21 0.6 ( 0.03 )  14.72 ( 0.57 ) 0.01 ( 0.01 )\n",
+       "2  100  50 0.1  0.50  0.23 0.34 ( 0.01 ) 13.7 ( 0.66 )  0 ( 0 )      \n",
+       "3  500  50 0.1  0.10  0.23 0.26 ( 0 )    13.59 ( 0.55 ) 0 ( 0 )      \n",
+       "4 1000  50 0.1  0.05  0.25 0.26 ( 0 )    12.64 ( 0.54 ) 0 ( 0 )      \n",
+       "5   50 100 0.1  2.00  0.20 0.69 ( 0.05 ) 19.33 ( 0.67 ) 0 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.22 0.38 ( 0.01 ) 17.77 ( 0.8 )  0 ( 0 )      \n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1 19.71      0.67 0.60     14.7    0.01   \n",
+       "2 18.70      0.64 0.34     13.7    0.00   \n",
+       "3 18.59      0.65 0.26     13.5    0.00   \n",
+       "4 17.64      0.63 0.26     12.6    0.00   \n",
+       "5 24.33      0.73 0.69     19.3    0.00   \n",
+       "6 22.77      0.7  0.38     17.7    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.12          </td><td>0.29 ( 0 )    </td><td>41.14 ( 1.02 )</td><td>0 ( 0 )       </td><td>46.14         </td><td>0.86          </td><td>0.29          </td><td>41.1          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>39.21 ( 0.84 )</td><td>0 ( 0 )       </td><td>44.21         </td><td>0.86          </td><td>0.27          </td><td>39.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.04          </td><td>0.94 ( 0.05 ) </td><td>36.59 ( 3.6 ) </td><td>3.72 ( 0.08 ) </td><td>37.87         </td><td>0.92          </td><td>0.94          </td><td>36.5          </td><td>3.72          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.09          </td><td>0.59 ( 0.02 ) </td><td>32.05 ( 1.85 )</td><td>2.59 ( 0.06 ) </td><td>34.46         </td><td>0.88          </td><td>0.59          </td><td>32.0          </td><td>2.59          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.09          </td><td>0.3 ( 0 )     </td><td>57.69 ( 1.57 )</td><td>0 ( 0 )       </td><td>62.69         </td><td>0.9           </td><td>0.30          </td><td>57.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.10          </td><td>0.27 ( 0 )    </td><td>52.35 ( 1.6 ) </td><td>0 ( 0 )       </td><td>57.35         </td><td>0.89          </td><td>0.27          </td><td>52.3          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.12           & 0.29 ( 0 )     & 41.14 ( 1.02 ) & 0 ( 0 )        & 46.14          & 0.86           & 0.29           & 41.1           & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.13           & 0.27 ( 0 )     & 39.21 ( 0.84 ) & 0 ( 0 )        & 44.21          & 0.86           & 0.27           & 39.2           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.04           & 0.94 ( 0.05 )  & 36.59 ( 3.6 )  & 3.72 ( 0.08 )  & 37.87          & 0.92           & 0.94           & 36.5           & 3.72          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.09           & 0.59 ( 0.02 )  & 32.05 ( 1.85 ) & 2.59 ( 0.06 )  & 34.46          & 0.88           & 0.59           & 32.0           & 2.59          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.09           & 0.3 ( 0 )      & 57.69 ( 1.57 ) & 0 ( 0 )        & 62.69          & 0.9            & 0.30           & 57.6           & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.10           & 0.27 ( 0 )     & 52.35 ( 1.6 )  & 0 ( 0 )        & 57.35          & 0.89           & 0.27           & 52.3           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.12           | 0.29 ( 0 )     | 41.14 ( 1.02 ) | 0 ( 0 )        | 46.14          | 0.86           | 0.29           | 41.1           | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.13           | 0.27 ( 0 )     | 39.21 ( 0.84 ) | 0 ( 0 )        | 44.21          | 0.86           | 0.27           | 39.2           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.04           | 0.94 ( 0.05 )  | 36.59 ( 3.6 )  | 3.72 ( 0.08 )  | 37.87          | 0.92           | 0.94           | 36.5           | 3.72           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.09           | 0.59 ( 0.02 )  | 32.05 ( 1.85 ) | 2.59 ( 0.06 )  | 34.46          | 0.88           | 0.59           | 32.0           | 2.59           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.09           | 0.3 ( 0 )      | 57.69 ( 1.57 ) | 0 ( 0 )        | 62.69          | 0.9            | 0.30           | 57.6           | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.10           | 0.27 ( 0 )     | 52.35 ( 1.6 )  | 0 ( 0 )        | 57.35          | 0.89           | 0.27           | 52.3           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.12 0.29 ( 0 )    41.14 ( 1.02 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.13 0.27 ( 0 )    39.21 ( 0.84 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.04 0.94 ( 0.05 ) 36.59 ( 3.6 )  3.72 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.09 0.59 ( 0.02 ) 32.05 ( 1.85 ) 2.59 ( 0.06 )\n",
+       "79  500 1000 0.9   2.0  0.09 0.3 ( 0 )     57.69 ( 1.57 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.10 0.27 ( 0 )    52.35 ( 1.6 )  0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 46.14      0.86 0.29     41.1    0.00   \n",
+       "76 44.21      0.86 0.27     39.2    0.00   \n",
+       "77 37.87      0.92 0.94     36.5    3.72   \n",
+       "78 34.46      0.88 0.59     32.0    2.59   \n",
+       "79 62.69      0.9  0.30     57.6    0.00   \n",
+       "80 57.35      0.89 0.27     52.3    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.3_sim_toe_rf_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.3_sim_toe_rf_cts-checkpoint.ipynb
new file mode 100755
index 0000000..38b3c7f
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.3_sim_toe_rf_cts-checkpoint.ipynb
@@ -0,0 +1,766 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_rf))\n",
+    "for (i in 1:length(results_toe_rf)){\n",
+    "    results_toe_rf[[i]]$OOB = paste(round(mean(results_toe_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_toe_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_toe = rbind(table_toe, results_toe_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.4 ( 0.06 ) </td><td>NaN          </td><td>1.98 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>0.86 ( 0.03 )</td><td>0.19         </td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>0.44 ( 0.01 )</td><td>0.77         </td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>0.34 ( 0 )   </td><td>0.86         </td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.36 ( 0.06 )</td><td>NaN          </td><td>2.19 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>0.93 ( 0.03 )</td><td>0.12         </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 1.4 ( 0.06 )  & NaN           & 1.98 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0.1           & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 0.86 ( 0.03 ) & 0.19          & 1.76 ( 0.01 ) & 3.62          & 0.48         \\\\\n",
+       "\t 500           & 50            & 0.1           & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 0.44 ( 0.01 ) & 0.77          & 1.29 ( 0 )    & 4.51          & 0.06         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 0.34 ( 0 )    & 0.86          & 1.15 ( 0 )    & 5.22          & 0.02         \\\\\n",
+       "\t 50            & 100           & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 1.36 ( 0.06 ) & NaN           & 2.19 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 100           & 0.1           & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 0.93 ( 0.03 ) & 0.12          & 1.99 ( 0.01 ) & 5.93          & 0.68         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 1.4 ( 0.06 )  | NaN           | 1.98 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0.1           | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 0.86 ( 0.03 ) | 0.19          | 1.76 ( 0.01 ) | 3.62          | 0.48          |\n",
+       "| 500           | 50            | 0.1           | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 0.44 ( 0.01 ) | 0.77          | 1.29 ( 0 )    | 4.51          | 0.06          |\n",
+       "| 1000          | 50            | 0.1           | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 0.34 ( 0 )    | 0.86          | 1.15 ( 0 )    | 5.22          | 0.02          |\n",
+       "| 50            | 100           | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 1.36 ( 0.06 ) | NaN           | 2.19 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 100           | 0.1           | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 0.93 ( 0.03 ) | 0.12          | 1.99 ( 0.01 ) | 5.93          | 0.68          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab OOB          \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1.4 ( 0.06 )  NaN  1.98 ( 0.02 )\n",
+       "2 100  50  0.1 1.87 ( 0.14 ) 4.25 ( 0.09 ) 0.86 ( 0.03 ) 0.19 1.76 ( 0.01 )\n",
+       "3 500  50  0.1 0.32 ( 0.05 ) 1.81 ( 0.07 ) 0.44 ( 0.01 ) 0.77 1.29 ( 0 )   \n",
+       "4 1000 50  0.1 0.15 ( 0.04 ) 0.93 ( 0.07 ) 0.34 ( 0 )    0.86 1.15 ( 0 )   \n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1.36 ( 0.06 ) NaN  2.19 ( 0.02 )\n",
+       "6 100  100 0.1 4.11 ( 0.18 ) 4.17 ( 0.1 )  0.93 ( 0.03 ) 0.12 1.99 ( 0.01 )\n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.62       0.48\n",
+       "3 4.51       0.06\n",
+       "4 5.22       0.02\n",
+       "5 0.00        NaN\n",
+       "6 5.93       0.68"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>NaN          </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0            </td><td>NaN          </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.19         </td><td>0.86 ( 0.03 )</td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.77         </td><td>0.44 ( 0.01 )</td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.86         </td><td>0.34 ( 0 )   </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>NaN          </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td>0            </td><td>NaN          </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.12         </td><td>0.93 ( 0.03 )</td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & NaN           & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0             & NaN          \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.19          & 0.86 ( 0.03 ) & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 1.76 ( 0.01 ) & 3.62          & 0.48         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.77          & 0.44 ( 0.01 ) & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 1.29 ( 0 )    & 4.51          & 0.06         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.86          & 0.34 ( 0 )    & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 1.15 ( 0 )    & 5.22          & 0.02         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & NaN           & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) & 0             & NaN          \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.12          & 0.93 ( 0.03 ) & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 1.99 ( 0.01 ) & 5.93          & 0.68         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | NaN           | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0             | NaN           |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.19          | 0.86 ( 0.03 ) | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 1.76 ( 0.01 ) | 3.62          | 0.48          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.77          | 0.44 ( 0.01 ) | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 1.29 ( 0 )    | 4.51          | 0.06          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.86          | 0.34 ( 0 )    | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 1.15 ( 0 )    | 5.22          | 0.02          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | NaN           | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) | 0             | NaN           |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.12          | 0.93 ( 0.03 ) | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 1.99 ( 0.01 ) | 5.93          | 0.68          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50  50 0.1  1.00  NaN  1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "2  100  50 0.1  0.50  0.19 0.86 ( 0.03 ) 1.87 ( 0.14 ) 4.25 ( 0.09 )\n",
+       "3  500  50 0.1  0.10  0.77 0.44 ( 0.01 ) 0.32 ( 0.05 ) 1.81 ( 0.07 )\n",
+       "4 1000  50 0.1  0.05  0.86 0.34 ( 0 )    0.15 ( 0.04 ) 0.93 ( 0.07 )\n",
+       "5   50 100 0.1  2.00  NaN  1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.12 0.93 ( 0.03 ) 4.11 ( 0.18 ) 4.17 ( 0.1 ) \n",
+       "  OOB           num_select FDR \n",
+       "1 1.98 ( 0.02 ) 0          NaN \n",
+       "2 1.76 ( 0.01 ) 3.62       0.48\n",
+       "3 1.29 ( 0 )    4.51       0.06\n",
+       "4 1.15 ( 0 )    5.22       0.02\n",
+       "5 2.19 ( 0.02 ) 0          NaN \n",
+       "6 1.99 ( 0.01 ) 5.93       0.68"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$OOB_mean = as.numeric(substr(result.table_toe$OOB, start=1, stop=4))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50          </td><td>  50         </td><td>0.1          </td><td> 1           </td><td> NaN         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.40         </td><td>  NA         </td><td>0.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50          </td><td> 100         </td><td>0.1          </td><td> 2           </td><td> NaN         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.36         </td><td>  NA         </td><td>0.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50          </td><td> 500         </td><td>0.1          </td><td>10           </td><td> NaN         </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.39 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.29         </td><td>  NA         </td><td>0.00         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50          </td><td>1000         </td><td>0.1          </td><td>20           </td><td> NaN         </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.42 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.35         </td><td>  NA         </td><td>0.00         </td><td>2.42         </td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100          </td><td>1000         </td><td>0.1          </td><td>10           </td><td>0.01         </td><td>1 ( 0.03 )   </td><td>49.42 ( 0.7 )</td><td>4.71 ( 0.09 )</td><td>2.39 ( 0.02 )</td><td>50.71        </td><td>0.97         </td><td>  NA         </td><td>49.4         </td><td>4.71         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50          </td><td>  50         </td><td>0.3          </td><td> 1           </td><td> NaN         </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.87 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.22         </td><td>  NA         </td><td>0.00         </td><td>1.87         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td> 100         </td><td>0.3          </td><td> 2           </td><td> NaN         </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.03 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.15         </td><td>  NA         </td><td>0.00         </td><td>2.03         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50          </td><td> 500         </td><td>0.3          </td><td>10           </td><td> NaN         </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.24 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.14         </td><td>  NA         </td><td>0.00         </td><td>2.24         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50          </td><td>1000         </td><td>0.3          </td><td>20           </td><td> NaN         </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.29 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.20         </td><td>  NA         </td><td>0.00         </td><td>2.29         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50          </td><td>  50         </td><td>0.5          </td><td> 1           </td><td> NaN         </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.99         </td><td>  NA         </td><td>0.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50          </td><td> 100         </td><td>0.5          </td><td> 2           </td><td> NaN         </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.84 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.91         </td><td>  NA         </td><td>0.00         </td><td>1.84         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50          </td><td> 500         </td><td>0.5          </td><td>10           </td><td> NaN         </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.95         </td><td>  NA         </td><td>0.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50          </td><td>1000         </td><td>0.5          </td><td>20           </td><td> NaN         </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.94         </td><td>  NA         </td><td>0.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50          </td><td>  50         </td><td>0.7          </td><td> 1           </td><td> NaN         </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.44 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.66         </td><td>  NA         </td><td>0.00         </td><td>1.44         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50          </td><td> 100         </td><td>0.7          </td><td> 2           </td><td> NaN         </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.5 ( 0.02 ) </td><td> 0.00        </td><td>NaN          </td><td>0.67         </td><td>  NA         </td><td>0.00         </td><td>1.50         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50          </td><td> 500         </td><td>0.7          </td><td>10           </td><td> NaN         </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.70         </td><td>  NA         </td><td>0.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50          </td><td>1000         </td><td>0.7          </td><td>20           </td><td> NaN         </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.69 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.74         </td><td>  NA         </td><td>0.00         </td><td>1.69         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50          </td><td>  50         </td><td>0.9          </td><td> 1           </td><td> NaN         </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.96 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.27         </td><td>  NA         </td><td>0.00         </td><td>0.96         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50          </td><td> 100         </td><td>0.9          </td><td> 2           </td><td> NaN         </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1 ( 0.01 )   </td><td> 0.00        </td><td>NaN          </td><td>0.30         </td><td>  NA         </td><td>0.00         </td><td>  NA         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50          </td><td> 500         </td><td>0.9          </td><td>10           </td><td> NaN         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.1 ( 0.01 ) </td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td>  NA         </td><td>0.00         </td><td>1.10         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50          </td><td>1000         </td><td>0.9          </td><td>20           </td><td> NaN         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.11 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td>  NA         </td><td>0.00         </td><td>1.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50           &   50          & 0.1           &  1            &  NaN          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) &  0.00         & NaN           & 1.40          &   NA          & 0.00          & 1.98         \\\\\n",
+       "\t5 &  50           &  100          & 0.1           &  2            &  NaN          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) &  0.00         & NaN           & 1.36          &   NA          & 0.00          & 2.19         \\\\\n",
+       "\t9 &  50           &  500          & 0.1           & 10            &  NaN          & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.39 ( 0.02 ) &  0.00         & NaN           & 1.29          &   NA          & 0.00          & 2.39         \\\\\n",
+       "\t13 &  50           & 1000          & 0.1           & 20            &  NaN          & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.42 ( 0.02 ) &  0.00         & NaN           & 1.35          &   NA          & 0.00          & 2.42         \\\\\n",
+       "\t14 & 100           & 1000          & 0.1           & 10            & 0.01          & 1 ( 0.03 )    & 49.42 ( 0.7 ) & 4.71 ( 0.09 ) & 2.39 ( 0.02 ) & 50.71         & 0.97          &   NA          & 49.4          & 4.71          & 2.39         \\\\\n",
+       "\t17 &  50           &   50          & 0.3           &  1            &  NaN          & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.87 ( 0.02 ) &  0.00         & NaN           & 1.22          &   NA          & 0.00          & 1.87         \\\\\n",
+       "\t21 &  50           &  100          & 0.3           &  2            &  NaN          & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.03 ( 0.02 ) &  0.00         & NaN           & 1.15          &   NA          & 0.00          & 2.03         \\\\\n",
+       "\t25 &  50           &  500          & 0.3           & 10            &  NaN          & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.24 ( 0.02 ) &  0.00         & NaN           & 1.14          &   NA          & 0.00          & 2.24         \\\\\n",
+       "\t29 &  50           & 1000          & 0.3           & 20            &  NaN          & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 2.29 ( 0.02 ) &  0.00         & NaN           & 1.20          &   NA          & 0.00          & 2.29         \\\\\n",
+       "\t33 &  50           &   50          & 0.5           &  1            &  NaN          & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.99          &   NA          & 0.00          & 1.68         \\\\\n",
+       "\t37 &  50           &  100          & 0.5           &  2            &  NaN          & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.84 ( 0.02 ) &  0.00         & NaN           & 0.91          &   NA          & 0.00          & 1.84         \\\\\n",
+       "\t41 &  50           &  500          & 0.5           & 10            &  NaN          & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.95          &   NA          & 0.00          & 2.04         \\\\\n",
+       "\t45 &  50           & 1000          & 0.5           & 20            &  NaN          & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.94          &   NA          & 0.00          & 2.04         \\\\\n",
+       "\t49 &  50           &   50          & 0.7           &  1            &  NaN          & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.44 ( 0.01 ) &  0.00         & NaN           & 0.66          &   NA          & 0.00          & 1.44         \\\\\n",
+       "\t53 &  50           &  100          & 0.7           &  2            &  NaN          & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.5 ( 0.02 )  &  0.00         & NaN           & 0.67          &   NA          & 0.00          & 1.50         \\\\\n",
+       "\t57 &  50           &  500          & 0.7           & 10            &  NaN          & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.70          &   NA          & 0.00          & 1.68         \\\\\n",
+       "\t61 &  50           & 1000          & 0.7           & 20            &  NaN          & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.69 ( 0.02 ) &  0.00         & NaN           & 0.74          &   NA          & 0.00          & 1.69         \\\\\n",
+       "\t65 &  50           &   50          & 0.9           &  1            &  NaN          & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0.96 ( 0.01 ) &  0.00         & NaN           & 0.27          &   NA          & 0.00          & 0.96         \\\\\n",
+       "\t69 &  50           &  100          & 0.9           &  2            &  NaN          & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 1 ( 0.01 )    &  0.00         & NaN           & 0.30          &   NA          & 0.00          &   NA         \\\\\n",
+       "\t73 &  50           &  500          & 0.9           & 10            &  NaN          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.1 ( 0.01 )  &  0.00         & NaN           & 0.29          &   NA          & 0.00          & 1.10         \\\\\n",
+       "\t77 &  50           & 1000          & 0.9           & 20            &  NaN          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.11 ( 0.01 ) &  0.00         & NaN           & 0.29          &   NA          & 0.00          & 1.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50           |   50          | 0.1           |  1            |  NaN          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) |  0.00         | NaN           | 1.40          |   NA          | 0.00          | 1.98          |\n",
+       "| 5 |  50           |  100          | 0.1           |  2            |  NaN          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) |  0.00         | NaN           | 1.36          |   NA          | 0.00          | 2.19          |\n",
+       "| 9 |  50           |  500          | 0.1           | 10            |  NaN          | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.39 ( 0.02 ) |  0.00         | NaN           | 1.29          |   NA          | 0.00          | 2.39          |\n",
+       "| 13 |  50           | 1000          | 0.1           | 20            |  NaN          | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.42 ( 0.02 ) |  0.00         | NaN           | 1.35          |   NA          | 0.00          | 2.42          |\n",
+       "| 14 | 100           | 1000          | 0.1           | 10            | 0.01          | 1 ( 0.03 )    | 49.42 ( 0.7 ) | 4.71 ( 0.09 ) | 2.39 ( 0.02 ) | 50.71         | 0.97          |   NA          | 49.4          | 4.71          | 2.39          |\n",
+       "| 17 |  50           |   50          | 0.3           |  1            |  NaN          | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.87 ( 0.02 ) |  0.00         | NaN           | 1.22          |   NA          | 0.00          | 1.87          |\n",
+       "| 21 |  50           |  100          | 0.3           |  2            |  NaN          | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.03 ( 0.02 ) |  0.00         | NaN           | 1.15          |   NA          | 0.00          | 2.03          |\n",
+       "| 25 |  50           |  500          | 0.3           | 10            |  NaN          | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.24 ( 0.02 ) |  0.00         | NaN           | 1.14          |   NA          | 0.00          | 2.24          |\n",
+       "| 29 |  50           | 1000          | 0.3           | 20            |  NaN          | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 2.29 ( 0.02 ) |  0.00         | NaN           | 1.20          |   NA          | 0.00          | 2.29          |\n",
+       "| 33 |  50           |   50          | 0.5           |  1            |  NaN          | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.99          |   NA          | 0.00          | 1.68          |\n",
+       "| 37 |  50           |  100          | 0.5           |  2            |  NaN          | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.84 ( 0.02 ) |  0.00         | NaN           | 0.91          |   NA          | 0.00          | 1.84          |\n",
+       "| 41 |  50           |  500          | 0.5           | 10            |  NaN          | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.95          |   NA          | 0.00          | 2.04          |\n",
+       "| 45 |  50           | 1000          | 0.5           | 20            |  NaN          | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.94          |   NA          | 0.00          | 2.04          |\n",
+       "| 49 |  50           |   50          | 0.7           |  1            |  NaN          | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.44 ( 0.01 ) |  0.00         | NaN           | 0.66          |   NA          | 0.00          | 1.44          |\n",
+       "| 53 |  50           |  100          | 0.7           |  2            |  NaN          | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.5 ( 0.02 )  |  0.00         | NaN           | 0.67          |   NA          | 0.00          | 1.50          |\n",
+       "| 57 |  50           |  500          | 0.7           | 10            |  NaN          | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.70          |   NA          | 0.00          | 1.68          |\n",
+       "| 61 |  50           | 1000          | 0.7           | 20            |  NaN          | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.69 ( 0.02 ) |  0.00         | NaN           | 0.74          |   NA          | 0.00          | 1.69          |\n",
+       "| 65 |  50           |   50          | 0.9           |  1            |  NaN          | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0.96 ( 0.01 ) |  0.00         | NaN           | 0.27          |   NA          | 0.00          | 0.96          |\n",
+       "| 69 |  50           |  100          | 0.9           |  2            |  NaN          | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 1 ( 0.01 )    |  0.00         | NaN           | 0.30          |   NA          | 0.00          |   NA          |\n",
+       "| 73 |  50           |  500          | 0.9           | 10            |  NaN          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.1 ( 0.01 )  |  0.00         | NaN           | 0.29          |   NA          | 0.00          | 1.10          |\n",
+       "| 77 |  50           | 1000          | 0.9           | 20            |  NaN          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.11 ( 0.01 ) |  0.00         | NaN           | 0.29          |   NA          | 0.00          | 1.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50   50 0.1   1     NaN 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "5   50  100 0.1   2     NaN 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "9   50  500 0.1  10     NaN 1.29 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "13  50 1000 0.1  20     NaN 1.35 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "14 100 1000 0.1  10    0.01 1 ( 0.03 )    49.42 ( 0.7 ) 4.71 ( 0.09 )\n",
+       "17  50   50 0.3   1     NaN 1.22 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "21  50  100 0.3   2     NaN 1.15 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "25  50  500 0.3  10     NaN 1.14 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "29  50 1000 0.3  20     NaN 1.2 ( 0.05 )  1 ( 0 )       6 ( 0 )      \n",
+       "33  50   50 0.5   1     NaN 0.99 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "37  50  100 0.5   2     NaN 0.91 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "41  50  500 0.5  10     NaN 0.95 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "45  50 1000 0.5  20     NaN 0.94 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "49  50   50 0.7   1     NaN 0.66 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "53  50  100 0.7   2     NaN 0.67 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "57  50  500 0.7  10     NaN 0.7 ( 0.03 )  1 ( 0 )       6 ( 0 )      \n",
+       "61  50 1000 0.7  20     NaN 0.74 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "65  50   50 0.9   1     NaN 0.27 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "69  50  100 0.9   2     NaN 0.3 ( 0.01 )  1 ( 0 )       6 ( 0 )      \n",
+       "73  50  500 0.9  10     NaN 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "77  50 1000 0.9  20     NaN 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  1.98 ( 0.02 )  0.00      NaN  1.40       NA    0.00    1.98    \n",
+       "5  2.19 ( 0.02 )  0.00      NaN  1.36       NA    0.00    2.19    \n",
+       "9  2.39 ( 0.02 )  0.00      NaN  1.29       NA    0.00    2.39    \n",
+       "13 2.42 ( 0.02 )  0.00      NaN  1.35       NA    0.00    2.42    \n",
+       "14 2.39 ( 0.02 ) 50.71      0.97   NA     49.4    4.71    2.39    \n",
+       "17 1.87 ( 0.02 )  0.00      NaN  1.22       NA    0.00    1.87    \n",
+       "21 2.03 ( 0.02 )  0.00      NaN  1.15       NA    0.00    2.03    \n",
+       "25 2.24 ( 0.02 )  0.00      NaN  1.14       NA    0.00    2.24    \n",
+       "29 2.29 ( 0.02 )  0.00      NaN  1.20       NA    0.00    2.29    \n",
+       "33 1.68 ( 0.02 )  0.00      NaN  0.99       NA    0.00    1.68    \n",
+       "37 1.84 ( 0.02 )  0.00      NaN  0.91       NA    0.00    1.84    \n",
+       "41 2.04 ( 0.02 )  0.00      NaN  0.95       NA    0.00    2.04    \n",
+       "45 2.04 ( 0.02 )  0.00      NaN  0.94       NA    0.00    2.04    \n",
+       "49 1.44 ( 0.01 )  0.00      NaN  0.66       NA    0.00    1.44    \n",
+       "53 1.5 ( 0.02 )   0.00      NaN  0.67       NA    0.00    1.50    \n",
+       "57 1.68 ( 0.02 )  0.00      NaN  0.70       NA    0.00    1.68    \n",
+       "61 1.69 ( 0.02 )  0.00      NaN  0.74       NA    0.00    1.69    \n",
+       "65 0.96 ( 0.01 )  0.00      NaN  0.27       NA    0.00    0.96    \n",
+       "69 1 ( 0.01 )     0.00      NaN  0.30       NA    0.00      NA    \n",
+       "73 1.1 ( 0.01 )   0.00      NaN  0.29       NA    0.00    1.10    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29       NA    0.00    1.11    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$Stab[is.na(result.table_toe$Stab)] = 0\n",
+    "result.table_toe$MSE_mean[is.na(result.table_toe$MSE_mean)] = 1\n",
+    "result.table_toe$FP_mean[is.na(result.table_toe$FP_mean)] = 1\n",
+    "result.table_toe$FN_mean[result.table_toe$num_select == 0] = 6\n",
+    "result.table_toe$OOB_mean[is.na(result.table_toe$OOB_mean)] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50          </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0.00         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.40         </td><td> 1.0         </td><td>6.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50          </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0.00         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.36         </td><td> 1.0         </td><td>6.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50          </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0.00         </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.39 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.29         </td><td> 1.0         </td><td>6.00         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50          </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0.00         </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.42 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.35         </td><td> 1.0         </td><td>6.00         </td><td>2.42         </td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100          </td><td>1000         </td><td>0.1          </td><td>10           </td><td>0.01         </td><td>1 ( 0.03 )   </td><td>49.42 ( 0.7 )</td><td>4.71 ( 0.09 )</td><td>2.39 ( 0.02 )</td><td>50.71        </td><td>0.97         </td><td>1.00         </td><td>49.4         </td><td>4.71         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50          </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0.00         </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.87 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.22         </td><td> 1.0         </td><td>6.00         </td><td>1.87         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0.00         </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.03 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.15         </td><td> 1.0         </td><td>6.00         </td><td>2.03         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50          </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0.00         </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.24 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.14         </td><td> 1.0         </td><td>6.00         </td><td>2.24         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50          </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0.00         </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.29 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.20         </td><td> 1.0         </td><td>6.00         </td><td>2.29         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50          </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0.00         </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.99         </td><td> 1.0         </td><td>6.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50          </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0.00         </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.84 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.91         </td><td> 1.0         </td><td>6.00         </td><td>1.84         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50          </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0.00         </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.95         </td><td> 1.0         </td><td>6.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50          </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0.00         </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.94         </td><td> 1.0         </td><td>6.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50          </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0.00         </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.44 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.66         </td><td> 1.0         </td><td>6.00         </td><td>1.44         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50          </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0.00         </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.5 ( 0.02 ) </td><td> 0.00        </td><td>NaN          </td><td>0.67         </td><td> 1.0         </td><td>6.00         </td><td>1.50         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50          </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0.00         </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.70         </td><td> 1.0         </td><td>6.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50          </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0.00         </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.69 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.74         </td><td> 1.0         </td><td>6.00         </td><td>1.69         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50          </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0.00         </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.96 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.27         </td><td> 1.0         </td><td>6.00         </td><td>0.96         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50          </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0.00         </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1 ( 0.01 )   </td><td> 0.00        </td><td>NaN          </td><td>0.30         </td><td> 1.0         </td><td>6.00         </td><td>1.00         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50          </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0.00         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.1 ( 0.01 ) </td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td> 1.0         </td><td>6.00         </td><td>1.10         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50          </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0.00         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.11 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td> 1.0         </td><td>6.00         </td><td>1.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50           &   50          & 0.1           &  1            & 0.00          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) &  0.00         & NaN           & 1.40          &  1.0          & 6.00          & 1.98         \\\\\n",
+       "\t5 &  50           &  100          & 0.1           &  2            & 0.00          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) &  0.00         & NaN           & 1.36          &  1.0          & 6.00          & 2.19         \\\\\n",
+       "\t9 &  50           &  500          & 0.1           & 10            & 0.00          & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.39 ( 0.02 ) &  0.00         & NaN           & 1.29          &  1.0          & 6.00          & 2.39         \\\\\n",
+       "\t13 &  50           & 1000          & 0.1           & 20            & 0.00          & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.42 ( 0.02 ) &  0.00         & NaN           & 1.35          &  1.0          & 6.00          & 2.42         \\\\\n",
+       "\t14 & 100           & 1000          & 0.1           & 10            & 0.01          & 1 ( 0.03 )    & 49.42 ( 0.7 ) & 4.71 ( 0.09 ) & 2.39 ( 0.02 ) & 50.71         & 0.97          & 1.00          & 49.4          & 4.71          & 2.39         \\\\\n",
+       "\t17 &  50           &   50          & 0.3           &  1            & 0.00          & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.87 ( 0.02 ) &  0.00         & NaN           & 1.22          &  1.0          & 6.00          & 1.87         \\\\\n",
+       "\t21 &  50           &  100          & 0.3           &  2            & 0.00          & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.03 ( 0.02 ) &  0.00         & NaN           & 1.15          &  1.0          & 6.00          & 2.03         \\\\\n",
+       "\t25 &  50           &  500          & 0.3           & 10            & 0.00          & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.24 ( 0.02 ) &  0.00         & NaN           & 1.14          &  1.0          & 6.00          & 2.24         \\\\\n",
+       "\t29 &  50           & 1000          & 0.3           & 20            & 0.00          & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 2.29 ( 0.02 ) &  0.00         & NaN           & 1.20          &  1.0          & 6.00          & 2.29         \\\\\n",
+       "\t33 &  50           &   50          & 0.5           &  1            & 0.00          & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.99          &  1.0          & 6.00          & 1.68         \\\\\n",
+       "\t37 &  50           &  100          & 0.5           &  2            & 0.00          & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.84 ( 0.02 ) &  0.00         & NaN           & 0.91          &  1.0          & 6.00          & 1.84         \\\\\n",
+       "\t41 &  50           &  500          & 0.5           & 10            & 0.00          & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.95          &  1.0          & 6.00          & 2.04         \\\\\n",
+       "\t45 &  50           & 1000          & 0.5           & 20            & 0.00          & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.94          &  1.0          & 6.00          & 2.04         \\\\\n",
+       "\t49 &  50           &   50          & 0.7           &  1            & 0.00          & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.44 ( 0.01 ) &  0.00         & NaN           & 0.66          &  1.0          & 6.00          & 1.44         \\\\\n",
+       "\t53 &  50           &  100          & 0.7           &  2            & 0.00          & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.5 ( 0.02 )  &  0.00         & NaN           & 0.67          &  1.0          & 6.00          & 1.50         \\\\\n",
+       "\t57 &  50           &  500          & 0.7           & 10            & 0.00          & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.70          &  1.0          & 6.00          & 1.68         \\\\\n",
+       "\t61 &  50           & 1000          & 0.7           & 20            & 0.00          & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.69 ( 0.02 ) &  0.00         & NaN           & 0.74          &  1.0          & 6.00          & 1.69         \\\\\n",
+       "\t65 &  50           &   50          & 0.9           &  1            & 0.00          & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0.96 ( 0.01 ) &  0.00         & NaN           & 0.27          &  1.0          & 6.00          & 0.96         \\\\\n",
+       "\t69 &  50           &  100          & 0.9           &  2            & 0.00          & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 1 ( 0.01 )    &  0.00         & NaN           & 0.30          &  1.0          & 6.00          & 1.00         \\\\\n",
+       "\t73 &  50           &  500          & 0.9           & 10            & 0.00          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.1 ( 0.01 )  &  0.00         & NaN           & 0.29          &  1.0          & 6.00          & 1.10         \\\\\n",
+       "\t77 &  50           & 1000          & 0.9           & 20            & 0.00          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.11 ( 0.01 ) &  0.00         & NaN           & 0.29          &  1.0          & 6.00          & 1.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50           |   50          | 0.1           |  1            | 0.00          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) |  0.00         | NaN           | 1.40          |  1.0          | 6.00          | 1.98          |\n",
+       "| 5 |  50           |  100          | 0.1           |  2            | 0.00          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) |  0.00         | NaN           | 1.36          |  1.0          | 6.00          | 2.19          |\n",
+       "| 9 |  50           |  500          | 0.1           | 10            | 0.00          | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.39 ( 0.02 ) |  0.00         | NaN           | 1.29          |  1.0          | 6.00          | 2.39          |\n",
+       "| 13 |  50           | 1000          | 0.1           | 20            | 0.00          | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.42 ( 0.02 ) |  0.00         | NaN           | 1.35          |  1.0          | 6.00          | 2.42          |\n",
+       "| 14 | 100           | 1000          | 0.1           | 10            | 0.01          | 1 ( 0.03 )    | 49.42 ( 0.7 ) | 4.71 ( 0.09 ) | 2.39 ( 0.02 ) | 50.71         | 0.97          | 1.00          | 49.4          | 4.71          | 2.39          |\n",
+       "| 17 |  50           |   50          | 0.3           |  1            | 0.00          | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.87 ( 0.02 ) |  0.00         | NaN           | 1.22          |  1.0          | 6.00          | 1.87          |\n",
+       "| 21 |  50           |  100          | 0.3           |  2            | 0.00          | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.03 ( 0.02 ) |  0.00         | NaN           | 1.15          |  1.0          | 6.00          | 2.03          |\n",
+       "| 25 |  50           |  500          | 0.3           | 10            | 0.00          | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.24 ( 0.02 ) |  0.00         | NaN           | 1.14          |  1.0          | 6.00          | 2.24          |\n",
+       "| 29 |  50           | 1000          | 0.3           | 20            | 0.00          | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 2.29 ( 0.02 ) |  0.00         | NaN           | 1.20          |  1.0          | 6.00          | 2.29          |\n",
+       "| 33 |  50           |   50          | 0.5           |  1            | 0.00          | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.99          |  1.0          | 6.00          | 1.68          |\n",
+       "| 37 |  50           |  100          | 0.5           |  2            | 0.00          | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.84 ( 0.02 ) |  0.00         | NaN           | 0.91          |  1.0          | 6.00          | 1.84          |\n",
+       "| 41 |  50           |  500          | 0.5           | 10            | 0.00          | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.95          |  1.0          | 6.00          | 2.04          |\n",
+       "| 45 |  50           | 1000          | 0.5           | 20            | 0.00          | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.94          |  1.0          | 6.00          | 2.04          |\n",
+       "| 49 |  50           |   50          | 0.7           |  1            | 0.00          | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.44 ( 0.01 ) |  0.00         | NaN           | 0.66          |  1.0          | 6.00          | 1.44          |\n",
+       "| 53 |  50           |  100          | 0.7           |  2            | 0.00          | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.5 ( 0.02 )  |  0.00         | NaN           | 0.67          |  1.0          | 6.00          | 1.50          |\n",
+       "| 57 |  50           |  500          | 0.7           | 10            | 0.00          | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.70          |  1.0          | 6.00          | 1.68          |\n",
+       "| 61 |  50           | 1000          | 0.7           | 20            | 0.00          | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.69 ( 0.02 ) |  0.00         | NaN           | 0.74          |  1.0          | 6.00          | 1.69          |\n",
+       "| 65 |  50           |   50          | 0.9           |  1            | 0.00          | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0.96 ( 0.01 ) |  0.00         | NaN           | 0.27          |  1.0          | 6.00          | 0.96          |\n",
+       "| 69 |  50           |  100          | 0.9           |  2            | 0.00          | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 1 ( 0.01 )    |  0.00         | NaN           | 0.30          |  1.0          | 6.00          | 1.00          |\n",
+       "| 73 |  50           |  500          | 0.9           | 10            | 0.00          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.1 ( 0.01 )  |  0.00         | NaN           | 0.29          |  1.0          | 6.00          | 1.10          |\n",
+       "| 77 |  50           | 1000          | 0.9           | 20            | 0.00          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.11 ( 0.01 ) |  0.00         | NaN           | 0.29          |  1.0          | 6.00          | 1.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50   50 0.1   1    0.00 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "5   50  100 0.1   2    0.00 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "9   50  500 0.1  10    0.00 1.29 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "13  50 1000 0.1  20    0.00 1.35 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "14 100 1000 0.1  10    0.01 1 ( 0.03 )    49.42 ( 0.7 ) 4.71 ( 0.09 )\n",
+       "17  50   50 0.3   1    0.00 1.22 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "21  50  100 0.3   2    0.00 1.15 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "25  50  500 0.3  10    0.00 1.14 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "29  50 1000 0.3  20    0.00 1.2 ( 0.05 )  1 ( 0 )       6 ( 0 )      \n",
+       "33  50   50 0.5   1    0.00 0.99 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "37  50  100 0.5   2    0.00 0.91 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "41  50  500 0.5  10    0.00 0.95 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "45  50 1000 0.5  20    0.00 0.94 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "49  50   50 0.7   1    0.00 0.66 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "53  50  100 0.7   2    0.00 0.67 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "57  50  500 0.7  10    0.00 0.7 ( 0.03 )  1 ( 0 )       6 ( 0 )      \n",
+       "61  50 1000 0.7  20    0.00 0.74 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "65  50   50 0.9   1    0.00 0.27 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "69  50  100 0.9   2    0.00 0.3 ( 0.01 )  1 ( 0 )       6 ( 0 )      \n",
+       "73  50  500 0.9  10    0.00 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "77  50 1000 0.9  20    0.00 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  1.98 ( 0.02 )  0.00      NaN  1.40      1.0    6.00    1.98    \n",
+       "5  2.19 ( 0.02 )  0.00      NaN  1.36      1.0    6.00    2.19    \n",
+       "9  2.39 ( 0.02 )  0.00      NaN  1.29      1.0    6.00    2.39    \n",
+       "13 2.42 ( 0.02 )  0.00      NaN  1.35      1.0    6.00    2.42    \n",
+       "14 2.39 ( 0.02 ) 50.71      0.97 1.00     49.4    4.71    2.39    \n",
+       "17 1.87 ( 0.02 )  0.00      NaN  1.22      1.0    6.00    1.87    \n",
+       "21 2.03 ( 0.02 )  0.00      NaN  1.15      1.0    6.00    2.03    \n",
+       "25 2.24 ( 0.02 )  0.00      NaN  1.14      1.0    6.00    2.24    \n",
+       "29 2.29 ( 0.02 )  0.00      NaN  1.20      1.0    6.00    2.29    \n",
+       "33 1.68 ( 0.02 )  0.00      NaN  0.99      1.0    6.00    1.68    \n",
+       "37 1.84 ( 0.02 )  0.00      NaN  0.91      1.0    6.00    1.84    \n",
+       "41 2.04 ( 0.02 )  0.00      NaN  0.95      1.0    6.00    2.04    \n",
+       "45 2.04 ( 0.02 )  0.00      NaN  0.94      1.0    6.00    2.04    \n",
+       "49 1.44 ( 0.01 )  0.00      NaN  0.66      1.0    6.00    1.44    \n",
+       "53 1.5 ( 0.02 )   0.00      NaN  0.67      1.0    6.00    1.50    \n",
+       "57 1.68 ( 0.02 )  0.00      NaN  0.70      1.0    6.00    1.68    \n",
+       "61 1.69 ( 0.02 )  0.00      NaN  0.74      1.0    6.00    1.69    \n",
+       "65 0.96 ( 0.01 )  0.00      NaN  0.27      1.0    6.00    0.96    \n",
+       "69 1 ( 0.01 )     0.00      NaN  0.30      1.0    6.00    1.00    \n",
+       "73 1.1 ( 0.01 )   0.00      NaN  0.29      1.0    6.00    1.10    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29      1.0    6.00    1.11    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[c(1,5,9,13,14,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.00         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0.00         </td><td>NaN          </td><td>1.40         </td><td>1.00         </td><td>6.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.19         </td><td>0.86 ( 0.03 )</td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td><td>0.86         </td><td>1.87         </td><td>4.25         </td><td>1.76         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.77         </td><td>0.44 ( 0.01 )</td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td><td>0.44         </td><td>0.32         </td><td>1.81         </td><td>1.29         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.86         </td><td>0.34 ( 0 )   </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td><td>0.34         </td><td>0.15         </td><td>0.93         </td><td>1.15         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.00         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td>0.00         </td><td>NaN          </td><td>1.36         </td><td>1.00         </td><td>6.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.12         </td><td>0.93 ( 0.03 )</td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td><td>0.93         </td><td>4.11         </td><td>4.17         </td><td>1.99         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.00          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0.00          & NaN           & 1.40          & 1.00          & 6.00          & 1.98         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.19          & 0.86 ( 0.03 ) & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 1.76 ( 0.01 ) & 3.62          & 0.48          & 0.86          & 1.87          & 4.25          & 1.76         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.77          & 0.44 ( 0.01 ) & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 1.29 ( 0 )    & 4.51          & 0.06          & 0.44          & 0.32          & 1.81          & 1.29         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.86          & 0.34 ( 0 )    & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 1.15 ( 0 )    & 5.22          & 0.02          & 0.34          & 0.15          & 0.93          & 1.15         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.00          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) & 0.00          & NaN           & 1.36          & 1.00          & 6.00          & 2.19         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.12          & 0.93 ( 0.03 ) & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 1.99 ( 0.01 ) & 5.93          & 0.68          & 0.93          & 4.11          & 4.17          & 1.99         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.00          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0.00          | NaN           | 1.40          | 1.00          | 6.00          | 1.98          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.19          | 0.86 ( 0.03 ) | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 1.76 ( 0.01 ) | 3.62          | 0.48          | 0.86          | 1.87          | 4.25          | 1.76          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.77          | 0.44 ( 0.01 ) | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 1.29 ( 0 )    | 4.51          | 0.06          | 0.44          | 0.32          | 1.81          | 1.29          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.86          | 0.34 ( 0 )    | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 1.15 ( 0 )    | 5.22          | 0.02          | 0.34          | 0.15          | 0.93          | 1.15          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.00          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) | 0.00          | NaN           | 1.36          | 1.00          | 6.00          | 2.19          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.12          | 0.93 ( 0.03 ) | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 1.99 ( 0.01 ) | 5.93          | 0.68          | 0.93          | 4.11          | 4.17          | 1.99          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50  50 0.1  1.00  0.00 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "2  100  50 0.1  0.50  0.19 0.86 ( 0.03 ) 1.87 ( 0.14 ) 4.25 ( 0.09 )\n",
+       "3  500  50 0.1  0.10  0.77 0.44 ( 0.01 ) 0.32 ( 0.05 ) 1.81 ( 0.07 )\n",
+       "4 1000  50 0.1  0.05  0.86 0.34 ( 0 )    0.15 ( 0.04 ) 0.93 ( 0.07 )\n",
+       "5   50 100 0.1  2.00  0.00 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.12 0.93 ( 0.03 ) 4.11 ( 0.18 ) 4.17 ( 0.1 ) \n",
+       "  OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1 1.98 ( 0.02 ) 0.00       NaN  1.40     1.00    6.00    1.98    \n",
+       "2 1.76 ( 0.01 ) 3.62       0.48 0.86     1.87    4.25    1.76    \n",
+       "3 1.29 ( 0 )    4.51       0.06 0.44     0.32    1.81    1.29    \n",
+       "4 1.15 ( 0 )    5.22       0.02 0.34     0.15    0.93    1.15    \n",
+       "5 2.19 ( 0.02 ) 0.00       NaN  1.36     1.00    6.00    2.19    \n",
+       "6 1.99 ( 0.01 ) 5.93       0.68 0.93     4.11    4.17    1.99    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.14          </td><td>0.17 ( 0 )    </td><td>21.38 ( 0.6 ) </td><td>3 ( 0.07 )    </td><td>0.99 ( 0 )    </td><td>24.38         </td><td>0.87          </td><td>0.17          </td><td>21.3          </td><td>0.00          </td><td>0.99          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.20          </td><td>0.16 ( 0 )    </td><td>18.68 ( 0.53 )</td><td>2.35 ( 0.06 ) </td><td>0.96 ( 0 )    </td><td>22.33         </td><td>0.83          </td><td>0.16          </td><td>18.6          </td><td>2.35          </td><td>0.96          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.00          </td><td>0.29 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.11 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.29          </td><td> 1.0          </td><td>6.00          </td><td>1.11          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.01          </td><td>0.2 ( 0.01 )  </td><td>49.91 ( 0.83 )</td><td>4.99 ( 0.09 ) </td><td>1.09 ( 0.01 ) </td><td>50.92         </td><td>0.98          </td><td>0.20          </td><td>49.9          </td><td>4.99          </td><td>1.09          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.06          </td><td>0.19 ( 0 )    </td><td>46.34 ( 0.86 )</td><td>3.13 ( 0.08 ) </td><td>1.04 ( 0 )    </td><td>49.21         </td><td>0.94          </td><td>0.19          </td><td>46.3          </td><td>3.13          </td><td>1.04          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.09          </td><td>0.18 ( 0 )    </td><td>43.52 ( 0.89 )</td><td>2.7 ( 0.07 )  </td><td>1.02 ( 0 )    </td><td>46.82         </td><td>0.93          </td><td>0.18          </td><td>43.5          </td><td>2.70          </td><td>1.02          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.14           & 0.17 ( 0 )     & 21.38 ( 0.6 )  & 3 ( 0.07 )     & 0.99 ( 0 )     & 24.38          & 0.87           & 0.17           & 21.3           & 0.00           & 0.99          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.20           & 0.16 ( 0 )     & 18.68 ( 0.53 ) & 2.35 ( 0.06 )  & 0.96 ( 0 )     & 22.33          & 0.83           & 0.16           & 18.6           & 2.35           & 0.96          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.00           & 0.29 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.11 ( 0.01 )  &  0.00          & NaN            & 0.29           &  1.0           & 6.00           & 1.11          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.01           & 0.2 ( 0.01 )   & 49.91 ( 0.83 ) & 4.99 ( 0.09 )  & 1.09 ( 0.01 )  & 50.92          & 0.98           & 0.20           & 49.9           & 4.99           & 1.09          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.06           & 0.19 ( 0 )     & 46.34 ( 0.86 ) & 3.13 ( 0.08 )  & 1.04 ( 0 )     & 49.21          & 0.94           & 0.19           & 46.3           & 3.13           & 1.04          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.09           & 0.18 ( 0 )     & 43.52 ( 0.89 ) & 2.7 ( 0.07 )   & 1.02 ( 0 )     & 46.82          & 0.93           & 0.18           & 43.5           & 2.70           & 1.02          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.14           | 0.17 ( 0 )     | 21.38 ( 0.6 )  | 3 ( 0.07 )     | 0.99 ( 0 )     | 24.38          | 0.87           | 0.17           | 21.3           | 0.00           | 0.99           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.20           | 0.16 ( 0 )     | 18.68 ( 0.53 ) | 2.35 ( 0.06 )  | 0.96 ( 0 )     | 22.33          | 0.83           | 0.16           | 18.6           | 2.35           | 0.96           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.00           | 0.29 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.11 ( 0.01 )  |  0.00          | NaN            | 0.29           |  1.0           | 6.00           | 1.11           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.01           | 0.2 ( 0.01 )   | 49.91 ( 0.83 ) | 4.99 ( 0.09 )  | 1.09 ( 0.01 )  | 50.92          | 0.98           | 0.20           | 49.9           | 4.99           | 1.09           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.06           | 0.19 ( 0 )     | 46.34 ( 0.86 ) | 3.13 ( 0.08 )  | 1.04 ( 0 )     | 49.21          | 0.94           | 0.19           | 46.3           | 3.13           | 1.04           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.09           | 0.18 ( 0 )     | 43.52 ( 0.89 ) | 2.7 ( 0.07 )   | 1.02 ( 0 )     | 46.82          | 0.93           | 0.18           | 43.5           | 2.70           | 1.02           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.14 0.17 ( 0 )    21.38 ( 0.6 )  3 ( 0.07 )   \n",
+       "76 1000  500 0.9   0.5  0.20 0.16 ( 0 )    18.68 ( 0.53 ) 2.35 ( 0.06 )\n",
+       "77   50 1000 0.9  20.0  0.00 0.29 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "78  100 1000 0.9  10.0  0.01 0.2 ( 0.01 )  49.91 ( 0.83 ) 4.99 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.06 0.19 ( 0 )    46.34 ( 0.86 ) 3.13 ( 0.08 )\n",
+       "80 1000 1000 0.9   1.0  0.09 0.18 ( 0 )    43.52 ( 0.89 ) 2.7 ( 0.07 ) \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "75 0.99 ( 0 )    24.38      0.87 0.17     21.3    0.00    0.99    \n",
+       "76 0.96 ( 0 )    22.33      0.83 0.16     18.6    2.35    0.96    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29      1.0    6.00    1.11    \n",
+       "78 1.09 ( 0.01 ) 50.92      0.98 0.20     49.9    4.99    1.09    \n",
+       "79 1.04 ( 0 )    49.21      0.94 0.19     46.3    3.13    1.04    \n",
+       "80 1.02 ( 0 )    46.82      0.93 0.18     43.5    2.70    1.02    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.4_sim_toe_compLasso_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.4_sim_toe_compLasso_cts-checkpoint.ipynb
new file mode 100755
index 0000000..a071ca2
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/1.4_sim_toe_compLasso_cts-checkpoint.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_compLasso))\n",
+    "for (i in 1:length(results_toe_compLasso)){\n",
+    "    table_toe = rbind(table_toe, results_toe_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>3.68 ( 0.29 )</td><td>0.06 ( 0.03 )</td><td>0.88 ( 0.05 )</td><td>0.56         </td><td> 9.62        </td><td>0.33         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>1.51 ( 0.35 )</td><td>0 ( 0 )      </td><td>0.87 ( 0.05 )</td><td>0.77         </td><td> 7.51        </td><td>0.12         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>0.73 ( 0.19 )</td><td>0 ( 0 )      </td><td>0.99 ( 0.05 )</td><td>0.88         </td><td> 6.73        </td><td>0.07         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>0.55 ( 0.17 )</td><td>0 ( 0 )      </td><td>1.09 ( 0.04 )</td><td>0.9          </td><td> 6.55        </td><td>0.05         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>4.24 ( 0.3 ) </td><td>0.23 ( 0.06 )</td><td>1.12 ( 0.1 ) </td><td>0.53         </td><td>10.01        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>1.51 ( 0.34 )</td><td>0 ( 0 )      </td><td>0.85 ( 0.04 )</td><td>0.79         </td><td> 7.51        </td><td>0.13         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 3.68 ( 0.29 ) & 0.06 ( 0.03 ) & 0.88 ( 0.05 ) & 0.56          &  9.62         & 0.33         \\\\\n",
+       "\t 100           & 50            & 0.1           & 1.51 ( 0.35 ) & 0 ( 0 )       & 0.87 ( 0.05 ) & 0.77          &  7.51         & 0.12         \\\\\n",
+       "\t 500           & 50            & 0.1           & 0.73 ( 0.19 ) & 0 ( 0 )       & 0.99 ( 0.05 ) & 0.88          &  6.73         & 0.07         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 0.55 ( 0.17 ) & 0 ( 0 )       & 1.09 ( 0.04 ) & 0.9           &  6.55         & 0.05         \\\\\n",
+       "\t 50            & 100           & 0.1           & 4.24 ( 0.3 )  & 0.23 ( 0.06 ) & 1.12 ( 0.1 )  & 0.53          & 10.01         & 0.37         \\\\\n",
+       "\t 100           & 100           & 0.1           & 1.51 ( 0.34 ) & 0 ( 0 )       & 0.85 ( 0.04 ) & 0.79          &  7.51         & 0.13         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 3.68 ( 0.29 ) | 0.06 ( 0.03 ) | 0.88 ( 0.05 ) | 0.56          |  9.62         | 0.33          |\n",
+       "| 100           | 50            | 0.1           | 1.51 ( 0.35 ) | 0 ( 0 )       | 0.87 ( 0.05 ) | 0.77          |  7.51         | 0.12          |\n",
+       "| 500           | 50            | 0.1           | 0.73 ( 0.19 ) | 0 ( 0 )       | 0.99 ( 0.05 ) | 0.88          |  6.73         | 0.07          |\n",
+       "| 1000          | 50            | 0.1           | 0.55 ( 0.17 ) | 0 ( 0 )       | 1.09 ( 0.04 ) | 0.9           |  6.55         | 0.05          |\n",
+       "| 50            | 100           | 0.1           | 4.24 ( 0.3 )  | 0.23 ( 0.06 ) | 1.12 ( 0.1 )  | 0.53          | 10.01         | 0.37          |\n",
+       "| 100           | 100           | 0.1           | 1.51 ( 0.34 ) | 0 ( 0 )       | 0.85 ( 0.04 ) | 0.79          |  7.51         | 0.13          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 3.68 ( 0.29 ) 0.06 ( 0.03 ) 0.88 ( 0.05 ) 0.56  9.62      0.33\n",
+       "2 100  50  0.1 1.51 ( 0.35 ) 0 ( 0 )       0.87 ( 0.05 ) 0.77  7.51      0.12\n",
+       "3 500  50  0.1 0.73 ( 0.19 ) 0 ( 0 )       0.99 ( 0.05 ) 0.88  6.73      0.07\n",
+       "4 1000 50  0.1 0.55 ( 0.17 ) 0 ( 0 )       1.09 ( 0.04 ) 0.9   6.55      0.05\n",
+       "5 50   100 0.1 4.24 ( 0.3 )  0.23 ( 0.06 ) 1.12 ( 0.1 )  0.53 10.01      0.37\n",
+       "6 100  100 0.1 1.51 ( 0.34 ) 0 ( 0 )       0.85 ( 0.04 ) 0.79  7.51      0.13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab MSE FP FN num_select FDR MSE_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.56         </td><td>0.88 ( 0.05 )</td><td>3.68 ( 0.29 )</td><td>0.06 ( 0.03 )</td><td> 9.62        </td><td>0.33         </td><td>0.88         </td><td>3.68         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.77         </td><td>0.87 ( 0.05 )</td><td>1.51 ( 0.35 )</td><td>0 ( 0 )      </td><td> 7.51        </td><td>0.12         </td><td>0.87         </td><td>1.51         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.88         </td><td>0.99 ( 0.05 )</td><td>0.73 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.73        </td><td>0.07         </td><td>0.99         </td><td>0.73         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.90         </td><td>1.09 ( 0.04 )</td><td>0.55 ( 0.17 )</td><td>0 ( 0 )      </td><td> 6.55        </td><td>0.05         </td><td>1.09         </td><td>0.55         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.53         </td><td>1.12 ( 0.1 ) </td><td>4.24 ( 0.3 ) </td><td>0.23 ( 0.06 )</td><td>10.01        </td><td>0.37         </td><td>1.12         </td><td>4.24         </td><td>0.23         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.79         </td><td>0.85 ( 0.04 )</td><td>1.51 ( 0.34 )</td><td>0 ( 0 )      </td><td> 7.51        </td><td>0.13         </td><td>0.85         </td><td>1.51         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.56          & 0.88 ( 0.05 ) & 3.68 ( 0.29 ) & 0.06 ( 0.03 ) &  9.62         & 0.33          & 0.88          & 3.68          & 0.06         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.77          & 0.87 ( 0.05 ) & 1.51 ( 0.35 ) & 0 ( 0 )       &  7.51         & 0.12          & 0.87          & 1.51          & 0.00         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.88          & 0.99 ( 0.05 ) & 0.73 ( 0.19 ) & 0 ( 0 )       &  6.73         & 0.07          & 0.99          & 0.73          & 0.00         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.90          & 1.09 ( 0.04 ) & 0.55 ( 0.17 ) & 0 ( 0 )       &  6.55         & 0.05          & 1.09          & 0.55          & 0.00         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.53          & 1.12 ( 0.1 )  & 4.24 ( 0.3 )  & 0.23 ( 0.06 ) & 10.01         & 0.37          & 1.12          & 4.24          & 0.23         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.79          & 0.85 ( 0.04 ) & 1.51 ( 0.34 ) & 0 ( 0 )       &  7.51         & 0.13          & 0.85          & 1.51          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.56          | 0.88 ( 0.05 ) | 3.68 ( 0.29 ) | 0.06 ( 0.03 ) |  9.62         | 0.33          | 0.88          | 3.68          | 0.06          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.77          | 0.87 ( 0.05 ) | 1.51 ( 0.35 ) | 0 ( 0 )       |  7.51         | 0.12          | 0.87          | 1.51          | 0.00          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.88          | 0.99 ( 0.05 ) | 0.73 ( 0.19 ) | 0 ( 0 )       |  6.73         | 0.07          | 0.99          | 0.73          | 0.00          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.90          | 1.09 ( 0.04 ) | 0.55 ( 0.17 ) | 0 ( 0 )       |  6.55         | 0.05          | 1.09          | 0.55          | 0.00          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.53          | 1.12 ( 0.1 )  | 4.24 ( 0.3 )  | 0.23 ( 0.06 ) | 10.01         | 0.37          | 1.12          | 4.24          | 0.23          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.79          | 0.85 ( 0.04 ) | 1.51 ( 0.34 ) | 0 ( 0 )       |  7.51         | 0.13          | 0.85          | 1.51          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.56 0.88 ( 0.05 ) 3.68 ( 0.29 ) 0.06 ( 0.03 )  9.62     \n",
+       "2  100  50 0.1  0.50  0.77 0.87 ( 0.05 ) 1.51 ( 0.35 ) 0 ( 0 )        7.51     \n",
+       "3  500  50 0.1  0.10  0.88 0.99 ( 0.05 ) 0.73 ( 0.19 ) 0 ( 0 )        6.73     \n",
+       "4 1000  50 0.1  0.05  0.90 1.09 ( 0.04 ) 0.55 ( 0.17 ) 0 ( 0 )        6.55     \n",
+       "5   50 100 0.1  2.00  0.53 1.12 ( 0.1 )  4.24 ( 0.3 )  0.23 ( 0.06 ) 10.01     \n",
+       "6  100 100 0.1  1.00  0.79 0.85 ( 0.04 ) 1.51 ( 0.34 ) 0 ( 0 )        7.51     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.33 0.88     3.68    0.06   \n",
+       "2 0.12 0.87     1.51    0.00   \n",
+       "3 0.07 0.99     0.73    0.00   \n",
+       "4 0.05 1.09     0.55    0.00   \n",
+       "5 0.37 1.12     4.24    0.23   \n",
+       "6 0.13 0.85     1.51    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.41          </td><td>0.34 ( 0.01 ) </td><td>8.17 ( 0.48 ) </td><td>0.17 ( 0.04 ) </td><td>14.00         </td><td>0.54          </td><td>0.34          </td><td> 8.17         </td><td>0.17          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.58          </td><td>0.32 ( 0 )    </td><td>4.24 ( 0.3 )  </td><td>0.01 ( 0.01 ) </td><td>10.23         </td><td>0.37          </td><td>0.32          </td><td> 4.24         </td><td>0.01          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.19          </td><td>1.04 ( 0.06 ) </td><td>5.24 ( 0.46 ) </td><td>4.23 ( 0.08 ) </td><td> 7.01         </td><td>0.6           </td><td>1.04          </td><td> 5.24         </td><td>4.23          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.22          </td><td>0.67 ( 0.02 ) </td><td>9.03 ( 0.73 ) </td><td>3.02 ( 0.07 ) </td><td>12.01         </td><td>0.66          </td><td>0.67          </td><td> 9.03         </td><td>3.02          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.34          </td><td>0.35 ( 0.01 ) </td><td>10.52 ( 0.68 )</td><td>0.32 ( 0.05 ) </td><td>16.20         </td><td>0.59          </td><td>0.35          </td><td>10.50         </td><td>0.32          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.57          </td><td>0.33 ( 0 )    </td><td>4.48 ( 0.3 )  </td><td>0.01 ( 0.01 ) </td><td>10.47         </td><td>0.39          </td><td>0.33          </td><td> 4.48         </td><td>0.01          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.41           & 0.34 ( 0.01 )  & 8.17 ( 0.48 )  & 0.17 ( 0.04 )  & 14.00          & 0.54           & 0.34           &  8.17          & 0.17          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.58           & 0.32 ( 0 )     & 4.24 ( 0.3 )   & 0.01 ( 0.01 )  & 10.23          & 0.37           & 0.32           &  4.24          & 0.01          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.19           & 1.04 ( 0.06 )  & 5.24 ( 0.46 )  & 4.23 ( 0.08 )  &  7.01          & 0.6            & 1.04           &  5.24          & 4.23          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.22           & 0.67 ( 0.02 )  & 9.03 ( 0.73 )  & 3.02 ( 0.07 )  & 12.01          & 0.66           & 0.67           &  9.03          & 3.02          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.34           & 0.35 ( 0.01 )  & 10.52 ( 0.68 ) & 0.32 ( 0.05 )  & 16.20          & 0.59           & 0.35           & 10.50          & 0.32          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.57           & 0.33 ( 0 )     & 4.48 ( 0.3 )   & 0.01 ( 0.01 )  & 10.47          & 0.39           & 0.33           &  4.48          & 0.01          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.41           | 0.34 ( 0.01 )  | 8.17 ( 0.48 )  | 0.17 ( 0.04 )  | 14.00          | 0.54           | 0.34           |  8.17          | 0.17           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.58           | 0.32 ( 0 )     | 4.24 ( 0.3 )   | 0.01 ( 0.01 )  | 10.23          | 0.37           | 0.32           |  4.24          | 0.01           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.19           | 1.04 ( 0.06 )  | 5.24 ( 0.46 )  | 4.23 ( 0.08 )  |  7.01          | 0.6            | 1.04           |  5.24          | 4.23           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.22           | 0.67 ( 0.02 )  | 9.03 ( 0.73 )  | 3.02 ( 0.07 )  | 12.01          | 0.66           | 0.67           |  9.03          | 3.02           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.34           | 0.35 ( 0.01 )  | 10.52 ( 0.68 ) | 0.32 ( 0.05 )  | 16.20          | 0.59           | 0.35           | 10.50          | 0.32           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.57           | 0.33 ( 0 )     | 4.48 ( 0.3 )   | 0.01 ( 0.01 )  | 10.47          | 0.39           | 0.33           |  4.48          | 0.01           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.41 0.34 ( 0.01 ) 8.17 ( 0.48 )  0.17 ( 0.04 )\n",
+       "76 1000  500 0.9   0.5  0.58 0.32 ( 0 )    4.24 ( 0.3 )   0.01 ( 0.01 )\n",
+       "77   50 1000 0.9  20.0  0.19 1.04 ( 0.06 ) 5.24 ( 0.46 )  4.23 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.22 0.67 ( 0.02 ) 9.03 ( 0.73 )  3.02 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.34 0.35 ( 0.01 ) 10.52 ( 0.68 ) 0.32 ( 0.05 )\n",
+       "80 1000 1000 0.9   1.0  0.57 0.33 ( 0 )    4.48 ( 0.3 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 14.00      0.54 0.34      8.17   0.17   \n",
+       "76 10.23      0.37 0.32      4.24   0.01   \n",
+       "77  7.01      0.6  1.04      5.24   4.23   \n",
+       "78 12.01      0.66 0.67      9.03   3.02   \n",
+       "79 16.20      0.59 0.35     10.50   0.32   \n",
+       "80 10.47      0.39 0.33      4.48   0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.1_sim_block_lasso_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.1_sim_block_lasso_cts-checkpoint.ipynb
new file mode 100755
index 0000000..427211a
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.1_sim_block_lasso_cts-checkpoint.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_lasso))\n",
+    "for (i in 1:length(results_block_lasso)){\n",
+    "    table_block = rbind(table_block, results_block_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>3.52 ( 0.22 )</td><td>4.92 ( 0.08 )</td><td>0.36 ( 0.01 )</td><td>0.05         </td><td>3.60         </td><td>0.65         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>2.71 ( 0.25 )</td><td>4.4 ( 0.1 )  </td><td>0.31 ( 0.01 )</td><td>0.18         </td><td>3.31         </td><td>0.42         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>5.42 ( 0.25 )</td><td>1.97 ( 0.11 )</td><td>0.29 ( 0 )   </td><td>0.35         </td><td>8.45         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>4.91 ( 0.2 ) </td><td>1.57 ( 0.09 )</td><td>0.28 ( 0 )   </td><td>0.44         </td><td>8.34         </td><td>0.44         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>4.73 ( 0.22 )</td><td>4.79 ( 0.08 )</td><td>0.37 ( 0.02 )</td><td>0.06         </td><td>4.94         </td><td>0.72         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>0.34 ( 0.01 )</td><td>0.13         </td><td>3.25         </td><td>0.57         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 3.52 ( 0.22 ) & 4.92 ( 0.08 ) & 0.36 ( 0.01 ) & 0.05          & 3.60          & 0.65         \\\\\n",
+       "\t 100           & 50            & 0.1           & 2.71 ( 0.25 ) & 4.4 ( 0.1 )   & 0.31 ( 0.01 ) & 0.18          & 3.31          & 0.42         \\\\\n",
+       "\t 500           & 50            & 0.1           & 5.42 ( 0.25 ) & 1.97 ( 0.11 ) & 0.29 ( 0 )    & 0.35          & 8.45          & 0.48         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 4.91 ( 0.2 )  & 1.57 ( 0.09 ) & 0.28 ( 0 )    & 0.44          & 8.34          & 0.44         \\\\\n",
+       "\t 50            & 100           & 0.1           & 4.73 ( 0.22 ) & 4.79 ( 0.08 ) & 0.37 ( 0.02 ) & 0.06          & 4.94          & 0.72         \\\\\n",
+       "\t 100           & 100           & 0.1           & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 0.34 ( 0.01 ) & 0.13          & 3.25          & 0.57         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 3.52 ( 0.22 ) | 4.92 ( 0.08 ) | 0.36 ( 0.01 ) | 0.05          | 3.60          | 0.65          |\n",
+       "| 100           | 50            | 0.1           | 2.71 ( 0.25 ) | 4.4 ( 0.1 )   | 0.31 ( 0.01 ) | 0.18          | 3.31          | 0.42          |\n",
+       "| 500           | 50            | 0.1           | 5.42 ( 0.25 ) | 1.97 ( 0.11 ) | 0.29 ( 0 )    | 0.35          | 8.45          | 0.48          |\n",
+       "| 1000          | 50            | 0.1           | 4.91 ( 0.2 )  | 1.57 ( 0.09 ) | 0.28 ( 0 )    | 0.44          | 8.34          | 0.44          |\n",
+       "| 50            | 100           | 0.1           | 4.73 ( 0.22 ) | 4.79 ( 0.08 ) | 0.37 ( 0.02 ) | 0.06          | 4.94          | 0.72          |\n",
+       "| 100           | 100           | 0.1           | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 0.34 ( 0.01 ) | 0.13          | 3.25          | 0.57          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 3.52 ( 0.22 ) 4.92 ( 0.08 ) 0.36 ( 0.01 ) 0.05 3.60       0.65\n",
+       "2 100  50  0.1 2.71 ( 0.25 ) 4.4 ( 0.1 )   0.31 ( 0.01 ) 0.18 3.31       0.42\n",
+       "3 500  50  0.1 5.42 ( 0.25 ) 1.97 ( 0.11 ) 0.29 ( 0 )    0.35 8.45       0.48\n",
+       "4 1000 50  0.1 4.91 ( 0.2 )  1.57 ( 0.09 ) 0.28 ( 0 )    0.44 8.34       0.44\n",
+       "5 50   100 0.1 4.73 ( 0.22 ) 4.79 ( 0.08 ) 0.37 ( 0.02 ) 0.06 4.94       0.72\n",
+       "6 100  100 0.1 3 ( 0.15 )    4.75 ( 0.09 ) 0.34 ( 0.01 ) 0.13 3.25       0.57"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>6</th><td>100          </td><td>100          </td><td>0.1          </td><td>1            </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>NA           </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t6 & 100           & 100           & 0.1           & 1             & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & NA            & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 6 | 100           | 100           | 0.1           | 1             | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | NA            | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N   P   Corr Ratio Stab MSE           FP         FN            num_select\n",
+       "6 100 100 0.1  1     0.13 0.34 ( 0.01 ) 3 ( 0.15 ) 4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "6 0.57 0.34     NA      4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>6</th><td>100          </td><td>100          </td><td>0.1          </td><td>1            </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>3            </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t6 & 100           & 100           & 0.1           & 1             & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & 3             & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 6 | 100           | 100           | 0.1           | 1             | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | 3             | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N   P   Corr Ratio Stab MSE           FP         FN            num_select\n",
+       "6 100 100 0.1  1     0.13 0.34 ( 0.01 ) 3 ( 0.15 ) 4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "6 0.57 0.34     3       4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[6, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.05         </td><td>0.36 ( 0.01 )</td><td>3.52 ( 0.22 )</td><td>4.92 ( 0.08 )</td><td>3.60         </td><td>0.65         </td><td>0.36         </td><td>3.52         </td><td>4.92         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.18         </td><td>0.31 ( 0.01 )</td><td>2.71 ( 0.25 )</td><td>4.4 ( 0.1 )  </td><td>3.31         </td><td>0.42         </td><td>0.31         </td><td>2.71         </td><td>4.40         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.35         </td><td>0.29 ( 0 )   </td><td>5.42 ( 0.25 )</td><td>1.97 ( 0.11 )</td><td>8.45         </td><td>0.48         </td><td>0.29         </td><td>5.42         </td><td>1.97         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.44         </td><td>0.28 ( 0 )   </td><td>4.91 ( 0.2 ) </td><td>1.57 ( 0.09 )</td><td>8.34         </td><td>0.44         </td><td>0.28         </td><td>4.91         </td><td>1.57         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.06         </td><td>0.37 ( 0.02 )</td><td>4.73 ( 0.22 )</td><td>4.79 ( 0.08 )</td><td>4.94         </td><td>0.72         </td><td>0.37         </td><td>4.73         </td><td>4.79         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>3.00         </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.05          & 0.36 ( 0.01 ) & 3.52 ( 0.22 ) & 4.92 ( 0.08 ) & 3.60          & 0.65          & 0.36          & 3.52          & 4.92         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.18          & 0.31 ( 0.01 ) & 2.71 ( 0.25 ) & 4.4 ( 0.1 )   & 3.31          & 0.42          & 0.31          & 2.71          & 4.40         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.35          & 0.29 ( 0 )    & 5.42 ( 0.25 ) & 1.97 ( 0.11 ) & 8.45          & 0.48          & 0.29          & 5.42          & 1.97         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.44          & 0.28 ( 0 )    & 4.91 ( 0.2 )  & 1.57 ( 0.09 ) & 8.34          & 0.44          & 0.28          & 4.91          & 1.57         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.06          & 0.37 ( 0.02 ) & 4.73 ( 0.22 ) & 4.79 ( 0.08 ) & 4.94          & 0.72          & 0.37          & 4.73          & 4.79         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & 3.00          & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.05          | 0.36 ( 0.01 ) | 3.52 ( 0.22 ) | 4.92 ( 0.08 ) | 3.60          | 0.65          | 0.36          | 3.52          | 4.92          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.18          | 0.31 ( 0.01 ) | 2.71 ( 0.25 ) | 4.4 ( 0.1 )   | 3.31          | 0.42          | 0.31          | 2.71          | 4.40          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.35          | 0.29 ( 0 )    | 5.42 ( 0.25 ) | 1.97 ( 0.11 ) | 8.45          | 0.48          | 0.29          | 5.42          | 1.97          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.44          | 0.28 ( 0 )    | 4.91 ( 0.2 )  | 1.57 ( 0.09 ) | 8.34          | 0.44          | 0.28          | 4.91          | 1.57          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.06          | 0.37 ( 0.02 ) | 4.73 ( 0.22 ) | 4.79 ( 0.08 ) | 4.94          | 0.72          | 0.37          | 4.73          | 4.79          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | 3.00          | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.05 0.36 ( 0.01 ) 3.52 ( 0.22 ) 4.92 ( 0.08 ) 3.60      \n",
+       "2  100  50 0.1  0.50  0.18 0.31 ( 0.01 ) 2.71 ( 0.25 ) 4.4 ( 0.1 )   3.31      \n",
+       "3  500  50 0.1  0.10  0.35 0.29 ( 0 )    5.42 ( 0.25 ) 1.97 ( 0.11 ) 8.45      \n",
+       "4 1000  50 0.1  0.05  0.44 0.28 ( 0 )    4.91 ( 0.2 )  1.57 ( 0.09 ) 8.34      \n",
+       "5   50 100 0.1  2.00  0.06 0.37 ( 0.02 ) 4.73 ( 0.22 ) 4.79 ( 0.08 ) 4.94      \n",
+       "6  100 100 0.1  1.00  0.13 0.34 ( 0.01 ) 3 ( 0.15 )    4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.65 0.36     3.52    4.92   \n",
+       "2 0.42 0.31     2.71    4.40   \n",
+       "3 0.48 0.29     5.42    1.97   \n",
+       "4 0.44 0.28     4.91    1.57   \n",
+       "5 0.72 0.37     4.73    4.79   \n",
+       "6 0.57 0.34     3.00    4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.50          </td><td>0.29 ( 0 )    </td><td>6.87 ( 0.54 ) </td><td>0 ( 0 )       </td><td>11.87         </td><td>0.41          </td><td>0.29          </td><td> 6.87         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.71          </td><td>0.27 ( 0 )    </td><td>3.37 ( 0.27 ) </td><td>0 ( 0 )       </td><td> 8.37         </td><td>0.23          </td><td>0.27          </td><td> 3.37         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.11          </td><td>2.46 ( 0.16 ) </td><td>25.37 ( 0.35 )</td><td>1.73 ( 0.12 ) </td><td>28.64         </td><td>0.85          </td><td>2.46          </td><td>25.30         </td><td>1.73          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.22          </td><td>0.62 ( 0.03 ) </td><td>21.43 ( 0.62 )</td><td>0 ( 0 )       </td><td>26.43         </td><td>0.76          </td><td>0.62          </td><td>21.40         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.46          </td><td>0.3 ( 0 )     </td><td>7.99 ( 0.79 ) </td><td>0 ( 0 )       </td><td>12.99         </td><td>0.42          </td><td>0.30          </td><td> 7.99         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.60          </td><td>0.28 ( 0 )    </td><td>4.93 ( 0.43 ) </td><td>0 ( 0 )       </td><td> 9.93         </td><td>0.31          </td><td>0.28          </td><td> 4.93         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.50           & 0.29 ( 0 )     & 6.87 ( 0.54 )  & 0 ( 0 )        & 11.87          & 0.41           & 0.29           &  6.87          & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.71           & 0.27 ( 0 )     & 3.37 ( 0.27 )  & 0 ( 0 )        &  8.37          & 0.23           & 0.27           &  3.37          & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.11           & 2.46 ( 0.16 )  & 25.37 ( 0.35 ) & 1.73 ( 0.12 )  & 28.64          & 0.85           & 2.46           & 25.30          & 1.73          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.22           & 0.62 ( 0.03 )  & 21.43 ( 0.62 ) & 0 ( 0 )        & 26.43          & 0.76           & 0.62           & 21.40          & 0.00          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.46           & 0.3 ( 0 )      & 7.99 ( 0.79 )  & 0 ( 0 )        & 12.99          & 0.42           & 0.30           &  7.99          & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.60           & 0.28 ( 0 )     & 4.93 ( 0.43 )  & 0 ( 0 )        &  9.93          & 0.31           & 0.28           &  4.93          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.50           | 0.29 ( 0 )     | 6.87 ( 0.54 )  | 0 ( 0 )        | 11.87          | 0.41           | 0.29           |  6.87          | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.71           | 0.27 ( 0 )     | 3.37 ( 0.27 )  | 0 ( 0 )        |  8.37          | 0.23           | 0.27           |  3.37          | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.11           | 2.46 ( 0.16 )  | 25.37 ( 0.35 ) | 1.73 ( 0.12 )  | 28.64          | 0.85           | 2.46           | 25.30          | 1.73           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.22           | 0.62 ( 0.03 )  | 21.43 ( 0.62 ) | 0 ( 0 )        | 26.43          | 0.76           | 0.62           | 21.40          | 0.00           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.46           | 0.3 ( 0 )      | 7.99 ( 0.79 )  | 0 ( 0 )        | 12.99          | 0.42           | 0.30           |  7.99          | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.60           | 0.28 ( 0 )     | 4.93 ( 0.43 )  | 0 ( 0 )        |  9.93          | 0.31           | 0.28           |  4.93          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.50 0.29 ( 0 )    6.87 ( 0.54 )  0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.71 0.27 ( 0 )    3.37 ( 0.27 )  0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.11 2.46 ( 0.16 ) 25.37 ( 0.35 ) 1.73 ( 0.12 )\n",
+       "78  100 1000 0.9  10.0  0.22 0.62 ( 0.03 ) 21.43 ( 0.62 ) 0 ( 0 )      \n",
+       "79  500 1000 0.9   2.0  0.46 0.3 ( 0 )     7.99 ( 0.79 )  0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.60 0.28 ( 0 )    4.93 ( 0.43 )  0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 11.87      0.41 0.29      6.87   0.00   \n",
+       "76  8.37      0.23 0.27      3.37   0.00   \n",
+       "77 28.64      0.85 2.46     25.30   1.73   \n",
+       "78 26.43      0.76 0.62     21.40   0.00   \n",
+       "79 12.99      0.42 0.30      7.99   0.00   \n",
+       "80  9.93      0.31 0.28      4.93   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.2_sim_block_elnet_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.2_sim_block_elnet_cts-checkpoint.ipynb
new file mode 100755
index 0000000..c718e78
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.2_sim_block_elnet_cts-checkpoint.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_elnet))\n",
+    "for (i in 1:length(results_block_elnet)){\n",
+    "    table_block = rbind(table_block, results_block_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>10.9 ( 0.97 )</td><td>3.91 ( 0.15 )</td><td>0.38 ( 0.02 )</td><td>0.02         </td><td>11.99        </td><td>0.77         </td></tr>\n",
+       "\t<tr><td>100           </td><td>50            </td><td>0.1           </td><td>13.39 ( 1.06 )</td><td>2.45 ( 0.16 ) </td><td>0.34 ( 0.01 ) </td><td>0.06          </td><td>15.94         </td><td>0.68          </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0.1           </td><td>19.21 ( 0.81 )</td><td>0.14 ( 0.04 ) </td><td>0.27 ( 0 )    </td><td>0.13          </td><td>24.07         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0.1           </td><td>18.76 ( 0.63 )</td><td>0.02 ( 0.01 ) </td><td>0.26 ( 0 )    </td><td>0.15          </td><td>23.74         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>9.27 ( 0.76 )</td><td>4.63 ( 0.09 )</td><td>0.41 ( 0.02 )</td><td>0.03         </td><td> 9.64        </td><td>0.82         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>10.1 ( 1.04 )</td><td>3.87 ( 0.13 )</td><td>0.34 ( 0.01 )</td><td>0.07         </td><td>11.23        </td><td>0.70         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 10.9 ( 0.97 ) & 3.91 ( 0.15 ) & 0.38 ( 0.02 ) & 0.02          & 11.99         & 0.77         \\\\\n",
+       "\t 100            & 50             & 0.1            & 13.39 ( 1.06 ) & 2.45 ( 0.16 )  & 0.34 ( 0.01 )  & 0.06           & 15.94          & 0.68          \\\\\n",
+       "\t 500            & 50             & 0.1            & 19.21 ( 0.81 ) & 0.14 ( 0.04 )  & 0.27 ( 0 )     & 0.13           & 24.07          & 0.73          \\\\\n",
+       "\t 1000           & 50             & 0.1            & 18.76 ( 0.63 ) & 0.02 ( 0.01 )  & 0.26 ( 0 )     & 0.15           & 23.74          & 0.73          \\\\\n",
+       "\t 50            & 100           & 0.1           & 9.27 ( 0.76 ) & 4.63 ( 0.09 ) & 0.41 ( 0.02 ) & 0.03          &  9.64         & 0.82         \\\\\n",
+       "\t 100           & 100           & 0.1           & 10.1 ( 1.04 ) & 3.87 ( 0.13 ) & 0.34 ( 0.01 ) & 0.07          & 11.23         & 0.70         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 10.9 ( 0.97 ) | 3.91 ( 0.15 ) | 0.38 ( 0.02 ) | 0.02          | 11.99         | 0.77          |\n",
+       "| 100            | 50             | 0.1            | 13.39 ( 1.06 ) | 2.45 ( 0.16 )  | 0.34 ( 0.01 )  | 0.06           | 15.94          | 0.68           |\n",
+       "| 500            | 50             | 0.1            | 19.21 ( 0.81 ) | 0.14 ( 0.04 )  | 0.27 ( 0 )     | 0.13           | 24.07          | 0.73           |\n",
+       "| 1000           | 50             | 0.1            | 18.76 ( 0.63 ) | 0.02 ( 0.01 )  | 0.26 ( 0 )     | 0.15           | 23.74          | 0.73           |\n",
+       "| 50            | 100           | 0.1           | 9.27 ( 0.76 ) | 4.63 ( 0.09 ) | 0.41 ( 0.02 ) | 0.03          |  9.64         | 0.82          |\n",
+       "| 100           | 100           | 0.1           | 10.1 ( 1.04 ) | 3.87 ( 0.13 ) | 0.34 ( 0.01 ) | 0.07          | 11.23         | 0.70          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 10.9 ( 0.97 )  3.91 ( 0.15 ) 0.38 ( 0.02 ) 0.02 11.99      0.77\n",
+       "2 100  50  0.1 13.39 ( 1.06 ) 2.45 ( 0.16 ) 0.34 ( 0.01 ) 0.06 15.94      0.68\n",
+       "3 500  50  0.1 19.21 ( 0.81 ) 0.14 ( 0.04 ) 0.27 ( 0 )    0.13 24.07      0.73\n",
+       "4 1000 50  0.1 18.76 ( 0.63 ) 0.02 ( 0.01 ) 0.26 ( 0 )    0.15 23.74      0.73\n",
+       "5 50   100 0.1 9.27 ( 0.76 )  4.63 ( 0.09 ) 0.41 ( 0.02 ) 0.03  9.64      0.82\n",
+       "6 100  100 0.1 10.1 ( 1.04 )  3.87 ( 0.13 ) 0.34 ( 0.01 ) 0.07 11.23      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>45</th><td>50            </td><td>1000          </td><td>0.5           </td><td>20            </td><td>0.04          </td><td>2 ( 0.1 )     </td><td>52.83 ( 3.84 )</td><td>2.29 ( 0.1 )  </td><td>55.54         </td><td>0.91          </td><td>NA            </td><td>52.8          </td><td>2.29          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t45 & 50             & 1000           & 0.5            & 20             & 0.04           & 2 ( 0.1 )      & 52.83 ( 3.84 ) & 2.29 ( 0.1 )   & 55.54          & 0.91           & NA             & 52.8           & 2.29          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 45 | 50             | 1000           | 0.5            | 20             | 0.04           | 2 ( 0.1 )      | 52.83 ( 3.84 ) | 2.29 ( 0.1 )   | 55.54          | 0.91           | NA             | 52.8           | 2.29           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE       FP             FN           num_select\n",
+       "45 50 1000 0.5  20    0.04 2 ( 0.1 ) 52.83 ( 3.84 ) 2.29 ( 0.1 ) 55.54     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "45 0.91 NA       52.8    2.29   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$MSE_mean[45] = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>45</th><td>50            </td><td>1000          </td><td>0.5           </td><td>20            </td><td>0.04          </td><td>2 ( 0.1 )     </td><td>52.83 ( 3.84 )</td><td>2.29 ( 0.1 )  </td><td>55.54         </td><td>0.91          </td><td>2             </td><td>52.8          </td><td>2.29          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t45 & 50             & 1000           & 0.5            & 20             & 0.04           & 2 ( 0.1 )      & 52.83 ( 3.84 ) & 2.29 ( 0.1 )   & 55.54          & 0.91           & 2              & 52.8           & 2.29          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 45 | 50             | 1000           | 0.5            | 20             | 0.04           | 2 ( 0.1 )      | 52.83 ( 3.84 ) | 2.29 ( 0.1 )   | 55.54          | 0.91           | 2              | 52.8           | 2.29           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE       FP             FN           num_select\n",
+       "45 50 1000 0.5  20    0.04 2 ( 0.1 ) 52.83 ( 3.84 ) 2.29 ( 0.1 ) 55.54     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "45 0.91 2        52.8    2.29   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[45, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td> 50           </td><td>0.1           </td><td>1.00          </td><td>0.02          </td><td>0.38 ( 0.02 ) </td><td>10.9 ( 0.97 ) </td><td>3.91 ( 0.15 ) </td><td>11.99         </td><td>0.77          </td><td>0.38          </td><td>10.90         </td><td>3.91          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 50           </td><td>0.1           </td><td>0.50          </td><td>0.06          </td><td>0.34 ( 0.01 ) </td><td>13.39 ( 1.06 )</td><td>2.45 ( 0.16 ) </td><td>15.94         </td><td>0.68          </td><td>0.34          </td><td>13.30         </td><td>2.45          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 50           </td><td>0.1           </td><td>0.10          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>19.21 ( 0.81 )</td><td>0.14 ( 0.04 ) </td><td>24.07         </td><td>0.73          </td><td>0.27          </td><td>19.20         </td><td>0.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 50           </td><td>0.1           </td><td>0.05          </td><td>0.15          </td><td>0.26 ( 0 )    </td><td>18.76 ( 0.63 )</td><td>0.02 ( 0.01 ) </td><td>23.74         </td><td>0.73          </td><td>0.26          </td><td>18.70         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>100           </td><td>0.1           </td><td>2.00          </td><td>0.03          </td><td>0.41 ( 0.02 ) </td><td>9.27 ( 0.76 ) </td><td>4.63 ( 0.09 ) </td><td> 9.64         </td><td>0.82          </td><td>0.41          </td><td> 9.27         </td><td>4.63          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>100           </td><td>0.1           </td><td>1.00          </td><td>0.07          </td><td>0.34 ( 0.01 ) </td><td>10.1 ( 1.04 ) </td><td>3.87 ( 0.13 ) </td><td>11.23         </td><td>0.7           </td><td>0.34          </td><td>10.10         </td><td>3.87          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &  50            & 0.1            & 1.00           & 0.02           & 0.38 ( 0.02 )  & 10.9 ( 0.97 )  & 3.91 ( 0.15 )  & 11.99          & 0.77           & 0.38           & 10.90          & 3.91          \\\\\n",
+       "\t  100           &  50            & 0.1            & 0.50           & 0.06           & 0.34 ( 0.01 )  & 13.39 ( 1.06 ) & 2.45 ( 0.16 )  & 15.94          & 0.68           & 0.34           & 13.30          & 2.45          \\\\\n",
+       "\t  500           &  50            & 0.1            & 0.10           & 0.13           & 0.27 ( 0 )     & 19.21 ( 0.81 ) & 0.14 ( 0.04 )  & 24.07          & 0.73           & 0.27           & 19.20          & 0.14          \\\\\n",
+       "\t 1000           &  50            & 0.1            & 0.05           & 0.15           & 0.26 ( 0 )     & 18.76 ( 0.63 ) & 0.02 ( 0.01 )  & 23.74          & 0.73           & 0.26           & 18.70          & 0.02          \\\\\n",
+       "\t   50           & 100            & 0.1            & 2.00           & 0.03           & 0.41 ( 0.02 )  & 9.27 ( 0.76 )  & 4.63 ( 0.09 )  &  9.64          & 0.82           & 0.41           &  9.27          & 4.63          \\\\\n",
+       "\t  100           & 100            & 0.1            & 1.00           & 0.07           & 0.34 ( 0.01 )  & 10.1 ( 1.04 )  & 3.87 ( 0.13 )  & 11.23          & 0.7            & 0.34           & 10.10          & 3.87          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |  50            | 0.1            | 1.00           | 0.02           | 0.38 ( 0.02 )  | 10.9 ( 0.97 )  | 3.91 ( 0.15 )  | 11.99          | 0.77           | 0.38           | 10.90          | 3.91           |\n",
+       "|  100           |  50            | 0.1            | 0.50           | 0.06           | 0.34 ( 0.01 )  | 13.39 ( 1.06 ) | 2.45 ( 0.16 )  | 15.94          | 0.68           | 0.34           | 13.30          | 2.45           |\n",
+       "|  500           |  50            | 0.1            | 0.10           | 0.13           | 0.27 ( 0 )     | 19.21 ( 0.81 ) | 0.14 ( 0.04 )  | 24.07          | 0.73           | 0.27           | 19.20          | 0.14           |\n",
+       "| 1000           |  50            | 0.1            | 0.05           | 0.15           | 0.26 ( 0 )     | 18.76 ( 0.63 ) | 0.02 ( 0.01 )  | 23.74          | 0.73           | 0.26           | 18.70          | 0.02           |\n",
+       "|   50           | 100            | 0.1            | 2.00           | 0.03           | 0.41 ( 0.02 )  | 9.27 ( 0.76 )  | 4.63 ( 0.09 )  |  9.64          | 0.82           | 0.41           |  9.27          | 4.63           |\n",
+       "|  100           | 100            | 0.1            | 1.00           | 0.07           | 0.34 ( 0.01 )  | 10.1 ( 1.04 )  | 3.87 ( 0.13 )  | 11.23          | 0.7            | 0.34           | 10.10          | 3.87           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.02 0.38 ( 0.02 ) 10.9 ( 0.97 )  3.91 ( 0.15 )\n",
+       "2  100  50 0.1  0.50  0.06 0.34 ( 0.01 ) 13.39 ( 1.06 ) 2.45 ( 0.16 )\n",
+       "3  500  50 0.1  0.10  0.13 0.27 ( 0 )    19.21 ( 0.81 ) 0.14 ( 0.04 )\n",
+       "4 1000  50 0.1  0.05  0.15 0.26 ( 0 )    18.76 ( 0.63 ) 0.02 ( 0.01 )\n",
+       "5   50 100 0.1  2.00  0.03 0.41 ( 0.02 ) 9.27 ( 0.76 )  4.63 ( 0.09 )\n",
+       "6  100 100 0.1  1.00  0.07 0.34 ( 0.01 ) 10.1 ( 1.04 )  3.87 ( 0.13 )\n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1 11.99      0.77 0.38     10.90   3.91   \n",
+       "2 15.94      0.68 0.34     13.30   2.45   \n",
+       "3 24.07      0.73 0.27     19.20   0.14   \n",
+       "4 23.74      0.73 0.26     18.70   0.02   \n",
+       "5  9.64      0.82 0.41      9.27   4.63   \n",
+       "6 11.23      0.7  0.34     10.10   3.87   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.15          </td><td>0.28 ( 0 )    </td><td>32.6 ( 1.76 ) </td><td>0 ( 0 )       </td><td>37.60         </td><td>0.81          </td><td>0.28          </td><td>32.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.16          </td><td>0.26 ( 0 )    </td><td>30.4 ( 1.66 ) </td><td>0 ( 0 )       </td><td>35.40         </td><td>0.79          </td><td>0.26          </td><td>30.4          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.07          </td><td>3.06 ( 0.18 ) </td><td>43.09 ( 2.82 )</td><td>1.57 ( 0.1 )  </td><td>46.52         </td><td>0.88          </td><td>3.06          </td><td>43.0          </td><td>1.57          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.12          </td><td>0.59 ( 0.03 ) </td><td>43.91 ( 1.31 )</td><td>0 ( 0 )       </td><td>48.91         </td><td>0.87          </td><td>0.59          </td><td>43.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.12          </td><td>0.29 ( 0 )    </td><td>41.57 ( 2.28 )</td><td>0 ( 0 )       </td><td>46.57         </td><td>0.84          </td><td>0.29          </td><td>41.5          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>38.76 ( 2.16 )</td><td>0 ( 0 )       </td><td>43.76         </td><td>0.82          </td><td>0.27          </td><td>38.7          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.15           & 0.28 ( 0 )     & 32.6 ( 1.76 )  & 0 ( 0 )        & 37.60          & 0.81           & 0.28           & 32.6           & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.16           & 0.26 ( 0 )     & 30.4 ( 1.66 )  & 0 ( 0 )        & 35.40          & 0.79           & 0.26           & 30.4           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.07           & 3.06 ( 0.18 )  & 43.09 ( 2.82 ) & 1.57 ( 0.1 )   & 46.52          & 0.88           & 3.06           & 43.0           & 1.57          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.12           & 0.59 ( 0.03 )  & 43.91 ( 1.31 ) & 0 ( 0 )        & 48.91          & 0.87           & 0.59           & 43.9           & 0.00          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.12           & 0.29 ( 0 )     & 41.57 ( 2.28 ) & 0 ( 0 )        & 46.57          & 0.84           & 0.29           & 41.5           & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.13           & 0.27 ( 0 )     & 38.76 ( 2.16 ) & 0 ( 0 )        & 43.76          & 0.82           & 0.27           & 38.7           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.15           | 0.28 ( 0 )     | 32.6 ( 1.76 )  | 0 ( 0 )        | 37.60          | 0.81           | 0.28           | 32.6           | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.16           | 0.26 ( 0 )     | 30.4 ( 1.66 )  | 0 ( 0 )        | 35.40          | 0.79           | 0.26           | 30.4           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.07           | 3.06 ( 0.18 )  | 43.09 ( 2.82 ) | 1.57 ( 0.1 )   | 46.52          | 0.88           | 3.06           | 43.0           | 1.57           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.12           | 0.59 ( 0.03 )  | 43.91 ( 1.31 ) | 0 ( 0 )        | 48.91          | 0.87           | 0.59           | 43.9           | 0.00           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.12           | 0.29 ( 0 )     | 41.57 ( 2.28 ) | 0 ( 0 )        | 46.57          | 0.84           | 0.29           | 41.5           | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.13           | 0.27 ( 0 )     | 38.76 ( 2.16 ) | 0 ( 0 )        | 43.76          | 0.82           | 0.27           | 38.7           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN          \n",
+       "75  500  500 0.9   1.0  0.15 0.28 ( 0 )    32.6 ( 1.76 )  0 ( 0 )     \n",
+       "76 1000  500 0.9   0.5  0.16 0.26 ( 0 )    30.4 ( 1.66 )  0 ( 0 )     \n",
+       "77   50 1000 0.9  20.0  0.07 3.06 ( 0.18 ) 43.09 ( 2.82 ) 1.57 ( 0.1 )\n",
+       "78  100 1000 0.9  10.0  0.12 0.59 ( 0.03 ) 43.91 ( 1.31 ) 0 ( 0 )     \n",
+       "79  500 1000 0.9   2.0  0.12 0.29 ( 0 )    41.57 ( 2.28 ) 0 ( 0 )     \n",
+       "80 1000 1000 0.9   1.0  0.13 0.27 ( 0 )    38.76 ( 2.16 ) 0 ( 0 )     \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 37.60      0.81 0.28     32.6    0.00   \n",
+       "76 35.40      0.79 0.26     30.4    0.00   \n",
+       "77 46.52      0.88 3.06     43.0    1.57   \n",
+       "78 48.91      0.87 0.59     43.9    0.00   \n",
+       "79 46.57      0.84 0.29     41.5    0.00   \n",
+       "80 43.76      0.82 0.27     38.7    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.3_sim_block_rf_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.3_sim_block_rf_cts-checkpoint.ipynb
new file mode 100755
index 0000000..669d0af
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.3_sim_block_rf_cts-checkpoint.ipynb
@@ -0,0 +1,690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_rf))\n",
+    "for (i in 1:length(results_block_rf)){\n",
+    "    results_block_rf[[i]]$OOB = paste(round(mean(results_block_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_block_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_block = rbind(table_block, results_block_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.08 ( 0 )   </td><td>NaN          </td><td>0.59 ( 0.01 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>2.26 ( 0.17 )</td><td>5.06 ( 0.12 )</td><td>0.07 ( 0 )   </td><td>0.03         </td><td>0.58 ( 0 )   </td><td>3.04         </td><td>0.70         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>2.34 ( 0.16 )</td><td>3.77 ( 0.14 )</td><td>0.05 ( 0 )   </td><td>0.23         </td><td>0.56 ( 0 )   </td><td>4.56         </td><td>0.49         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>2.37 ( 0.16 )</td><td>2.54 ( 0.11 )</td><td>0.05 ( 0 )   </td><td>0.51         </td><td>0.55 ( 0 )   </td><td>5.83         </td><td>0.38         </td></tr>\n",
+       "\t<tr><td>50          </td><td>100         </td><td>0.1         </td><td>1 ( 0 )     </td><td>6 ( 0 )     </td><td>0.08 ( 0 )  </td><td>NaN         </td><td>0.6 ( 0.01 )</td><td>0.00        </td><td> NaN        </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>4.76 ( 0.27 )</td><td>5.51 ( 0.09 )</td><td>0.07 ( 0 )   </td><td>0.01         </td><td>0.58 ( 0 )   </td><td>5.24         </td><td>0.91         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 0.08 ( 0 )    & NaN           & 0.59 ( 0.01 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0.1           & 2.26 ( 0.17 ) & 5.06 ( 0.12 ) & 0.07 ( 0 )    & 0.03          & 0.58 ( 0 )    & 3.04          & 0.70         \\\\\n",
+       "\t 500           & 50            & 0.1           & 2.34 ( 0.16 ) & 3.77 ( 0.14 ) & 0.05 ( 0 )    & 0.23          & 0.56 ( 0 )    & 4.56          & 0.49         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 2.37 ( 0.16 ) & 2.54 ( 0.11 ) & 0.05 ( 0 )    & 0.51          & 0.55 ( 0 )    & 5.83          & 0.38         \\\\\n",
+       "\t 50           & 100          & 0.1          & 1 ( 0 )      & 6 ( 0 )      & 0.08 ( 0 )   & NaN          & 0.6 ( 0.01 ) & 0.00         &  NaN        \\\\\n",
+       "\t 100           & 100           & 0.1           & 4.76 ( 0.27 ) & 5.51 ( 0.09 ) & 0.07 ( 0 )    & 0.01          & 0.58 ( 0 )    & 5.24          & 0.91         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 0.08 ( 0 )    | NaN           | 0.59 ( 0.01 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0.1           | 2.26 ( 0.17 ) | 5.06 ( 0.12 ) | 0.07 ( 0 )    | 0.03          | 0.58 ( 0 )    | 3.04          | 0.70          |\n",
+       "| 500           | 50            | 0.1           | 2.34 ( 0.16 ) | 3.77 ( 0.14 ) | 0.05 ( 0 )    | 0.23          | 0.56 ( 0 )    | 4.56          | 0.49          |\n",
+       "| 1000          | 50            | 0.1           | 2.37 ( 0.16 ) | 2.54 ( 0.11 ) | 0.05 ( 0 )    | 0.51          | 0.55 ( 0 )    | 5.83          | 0.38          |\n",
+       "| 50           | 100          | 0.1          | 1 ( 0 )      | 6 ( 0 )      | 0.08 ( 0 )   | NaN          | 0.6 ( 0.01 ) | 0.00         |  NaN         |\n",
+       "| 100           | 100           | 0.1           | 4.76 ( 0.27 ) | 5.51 ( 0.09 ) | 0.07 ( 0 )    | 0.01          | 0.58 ( 0 )    | 5.24          | 0.91          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE        Stab OOB          \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       0.08 ( 0 ) NaN  0.59 ( 0.01 )\n",
+       "2 100  50  0.1 2.26 ( 0.17 ) 5.06 ( 0.12 ) 0.07 ( 0 ) 0.03 0.58 ( 0 )   \n",
+       "3 500  50  0.1 2.34 ( 0.16 ) 3.77 ( 0.14 ) 0.05 ( 0 ) 0.23 0.56 ( 0 )   \n",
+       "4 1000 50  0.1 2.37 ( 0.16 ) 2.54 ( 0.11 ) 0.05 ( 0 ) 0.51 0.55 ( 0 )   \n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       0.08 ( 0 ) NaN  0.6 ( 0.01 ) \n",
+       "6 100  100 0.1 4.76 ( 0.27 ) 5.51 ( 0.09 ) 0.07 ( 0 ) 0.01 0.58 ( 0 )   \n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.04       0.70\n",
+       "3 4.56       0.49\n",
+       "4 5.83       0.38\n",
+       "5 0.00        NaN\n",
+       "6 5.24       0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$OOB_mean = as.numeric(substr(result.table_block$OOB, start=1, stop=4))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50           </td><td>  50          </td><td>0.1           </td><td> 1            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50           </td><td> 100          </td><td>0.1           </td><td> 2            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.6 ( 0.01 )  </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.60          </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50           </td><td> 500          </td><td>0.1           </td><td>10            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50           </td><td>1000          </td><td>0.1           </td><td>20            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50           </td><td>  50          </td><td>0.3           </td><td> 1            </td><td> NaN          </td><td>0.31 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.14 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.31          </td><td>  NA          </td><td>0.00          </td><td>1.14          </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50           </td><td> 100          </td><td>0.3           </td><td> 2            </td><td> NaN          </td><td>0.32 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.2 ( 0.01 )  </td><td> 0.00         </td><td>NaN           </td><td>0.32          </td><td>  NA          </td><td>0.00          </td><td>1.20          </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50           </td><td> 500          </td><td>0.3           </td><td>10            </td><td> NaN          </td><td>0.36 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.23 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.36          </td><td>  NA          </td><td>0.00          </td><td>1.23          </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50           </td><td>1000          </td><td>0.3           </td><td>20            </td><td> NaN          </td><td>0.34 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.26 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.34          </td><td>  NA          </td><td>0.00          </td><td>1.26          </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50           </td><td>  50          </td><td>0.5           </td><td> 1            </td><td> NaN          </td><td>0.6 ( 0.03 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.46 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.60          </td><td>  NA          </td><td>0.00          </td><td>1.46          </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50           </td><td> 100          </td><td>0.5           </td><td> 2            </td><td> NaN          </td><td>0.6 ( 0.02 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.56 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.60          </td><td>  NA          </td><td>0.00          </td><td>1.56          </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50           </td><td> 500          </td><td>0.5           </td><td>10            </td><td> NaN          </td><td>0.58 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.64 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.58          </td><td>  NA          </td><td>0.00          </td><td>1.64          </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50           </td><td>1000          </td><td>0.5           </td><td>20            </td><td> NaN          </td><td>0.57 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.64 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.57          </td><td>  NA          </td><td>0.00          </td><td>1.64          </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50           </td><td>  50          </td><td>0.7           </td><td> 1            </td><td> NaN          </td><td>0.8 ( 0.04 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.72 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.80          </td><td>  NA          </td><td>0.00          </td><td>1.72          </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50           </td><td> 100          </td><td>0.7           </td><td> 2            </td><td> NaN          </td><td>0.92 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.85 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.92          </td><td>  NA          </td><td>0.00          </td><td>1.85          </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50           </td><td> 500          </td><td>0.7           </td><td>10            </td><td> NaN          </td><td>0.91 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2 ( 0.02 )    </td><td> 0.00         </td><td>NaN           </td><td>0.91          </td><td>  NA          </td><td>0.00          </td><td>  NA          </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50           </td><td>1000          </td><td>0.7           </td><td>20            </td><td> NaN          </td><td>0.91 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.98 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.91          </td><td>  NA          </td><td>0.00          </td><td>1.98          </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50           </td><td>  50          </td><td>0.9           </td><td> 1            </td><td> NaN          </td><td>1.1 ( 0.04 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.99 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>1.10          </td><td>  NA          </td><td>0.00          </td><td>1.99          </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50           </td><td> 100          </td><td>0.9           </td><td> 2            </td><td> NaN          </td><td>1.11 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.06 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>1.11          </td><td>  NA          </td><td>0.00          </td><td>2.06          </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50           </td><td> 500          </td><td>0.9           </td><td>10            </td><td> NaN          </td><td>1.18 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.2 ( 0.02 )  </td><td> 0.00         </td><td>NaN           </td><td>1.18          </td><td>  NA          </td><td>0.00          </td><td>2.20          </td></tr>\n",
+       "\t<tr><th scope=row>75</th><td>500           </td><td> 500          </td><td>0.9           </td><td> 1            </td><td>0.12          </td><td>0.73 ( 0.01 ) </td><td>20.83 ( 0.45 )</td><td>2.04 ( 0.09 ) </td><td>2 ( 0.01 )    </td><td>24.79         </td><td>0.84          </td><td>0.73          </td><td>20.8          </td><td>2.04          </td><td>  NA          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50           </td><td>1000          </td><td>0.9           </td><td>20            </td><td> NaN          </td><td>1.22 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.3 ( 0.03 )  </td><td> 0.00         </td><td>NaN           </td><td>1.22          </td><td>  NA          </td><td>0.00          </td><td>2.30          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50            &   50           & 0.1            &  1             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t5 &  50            &  100           & 0.1            &  2             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.6 ( 0.01 )   &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.60          \\\\\n",
+       "\t9 &  50            &  500           & 0.1            & 10             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t13 &  50            & 1000           & 0.1            & 20             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t17 &  50            &   50           & 0.3            &  1             &  NaN           & 0.31 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.14 ( 0.01 )  &  0.00          & NaN            & 0.31           &   NA           & 0.00           & 1.14          \\\\\n",
+       "\t21 &  50            &  100           & 0.3            &  2             &  NaN           & 0.32 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.2 ( 0.01 )   &  0.00          & NaN            & 0.32           &   NA           & 0.00           & 1.20          \\\\\n",
+       "\t25 &  50            &  500           & 0.3            & 10             &  NaN           & 0.36 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.23 ( 0.01 )  &  0.00          & NaN            & 0.36           &   NA           & 0.00           & 1.23          \\\\\n",
+       "\t29 &  50            & 1000           & 0.3            & 20             &  NaN           & 0.34 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.26 ( 0.01 )  &  0.00          & NaN            & 0.34           &   NA           & 0.00           & 1.26          \\\\\n",
+       "\t33 &  50            &   50           & 0.5            &  1             &  NaN           & 0.6 ( 0.03 )   & 1 ( 0 )        & 6 ( 0 )        & 1.46 ( 0.01 )  &  0.00          & NaN            & 0.60           &   NA           & 0.00           & 1.46          \\\\\n",
+       "\t37 &  50            &  100           & 0.5            &  2             &  NaN           & 0.6 ( 0.02 )   & 1 ( 0 )        & 6 ( 0 )        & 1.56 ( 0.01 )  &  0.00          & NaN            & 0.60           &   NA           & 0.00           & 1.56          \\\\\n",
+       "\t41 &  50            &  500           & 0.5            & 10             &  NaN           & 0.58 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.64 ( 0.02 )  &  0.00          & NaN            & 0.58           &   NA           & 0.00           & 1.64          \\\\\n",
+       "\t45 &  50            & 1000           & 0.5            & 20             &  NaN           & 0.57 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.64 ( 0.02 )  &  0.00          & NaN            & 0.57           &   NA           & 0.00           & 1.64          \\\\\n",
+       "\t49 &  50            &   50           & 0.7            &  1             &  NaN           & 0.8 ( 0.04 )   & 1 ( 0 )        & 6 ( 0 )        & 1.72 ( 0.02 )  &  0.00          & NaN            & 0.80           &   NA           & 0.00           & 1.72          \\\\\n",
+       "\t53 &  50            &  100           & 0.7            &  2             &  NaN           & 0.92 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 1.85 ( 0.02 )  &  0.00          & NaN            & 0.92           &   NA           & 0.00           & 1.85          \\\\\n",
+       "\t57 &  50            &  500           & 0.7            & 10             &  NaN           & 0.91 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 2 ( 0.02 )     &  0.00          & NaN            & 0.91           &   NA           & 0.00           &   NA          \\\\\n",
+       "\t61 &  50            & 1000           & 0.7            & 20             &  NaN           & 0.91 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 1.98 ( 0.02 )  &  0.00          & NaN            & 0.91           &   NA           & 0.00           & 1.98          \\\\\n",
+       "\t65 &  50            &   50           & 0.9            &  1             &  NaN           & 1.1 ( 0.04 )   & 1 ( 0 )        & 6 ( 0 )        & 1.99 ( 0.02 )  &  0.00          & NaN            & 1.10           &   NA           & 0.00           & 1.99          \\\\\n",
+       "\t69 &  50            &  100           & 0.9            &  2             &  NaN           & 1.11 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.06 ( 0.02 )  &  0.00          & NaN            & 1.11           &   NA           & 0.00           & 2.06          \\\\\n",
+       "\t73 &  50            &  500           & 0.9            & 10             &  NaN           & 1.18 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.2 ( 0.02 )   &  0.00          & NaN            & 1.18           &   NA           & 0.00           & 2.20          \\\\\n",
+       "\t75 & 500            &  500           & 0.9            &  1             & 0.12           & 0.73 ( 0.01 )  & 20.83 ( 0.45 ) & 2.04 ( 0.09 )  & 2 ( 0.01 )     & 24.79          & 0.84           & 0.73           & 20.8           & 2.04           &   NA          \\\\\n",
+       "\t77 &  50            & 1000           & 0.9            & 20             &  NaN           & 1.22 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.3 ( 0.03 )   &  0.00          & NaN            & 1.22           &   NA           & 0.00           & 2.30          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50            |   50           | 0.1            |  1             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 5 |  50            |  100           | 0.1            |  2             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.6 ( 0.01 )   |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.60           |\n",
+       "| 9 |  50            |  500           | 0.1            | 10             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 13 |  50            | 1000           | 0.1            | 20             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 17 |  50            |   50           | 0.3            |  1             |  NaN           | 0.31 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.14 ( 0.01 )  |  0.00          | NaN            | 0.31           |   NA           | 0.00           | 1.14           |\n",
+       "| 21 |  50            |  100           | 0.3            |  2             |  NaN           | 0.32 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.2 ( 0.01 )   |  0.00          | NaN            | 0.32           |   NA           | 0.00           | 1.20           |\n",
+       "| 25 |  50            |  500           | 0.3            | 10             |  NaN           | 0.36 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.23 ( 0.01 )  |  0.00          | NaN            | 0.36           |   NA           | 0.00           | 1.23           |\n",
+       "| 29 |  50            | 1000           | 0.3            | 20             |  NaN           | 0.34 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.26 ( 0.01 )  |  0.00          | NaN            | 0.34           |   NA           | 0.00           | 1.26           |\n",
+       "| 33 |  50            |   50           | 0.5            |  1             |  NaN           | 0.6 ( 0.03 )   | 1 ( 0 )        | 6 ( 0 )        | 1.46 ( 0.01 )  |  0.00          | NaN            | 0.60           |   NA           | 0.00           | 1.46           |\n",
+       "| 37 |  50            |  100           | 0.5            |  2             |  NaN           | 0.6 ( 0.02 )   | 1 ( 0 )        | 6 ( 0 )        | 1.56 ( 0.01 )  |  0.00          | NaN            | 0.60           |   NA           | 0.00           | 1.56           |\n",
+       "| 41 |  50            |  500           | 0.5            | 10             |  NaN           | 0.58 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.64 ( 0.02 )  |  0.00          | NaN            | 0.58           |   NA           | 0.00           | 1.64           |\n",
+       "| 45 |  50            | 1000           | 0.5            | 20             |  NaN           | 0.57 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.64 ( 0.02 )  |  0.00          | NaN            | 0.57           |   NA           | 0.00           | 1.64           |\n",
+       "| 49 |  50            |   50           | 0.7            |  1             |  NaN           | 0.8 ( 0.04 )   | 1 ( 0 )        | 6 ( 0 )        | 1.72 ( 0.02 )  |  0.00          | NaN            | 0.80           |   NA           | 0.00           | 1.72           |\n",
+       "| 53 |  50            |  100           | 0.7            |  2             |  NaN           | 0.92 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 1.85 ( 0.02 )  |  0.00          | NaN            | 0.92           |   NA           | 0.00           | 1.85           |\n",
+       "| 57 |  50            |  500           | 0.7            | 10             |  NaN           | 0.91 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 2 ( 0.02 )     |  0.00          | NaN            | 0.91           |   NA           | 0.00           |   NA           |\n",
+       "| 61 |  50            | 1000           | 0.7            | 20             |  NaN           | 0.91 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 1.98 ( 0.02 )  |  0.00          | NaN            | 0.91           |   NA           | 0.00           | 1.98           |\n",
+       "| 65 |  50            |   50           | 0.9            |  1             |  NaN           | 1.1 ( 0.04 )   | 1 ( 0 )        | 6 ( 0 )        | 1.99 ( 0.02 )  |  0.00          | NaN            | 1.10           |   NA           | 0.00           | 1.99           |\n",
+       "| 69 |  50            |  100           | 0.9            |  2             |  NaN           | 1.11 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.06 ( 0.02 )  |  0.00          | NaN            | 1.11           |   NA           | 0.00           | 2.06           |\n",
+       "| 73 |  50            |  500           | 0.9            | 10             |  NaN           | 1.18 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.2 ( 0.02 )   |  0.00          | NaN            | 1.18           |   NA           | 0.00           | 2.20           |\n",
+       "| 75 | 500            |  500           | 0.9            |  1             | 0.12           | 0.73 ( 0.01 )  | 20.83 ( 0.45 ) | 2.04 ( 0.09 )  | 2 ( 0.01 )     | 24.79          | 0.84           | 0.73           | 20.8           | 2.04           |   NA           |\n",
+       "| 77 |  50            | 1000           | 0.9            | 20             |  NaN           | 1.22 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.3 ( 0.03 )   |  0.00          | NaN            | 1.22           |   NA           | 0.00           | 2.30           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50   50 0.1   1     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "5   50  100 0.1   2     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "9   50  500 0.1  10     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "13  50 1000 0.1  20     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "17  50   50 0.3   1     NaN 0.31 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "21  50  100 0.3   2     NaN 0.32 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "25  50  500 0.3  10     NaN 0.36 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "29  50 1000 0.3  20     NaN 0.34 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "33  50   50 0.5   1     NaN 0.6 ( 0.03 )  1 ( 0 )        6 ( 0 )      \n",
+       "37  50  100 0.5   2     NaN 0.6 ( 0.02 )  1 ( 0 )        6 ( 0 )      \n",
+       "41  50  500 0.5  10     NaN 0.58 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "45  50 1000 0.5  20     NaN 0.57 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "49  50   50 0.7   1     NaN 0.8 ( 0.04 )  1 ( 0 )        6 ( 0 )      \n",
+       "53  50  100 0.7   2     NaN 0.92 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "57  50  500 0.7  10     NaN 0.91 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "61  50 1000 0.7  20     NaN 0.91 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "65  50   50 0.9   1     NaN 1.1 ( 0.04 )  1 ( 0 )        6 ( 0 )      \n",
+       "69  50  100 0.9   2     NaN 1.11 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "73  50  500 0.9  10     NaN 1.18 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "75 500  500 0.9   1    0.12 0.73 ( 0.01 ) 20.83 ( 0.45 ) 2.04 ( 0.09 )\n",
+       "77  50 1000 0.9  20     NaN 1.22 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "5  0.6 ( 0.01 )   0.00      NaN  0.08       NA    0.00    0.60    \n",
+       "9  0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "13 0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "17 1.14 ( 0.01 )  0.00      NaN  0.31       NA    0.00    1.14    \n",
+       "21 1.2 ( 0.01 )   0.00      NaN  0.32       NA    0.00    1.20    \n",
+       "25 1.23 ( 0.01 )  0.00      NaN  0.36       NA    0.00    1.23    \n",
+       "29 1.26 ( 0.01 )  0.00      NaN  0.34       NA    0.00    1.26    \n",
+       "33 1.46 ( 0.01 )  0.00      NaN  0.60       NA    0.00    1.46    \n",
+       "37 1.56 ( 0.01 )  0.00      NaN  0.60       NA    0.00    1.56    \n",
+       "41 1.64 ( 0.02 )  0.00      NaN  0.58       NA    0.00    1.64    \n",
+       "45 1.64 ( 0.02 )  0.00      NaN  0.57       NA    0.00    1.64    \n",
+       "49 1.72 ( 0.02 )  0.00      NaN  0.80       NA    0.00    1.72    \n",
+       "53 1.85 ( 0.02 )  0.00      NaN  0.92       NA    0.00    1.85    \n",
+       "57 2 ( 0.02 )     0.00      NaN  0.91       NA    0.00      NA    \n",
+       "61 1.98 ( 0.02 )  0.00      NaN  0.91       NA    0.00    1.98    \n",
+       "65 1.99 ( 0.02 )  0.00      NaN  1.10       NA    0.00    1.99    \n",
+       "69 2.06 ( 0.02 )  0.00      NaN  1.11       NA    0.00    2.06    \n",
+       "73 2.2 ( 0.02 )   0.00      NaN  1.18       NA    0.00    2.20    \n",
+       "75 2 ( 0.01 )    24.79      0.84 0.73     20.8    2.04      NA    \n",
+       "77 2.3 ( 0.03 )   0.00      NaN  1.22       NA    0.00    2.30    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$Stab[is.na(result.table_block$Stab)] = 0\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 1\n",
+    "result.table_block$OOB_mean[is.na(result.table_block$OOB_mean)] = 2\n",
+    "result.table_block$FN_mean[result.table_block$num_select == 0] = 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50           </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50           </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.6 ( 0.01 ) </td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.60         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50           </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50           </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50           </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0            </td><td>0.31 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.14 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.31         </td><td>1            </td><td>6            </td><td>1.14         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50           </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0            </td><td>0.32 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.2 ( 0.01 ) </td><td>0            </td><td>NaN          </td><td>0.32         </td><td>1            </td><td>6            </td><td>1.20         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50           </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0            </td><td>0.36 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.23 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.36         </td><td>1            </td><td>6            </td><td>1.23         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50           </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0            </td><td>0.34 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.26 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.34         </td><td>1            </td><td>6            </td><td>1.26         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50           </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0            </td><td>0.6 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.46 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.60         </td><td>1            </td><td>6            </td><td>1.46         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50           </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0            </td><td>0.6 ( 0.02 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.56 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.60         </td><td>1            </td><td>6            </td><td>1.56         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50           </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0            </td><td>0.58 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.64 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.58         </td><td>1            </td><td>6            </td><td>1.64         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50           </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0            </td><td>0.57 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.64 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.57         </td><td>1            </td><td>6            </td><td>1.64         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50           </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0            </td><td>0.8 ( 0.04 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.72 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.80         </td><td>1            </td><td>6            </td><td>1.72         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50           </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0            </td><td>0.92 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.85 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.92         </td><td>1            </td><td>6            </td><td>1.85         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50           </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2 ( 0.02 )   </td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>2.00         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50           </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50           </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0            </td><td>1.1 ( 0.04 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.99 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>1.10         </td><td>1            </td><td>6            </td><td>1.99         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50           </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0            </td><td>1.11 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.06 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>1.11         </td><td>1            </td><td>6            </td><td>2.06         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50           </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0            </td><td>1.18 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.2 ( 0.02 ) </td><td>0            </td><td>NaN          </td><td>1.18         </td><td>1            </td><td>6            </td><td>2.20         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50           </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0            </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.3 ( 0.03 ) </td><td>0            </td><td>NaN          </td><td>1.22         </td><td>1            </td><td>6            </td><td>2.30         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 & 50            &   50          & 0.1           &  1            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t5 & 50            &  100          & 0.1           &  2            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.6 ( 0.01 )  & 0             & NaN           & 0.08          & 1             & 6             & 0.60         \\\\\n",
+       "\t9 & 50            &  500          & 0.1           & 10            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t13 & 50            & 1000          & 0.1           & 20            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t17 & 50            &   50          & 0.3           &  1            & 0             & 0.31 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.14 ( 0.01 ) & 0             & NaN           & 0.31          & 1             & 6             & 1.14         \\\\\n",
+       "\t21 & 50            &  100          & 0.3           &  2            & 0             & 0.32 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.2 ( 0.01 )  & 0             & NaN           & 0.32          & 1             & 6             & 1.20         \\\\\n",
+       "\t25 & 50            &  500          & 0.3           & 10            & 0             & 0.36 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.23 ( 0.01 ) & 0             & NaN           & 0.36          & 1             & 6             & 1.23         \\\\\n",
+       "\t29 & 50            & 1000          & 0.3           & 20            & 0             & 0.34 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.26 ( 0.01 ) & 0             & NaN           & 0.34          & 1             & 6             & 1.26         \\\\\n",
+       "\t33 & 50            &   50          & 0.5           &  1            & 0             & 0.6 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.46 ( 0.01 ) & 0             & NaN           & 0.60          & 1             & 6             & 1.46         \\\\\n",
+       "\t37 & 50            &  100          & 0.5           &  2            & 0             & 0.6 ( 0.02 )  & 1 ( 0 )       & 6 ( 0 )       & 1.56 ( 0.01 ) & 0             & NaN           & 0.60          & 1             & 6             & 1.56         \\\\\n",
+       "\t41 & 50            &  500          & 0.5           & 10            & 0             & 0.58 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.64 ( 0.02 ) & 0             & NaN           & 0.58          & 1             & 6             & 1.64         \\\\\n",
+       "\t45 & 50            & 1000          & 0.5           & 20            & 0             & 0.57 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.64 ( 0.02 ) & 0             & NaN           & 0.57          & 1             & 6             & 1.64         \\\\\n",
+       "\t49 & 50            &   50          & 0.7           &  1            & 0             & 0.8 ( 0.04 )  & 1 ( 0 )       & 6 ( 0 )       & 1.72 ( 0.02 ) & 0             & NaN           & 0.80          & 1             & 6             & 1.72         \\\\\n",
+       "\t53 & 50            &  100          & 0.7           &  2            & 0             & 0.92 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.85 ( 0.02 ) & 0             & NaN           & 0.92          & 1             & 6             & 1.85         \\\\\n",
+       "\t57 & 50            &  500          & 0.7           & 10            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2 ( 0.02 )    & 0             & NaN           & 0.91          & 1             & 6             & 2.00         \\\\\n",
+       "\t61 & 50            & 1000          & 0.7           & 20            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0             & NaN           & 0.91          & 1             & 6             & 1.98         \\\\\n",
+       "\t65 & 50            &   50          & 0.9           &  1            & 0             & 1.1 ( 0.04 )  & 1 ( 0 )       & 6 ( 0 )       & 1.99 ( 0.02 ) & 0             & NaN           & 1.10          & 1             & 6             & 1.99         \\\\\n",
+       "\t69 & 50            &  100          & 0.9           &  2            & 0             & 1.11 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.06 ( 0.02 ) & 0             & NaN           & 1.11          & 1             & 6             & 2.06         \\\\\n",
+       "\t73 & 50            &  500          & 0.9           & 10            & 0             & 1.18 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.2 ( 0.02 )  & 0             & NaN           & 1.18          & 1             & 6             & 2.20         \\\\\n",
+       "\t77 & 50            & 1000          & 0.9           & 20            & 0             & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.3 ( 0.03 )  & 0             & NaN           & 1.22          & 1             & 6             & 2.30         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50            |   50          | 0.1           |  1            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 5 | 50            |  100          | 0.1           |  2            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.6 ( 0.01 )  | 0             | NaN           | 0.08          | 1             | 6             | 0.60          |\n",
+       "| 9 | 50            |  500          | 0.1           | 10            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 13 | 50            | 1000          | 0.1           | 20            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 17 | 50            |   50          | 0.3           |  1            | 0             | 0.31 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.14 ( 0.01 ) | 0             | NaN           | 0.31          | 1             | 6             | 1.14          |\n",
+       "| 21 | 50            |  100          | 0.3           |  2            | 0             | 0.32 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.2 ( 0.01 )  | 0             | NaN           | 0.32          | 1             | 6             | 1.20          |\n",
+       "| 25 | 50            |  500          | 0.3           | 10            | 0             | 0.36 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.23 ( 0.01 ) | 0             | NaN           | 0.36          | 1             | 6             | 1.23          |\n",
+       "| 29 | 50            | 1000          | 0.3           | 20            | 0             | 0.34 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.26 ( 0.01 ) | 0             | NaN           | 0.34          | 1             | 6             | 1.26          |\n",
+       "| 33 | 50            |   50          | 0.5           |  1            | 0             | 0.6 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.46 ( 0.01 ) | 0             | NaN           | 0.60          | 1             | 6             | 1.46          |\n",
+       "| 37 | 50            |  100          | 0.5           |  2            | 0             | 0.6 ( 0.02 )  | 1 ( 0 )       | 6 ( 0 )       | 1.56 ( 0.01 ) | 0             | NaN           | 0.60          | 1             | 6             | 1.56          |\n",
+       "| 41 | 50            |  500          | 0.5           | 10            | 0             | 0.58 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.64 ( 0.02 ) | 0             | NaN           | 0.58          | 1             | 6             | 1.64          |\n",
+       "| 45 | 50            | 1000          | 0.5           | 20            | 0             | 0.57 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.64 ( 0.02 ) | 0             | NaN           | 0.57          | 1             | 6             | 1.64          |\n",
+       "| 49 | 50            |   50          | 0.7           |  1            | 0             | 0.8 ( 0.04 )  | 1 ( 0 )       | 6 ( 0 )       | 1.72 ( 0.02 ) | 0             | NaN           | 0.80          | 1             | 6             | 1.72          |\n",
+       "| 53 | 50            |  100          | 0.7           |  2            | 0             | 0.92 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.85 ( 0.02 ) | 0             | NaN           | 0.92          | 1             | 6             | 1.85          |\n",
+       "| 57 | 50            |  500          | 0.7           | 10            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2 ( 0.02 )    | 0             | NaN           | 0.91          | 1             | 6             | 2.00          |\n",
+       "| 61 | 50            | 1000          | 0.7           | 20            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0             | NaN           | 0.91          | 1             | 6             | 1.98          |\n",
+       "| 65 | 50            |   50          | 0.9           |  1            | 0             | 1.1 ( 0.04 )  | 1 ( 0 )       | 6 ( 0 )       | 1.99 ( 0.02 ) | 0             | NaN           | 1.10          | 1             | 6             | 1.99          |\n",
+       "| 69 | 50            |  100          | 0.9           |  2            | 0             | 1.11 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.06 ( 0.02 ) | 0             | NaN           | 1.11          | 1             | 6             | 2.06          |\n",
+       "| 73 | 50            |  500          | 0.9           | 10            | 0             | 1.18 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.2 ( 0.02 )  | 0             | NaN           | 1.18          | 1             | 6             | 2.20          |\n",
+       "| 77 | 50            | 1000          | 0.9           | 20            | 0             | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.3 ( 0.03 )  | 0             | NaN           | 1.22          | 1             | 6             | 2.30          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE           FP      FN      OOB          \n",
+       "1  50   50 0.1   1    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "5  50  100 0.1   2    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.6 ( 0.01 ) \n",
+       "9  50  500 0.1  10    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "13 50 1000 0.1  20    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "17 50   50 0.3   1    0    0.31 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.14 ( 0.01 )\n",
+       "21 50  100 0.3   2    0    0.32 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.2 ( 0.01 ) \n",
+       "25 50  500 0.3  10    0    0.36 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.23 ( 0.01 )\n",
+       "29 50 1000 0.3  20    0    0.34 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.26 ( 0.01 )\n",
+       "33 50   50 0.5   1    0    0.6 ( 0.03 )  1 ( 0 ) 6 ( 0 ) 1.46 ( 0.01 )\n",
+       "37 50  100 0.5   2    0    0.6 ( 0.02 )  1 ( 0 ) 6 ( 0 ) 1.56 ( 0.01 )\n",
+       "41 50  500 0.5  10    0    0.58 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.64 ( 0.02 )\n",
+       "45 50 1000 0.5  20    0    0.57 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.64 ( 0.02 )\n",
+       "49 50   50 0.7   1    0    0.8 ( 0.04 )  1 ( 0 ) 6 ( 0 ) 1.72 ( 0.02 )\n",
+       "53 50  100 0.7   2    0    0.92 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 1.85 ( 0.02 )\n",
+       "57 50  500 0.7  10    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 2 ( 0.02 )   \n",
+       "61 50 1000 0.7  20    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 1.98 ( 0.02 )\n",
+       "65 50   50 0.9   1    0    1.1 ( 0.04 )  1 ( 0 ) 6 ( 0 ) 1.99 ( 0.02 )\n",
+       "69 50  100 0.9   2    0    1.11 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.06 ( 0.02 )\n",
+       "73 50  500 0.9  10    0    1.18 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.2 ( 0.02 ) \n",
+       "77 50 1000 0.9  20    0    1.22 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.3 ( 0.03 ) \n",
+       "   num_select FDR MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  0          NaN 0.08     1       6       0.59    \n",
+       "5  0          NaN 0.08     1       6       0.60    \n",
+       "9  0          NaN 0.08     1       6       0.59    \n",
+       "13 0          NaN 0.08     1       6       0.59    \n",
+       "17 0          NaN 0.31     1       6       1.14    \n",
+       "21 0          NaN 0.32     1       6       1.20    \n",
+       "25 0          NaN 0.36     1       6       1.23    \n",
+       "29 0          NaN 0.34     1       6       1.26    \n",
+       "33 0          NaN 0.60     1       6       1.46    \n",
+       "37 0          NaN 0.60     1       6       1.56    \n",
+       "41 0          NaN 0.58     1       6       1.64    \n",
+       "45 0          NaN 0.57     1       6       1.64    \n",
+       "49 0          NaN 0.80     1       6       1.72    \n",
+       "53 0          NaN 0.92     1       6       1.85    \n",
+       "57 0          NaN 0.91     1       6       2.00    \n",
+       "61 0          NaN 0.91     1       6       1.98    \n",
+       "65 0          NaN 1.10     1       6       1.99    \n",
+       "69 0          NaN 1.11     1       6       2.06    \n",
+       "73 0          NaN 1.18     1       6       2.20    \n",
+       "77 0          NaN 1.22     1       6       2.30    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[c(1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.00         </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0.00         </td><td>NaN          </td><td>0.08         </td><td>1.00         </td><td>6.00         </td><td>0.59         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.03         </td><td>0.07 ( 0 )   </td><td>2.26 ( 0.17 )</td><td>5.06 ( 0.12 )</td><td>0.58 ( 0 )   </td><td>3.04         </td><td>0.7          </td><td>0.07         </td><td>2.26         </td><td>5.06         </td><td>0.58         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.23         </td><td>0.05 ( 0 )   </td><td>2.34 ( 0.16 )</td><td>3.77 ( 0.14 )</td><td>0.56 ( 0 )   </td><td>4.56         </td><td>0.49         </td><td>0.05         </td><td>2.34         </td><td>3.77         </td><td>0.56         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.51         </td><td>0.05 ( 0 )   </td><td>2.37 ( 0.16 )</td><td>2.54 ( 0.11 )</td><td>0.55 ( 0 )   </td><td>5.83         </td><td>0.38         </td><td>0.05         </td><td>2.37         </td><td>2.54         </td><td>0.55         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.00         </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.6 ( 0.01 ) </td><td>0.00         </td><td>NaN          </td><td>0.08         </td><td>1.00         </td><td>6.00         </td><td>0.60         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.01         </td><td>0.07 ( 0 )   </td><td>4.76 ( 0.27 )</td><td>5.51 ( 0.09 )</td><td>0.58 ( 0 )   </td><td>5.24         </td><td>0.91         </td><td>0.07         </td><td>4.76         </td><td>5.51         </td><td>0.58         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.00          & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0.00          & NaN           & 0.08          & 1.00          & 6.00          & 0.59         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.03          & 0.07 ( 0 )    & 2.26 ( 0.17 ) & 5.06 ( 0.12 ) & 0.58 ( 0 )    & 3.04          & 0.7           & 0.07          & 2.26          & 5.06          & 0.58         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.23          & 0.05 ( 0 )    & 2.34 ( 0.16 ) & 3.77 ( 0.14 ) & 0.56 ( 0 )    & 4.56          & 0.49          & 0.05          & 2.34          & 3.77          & 0.56         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.51          & 0.05 ( 0 )    & 2.37 ( 0.16 ) & 2.54 ( 0.11 ) & 0.55 ( 0 )    & 5.83          & 0.38          & 0.05          & 2.37          & 2.54          & 0.55         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.00          & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.6 ( 0.01 )  & 0.00          & NaN           & 0.08          & 1.00          & 6.00          & 0.60         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.01          & 0.07 ( 0 )    & 4.76 ( 0.27 ) & 5.51 ( 0.09 ) & 0.58 ( 0 )    & 5.24          & 0.91          & 0.07          & 4.76          & 5.51          & 0.58         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.00          | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0.00          | NaN           | 0.08          | 1.00          | 6.00          | 0.59          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.03          | 0.07 ( 0 )    | 2.26 ( 0.17 ) | 5.06 ( 0.12 ) | 0.58 ( 0 )    | 3.04          | 0.7           | 0.07          | 2.26          | 5.06          | 0.58          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.23          | 0.05 ( 0 )    | 2.34 ( 0.16 ) | 3.77 ( 0.14 ) | 0.56 ( 0 )    | 4.56          | 0.49          | 0.05          | 2.34          | 3.77          | 0.56          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.51          | 0.05 ( 0 )    | 2.37 ( 0.16 ) | 2.54 ( 0.11 ) | 0.55 ( 0 )    | 5.83          | 0.38          | 0.05          | 2.37          | 2.54          | 0.55          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.00          | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.6 ( 0.01 )  | 0.00          | NaN           | 0.08          | 1.00          | 6.00          | 0.60          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.01          | 0.07 ( 0 )    | 4.76 ( 0.27 ) | 5.51 ( 0.09 ) | 0.58 ( 0 )    | 5.24          | 0.91          | 0.07          | 4.76          | 5.51          | 0.58          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE        FP            FN            OOB          \n",
+       "1   50  50 0.1  1.00  0.00 0.08 ( 0 ) 1 ( 0 )       6 ( 0 )       0.59 ( 0.01 )\n",
+       "2  100  50 0.1  0.50  0.03 0.07 ( 0 ) 2.26 ( 0.17 ) 5.06 ( 0.12 ) 0.58 ( 0 )   \n",
+       "3  500  50 0.1  0.10  0.23 0.05 ( 0 ) 2.34 ( 0.16 ) 3.77 ( 0.14 ) 0.56 ( 0 )   \n",
+       "4 1000  50 0.1  0.05  0.51 0.05 ( 0 ) 2.37 ( 0.16 ) 2.54 ( 0.11 ) 0.55 ( 0 )   \n",
+       "5   50 100 0.1  2.00  0.00 0.08 ( 0 ) 1 ( 0 )       6 ( 0 )       0.6 ( 0.01 ) \n",
+       "6  100 100 0.1  1.00  0.01 0.07 ( 0 ) 4.76 ( 0.27 ) 5.51 ( 0.09 ) 0.58 ( 0 )   \n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1 0.00       NaN  0.08     1.00    6.00    0.59    \n",
+       "2 3.04       0.7  0.07     2.26    5.06    0.58    \n",
+       "3 4.56       0.49 0.05     2.34    3.77    0.56    \n",
+       "4 5.83       0.38 0.05     2.37    2.54    0.55    \n",
+       "5 0.00       NaN  0.08     1.00    6.00    0.60    \n",
+       "6 5.24       0.91 0.07     4.76    5.51    0.58    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.12          </td><td>0.73 ( 0.01 ) </td><td>20.83 ( 0.45 )</td><td>2.04 ( 0.09 ) </td><td>2 ( 0.01 )    </td><td>24.79         </td><td>0.84          </td><td>0.73          </td><td>20.8          </td><td>2.04          </td><td>2.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.18          </td><td>0.68 ( 0.01 ) </td><td>18.47 ( 0.43 )</td><td>1 ( 0.07 )    </td><td>1.92 ( 0 )    </td><td>23.47         </td><td>0.78          </td><td>0.68          </td><td>18.4          </td><td>0.00          </td><td>1.92          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.00          </td><td>1.22 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.3 ( 0.03 )  </td><td> 0.00         </td><td>NaN           </td><td>1.22          </td><td> 1.0          </td><td>6.00          </td><td>2.30          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.01          </td><td>0.93 ( 0.02 ) </td><td>49.18 ( 0.69 )</td><td>4.67 ( 0.09 ) </td><td>2.22 ( 0.02 ) </td><td>50.51         </td><td>0.97          </td><td>0.93          </td><td>49.1          </td><td>4.67          </td><td>2.22          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.06          </td><td>0.78 ( 0.01 ) </td><td>46.01 ( 0.7 ) </td><td>2.14 ( 0.09 ) </td><td>2.12 ( 0.01 ) </td><td>49.87         </td><td>0.92          </td><td>0.78          </td><td>46.0          </td><td>2.14          </td><td>2.12          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.08          </td><td>0.74 ( 0.01 ) </td><td>43.05 ( 0.72 )</td><td>1.17 ( 0.08 ) </td><td>2.07 ( 0 )    </td><td>47.88         </td><td>0.9           </td><td>0.74          </td><td>43.0          </td><td>1.17          </td><td>2.07          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.12           & 0.73 ( 0.01 )  & 20.83 ( 0.45 ) & 2.04 ( 0.09 )  & 2 ( 0.01 )     & 24.79          & 0.84           & 0.73           & 20.8           & 2.04           & 2.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.18           & 0.68 ( 0.01 )  & 18.47 ( 0.43 ) & 1 ( 0.07 )     & 1.92 ( 0 )     & 23.47          & 0.78           & 0.68           & 18.4           & 0.00           & 1.92          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.00           & 1.22 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.3 ( 0.03 )   &  0.00          & NaN            & 1.22           &  1.0           & 6.00           & 2.30          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.01           & 0.93 ( 0.02 )  & 49.18 ( 0.69 ) & 4.67 ( 0.09 )  & 2.22 ( 0.02 )  & 50.51          & 0.97           & 0.93           & 49.1           & 4.67           & 2.22          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.06           & 0.78 ( 0.01 )  & 46.01 ( 0.7 )  & 2.14 ( 0.09 )  & 2.12 ( 0.01 )  & 49.87          & 0.92           & 0.78           & 46.0           & 2.14           & 2.12          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.08           & 0.74 ( 0.01 )  & 43.05 ( 0.72 ) & 1.17 ( 0.08 )  & 2.07 ( 0 )     & 47.88          & 0.9            & 0.74           & 43.0           & 1.17           & 2.07          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.12           | 0.73 ( 0.01 )  | 20.83 ( 0.45 ) | 2.04 ( 0.09 )  | 2 ( 0.01 )     | 24.79          | 0.84           | 0.73           | 20.8           | 2.04           | 2.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.18           | 0.68 ( 0.01 )  | 18.47 ( 0.43 ) | 1 ( 0.07 )     | 1.92 ( 0 )     | 23.47          | 0.78           | 0.68           | 18.4           | 0.00           | 1.92           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.00           | 1.22 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.3 ( 0.03 )   |  0.00          | NaN            | 1.22           |  1.0           | 6.00           | 2.30           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.01           | 0.93 ( 0.02 )  | 49.18 ( 0.69 ) | 4.67 ( 0.09 )  | 2.22 ( 0.02 )  | 50.51          | 0.97           | 0.93           | 49.1           | 4.67           | 2.22           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.06           | 0.78 ( 0.01 )  | 46.01 ( 0.7 )  | 2.14 ( 0.09 )  | 2.12 ( 0.01 )  | 49.87          | 0.92           | 0.78           | 46.0           | 2.14           | 2.12           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.08           | 0.74 ( 0.01 )  | 43.05 ( 0.72 ) | 1.17 ( 0.08 )  | 2.07 ( 0 )     | 47.88          | 0.9            | 0.74           | 43.0           | 1.17           | 2.07           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.12 0.73 ( 0.01 ) 20.83 ( 0.45 ) 2.04 ( 0.09 )\n",
+       "76 1000  500 0.9   0.5  0.18 0.68 ( 0.01 ) 18.47 ( 0.43 ) 1 ( 0.07 )   \n",
+       "77   50 1000 0.9  20.0  0.00 1.22 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "78  100 1000 0.9  10.0  0.01 0.93 ( 0.02 ) 49.18 ( 0.69 ) 4.67 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.06 0.78 ( 0.01 ) 46.01 ( 0.7 )  2.14 ( 0.09 )\n",
+       "80 1000 1000 0.9   1.0  0.08 0.74 ( 0.01 ) 43.05 ( 0.72 ) 1.17 ( 0.08 )\n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "75 2 ( 0.01 )    24.79      0.84 0.73     20.8    2.04    2.00    \n",
+       "76 1.92 ( 0 )    23.47      0.78 0.68     18.4    0.00    1.92    \n",
+       "77 2.3 ( 0.03 )   0.00      NaN  1.22      1.0    6.00    2.30    \n",
+       "78 2.22 ( 0.02 ) 50.51      0.97 0.93     49.1    4.67    2.22    \n",
+       "79 2.12 ( 0.01 ) 49.87      0.92 0.78     46.0    2.14    2.12    \n",
+       "80 2.07 ( 0 )    47.88      0.9  0.74     43.0    1.17    2.07    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.4_sim_block_compLasso_cts-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.4_sim_block_compLasso_cts-checkpoint.ipynb
new file mode 100755
index 0000000..a50b32e
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/2.4_sim_block_compLasso_cts-checkpoint.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_compLasso))\n",
+    "for (i in 1:length(results_block_compLasso)){\n",
+    "    table_block = rbind(table_block, results_block_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>2.8 ( 0.36 ) </td><td>5.01 ( 0.09 )</td><td>0.36 ( 0.01 )</td><td>0.03         </td><td> 3.79        </td><td>0.62         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>4.96 ( 0.55 )</td><td>3.77 ( 0.14 )</td><td>0.32 ( 0.01 )</td><td>0.1          </td><td> 7.19        </td><td>0.55         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>8.81 ( 0.4 ) </td><td>0.78 ( 0.08 )</td><td>0.28 ( 0 )   </td><td>0.26         </td><td>14.03        </td><td>0.60         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>8.37 ( 0.37 )</td><td>0.24 ( 0.06 )</td><td>0.27 ( 0 )   </td><td>0.31         </td><td>14.13        </td><td>0.56         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>2.54 ( 0.37 )</td><td>5.22 ( 0.08 )</td><td>0.37 ( 0.02 )</td><td>0.04         </td><td> 3.32        </td><td>0.63         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>3.99 ( 0.57 )</td><td>4.63 ( 0.1 ) </td><td>0.35 ( 0.01 )</td><td>0.07         </td><td> 5.36        </td><td>0.56         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 2.8 ( 0.36 )  & 5.01 ( 0.09 ) & 0.36 ( 0.01 ) & 0.03          &  3.79         & 0.62         \\\\\n",
+       "\t 100           & 50            & 0.1           & 4.96 ( 0.55 ) & 3.77 ( 0.14 ) & 0.32 ( 0.01 ) & 0.1           &  7.19         & 0.55         \\\\\n",
+       "\t 500           & 50            & 0.1           & 8.81 ( 0.4 )  & 0.78 ( 0.08 ) & 0.28 ( 0 )    & 0.26          & 14.03         & 0.60         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 8.37 ( 0.37 ) & 0.24 ( 0.06 ) & 0.27 ( 0 )    & 0.31          & 14.13         & 0.56         \\\\\n",
+       "\t 50            & 100           & 0.1           & 2.54 ( 0.37 ) & 5.22 ( 0.08 ) & 0.37 ( 0.02 ) & 0.04          &  3.32         & 0.63         \\\\\n",
+       "\t 100           & 100           & 0.1           & 3.99 ( 0.57 ) & 4.63 ( 0.1 )  & 0.35 ( 0.01 ) & 0.07          &  5.36         & 0.56         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 2.8 ( 0.36 )  | 5.01 ( 0.09 ) | 0.36 ( 0.01 ) | 0.03          |  3.79         | 0.62          |\n",
+       "| 100           | 50            | 0.1           | 4.96 ( 0.55 ) | 3.77 ( 0.14 ) | 0.32 ( 0.01 ) | 0.1           |  7.19         | 0.55          |\n",
+       "| 500           | 50            | 0.1           | 8.81 ( 0.4 )  | 0.78 ( 0.08 ) | 0.28 ( 0 )    | 0.26          | 14.03         | 0.60          |\n",
+       "| 1000          | 50            | 0.1           | 8.37 ( 0.37 ) | 0.24 ( 0.06 ) | 0.27 ( 0 )    | 0.31          | 14.13         | 0.56          |\n",
+       "| 50            | 100           | 0.1           | 2.54 ( 0.37 ) | 5.22 ( 0.08 ) | 0.37 ( 0.02 ) | 0.04          |  3.32         | 0.63          |\n",
+       "| 100           | 100           | 0.1           | 3.99 ( 0.57 ) | 4.63 ( 0.1 )  | 0.35 ( 0.01 ) | 0.07          |  5.36         | 0.56          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 2.8 ( 0.36 )  5.01 ( 0.09 ) 0.36 ( 0.01 ) 0.03  3.79      0.62\n",
+       "2 100  50  0.1 4.96 ( 0.55 ) 3.77 ( 0.14 ) 0.32 ( 0.01 ) 0.1   7.19      0.55\n",
+       "3 500  50  0.1 8.81 ( 0.4 )  0.78 ( 0.08 ) 0.28 ( 0 )    0.26 14.03      0.60\n",
+       "4 1000 50  0.1 8.37 ( 0.37 ) 0.24 ( 0.06 ) 0.27 ( 0 )    0.31 14.13      0.56\n",
+       "5 50   100 0.1 2.54 ( 0.37 ) 5.22 ( 0.08 ) 0.37 ( 0.02 ) 0.04  3.32      0.63\n",
+       "6 100  100 0.1 3.99 ( 0.57 ) 4.63 ( 0.1 )  0.35 ( 0.01 ) 0.07  5.36      0.56"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td>100          </td><td>0.3          </td><td>2.0          </td><td>0.27         </td><td>0.94 ( 0.05 )</td><td>6 ( 0.34 )   </td><td>1.93 ( 0.14 )</td><td>10.07        </td><td>0.54         </td><td>0.94         </td><td>  NA         </td><td>1.93         </td></tr>\n",
+       "\t<tr><th scope=row>26</th><td>100          </td><td>500          </td><td>0.3          </td><td>5.0          </td><td>0.30         </td><td>0.73 ( 0.03 )</td><td>9 ( 0.76 )   </td><td>0.99 ( 0.12 )</td><td>14.01        </td><td>0.52         </td><td>0.73         </td><td>  NA         </td><td>0.99         </td></tr>\n",
+       "\t<tr><th scope=row>51</th><td>500          </td><td> 50          </td><td>0.7          </td><td>0.1          </td><td>0.90         </td><td>1 ( 0.04 )   </td><td>0.58 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.58        </td><td>0.06         </td><td>  NA         </td><td>0.58         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t21 &  50           & 100           & 0.3           & 2.0           & 0.27          & 0.94 ( 0.05 ) & 6 ( 0.34 )    & 1.93 ( 0.14 ) & 10.07         & 0.54          & 0.94          &   NA          & 1.93         \\\\\n",
+       "\t26 & 100           & 500           & 0.3           & 5.0           & 0.30          & 0.73 ( 0.03 ) & 9 ( 0.76 )    & 0.99 ( 0.12 ) & 14.01         & 0.52          & 0.73          &   NA          & 0.99         \\\\\n",
+       "\t51 & 500           &  50           & 0.7           & 0.1           & 0.90          & 1 ( 0.04 )    & 0.58 ( 0.19 ) & 0 ( 0 )       &  6.58         & 0.06          &   NA          & 0.58          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 21 |  50           | 100           | 0.3           | 2.0           | 0.27          | 0.94 ( 0.05 ) | 6 ( 0.34 )    | 1.93 ( 0.14 ) | 10.07         | 0.54          | 0.94          |   NA          | 1.93          |\n",
+       "| 26 | 100           | 500           | 0.3           | 5.0           | 0.30          | 0.73 ( 0.03 ) | 9 ( 0.76 )    | 0.99 ( 0.12 ) | 14.01         | 0.52          | 0.73          |   NA          | 0.99          |\n",
+       "| 51 | 500           |  50           | 0.7           | 0.1           | 0.90          | 1 ( 0.04 )    | 0.58 ( 0.19 ) | 0 ( 0 )       |  6.58         | 0.06          |   NA          | 0.58          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "21  50 100 0.3  2.0   0.27 0.94 ( 0.05 ) 6 ( 0.34 )    1.93 ( 0.14 ) 10.07     \n",
+       "26 100 500 0.3  5.0   0.30 0.73 ( 0.03 ) 9 ( 0.76 )    0.99 ( 0.12 ) 14.01     \n",
+       "51 500  50 0.7  0.1   0.90 1 ( 0.04 )    0.58 ( 0.19 ) 0 ( 0 )        6.58     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "21 0.54 0.94       NA    1.93   \n",
+       "26 0.52 0.73       NA    0.99   \n",
+       "51 0.06   NA     0.58    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 6\n",
+    "result.table_block$MSE_mean[is.na(result.table_block$MSE_mean)] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td>100          </td><td>0.3          </td><td>2.0          </td><td>0.27         </td><td>0.94 ( 0.05 )</td><td>6 ( 0.34 )   </td><td>1.93 ( 0.14 )</td><td>10.07        </td><td>0.54         </td><td>0.94         </td><td>6.00         </td><td>1.93         </td></tr>\n",
+       "\t<tr><th scope=row>26</th><td>100          </td><td>500          </td><td>0.3          </td><td>5.0          </td><td>0.30         </td><td>0.73 ( 0.03 )</td><td>9 ( 0.76 )   </td><td>0.99 ( 0.12 )</td><td>14.01        </td><td>0.52         </td><td>0.73         </td><td>6.00         </td><td>0.99         </td></tr>\n",
+       "\t<tr><th scope=row>51</th><td>500          </td><td> 50          </td><td>0.7          </td><td>0.1          </td><td>0.90         </td><td>1 ( 0.04 )   </td><td>0.58 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.58        </td><td>0.06         </td><td>1.00         </td><td>0.58         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t21 &  50           & 100           & 0.3           & 2.0           & 0.27          & 0.94 ( 0.05 ) & 6 ( 0.34 )    & 1.93 ( 0.14 ) & 10.07         & 0.54          & 0.94          & 6.00          & 1.93         \\\\\n",
+       "\t26 & 100           & 500           & 0.3           & 5.0           & 0.30          & 0.73 ( 0.03 ) & 9 ( 0.76 )    & 0.99 ( 0.12 ) & 14.01         & 0.52          & 0.73          & 6.00          & 0.99         \\\\\n",
+       "\t51 & 500           &  50           & 0.7           & 0.1           & 0.90          & 1 ( 0.04 )    & 0.58 ( 0.19 ) & 0 ( 0 )       &  6.58         & 0.06          & 1.00          & 0.58          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 21 |  50           | 100           | 0.3           | 2.0           | 0.27          | 0.94 ( 0.05 ) | 6 ( 0.34 )    | 1.93 ( 0.14 ) | 10.07         | 0.54          | 0.94          | 6.00          | 1.93          |\n",
+       "| 26 | 100           | 500           | 0.3           | 5.0           | 0.30          | 0.73 ( 0.03 ) | 9 ( 0.76 )    | 0.99 ( 0.12 ) | 14.01         | 0.52          | 0.73          | 6.00          | 0.99          |\n",
+       "| 51 | 500           |  50           | 0.7           | 0.1           | 0.90          | 1 ( 0.04 )    | 0.58 ( 0.19 ) | 0 ( 0 )       |  6.58         | 0.06          | 1.00          | 0.58          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "21  50 100 0.3  2.0   0.27 0.94 ( 0.05 ) 6 ( 0.34 )    1.93 ( 0.14 ) 10.07     \n",
+       "26 100 500 0.3  5.0   0.30 0.73 ( 0.03 ) 9 ( 0.76 )    0.99 ( 0.12 ) 14.01     \n",
+       "51 500  50 0.7  0.1   0.90 1 ( 0.04 )    0.58 ( 0.19 ) 0 ( 0 )        6.58     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "21 0.54 0.94     6.00    1.93   \n",
+       "26 0.52 0.73     6.00    0.99   \n",
+       "51 0.06 1.00     0.58    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[c(21,26,51), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.03         </td><td>0.36 ( 0.01 )</td><td>2.8 ( 0.36 ) </td><td>5.01 ( 0.09 )</td><td> 3.79        </td><td>0.62         </td><td>0.36         </td><td>2.80         </td><td>5.01         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.10         </td><td>0.32 ( 0.01 )</td><td>4.96 ( 0.55 )</td><td>3.77 ( 0.14 )</td><td> 7.19        </td><td>0.55         </td><td>0.32         </td><td>4.96         </td><td>3.77         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.26         </td><td>0.28 ( 0 )   </td><td>8.81 ( 0.4 ) </td><td>0.78 ( 0.08 )</td><td>14.03        </td><td>0.6          </td><td>0.28         </td><td>8.81         </td><td>0.78         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.31         </td><td>0.27 ( 0 )   </td><td>8.37 ( 0.37 )</td><td>0.24 ( 0.06 )</td><td>14.13        </td><td>0.56         </td><td>0.27         </td><td>8.37         </td><td>0.24         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.04         </td><td>0.37 ( 0.02 )</td><td>2.54 ( 0.37 )</td><td>5.22 ( 0.08 )</td><td> 3.32        </td><td>0.63         </td><td>0.37         </td><td>2.54         </td><td>5.22         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.07         </td><td>0.35 ( 0.01 )</td><td>3.99 ( 0.57 )</td><td>4.63 ( 0.1 ) </td><td> 5.36        </td><td>0.56         </td><td>0.35         </td><td>3.99         </td><td>4.63         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.03          & 0.36 ( 0.01 ) & 2.8 ( 0.36 )  & 5.01 ( 0.09 ) &  3.79         & 0.62          & 0.36          & 2.80          & 5.01         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.10          & 0.32 ( 0.01 ) & 4.96 ( 0.55 ) & 3.77 ( 0.14 ) &  7.19         & 0.55          & 0.32          & 4.96          & 3.77         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.26          & 0.28 ( 0 )    & 8.81 ( 0.4 )  & 0.78 ( 0.08 ) & 14.03         & 0.6           & 0.28          & 8.81          & 0.78         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.31          & 0.27 ( 0 )    & 8.37 ( 0.37 ) & 0.24 ( 0.06 ) & 14.13         & 0.56          & 0.27          & 8.37          & 0.24         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.04          & 0.37 ( 0.02 ) & 2.54 ( 0.37 ) & 5.22 ( 0.08 ) &  3.32         & 0.63          & 0.37          & 2.54          & 5.22         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.07          & 0.35 ( 0.01 ) & 3.99 ( 0.57 ) & 4.63 ( 0.1 )  &  5.36         & 0.56          & 0.35          & 3.99          & 4.63         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.03          | 0.36 ( 0.01 ) | 2.8 ( 0.36 )  | 5.01 ( 0.09 ) |  3.79         | 0.62          | 0.36          | 2.80          | 5.01          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.10          | 0.32 ( 0.01 ) | 4.96 ( 0.55 ) | 3.77 ( 0.14 ) |  7.19         | 0.55          | 0.32          | 4.96          | 3.77          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.26          | 0.28 ( 0 )    | 8.81 ( 0.4 )  | 0.78 ( 0.08 ) | 14.03         | 0.6           | 0.28          | 8.81          | 0.78          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.31          | 0.27 ( 0 )    | 8.37 ( 0.37 ) | 0.24 ( 0.06 ) | 14.13         | 0.56          | 0.27          | 8.37          | 0.24          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.04          | 0.37 ( 0.02 ) | 2.54 ( 0.37 ) | 5.22 ( 0.08 ) |  3.32         | 0.63          | 0.37          | 2.54          | 5.22          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.07          | 0.35 ( 0.01 ) | 3.99 ( 0.57 ) | 4.63 ( 0.1 )  |  5.36         | 0.56          | 0.35          | 3.99          | 4.63          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.03 0.36 ( 0.01 ) 2.8 ( 0.36 )  5.01 ( 0.09 )  3.79     \n",
+       "2  100  50 0.1  0.50  0.10 0.32 ( 0.01 ) 4.96 ( 0.55 ) 3.77 ( 0.14 )  7.19     \n",
+       "3  500  50 0.1  0.10  0.26 0.28 ( 0 )    8.81 ( 0.4 )  0.78 ( 0.08 ) 14.03     \n",
+       "4 1000  50 0.1  0.05  0.31 0.27 ( 0 )    8.37 ( 0.37 ) 0.24 ( 0.06 ) 14.13     \n",
+       "5   50 100 0.1  2.00  0.04 0.37 ( 0.02 ) 2.54 ( 0.37 ) 5.22 ( 0.08 )  3.32     \n",
+       "6  100 100 0.1  1.00  0.07 0.35 ( 0.01 ) 3.99 ( 0.57 ) 4.63 ( 0.1 )   5.36     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.62 0.36     2.80    5.01   \n",
+       "2 0.55 0.32     4.96    3.77   \n",
+       "3 0.6  0.28     8.81    0.78   \n",
+       "4 0.56 0.27     8.37    0.24   \n",
+       "5 0.63 0.37     2.54    5.22   \n",
+       "6 0.56 0.35     3.99    4.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500         </td><td> 500         </td><td>0.9          </td><td> 1.0         </td><td>0.93         </td><td>1.27 ( 0.05 )</td><td>0.44 ( 0.18 )</td><td>0 ( 0 )      </td><td> 6.44        </td><td>0.04         </td><td>1.27         </td><td>0.44         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000         </td><td> 500         </td><td>0.9          </td><td> 0.5         </td><td>0.96         </td><td>1.31 ( 0.05 )</td><td>0.23 ( 0.06 )</td><td>0 ( 0 )      </td><td> 6.23        </td><td>0.03         </td><td>1.31         </td><td>0.23         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50         </td><td>1000         </td><td>0.9          </td><td>20.0         </td><td>0.23         </td><td>3.37 ( 0.19 )</td><td>5.29 ( 0.41 )</td><td>3.13 ( 0.15 )</td><td> 8.16        </td><td>0.57         </td><td>3.37         </td><td>5.29         </td><td>3.13         </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100         </td><td>1000         </td><td>0.9          </td><td>10.0         </td><td>0.56         </td><td>1.14 ( 0.05 )</td><td>4.56 ( 0.38 )</td><td>0.07 ( 0.03 )</td><td>10.49        </td><td>0.37         </td><td>1.14         </td><td>4.56         </td><td>0.07         </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500         </td><td>1000         </td><td>0.9          </td><td> 2.0         </td><td>0.96         </td><td>1.28 ( 0.04 )</td><td>0.27 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.28         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000         </td><td>1000         </td><td>0.9          </td><td> 1.0         </td><td>0.95         </td><td>1.35 ( 0.05 )</td><td>0.29 ( 0.09 )</td><td>0 ( 0 )      </td><td> 6.29        </td><td>0.03         </td><td>1.35         </td><td>0.29         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500          &  500          & 0.9           &  1.0          & 0.93          & 1.27 ( 0.05 ) & 0.44 ( 0.18 ) & 0 ( 0 )       &  6.44         & 0.04          & 1.27          & 0.44          & 0.00         \\\\\n",
+       "\t76 & 1000          &  500          & 0.9           &  0.5          & 0.96          & 1.31 ( 0.05 ) & 0.23 ( 0.06 ) & 0 ( 0 )       &  6.23         & 0.03          & 1.31          & 0.23          & 0.00         \\\\\n",
+       "\t77 &   50          & 1000          & 0.9           & 20.0          & 0.23          & 3.37 ( 0.19 ) & 5.29 ( 0.41 ) & 3.13 ( 0.15 ) &  8.16         & 0.57          & 3.37          & 5.29          & 3.13         \\\\\n",
+       "\t78 &  100          & 1000          & 0.9           & 10.0          & 0.56          & 1.14 ( 0.05 ) & 4.56 ( 0.38 ) & 0.07 ( 0.03 ) & 10.49         & 0.37          & 1.14          & 4.56          & 0.07         \\\\\n",
+       "\t79 &  500          & 1000          & 0.9           &  2.0          & 0.96          & 1.28 ( 0.04 ) & 0.27 ( 0.11 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.28          & 0.27          & 0.00         \\\\\n",
+       "\t80 & 1000          & 1000          & 0.9           &  1.0          & 0.95          & 1.35 ( 0.05 ) & 0.29 ( 0.09 ) & 0 ( 0 )       &  6.29         & 0.03          & 1.35          & 0.29          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500          |  500          | 0.9           |  1.0          | 0.93          | 1.27 ( 0.05 ) | 0.44 ( 0.18 ) | 0 ( 0 )       |  6.44         | 0.04          | 1.27          | 0.44          | 0.00          |\n",
+       "| 76 | 1000          |  500          | 0.9           |  0.5          | 0.96          | 1.31 ( 0.05 ) | 0.23 ( 0.06 ) | 0 ( 0 )       |  6.23         | 0.03          | 1.31          | 0.23          | 0.00          |\n",
+       "| 77 |   50          | 1000          | 0.9           | 20.0          | 0.23          | 3.37 ( 0.19 ) | 5.29 ( 0.41 ) | 3.13 ( 0.15 ) |  8.16         | 0.57          | 3.37          | 5.29          | 3.13          |\n",
+       "| 78 |  100          | 1000          | 0.9           | 10.0          | 0.56          | 1.14 ( 0.05 ) | 4.56 ( 0.38 ) | 0.07 ( 0.03 ) | 10.49         | 0.37          | 1.14          | 4.56          | 0.07          |\n",
+       "| 79 |  500          | 1000          | 0.9           |  2.0          | 0.96          | 1.28 ( 0.04 ) | 0.27 ( 0.11 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.28          | 0.27          | 0.00          |\n",
+       "| 80 | 1000          | 1000          | 0.9           |  1.0          | 0.95          | 1.35 ( 0.05 ) | 0.29 ( 0.09 ) | 0 ( 0 )       |  6.29         | 0.03          | 1.35          | 0.29          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP            FN           \n",
+       "75  500  500 0.9   1.0  0.93 1.27 ( 0.05 ) 0.44 ( 0.18 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.96 1.31 ( 0.05 ) 0.23 ( 0.06 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.23 3.37 ( 0.19 ) 5.29 ( 0.41 ) 3.13 ( 0.15 )\n",
+       "78  100 1000 0.9  10.0  0.56 1.14 ( 0.05 ) 4.56 ( 0.38 ) 0.07 ( 0.03 )\n",
+       "79  500 1000 0.9   2.0  0.96 1.28 ( 0.04 ) 0.27 ( 0.11 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.95 1.35 ( 0.05 ) 0.29 ( 0.09 ) 0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75  6.44      0.04 1.27     0.44    0.00   \n",
+       "76  6.23      0.03 1.31     0.23    0.00   \n",
+       "77  8.16      0.57 3.37     5.29    3.13   \n",
+       "78 10.49      0.37 1.14     4.56    0.07   \n",
+       "79  6.27      0.03 1.28     0.27    0.00   \n",
+       "80  6.29      0.03 1.35     0.29    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/3_sim_cts_summary-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/3_sim_cts_summary-checkpoint.ipynb
new file mode 100755
index 0000000..5487719
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/3_sim_cts_summary-checkpoint.ipynb
@@ -0,0 +1,998 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize method comparison results across 4 feature selection methods in different simulation scenarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Independent Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_lasso = read.csv('../results_summary_cts/sim_ind_lasso.txt', sep='\\t')\n",
+    "ind_elnet = read.csv('../results_summary_cts/sim_ind_elnet.txt', sep='\\t')\n",
+    "ind_rf = read.csv('../results_summary_cts/sim_ind_rf.txt', sep='\\t')\n",
+    "ind_compLasso = read.csv('../results_summary_cts/sim_ind_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "ind_lasso$method = rep('lasso', dim(ind_lasso)[1])\n",
+    "ind_elnet$method = rep('elnet', dim(ind_elnet)[1])\n",
+    "ind_rf$method = rep('rf', dim(ind_rf)[1])\n",
+    "ind_compLasso$method = rep('compLasso', dim(ind_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(ind_lasso)\n",
+    "dim(ind_elnet)\n",
+    "dim(ind_rf)\n",
+    "dim(ind_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind_rf = ind_rf[!names(ind_rf)%in% c('OOB', 'OOB_mean', 'Index')]\n",
+    "dim(ind_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>64</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 64\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 64\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 64 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind = rbind(ind_lasso, ind_elnet)\n",
+    "ind = rbind(ind, ind_rf)\n",
+    "ind = rbind(ind, ind_compLasso)\n",
+    "dim(ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "ind$FP_mean[ind$num_select ==0] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(ind, '../results_summary_cts/table_ind_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>method</th><th scope=col>Stab</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>16</th><td>1000     </td><td>1000     </td><td>lasso    </td><td>0.64     </td><td>0.27     </td><td> 4.39    </td><td>0.00     </td><td> 9.39    </td><td>0.26     </td></tr>\n",
+       "\t<tr><th scope=row>32</th><td>1000     </td><td>1000     </td><td>elnet    </td><td>0.15     </td><td>0.27     </td><td>34.60    </td><td>0.00     </td><td>39.65    </td><td>0.79     </td></tr>\n",
+       "\t<tr><th scope=row>48</th><td>1000     </td><td>1000     </td><td>rf       </td><td>0.09     </td><td>0.85     </td><td>41.70    </td><td>0.99     </td><td>46.78    </td><td>0.89     </td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000     </td><td>1000     </td><td>compLasso</td><td>0.96     </td><td>1.16     </td><td> 0.24    </td><td>0.00     </td><td> 6.24    </td><td>0.03     </td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000     </td><td> 500     </td><td>lasso    </td><td>0.72     </td><td>0.27     </td><td> 3.35    </td><td>0.00     </td><td> 8.35    </td><td>0.21     </td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000     </td><td> 500     </td><td>elnet    </td><td>0.18     </td><td>0.26     </td><td>27.20    </td><td>0.00     </td><td>32.21    </td><td>0.77     </td></tr>\n",
+       "\t<tr><th scope=row>44</th><td>1000     </td><td> 500     </td><td>rf       </td><td>0.19     </td><td>0.78     </td><td>18.70    </td><td>0.84     </td><td>23.89    </td><td>0.78     </td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000     </td><td> 500     </td><td>compLasso</td><td>0.96     </td><td>1.24     </td><td> 0.25    </td><td>0.00     </td><td> 6.25    </td><td>0.03     </td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000     </td><td> 100     </td><td>lasso    </td><td>0.82     </td><td>0.27     </td><td> 2.25    </td><td>0.00     </td><td> 7.25    </td><td>0.15     </td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000     </td><td> 100     </td><td>elnet    </td><td>0.23     </td><td>0.26     </td><td>17.00    </td><td>0.00     </td><td>22.05    </td><td>0.69     </td></tr>\n",
+       "\t<tr><th scope=row>40</th><td>1000     </td><td> 100     </td><td>rf       </td><td>0.81     </td><td>0.47     </td><td> 0.71    </td><td>0.53     </td><td> 6.18    </td><td>0.10     </td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000     </td><td> 100     </td><td>compLasso</td><td>0.89     </td><td>1.04     </td><td> 0.71    </td><td>0.00     </td><td> 6.71    </td><td>0.08     </td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000     </td><td>  50     </td><td>lasso    </td><td>0.86     </td><td>0.26     </td><td> 1.82    </td><td>0.00     </td><td> 6.82    </td><td>0.10     </td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000     </td><td>  50     </td><td>elnet    </td><td>0.27     </td><td>0.25     </td><td>11.70    </td><td>0.00     </td><td>16.76    </td><td>0.60     </td></tr>\n",
+       "\t<tr><th scope=row>36</th><td>1000     </td><td>  50     </td><td>rf       </td><td>0.88     </td><td>0.35     </td><td> 0.07    </td><td>0.78     </td><td> 5.29    </td><td>0.01     </td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000     </td><td>  50     </td><td>compLasso</td><td>0.93     </td><td>1.18     </td><td> 0.39    </td><td>0.00     </td><td> 6.39    </td><td>0.04     </td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500     </td><td>1000     </td><td>lasso    </td><td>0.52     </td><td>0.29     </td><td> 6.51    </td><td>0.00     </td><td>11.51    </td><td>0.40     </td></tr>\n",
+       "\t<tr><th scope=row>31</th><td> 500     </td><td>1000     </td><td>elnet    </td><td>0.14     </td><td>0.27     </td><td>35.90    </td><td>0.00     </td><td>40.98    </td><td>0.80     </td></tr>\n",
+       "\t<tr><th scope=row>47</th><td> 500     </td><td>1000     </td><td>rf       </td><td>0.06     </td><td>0.89     </td><td>44.70    </td><td>1.81     </td><td>48.96    </td><td>0.91     </td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500     </td><td>1000     </td><td>compLasso</td><td>0.96     </td><td>1.12     </td><td> 0.27    </td><td>0.00     </td><td> 6.27    </td><td>0.03     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & N & P & method & Stab & MSE\\_mean & FP\\_mean & FN\\_mean & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t16 & 1000      & 1000      & lasso     & 0.64      & 0.27      &  4.39     & 0.00      &  9.39     & 0.26     \\\\\n",
+       "\t32 & 1000      & 1000      & elnet     & 0.15      & 0.27      & 34.60     & 0.00      & 39.65     & 0.79     \\\\\n",
+       "\t48 & 1000      & 1000      & rf        & 0.09      & 0.85      & 41.70     & 0.99      & 46.78     & 0.89     \\\\\n",
+       "\t64 & 1000      & 1000      & compLasso & 0.96      & 1.16      &  0.24     & 0.00      &  6.24     & 0.03     \\\\\n",
+       "\t12 & 1000      &  500      & lasso     & 0.72      & 0.27      &  3.35     & 0.00      &  8.35     & 0.21     \\\\\n",
+       "\t28 & 1000      &  500      & elnet     & 0.18      & 0.26      & 27.20     & 0.00      & 32.21     & 0.77     \\\\\n",
+       "\t44 & 1000      &  500      & rf        & 0.19      & 0.78      & 18.70     & 0.84      & 23.89     & 0.78     \\\\\n",
+       "\t60 & 1000      &  500      & compLasso & 0.96      & 1.24      &  0.25     & 0.00      &  6.25     & 0.03     \\\\\n",
+       "\t8 & 1000      &  100      & lasso     & 0.82      & 0.27      &  2.25     & 0.00      &  7.25     & 0.15     \\\\\n",
+       "\t24 & 1000      &  100      & elnet     & 0.23      & 0.26      & 17.00     & 0.00      & 22.05     & 0.69     \\\\\n",
+       "\t40 & 1000      &  100      & rf        & 0.81      & 0.47      &  0.71     & 0.53      &  6.18     & 0.10     \\\\\n",
+       "\t56 & 1000      &  100      & compLasso & 0.89      & 1.04      &  0.71     & 0.00      &  6.71     & 0.08     \\\\\n",
+       "\t4 & 1000      &   50      & lasso     & 0.86      & 0.26      &  1.82     & 0.00      &  6.82     & 0.10     \\\\\n",
+       "\t20 & 1000      &   50      & elnet     & 0.27      & 0.25      & 11.70     & 0.00      & 16.76     & 0.60     \\\\\n",
+       "\t36 & 1000      &   50      & rf        & 0.88      & 0.35      &  0.07     & 0.78      &  5.29     & 0.01     \\\\\n",
+       "\t52 & 1000      &   50      & compLasso & 0.93      & 1.18      &  0.39     & 0.00      &  6.39     & 0.04     \\\\\n",
+       "\t15 &  500      & 1000      & lasso     & 0.52      & 0.29      &  6.51     & 0.00      & 11.51     & 0.40     \\\\\n",
+       "\t31 &  500      & 1000      & elnet     & 0.14      & 0.27      & 35.90     & 0.00      & 40.98     & 0.80     \\\\\n",
+       "\t47 &  500      & 1000      & rf        & 0.06      & 0.89      & 44.70     & 1.81      & 48.96     & 0.91     \\\\\n",
+       "\t63 &  500      & 1000      & compLasso & 0.96      & 1.12      &  0.27     & 0.00      &  6.27     & 0.03     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | method | Stab | MSE_mean | FP_mean | FN_mean | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 16 | 1000      | 1000      | lasso     | 0.64      | 0.27      |  4.39     | 0.00      |  9.39     | 0.26      |\n",
+       "| 32 | 1000      | 1000      | elnet     | 0.15      | 0.27      | 34.60     | 0.00      | 39.65     | 0.79      |\n",
+       "| 48 | 1000      | 1000      | rf        | 0.09      | 0.85      | 41.70     | 0.99      | 46.78     | 0.89      |\n",
+       "| 64 | 1000      | 1000      | compLasso | 0.96      | 1.16      |  0.24     | 0.00      |  6.24     | 0.03      |\n",
+       "| 12 | 1000      |  500      | lasso     | 0.72      | 0.27      |  3.35     | 0.00      |  8.35     | 0.21      |\n",
+       "| 28 | 1000      |  500      | elnet     | 0.18      | 0.26      | 27.20     | 0.00      | 32.21     | 0.77      |\n",
+       "| 44 | 1000      |  500      | rf        | 0.19      | 0.78      | 18.70     | 0.84      | 23.89     | 0.78      |\n",
+       "| 60 | 1000      |  500      | compLasso | 0.96      | 1.24      |  0.25     | 0.00      |  6.25     | 0.03      |\n",
+       "| 8 | 1000      |  100      | lasso     | 0.82      | 0.27      |  2.25     | 0.00      |  7.25     | 0.15      |\n",
+       "| 24 | 1000      |  100      | elnet     | 0.23      | 0.26      | 17.00     | 0.00      | 22.05     | 0.69      |\n",
+       "| 40 | 1000      |  100      | rf        | 0.81      | 0.47      |  0.71     | 0.53      |  6.18     | 0.10      |\n",
+       "| 56 | 1000      |  100      | compLasso | 0.89      | 1.04      |  0.71     | 0.00      |  6.71     | 0.08      |\n",
+       "| 4 | 1000      |   50      | lasso     | 0.86      | 0.26      |  1.82     | 0.00      |  6.82     | 0.10      |\n",
+       "| 20 | 1000      |   50      | elnet     | 0.27      | 0.25      | 11.70     | 0.00      | 16.76     | 0.60      |\n",
+       "| 36 | 1000      |   50      | rf        | 0.88      | 0.35      |  0.07     | 0.78      |  5.29     | 0.01      |\n",
+       "| 52 | 1000      |   50      | compLasso | 0.93      | 1.18      |  0.39     | 0.00      |  6.39     | 0.04      |\n",
+       "| 15 |  500      | 1000      | lasso     | 0.52      | 0.29      |  6.51     | 0.00      | 11.51     | 0.40      |\n",
+       "| 31 |  500      | 1000      | elnet     | 0.14      | 0.27      | 35.90     | 0.00      | 40.98     | 0.80      |\n",
+       "| 47 |  500      | 1000      | rf        | 0.06      | 0.89      | 44.70     | 1.81      | 48.96     | 0.91      |\n",
+       "| 63 |  500      | 1000      | compLasso | 0.96      | 1.12      |  0.27     | 0.00      |  6.27     | 0.03      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    method    Stab MSE_mean FP_mean FN_mean num_select FDR \n",
+       "16 1000 1000 lasso     0.64 0.27      4.39   0.00     9.39      0.26\n",
+       "32 1000 1000 elnet     0.15 0.27     34.60   0.00    39.65      0.79\n",
+       "48 1000 1000 rf        0.09 0.85     41.70   0.99    46.78      0.89\n",
+       "64 1000 1000 compLasso 0.96 1.16      0.24   0.00     6.24      0.03\n",
+       "12 1000  500 lasso     0.72 0.27      3.35   0.00     8.35      0.21\n",
+       "28 1000  500 elnet     0.18 0.26     27.20   0.00    32.21      0.77\n",
+       "44 1000  500 rf        0.19 0.78     18.70   0.84    23.89      0.78\n",
+       "60 1000  500 compLasso 0.96 1.24      0.25   0.00     6.25      0.03\n",
+       "8  1000  100 lasso     0.82 0.27      2.25   0.00     7.25      0.15\n",
+       "24 1000  100 elnet     0.23 0.26     17.00   0.00    22.05      0.69\n",
+       "40 1000  100 rf        0.81 0.47      0.71   0.53     6.18      0.10\n",
+       "56 1000  100 compLasso 0.89 1.04      0.71   0.00     6.71      0.08\n",
+       "4  1000   50 lasso     0.86 0.26      1.82   0.00     6.82      0.10\n",
+       "20 1000   50 elnet     0.27 0.25     11.70   0.00    16.76      0.60\n",
+       "36 1000   50 rf        0.88 0.35      0.07   0.78     5.29      0.01\n",
+       "52 1000   50 compLasso 0.93 1.18      0.39   0.00     6.39      0.04\n",
+       "15  500 1000 lasso     0.52 0.29      6.51   0.00    11.51      0.40\n",
+       "31  500 1000 elnet     0.14 0.27     35.90   0.00    40.98      0.80\n",
+       "47  500 1000 rf        0.06 0.89     44.70   1.81    48.96      0.91\n",
+       "63  500 1000 compLasso 0.96 1.12      0.27   0.00     6.27      0.03"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind[order(ind$N, ind$P, decreasing=T), c('N', 'P', 'method', 'Stab', 'MSE_mean', 'FP_mean', 'FN_mean', 'num_select', 'FDR')][1:20, ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Toeplitz Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_lasso = read.csv('../results_summary_cts/sim_toe_lasso.txt', sep='\\t')\n",
+    "toe_elnet = read.csv('../results_summary_cts/sim_toe_elnet.txt', sep='\\t')\n",
+    "toe_rf = read.csv('../results_summary_cts/sim_toe_rf.txt', sep='\\t')\n",
+    "toe_compLasso = read.csv('../results_summary_cts/sim_toe_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "toe_lasso$method = rep('lasso', dim(toe_lasso)[1])\n",
+    "toe_elnet$method = rep('elnet', dim(toe_elnet)[1])\n",
+    "toe_rf$method = rep('rf', dim(toe_rf)[1])\n",
+    "toe_compLasso$method = rep('compLasso', dim(toe_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(toe_lasso)\n",
+    "dim(toe_elnet)\n",
+    "dim(toe_rf)\n",
+    "dim(toe_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe_rf = toe_rf[!names(toe_rf)%in% c('OOB', 'OOB_mean', 'toeex')]\n",
+    "dim(toe_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>320</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe = rbind(toe_lasso, toe_elnet)\n",
+    "toe = rbind(toe, toe_rf)\n",
+    "toe = rbind(toe, toe_compLasso)\n",
+    "dim(toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>50           </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0            </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.40         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50           </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0            </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.36         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50           </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0            </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50           </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0            </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.35         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>50           </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0            </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.22         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>50           </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0            </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.15         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>50           </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0            </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.14         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>50           </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0            </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.20         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>50           </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0            </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.99         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>50           </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>50           </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0            </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.95         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>50           </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0            </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.94         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>50           </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0            </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.66         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>50           </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0            </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.67         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>50           </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0            </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.70         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>50           </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0            </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.74         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>50           </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0            </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.27         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>50           </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0            </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.30         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>50           </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0            </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>50           </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0            </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "\\hline\n",
+       "\t161 & 50            &   50          & 0.1           &  1            & 0             & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.40          & 1             & 6             & rf           \\\\\n",
+       "\t165 & 50            &  100          & 0.1           &  2            & 0             & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.36          & 1             & 6             & rf           \\\\\n",
+       "\t169 & 50            &  500          & 0.1           & 10            & 0             & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.29          & 1             & 6             & rf           \\\\\n",
+       "\t173 & 50            & 1000          & 0.1           & 20            & 0             & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.35          & 1             & 6             & rf           \\\\\n",
+       "\t177 & 50            &   50          & 0.3           &  1            & 0             & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.22          & 1             & 6             & rf           \\\\\n",
+       "\t181 & 50            &  100          & 0.3           &  2            & 0             & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.15          & 1             & 6             & rf           \\\\\n",
+       "\t185 & 50            &  500          & 0.3           & 10            & 0             & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.14          & 1             & 6             & rf           \\\\\n",
+       "\t189 & 50            & 1000          & 0.3           & 20            & 0             & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.20          & 1             & 6             & rf           \\\\\n",
+       "\t193 & 50            &   50          & 0.5           &  1            & 0             & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.99          & 1             & 6             & rf           \\\\\n",
+       "\t197 & 50            &  100          & 0.5           &  2            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.91          & 1             & 6             & rf           \\\\\n",
+       "\t201 & 50            &  500          & 0.5           & 10            & 0             & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.95          & 1             & 6             & rf           \\\\\n",
+       "\t205 & 50            & 1000          & 0.5           & 20            & 0             & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.94          & 1             & 6             & rf           \\\\\n",
+       "\t209 & 50            &   50          & 0.7           &  1            & 0             & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.66          & 1             & 6             & rf           \\\\\n",
+       "\t213 & 50            &  100          & 0.7           &  2            & 0             & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.67          & 1             & 6             & rf           \\\\\n",
+       "\t217 & 50            &  500          & 0.7           & 10            & 0             & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.70          & 1             & 6             & rf           \\\\\n",
+       "\t221 & 50            & 1000          & 0.7           & 20            & 0             & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.74          & 1             & 6             & rf           \\\\\n",
+       "\t225 & 50            &   50          & 0.9           &  1            & 0             & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.27          & 1             & 6             & rf           \\\\\n",
+       "\t229 & 50            &  100          & 0.9           &  2            & 0             & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.30          & 1             & 6             & rf           \\\\\n",
+       "\t233 & 50            &  500          & 0.9           & 10            & 0             & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.29          & 1             & 6             & rf           \\\\\n",
+       "\t237 & 50            & 1000          & 0.9           & 20            & 0             & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.29          & 1             & 6             & rf           \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean | method |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 | 50            |   50          | 0.1           |  1            | 0             | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.40          | 1             | 6             | rf            |\n",
+       "| 165 | 50            |  100          | 0.1           |  2            | 0             | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.36          | 1             | 6             | rf            |\n",
+       "| 169 | 50            |  500          | 0.1           | 10            | 0             | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.29          | 1             | 6             | rf            |\n",
+       "| 173 | 50            | 1000          | 0.1           | 20            | 0             | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.35          | 1             | 6             | rf            |\n",
+       "| 177 | 50            |   50          | 0.3           |  1            | 0             | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.22          | 1             | 6             | rf            |\n",
+       "| 181 | 50            |  100          | 0.3           |  2            | 0             | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.15          | 1             | 6             | rf            |\n",
+       "| 185 | 50            |  500          | 0.3           | 10            | 0             | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.14          | 1             | 6             | rf            |\n",
+       "| 189 | 50            | 1000          | 0.3           | 20            | 0             | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.20          | 1             | 6             | rf            |\n",
+       "| 193 | 50            |   50          | 0.5           |  1            | 0             | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.99          | 1             | 6             | rf            |\n",
+       "| 197 | 50            |  100          | 0.5           |  2            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.91          | 1             | 6             | rf            |\n",
+       "| 201 | 50            |  500          | 0.5           | 10            | 0             | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.95          | 1             | 6             | rf            |\n",
+       "| 205 | 50            | 1000          | 0.5           | 20            | 0             | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.94          | 1             | 6             | rf            |\n",
+       "| 209 | 50            |   50          | 0.7           |  1            | 0             | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.66          | 1             | 6             | rf            |\n",
+       "| 213 | 50            |  100          | 0.7           |  2            | 0             | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.67          | 1             | 6             | rf            |\n",
+       "| 217 | 50            |  500          | 0.7           | 10            | 0             | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.70          | 1             | 6             | rf            |\n",
+       "| 221 | 50            | 1000          | 0.7           | 20            | 0             | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.74          | 1             | 6             | rf            |\n",
+       "| 225 | 50            |   50          | 0.9           |  1            | 0             | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.27          | 1             | 6             | rf            |\n",
+       "| 229 | 50            |  100          | 0.9           |  2            | 0             | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.30          | 1             | 6             | rf            |\n",
+       "| 233 | 50            |  500          | 0.9           | 10            | 0             | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.29          | 1             | 6             | rf            |\n",
+       "| 237 | 50            | 1000          | 0.9           | 20            | 0             | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.29          | 1             | 6             | rf            |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N  P    Corr Ratio Stab MSE           FP      FN      num_select FDR\n",
+       "161 50   50 0.1   1    0    1.4 ( 0.06 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "165 50  100 0.1   2    0    1.36 ( 0.06 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "169 50  500 0.1  10    0    1.29 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "173 50 1000 0.1  20    0    1.35 ( 0.06 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "177 50   50 0.3   1    0    1.22 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "181 50  100 0.3   2    0    1.15 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "185 50  500 0.3  10    0    1.14 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "189 50 1000 0.3  20    0    1.2 ( 0.05 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "193 50   50 0.5   1    0    0.99 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "197 50  100 0.5   2    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "201 50  500 0.5  10    0    0.95 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "205 50 1000 0.5  20    0    0.94 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "209 50   50 0.7   1    0    0.66 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "213 50  100 0.7   2    0    0.67 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "217 50  500 0.7  10    0    0.7 ( 0.03 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "221 50 1000 0.7  20    0    0.74 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "225 50   50 0.9   1    0    0.27 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "229 50  100 0.9   2    0    0.3 ( 0.01 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "233 50  500 0.9  10    0    0.29 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "237 50 1000 0.9  20    0    0.29 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "    MSE_mean FP_mean FN_mean method\n",
+       "161 1.40     1       6       rf    \n",
+       "165 1.36     1       6       rf    \n",
+       "169 1.29     1       6       rf    \n",
+       "173 1.35     1       6       rf    \n",
+       "177 1.22     1       6       rf    \n",
+       "181 1.15     1       6       rf    \n",
+       "185 1.14     1       6       rf    \n",
+       "189 1.20     1       6       rf    \n",
+       "193 0.99     1       6       rf    \n",
+       "197 0.91     1       6       rf    \n",
+       "201 0.95     1       6       rf    \n",
+       "205 0.94     1       6       rf    \n",
+       "209 0.66     1       6       rf    \n",
+       "213 0.67     1       6       rf    \n",
+       "217 0.70     1       6       rf    \n",
+       "221 0.74     1       6       rf    \n",
+       "225 0.27     1       6       rf    \n",
+       "229 0.30     1       6       rf    \n",
+       "233 0.29     1       6       rf    \n",
+       "237 0.29     1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe[toe$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "toe$FP_mean[toe$num_select ==0] = 0\n",
+    "write.table(toe, '../results_summary_cts/table_toe_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Block Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block_lasso = read.csv('../results_summary_cts/sim_block_lasso.txt', sep='\\t')\n",
+    "block_elnet = read.csv('../results_summary_cts/sim_block_elnet.txt', sep='\\t')\n",
+    "block_rf = read.csv('../results_summary_cts/sim_block_rf.txt', sep='\\t')\n",
+    "block_compLasso = read.csv('../results_summary_cts/sim_block_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "block_lasso$method = rep('lasso', dim(block_lasso)[1])\n",
+    "block_elnet$method = rep('elnet', dim(block_elnet)[1])\n",
+    "block_rf$method = rep('rf', dim(block_rf)[1])\n",
+    "block_compLasso$method = rep('compLasso', dim(block_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(block_lasso)\n",
+    "dim(block_elnet)\n",
+    "dim(block_rf)\n",
+    "dim(block_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block_rf = block_rf[!names(block_rf)%in% c('OOB', 'OOB_mean', 'blockex')]\n",
+    "dim(block_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>320</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block = rbind(block_lasso, block_elnet)\n",
+    "block = rbind(block, block_rf)\n",
+    "block = rbind(block, block_compLasso)\n",
+    "dim(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "block$FP_mean[block$num_select ==0] = 0\n",
+    "write.table(block, '../results_summary_cts/table_block_all.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/.ipynb_checkpoints/4_sim_cts_bootstrap_testing-checkpoint.ipynb b/simulations/notebooks_sim_cts/.ipynb_checkpoints/4_sim_cts_bootstrap_testing-checkpoint.ipynb
new file mode 100755
index 0000000..6ed9920
--- /dev/null
+++ b/simulations/notebooks_sim_cts/.ipynb_checkpoints/4_sim_cts_bootstrap_testing-checkpoint.ipynb
@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hypothesis testing based on boostrap for selected simulation scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/boot_toe_RF.RData'))\n",
+    "load(paste0(dir, '/boot_block_RF.RData'))\n",
+    "load(paste0(dir, '/boot_toe_compLasso.RData'))\n",
+    "load(paste0(dir, '/boot_block_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>toe_rf</th><th scope=col>toe_lin</th><th scope=col>block_rf</th><th scope=col>block_lin</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>0.01</td><td>0.25</td><td>0.01</td><td>0.26</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.01</td><td>0.23</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.28</td><td>0.00</td><td>0.20</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.25</td><td>0.01</td><td>0.26</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.01</td><td>0.25</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.00</td><td>0.26</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " toe\\_rf & toe\\_lin & block\\_rf & block\\_lin\\\\\n",
+       "\\hline\n",
+       "\t 0.01 & 0.25 & 0.01 & 0.26\\\\\n",
+       "\t 0.01 & 0.23 & 0.01 & 0.23\\\\\n",
+       "\t 0.01 & 0.28 & 0.00 & 0.20\\\\\n",
+       "\t 0.01 & 0.25 & 0.01 & 0.26\\\\\n",
+       "\t 0.01 & 0.23 & 0.01 & 0.25\\\\\n",
+       "\t 0.01 & 0.23 & 0.00 & 0.26\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| toe_rf | toe_lin | block_rf | block_lin |\n",
+       "|---|---|---|---|\n",
+       "| 0.01 | 0.25 | 0.01 | 0.26 |\n",
+       "| 0.01 | 0.23 | 0.01 | 0.23 |\n",
+       "| 0.01 | 0.28 | 0.00 | 0.20 |\n",
+       "| 0.01 | 0.25 | 0.01 | 0.26 |\n",
+       "| 0.01 | 0.23 | 0.01 | 0.25 |\n",
+       "| 0.01 | 0.23 | 0.00 | 0.26 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  toe_rf toe_lin block_rf block_lin\n",
+       "1 0.01   0.25    0.01     0.26     \n",
+       "2 0.01   0.23    0.01     0.23     \n",
+       "3 0.01   0.28    0.00     0.20     \n",
+       "4 0.01   0.25    0.01     0.26     \n",
+       "5 0.01   0.23    0.01     0.25     \n",
+       "6 0.01   0.23    0.00     0.26     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = as.data.frame(cbind(toe_rf$stab_index, toe_lin$stab_index, block_rf$stab_index, block_lin$stab_index))\n",
+    "colnames(table) = c('toe_rf', 'toe_lin', 'block_rf', 'block_lin')\n",
+    "head(table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two sample test based on Bootstrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## interpretation: if confidence interval doesn't contain zero, then significant\n",
+    "## ref: correct intrepretation of confidence intervals: https://journals.sagepub.com/doi/pdf/10.1177/201010581001900316"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2247"
+      ],
+      "text/latex": [
+       "0.2247"
+      ],
+      "text/markdown": [
+       "0.2247"
+      ],
+      "text/plain": [
+       "[1] 0.2247"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.19</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.28</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.19\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.28\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1997.5%\n",
+       ":   0.28\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.19  0.28 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (table$toe_lin - table$toe_rf)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2289"
+      ],
+      "text/latex": [
+       "0.2289"
+      ],
+      "text/markdown": [
+       "0.2289"
+      ],
+      "text/plain": [
+       "[1] 0.2289"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.17475</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.28525</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.17475\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.28525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1747597.5%\n",
+       ":   0.28525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.17475 0.28525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (table$block_lin - table$block_rf)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare based on MSE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### block 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.47 ( 0.01 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_block_rf = NULL\n",
+    "for (i in 1:length(results_block_rf)){\n",
+    "    table_block_rf = rbind(table_block_rf, results_block_rf[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_block_rf = as.data.frame(table_block_rf)\n",
+    "rf_block5_rf= table_block_rf[table_block_rf$n == 100 & table_block_rf$p == 1000 & table_block_rf$rou == 0.5, ]\n",
+    "rf_block5_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.91 ( 0.04 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_block_compLasso = NULL\n",
+    "for (i in 1:length(results_block_compLasso)){\n",
+    "    table_block_compLasso = rbind(table_block_compLasso, results_block_compLasso[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_block_compLasso = as.data.frame(table_block_compLasso)\n",
+    "rf_block5_compLasso= table_block_compLasso[table_block_compLasso$n == 100 & table_block_compLasso$p == 1000 & table_block_compLasso$rou == 0.5, ]\n",
+    "rf_block5_compLasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.439514359427722"
+      ],
+      "text/latex": [
+       "0.439514359427722"
+      ],
+      "text/markdown": [
+       "0.439514359427722"
+      ],
+      "text/plain": [
+       "[1] 0.4395144"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.454598954212658"
+      ],
+      "text/latex": [
+       "0.454598954212658"
+      ],
+      "text/markdown": [
+       "0.454598954212658"
+      ],
+      "text/plain": [
+       "[1] 0.454599"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.273304180160457</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>1.56863259003766</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.273304180160457\n",
+       "\\item[97.5\\textbackslash{}\\%] 1.56863259003766\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.27330418016045797.5%\n",
+       ":   1.56863259003766\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.2733042  1.5686326 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (unlist(rf_block5_compLasso$MSE.list) - unlist(rf_block5_rf$MSE.list))\n",
+    "mean(diff_block)\n",
+    "sd(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Toeplitz 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.72 ( 0.02 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_toe_rf = NULL\n",
+    "for (i in 1:length(results_toe_rf)){\n",
+    "    table_toe_rf = rbind(table_toe_rf, results_toe_rf[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_toe_rf = as.data.frame(table_toe_rf)\n",
+    "rf_toe5_rf= table_toe_rf[table_toe_rf$n == 100 & table_toe_rf$p == 1000 & table_toe_rf$rou == 0.5, ]\n",
+    "rf_toe5_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.96 ( 0.05 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_toe_compLasso = NULL\n",
+    "for (i in 1:length(results_toe_compLasso)){\n",
+    "    table_toe_compLasso = rbind(table_toe_compLasso, results_toe_compLasso[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_toe_compLasso = as.data.frame(table_toe_compLasso)\n",
+    "rf_toe5_compLasso= table_toe_compLasso[table_toe_compLasso$n == 100 & table_toe_compLasso$p == 1000 & table_toe_compLasso$rou == 0.5, ]\n",
+    "rf_toe5_compLasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.23096420572779"
+      ],
+      "text/latex": [
+       "0.23096420572779"
+      ],
+      "text/markdown": [
+       "0.23096420572779"
+      ],
+      "text/plain": [
+       "[1] 0.2309642"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.521190219773319"
+      ],
+      "text/latex": [
+       "0.521190219773319"
+      ],
+      "text/markdown": [
+       "0.521190219773319"
+      ],
+      "text/plain": [
+       "[1] 0.5211902"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.621269466414493</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>1.36106289789273</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.621269466414493\n",
+       "\\item[97.5\\textbackslash{}\\%] 1.36106289789273\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.62126946641449397.5%\n",
+       ":   1.36106289789273\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.6212695  1.3610629 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (unlist(rf_toe5_compLasso$MSE.list) - unlist(rf_toe5_rf$MSE.list))\n",
+    "mean(diff_toe)\n",
+    "sd(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/0.1_sim_indepdent_lasso.ipynb b/simulations/notebooks_sim_cts/0.1_sim_indepdent_lasso.ipynb
new file mode 100755
index 0000000..84c4b66
--- /dev/null
+++ b/simulations/notebooks_sim_cts/0.1_sim_indepdent_lasso.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_lasso))\n",
+    "for (i in 1:length(results_ind_lasso)){\n",
+    "    table_ind = rbind(table_ind, results_ind_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>8.36 ( 0.45 )</td><td>0.02 ( 0.01 )</td><td>0.54 ( 0.03 )</td><td>0.37         </td><td>13.34        </td><td>0.51         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>5.5 ( 0.42 ) </td><td>0 ( 0 )      </td><td>0.36 ( 0.01 )</td><td>0.51         </td><td>10.50        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>2.33 ( 0.14 )</td><td>0 ( 0 )      </td><td>0.28 ( 0 )   </td><td>0.79         </td><td> 7.33        </td><td>0.15         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>1.82 ( 0.13 )</td><td>0 ( 0 )      </td><td>0.26 ( 0 )   </td><td>0.86         </td><td> 6.82        </td><td>0.10         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>11.1 ( 0.38 )</td><td>0.08 ( 0.03 )</td><td>0.66 ( 0.04 )</td><td>0.32         </td><td>16.02        </td><td>0.61         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>7.23 ( 0.4 ) </td><td>0 ( 0 )      </td><td>0.41 ( 0.01 )</td><td>0.46         </td><td>12.23        </td><td>0.46         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 8.36 ( 0.45 ) & 0.02 ( 0.01 ) & 0.54 ( 0.03 ) & 0.37          & 13.34         & 0.51         \\\\\n",
+       "\t 100           & 50            & 0             & 5.5 ( 0.42 )  & 0 ( 0 )       & 0.36 ( 0.01 ) & 0.51          & 10.50         & 0.37         \\\\\n",
+       "\t 500           & 50            & 0             & 2.33 ( 0.14 ) & 0 ( 0 )       & 0.28 ( 0 )    & 0.79          &  7.33         & 0.15         \\\\\n",
+       "\t 1000          & 50            & 0             & 1.82 ( 0.13 ) & 0 ( 0 )       & 0.26 ( 0 )    & 0.86          &  6.82         & 0.10         \\\\\n",
+       "\t 50            & 100           & 0             & 11.1 ( 0.38 ) & 0.08 ( 0.03 ) & 0.66 ( 0.04 ) & 0.32          & 16.02         & 0.61         \\\\\n",
+       "\t 100           & 100           & 0             & 7.23 ( 0.4 )  & 0 ( 0 )       & 0.41 ( 0.01 ) & 0.46          & 12.23         & 0.46         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 8.36 ( 0.45 ) | 0.02 ( 0.01 ) | 0.54 ( 0.03 ) | 0.37          | 13.34         | 0.51          |\n",
+       "| 100           | 50            | 0             | 5.5 ( 0.42 )  | 0 ( 0 )       | 0.36 ( 0.01 ) | 0.51          | 10.50         | 0.37          |\n",
+       "| 500           | 50            | 0             | 2.33 ( 0.14 ) | 0 ( 0 )       | 0.28 ( 0 )    | 0.79          |  7.33         | 0.15          |\n",
+       "| 1000          | 50            | 0             | 1.82 ( 0.13 ) | 0 ( 0 )       | 0.26 ( 0 )    | 0.86          |  6.82         | 0.10          |\n",
+       "| 50            | 100           | 0             | 11.1 ( 0.38 ) | 0.08 ( 0.03 ) | 0.66 ( 0.04 ) | 0.32          | 16.02         | 0.61          |\n",
+       "| 100           | 100           | 0             | 7.23 ( 0.4 )  | 0 ( 0 )       | 0.41 ( 0.01 ) | 0.46          | 12.23         | 0.46          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   8.36 ( 0.45 ) 0.02 ( 0.01 ) 0.54 ( 0.03 ) 0.37 13.34      0.51\n",
+       "2 100  50  0   5.5 ( 0.42 )  0 ( 0 )       0.36 ( 0.01 ) 0.51 10.50      0.37\n",
+       "3 500  50  0   2.33 ( 0.14 ) 0 ( 0 )       0.28 ( 0 )    0.79  7.33      0.15\n",
+       "4 1000 50  0   1.82 ( 0.13 ) 0 ( 0 )       0.26 ( 0 )    0.86  6.82      0.10\n",
+       "5 50   100 0   11.1 ( 0.38 ) 0.08 ( 0.03 ) 0.66 ( 0.04 ) 0.32 16.02      0.61\n",
+       "6 100  100 0   7.23 ( 0.4 )  0 ( 0 )       0.41 ( 0.01 ) 0.46 12.23      0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.37          </td><td>0.54 ( 0.03 ) </td><td>8.36 ( 0.45 ) </td><td>0.02 ( 0.01 ) </td><td>13.34         </td><td>0.51          </td><td>0.54          </td><td> 8.36         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.51          </td><td>0.36 ( 0.01 ) </td><td>5.5 ( 0.42 )  </td><td>0 ( 0 )       </td><td>10.50         </td><td>0.37          </td><td>0.36          </td><td> 5.50         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.79          </td><td>0.28 ( 0 )    </td><td>2.33 ( 0.14 ) </td><td>0 ( 0 )       </td><td> 7.33         </td><td>0.15          </td><td>0.28          </td><td> 2.33         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.86          </td><td>0.26 ( 0 )    </td><td>1.82 ( 0.13 ) </td><td>0 ( 0 )       </td><td> 6.82         </td><td>0.1           </td><td>0.26          </td><td> 1.82         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.32          </td><td>0.66 ( 0.04 ) </td><td>11.1 ( 0.38 ) </td><td>0.08 ( 0.03 ) </td><td>16.02         </td><td>0.61          </td><td>0.66          </td><td>11.10         </td><td>0.08          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.46          </td><td>0.41 ( 0.01 ) </td><td>7.23 ( 0.4 )  </td><td>0 ( 0 )       </td><td>12.23         </td><td>0.46          </td><td>0.41          </td><td> 7.23         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.71          </td><td>0.29 ( 0 )    </td><td>3.26 ( 0.25 ) </td><td>0 ( 0 )       </td><td> 8.26         </td><td>0.22          </td><td>0.29          </td><td> 3.26         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.82          </td><td>0.27 ( 0 )    </td><td>2.25 ( 0.15 ) </td><td>0 ( 0 )       </td><td> 7.25         </td><td>0.15          </td><td>0.27          </td><td> 2.25         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.19          </td><td>1.41 ( 0.11 ) </td><td>19.89 ( 0.38 )</td><td>0.64 ( 0.08 ) </td><td>24.25         </td><td>0.77          </td><td>1.41          </td><td>19.80         </td><td>0.64          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.30          </td><td>0.48 ( 0.02 ) </td><td>14.71 ( 0.7 ) </td><td>0 ( 0 )       </td><td>19.71         </td><td>0.66          </td><td>0.48          </td><td>14.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.61          </td><td>0.28 ( 0 )    </td><td>4.82 ( 0.34 ) </td><td>0 ( 0 )       </td><td> 9.82         </td><td>0.32          </td><td>0.28          </td><td> 4.82         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.72          </td><td>0.27 ( 0 )    </td><td>3.35 ( 0.32 ) </td><td>0 ( 0 )       </td><td> 8.35         </td><td>0.21          </td><td>0.27          </td><td> 3.35         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.14          </td><td>2.24 ( 0.19 ) </td><td>23.95 ( 0.37 )</td><td>1.19 ( 0.09 ) </td><td>27.76         </td><td>0.82          </td><td>2.24          </td><td>23.90         </td><td>1.19          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.25          </td><td>0.53 ( 0.02 ) </td><td>18.53 ( 0.59 )</td><td>0 ( 0 )       </td><td>23.53         </td><td>0.73          </td><td>0.53          </td><td>18.50         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.52          </td><td>0.29 ( 0 )    </td><td>6.51 ( 0.45 ) </td><td>0 ( 0 )       </td><td>11.51         </td><td>0.4           </td><td>0.29          </td><td> 6.51         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.64          </td><td>0.27 ( 0 )    </td><td>4.39 ( 0.56 ) </td><td>0 ( 0 )       </td><td> 9.39         </td><td>0.26          </td><td>0.27          </td><td> 4.39         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.37           & 0.54 ( 0.03 )  & 8.36 ( 0.45 )  & 0.02 ( 0.01 )  & 13.34          & 0.51           & 0.54           &  8.36          & 0.02          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.51           & 0.36 ( 0.01 )  & 5.5 ( 0.42 )   & 0 ( 0 )        & 10.50          & 0.37           & 0.36           &  5.50          & 0.00          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.79           & 0.28 ( 0 )     & 2.33 ( 0.14 )  & 0 ( 0 )        &  7.33          & 0.15           & 0.28           &  2.33          & 0.00          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.86           & 0.26 ( 0 )     & 1.82 ( 0.13 )  & 0 ( 0 )        &  6.82          & 0.1            & 0.26           &  1.82          & 0.00          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.32           & 0.66 ( 0.04 )  & 11.1 ( 0.38 )  & 0.08 ( 0.03 )  & 16.02          & 0.61           & 0.66           & 11.10          & 0.08          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.46           & 0.41 ( 0.01 )  & 7.23 ( 0.4 )   & 0 ( 0 )        & 12.23          & 0.46           & 0.41           &  7.23          & 0.00          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.71           & 0.29 ( 0 )     & 3.26 ( 0.25 )  & 0 ( 0 )        &  8.26          & 0.22           & 0.29           &  3.26          & 0.00          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.82           & 0.27 ( 0 )     & 2.25 ( 0.15 )  & 0 ( 0 )        &  7.25          & 0.15           & 0.27           &  2.25          & 0.00          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.19           & 1.41 ( 0.11 )  & 19.89 ( 0.38 ) & 0.64 ( 0.08 )  & 24.25          & 0.77           & 1.41           & 19.80          & 0.64          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.30           & 0.48 ( 0.02 )  & 14.71 ( 0.7 )  & 0 ( 0 )        & 19.71          & 0.66           & 0.48           & 14.70          & 0.00          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.61           & 0.28 ( 0 )     & 4.82 ( 0.34 )  & 0 ( 0 )        &  9.82          & 0.32           & 0.28           &  4.82          & 0.00          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.72           & 0.27 ( 0 )     & 3.35 ( 0.32 )  & 0 ( 0 )        &  8.35          & 0.21           & 0.27           &  3.35          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.14           & 2.24 ( 0.19 )  & 23.95 ( 0.37 ) & 1.19 ( 0.09 )  & 27.76          & 0.82           & 2.24           & 23.90          & 1.19          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.25           & 0.53 ( 0.02 )  & 18.53 ( 0.59 ) & 0 ( 0 )        & 23.53          & 0.73           & 0.53           & 18.50          & 0.00          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.52           & 0.29 ( 0 )     & 6.51 ( 0.45 )  & 0 ( 0 )        & 11.51          & 0.4            & 0.29           &  6.51          & 0.00          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.64           & 0.27 ( 0 )     & 4.39 ( 0.56 )  & 0 ( 0 )        &  9.39          & 0.26           & 0.27           &  4.39          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.37           | 0.54 ( 0.03 )  | 8.36 ( 0.45 )  | 0.02 ( 0.01 )  | 13.34          | 0.51           | 0.54           |  8.36          | 0.02           |\n",
+       "|  100           |   50           |  0.50          | 0.51           | 0.36 ( 0.01 )  | 5.5 ( 0.42 )   | 0 ( 0 )        | 10.50          | 0.37           | 0.36           |  5.50          | 0.00           |\n",
+       "|  500           |   50           |  0.10          | 0.79           | 0.28 ( 0 )     | 2.33 ( 0.14 )  | 0 ( 0 )        |  7.33          | 0.15           | 0.28           |  2.33          | 0.00           |\n",
+       "| 1000           |   50           |  0.05          | 0.86           | 0.26 ( 0 )     | 1.82 ( 0.13 )  | 0 ( 0 )        |  6.82          | 0.1            | 0.26           |  1.82          | 0.00           |\n",
+       "|   50           |  100           |  2.00          | 0.32           | 0.66 ( 0.04 )  | 11.1 ( 0.38 )  | 0.08 ( 0.03 )  | 16.02          | 0.61           | 0.66           | 11.10          | 0.08           |\n",
+       "|  100           |  100           |  1.00          | 0.46           | 0.41 ( 0.01 )  | 7.23 ( 0.4 )   | 0 ( 0 )        | 12.23          | 0.46           | 0.41           |  7.23          | 0.00           |\n",
+       "|  500           |  100           |  0.20          | 0.71           | 0.29 ( 0 )     | 3.26 ( 0.25 )  | 0 ( 0 )        |  8.26          | 0.22           | 0.29           |  3.26          | 0.00           |\n",
+       "| 1000           |  100           |  0.10          | 0.82           | 0.27 ( 0 )     | 2.25 ( 0.15 )  | 0 ( 0 )        |  7.25          | 0.15           | 0.27           |  2.25          | 0.00           |\n",
+       "|   50           |  500           | 10.00          | 0.19           | 1.41 ( 0.11 )  | 19.89 ( 0.38 ) | 0.64 ( 0.08 )  | 24.25          | 0.77           | 1.41           | 19.80          | 0.64           |\n",
+       "|  100           |  500           |  5.00          | 0.30           | 0.48 ( 0.02 )  | 14.71 ( 0.7 )  | 0 ( 0 )        | 19.71          | 0.66           | 0.48           | 14.70          | 0.00           |\n",
+       "|  500           |  500           |  1.00          | 0.61           | 0.28 ( 0 )     | 4.82 ( 0.34 )  | 0 ( 0 )        |  9.82          | 0.32           | 0.28           |  4.82          | 0.00           |\n",
+       "| 1000           |  500           |  0.50          | 0.72           | 0.27 ( 0 )     | 3.35 ( 0.32 )  | 0 ( 0 )        |  8.35          | 0.21           | 0.27           |  3.35          | 0.00           |\n",
+       "|   50           | 1000           | 20.00          | 0.14           | 2.24 ( 0.19 )  | 23.95 ( 0.37 ) | 1.19 ( 0.09 )  | 27.76          | 0.82           | 2.24           | 23.90          | 1.19           |\n",
+       "|  100           | 1000           | 10.00          | 0.25           | 0.53 ( 0.02 )  | 18.53 ( 0.59 ) | 0 ( 0 )        | 23.53          | 0.73           | 0.53           | 18.50          | 0.00           |\n",
+       "|  500           | 1000           |  2.00          | 0.52           | 0.29 ( 0 )     | 6.51 ( 0.45 )  | 0 ( 0 )        | 11.51          | 0.4            | 0.29           |  6.51          | 0.00           |\n",
+       "| 1000           | 1000           |  1.00          | 0.64           | 0.27 ( 0 )     | 4.39 ( 0.56 )  | 0 ( 0 )        |  9.39          | 0.26           | 0.27           |  4.39          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN            num_select\n",
+       "1    50   50  1.00 0.37 0.54 ( 0.03 ) 8.36 ( 0.45 )  0.02 ( 0.01 ) 13.34     \n",
+       "2   100   50  0.50 0.51 0.36 ( 0.01 ) 5.5 ( 0.42 )   0 ( 0 )       10.50     \n",
+       "3   500   50  0.10 0.79 0.28 ( 0 )    2.33 ( 0.14 )  0 ( 0 )        7.33     \n",
+       "4  1000   50  0.05 0.86 0.26 ( 0 )    1.82 ( 0.13 )  0 ( 0 )        6.82     \n",
+       "5    50  100  2.00 0.32 0.66 ( 0.04 ) 11.1 ( 0.38 )  0.08 ( 0.03 ) 16.02     \n",
+       "6   100  100  1.00 0.46 0.41 ( 0.01 ) 7.23 ( 0.4 )   0 ( 0 )       12.23     \n",
+       "7   500  100  0.20 0.71 0.29 ( 0 )    3.26 ( 0.25 )  0 ( 0 )        8.26     \n",
+       "8  1000  100  0.10 0.82 0.27 ( 0 )    2.25 ( 0.15 )  0 ( 0 )        7.25     \n",
+       "9    50  500 10.00 0.19 1.41 ( 0.11 ) 19.89 ( 0.38 ) 0.64 ( 0.08 ) 24.25     \n",
+       "10  100  500  5.00 0.30 0.48 ( 0.02 ) 14.71 ( 0.7 )  0 ( 0 )       19.71     \n",
+       "11  500  500  1.00 0.61 0.28 ( 0 )    4.82 ( 0.34 )  0 ( 0 )        9.82     \n",
+       "12 1000  500  0.50 0.72 0.27 ( 0 )    3.35 ( 0.32 )  0 ( 0 )        8.35     \n",
+       "13   50 1000 20.00 0.14 2.24 ( 0.19 ) 23.95 ( 0.37 ) 1.19 ( 0.09 ) 27.76     \n",
+       "14  100 1000 10.00 0.25 0.53 ( 0.02 ) 18.53 ( 0.59 ) 0 ( 0 )       23.53     \n",
+       "15  500 1000  2.00 0.52 0.29 ( 0 )    6.51 ( 0.45 )  0 ( 0 )       11.51     \n",
+       "16 1000 1000  1.00 0.64 0.27 ( 0 )    4.39 ( 0.56 )  0 ( 0 )        9.39     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.51 0.54      8.36   0.02   \n",
+       "2  0.37 0.36      5.50   0.00   \n",
+       "3  0.15 0.28      2.33   0.00   \n",
+       "4  0.1  0.26      1.82   0.00   \n",
+       "5  0.61 0.66     11.10   0.08   \n",
+       "6  0.46 0.41      7.23   0.00   \n",
+       "7  0.22 0.29      3.26   0.00   \n",
+       "8  0.15 0.27      2.25   0.00   \n",
+       "9  0.77 1.41     19.80   0.64   \n",
+       "10 0.66 0.48     14.70   0.00   \n",
+       "11 0.32 0.28      4.82   0.00   \n",
+       "12 0.21 0.27      3.35   0.00   \n",
+       "13 0.82 2.24     23.90   1.19   \n",
+       "14 0.73 0.53     18.50   0.00   \n",
+       "15 0.4  0.29      6.51   0.00   \n",
+       "16 0.26 0.27      4.39   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/0.2_sim_indepdent_elnet_cts.ipynb b/simulations/notebooks_sim_cts/0.2_sim_indepdent_elnet_cts.ipynb
new file mode 100755
index 0000000..0db1fc6
--- /dev/null
+++ b/simulations/notebooks_sim_cts/0.2_sim_indepdent_elnet_cts.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_elnet))\n",
+    "for (i in 1:length(results_ind_elnet)){\n",
+    "    table_ind = rbind(table_ind, results_ind_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50            </td><td>50            </td><td>0             </td><td>14.99 ( 0.59 )</td><td>0 ( 0 )       </td><td>0.53 ( 0.03 ) </td><td>0.2           </td><td>19.99         </td><td>0.67          </td></tr>\n",
+       "\t<tr><td>100           </td><td>50            </td><td>0             </td><td>13.46 ( 0.55 )</td><td>0 ( 0 )       </td><td>0.34 ( 0.01 ) </td><td>0.23          </td><td>18.46         </td><td>0.65          </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0             </td><td>13.65 ( 0.56 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.23          </td><td>18.65         </td><td>0.64          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0             </td><td>11.76 ( 0.53 )</td><td>0 ( 0 )       </td><td>0.25 ( 0 )    </td><td>0.27          </td><td>16.76         </td><td>0.60          </td></tr>\n",
+       "\t<tr><td>50            </td><td>100           </td><td>0             </td><td>19.45 ( 0.66 )</td><td>0.04 ( 0.02 ) </td><td>0.74 ( 0.06 ) </td><td>0.19          </td><td>24.41         </td><td>0.74          </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>19.2 ( 1.02 )</td><td>0 ( 0 )      </td><td>0.38 ( 0.01 )</td><td>0.2          </td><td>24.20        </td><td>0.71         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50             & 50             & 0              & 14.99 ( 0.59 ) & 0 ( 0 )        & 0.53 ( 0.03 )  & 0.2            & 19.99          & 0.67          \\\\\n",
+       "\t 100            & 50             & 0              & 13.46 ( 0.55 ) & 0 ( 0 )        & 0.34 ( 0.01 )  & 0.23           & 18.46          & 0.65          \\\\\n",
+       "\t 500            & 50             & 0              & 13.65 ( 0.56 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.23           & 18.65          & 0.64          \\\\\n",
+       "\t 1000           & 50             & 0              & 11.76 ( 0.53 ) & 0 ( 0 )        & 0.25 ( 0 )     & 0.27           & 16.76          & 0.60          \\\\\n",
+       "\t 50             & 100            & 0              & 19.45 ( 0.66 ) & 0.04 ( 0.02 )  & 0.74 ( 0.06 )  & 0.19           & 24.41          & 0.74          \\\\\n",
+       "\t 100           & 100           & 0             & 19.2 ( 1.02 ) & 0 ( 0 )       & 0.38 ( 0.01 ) & 0.2           & 24.20         & 0.71         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50             | 50             | 0              | 14.99 ( 0.59 ) | 0 ( 0 )        | 0.53 ( 0.03 )  | 0.2            | 19.99          | 0.67           |\n",
+       "| 100            | 50             | 0              | 13.46 ( 0.55 ) | 0 ( 0 )        | 0.34 ( 0.01 )  | 0.23           | 18.46          | 0.65           |\n",
+       "| 500            | 50             | 0              | 13.65 ( 0.56 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.23           | 18.65          | 0.64           |\n",
+       "| 1000           | 50             | 0              | 11.76 ( 0.53 ) | 0 ( 0 )        | 0.25 ( 0 )     | 0.27           | 16.76          | 0.60           |\n",
+       "| 50             | 100            | 0              | 19.45 ( 0.66 ) | 0.04 ( 0.02 )  | 0.74 ( 0.06 )  | 0.19           | 24.41          | 0.74           |\n",
+       "| 100           | 100           | 0             | 19.2 ( 1.02 ) | 0 ( 0 )       | 0.38 ( 0.01 ) | 0.2           | 24.20         | 0.71          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   14.99 ( 0.59 ) 0 ( 0 )       0.53 ( 0.03 ) 0.2  19.99      0.67\n",
+       "2 100  50  0   13.46 ( 0.55 ) 0 ( 0 )       0.34 ( 0.01 ) 0.23 18.46      0.65\n",
+       "3 500  50  0   13.65 ( 0.56 ) 0 ( 0 )       0.26 ( 0 )    0.23 18.65      0.64\n",
+       "4 1000 50  0   11.76 ( 0.53 ) 0 ( 0 )       0.25 ( 0 )    0.27 16.76      0.60\n",
+       "5 50   100 0   19.45 ( 0.66 ) 0.04 ( 0.02 ) 0.74 ( 0.06 ) 0.19 24.41      0.74\n",
+       "6 100  100 0   19.2 ( 1.02 )  0 ( 0 )       0.38 ( 0.01 ) 0.2  24.20      0.71"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.20          </td><td>0.53 ( 0.03 ) </td><td>14.99 ( 0.59 )</td><td>0 ( 0 )       </td><td>19.99         </td><td>0.67          </td><td>0.53          </td><td>14.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.23          </td><td>0.34 ( 0.01 ) </td><td>13.46 ( 0.55 )</td><td>0 ( 0 )       </td><td>18.46         </td><td>0.65          </td><td>0.34          </td><td>13.4          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>13.65 ( 0.56 )</td><td>0 ( 0 )       </td><td>18.65         </td><td>0.64          </td><td>0.26          </td><td>13.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.27          </td><td>0.25 ( 0 )    </td><td>11.76 ( 0.53 )</td><td>0 ( 0 )       </td><td>16.76         </td><td>0.6           </td><td>0.25          </td><td>11.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.19          </td><td>0.74 ( 0.06 ) </td><td>19.45 ( 0.66 )</td><td>0.04 ( 0.02 ) </td><td>24.41         </td><td>0.74          </td><td>0.74          </td><td>19.4          </td><td>0.04          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.20          </td><td>0.38 ( 0.01 ) </td><td>19.2 ( 1.02 ) </td><td>0 ( 0 )       </td><td>24.20         </td><td>0.71          </td><td>0.38          </td><td>19.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.23          </td><td>0.27 ( 0 )    </td><td>16.28 ( 0.78 )</td><td>0 ( 0 )       </td><td>21.28         </td><td>0.67          </td><td>0.27          </td><td>16.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>17.05 ( 0.76 )</td><td>0 ( 0 )       </td><td>22.05         </td><td>0.69          </td><td>0.26          </td><td>17.0          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.13          </td><td>1.65 ( 0.13 ) </td><td>30.38 ( 1.24 )</td><td>0.58 ( 0.08 ) </td><td>34.80         </td><td>0.83          </td><td>1.65          </td><td>30.3          </td><td>0.58          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.15          </td><td>0.5 ( 0.02 )  </td><td>32.18 ( 1.44 )</td><td>0 ( 0 )       </td><td>37.18         </td><td>0.81          </td><td>0.50          </td><td>32.1          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.16          </td><td>0.28 ( 0 )    </td><td>29.79 ( 1.73 )</td><td>0 ( 0 )       </td><td>34.79         </td><td>0.78          </td><td>0.28          </td><td>29.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.18          </td><td>0.26 ( 0 )    </td><td>27.21 ( 1.58 )</td><td>0 ( 0 )       </td><td>32.21         </td><td>0.77          </td><td>0.26          </td><td>27.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.10          </td><td>2.5 ( 0.22 )  </td><td>36.6 ( 2.03 ) </td><td>1.12 ( 0.09 ) </td><td>40.48         </td><td>0.86          </td><td>2.50          </td><td>36.6          </td><td>1.12          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.13          </td><td>0.56 ( 0.02 ) </td><td>40.61 ( 1.71 )</td><td>0 ( 0 )       </td><td>45.61         </td><td>0.85          </td><td>0.56          </td><td>40.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.14          </td><td>0.27 ( 0 )    </td><td>35.98 ( 2.2 ) </td><td>0 ( 0 )       </td><td>40.98         </td><td>0.8           </td><td>0.27          </td><td>35.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.15          </td><td>0.27 ( 0 )    </td><td>34.65 ( 2.02 )</td><td>0 ( 0 )       </td><td>39.65         </td><td>0.79          </td><td>0.27          </td><td>34.6          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.20           & 0.53 ( 0.03 )  & 14.99 ( 0.59 ) & 0 ( 0 )        & 19.99          & 0.67           & 0.53           & 14.9           & 0.00          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.23           & 0.34 ( 0.01 )  & 13.46 ( 0.55 ) & 0 ( 0 )        & 18.46          & 0.65           & 0.34           & 13.4           & 0.00          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.23           & 0.26 ( 0 )     & 13.65 ( 0.56 ) & 0 ( 0 )        & 18.65          & 0.64           & 0.26           & 13.6           & 0.00          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.27           & 0.25 ( 0 )     & 11.76 ( 0.53 ) & 0 ( 0 )        & 16.76          & 0.6            & 0.25           & 11.7           & 0.00          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.19           & 0.74 ( 0.06 )  & 19.45 ( 0.66 ) & 0.04 ( 0.02 )  & 24.41          & 0.74           & 0.74           & 19.4           & 0.04          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.20           & 0.38 ( 0.01 )  & 19.2 ( 1.02 )  & 0 ( 0 )        & 24.20          & 0.71           & 0.38           & 19.2           & 0.00          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.23           & 0.27 ( 0 )     & 16.28 ( 0.78 ) & 0 ( 0 )        & 21.28          & 0.67           & 0.27           & 16.2           & 0.00          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.23           & 0.26 ( 0 )     & 17.05 ( 0.76 ) & 0 ( 0 )        & 22.05          & 0.69           & 0.26           & 17.0           & 0.00          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.13           & 1.65 ( 0.13 )  & 30.38 ( 1.24 ) & 0.58 ( 0.08 )  & 34.80          & 0.83           & 1.65           & 30.3           & 0.58          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.15           & 0.5 ( 0.02 )   & 32.18 ( 1.44 ) & 0 ( 0 )        & 37.18          & 0.81           & 0.50           & 32.1           & 0.00          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.16           & 0.28 ( 0 )     & 29.79 ( 1.73 ) & 0 ( 0 )        & 34.79          & 0.78           & 0.28           & 29.7           & 0.00          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.18           & 0.26 ( 0 )     & 27.21 ( 1.58 ) & 0 ( 0 )        & 32.21          & 0.77           & 0.26           & 27.2           & 0.00          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.10           & 2.5 ( 0.22 )   & 36.6 ( 2.03 )  & 1.12 ( 0.09 )  & 40.48          & 0.86           & 2.50           & 36.6           & 1.12          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.13           & 0.56 ( 0.02 )  & 40.61 ( 1.71 ) & 0 ( 0 )        & 45.61          & 0.85           & 0.56           & 40.6           & 0.00          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.14           & 0.27 ( 0 )     & 35.98 ( 2.2 )  & 0 ( 0 )        & 40.98          & 0.8            & 0.27           & 35.9           & 0.00          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.15           & 0.27 ( 0 )     & 34.65 ( 2.02 ) & 0 ( 0 )        & 39.65          & 0.79           & 0.27           & 34.6           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.20           | 0.53 ( 0.03 )  | 14.99 ( 0.59 ) | 0 ( 0 )        | 19.99          | 0.67           | 0.53           | 14.9           | 0.00           |\n",
+       "|  100           |   50           |  0.50          | 0.23           | 0.34 ( 0.01 )  | 13.46 ( 0.55 ) | 0 ( 0 )        | 18.46          | 0.65           | 0.34           | 13.4           | 0.00           |\n",
+       "|  500           |   50           |  0.10          | 0.23           | 0.26 ( 0 )     | 13.65 ( 0.56 ) | 0 ( 0 )        | 18.65          | 0.64           | 0.26           | 13.6           | 0.00           |\n",
+       "| 1000           |   50           |  0.05          | 0.27           | 0.25 ( 0 )     | 11.76 ( 0.53 ) | 0 ( 0 )        | 16.76          | 0.6            | 0.25           | 11.7           | 0.00           |\n",
+       "|   50           |  100           |  2.00          | 0.19           | 0.74 ( 0.06 )  | 19.45 ( 0.66 ) | 0.04 ( 0.02 )  | 24.41          | 0.74           | 0.74           | 19.4           | 0.04           |\n",
+       "|  100           |  100           |  1.00          | 0.20           | 0.38 ( 0.01 )  | 19.2 ( 1.02 )  | 0 ( 0 )        | 24.20          | 0.71           | 0.38           | 19.2           | 0.00           |\n",
+       "|  500           |  100           |  0.20          | 0.23           | 0.27 ( 0 )     | 16.28 ( 0.78 ) | 0 ( 0 )        | 21.28          | 0.67           | 0.27           | 16.2           | 0.00           |\n",
+       "| 1000           |  100           |  0.10          | 0.23           | 0.26 ( 0 )     | 17.05 ( 0.76 ) | 0 ( 0 )        | 22.05          | 0.69           | 0.26           | 17.0           | 0.00           |\n",
+       "|   50           |  500           | 10.00          | 0.13           | 1.65 ( 0.13 )  | 30.38 ( 1.24 ) | 0.58 ( 0.08 )  | 34.80          | 0.83           | 1.65           | 30.3           | 0.58           |\n",
+       "|  100           |  500           |  5.00          | 0.15           | 0.5 ( 0.02 )   | 32.18 ( 1.44 ) | 0 ( 0 )        | 37.18          | 0.81           | 0.50           | 32.1           | 0.00           |\n",
+       "|  500           |  500           |  1.00          | 0.16           | 0.28 ( 0 )     | 29.79 ( 1.73 ) | 0 ( 0 )        | 34.79          | 0.78           | 0.28           | 29.7           | 0.00           |\n",
+       "| 1000           |  500           |  0.50          | 0.18           | 0.26 ( 0 )     | 27.21 ( 1.58 ) | 0 ( 0 )        | 32.21          | 0.77           | 0.26           | 27.2           | 0.00           |\n",
+       "|   50           | 1000           | 20.00          | 0.10           | 2.5 ( 0.22 )   | 36.6 ( 2.03 )  | 1.12 ( 0.09 )  | 40.48          | 0.86           | 2.50           | 36.6           | 1.12           |\n",
+       "|  100           | 1000           | 10.00          | 0.13           | 0.56 ( 0.02 )  | 40.61 ( 1.71 ) | 0 ( 0 )        | 45.61          | 0.85           | 0.56           | 40.6           | 0.00           |\n",
+       "|  500           | 1000           |  2.00          | 0.14           | 0.27 ( 0 )     | 35.98 ( 2.2 )  | 0 ( 0 )        | 40.98          | 0.8            | 0.27           | 35.9           | 0.00           |\n",
+       "| 1000           | 1000           |  1.00          | 0.15           | 0.27 ( 0 )     | 34.65 ( 2.02 ) | 0 ( 0 )        | 39.65          | 0.79           | 0.27           | 34.6           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN            num_select\n",
+       "1    50   50  1.00 0.20 0.53 ( 0.03 ) 14.99 ( 0.59 ) 0 ( 0 )       19.99     \n",
+       "2   100   50  0.50 0.23 0.34 ( 0.01 ) 13.46 ( 0.55 ) 0 ( 0 )       18.46     \n",
+       "3   500   50  0.10 0.23 0.26 ( 0 )    13.65 ( 0.56 ) 0 ( 0 )       18.65     \n",
+       "4  1000   50  0.05 0.27 0.25 ( 0 )    11.76 ( 0.53 ) 0 ( 0 )       16.76     \n",
+       "5    50  100  2.00 0.19 0.74 ( 0.06 ) 19.45 ( 0.66 ) 0.04 ( 0.02 ) 24.41     \n",
+       "6   100  100  1.00 0.20 0.38 ( 0.01 ) 19.2 ( 1.02 )  0 ( 0 )       24.20     \n",
+       "7   500  100  0.20 0.23 0.27 ( 0 )    16.28 ( 0.78 ) 0 ( 0 )       21.28     \n",
+       "8  1000  100  0.10 0.23 0.26 ( 0 )    17.05 ( 0.76 ) 0 ( 0 )       22.05     \n",
+       "9    50  500 10.00 0.13 1.65 ( 0.13 ) 30.38 ( 1.24 ) 0.58 ( 0.08 ) 34.80     \n",
+       "10  100  500  5.00 0.15 0.5 ( 0.02 )  32.18 ( 1.44 ) 0 ( 0 )       37.18     \n",
+       "11  500  500  1.00 0.16 0.28 ( 0 )    29.79 ( 1.73 ) 0 ( 0 )       34.79     \n",
+       "12 1000  500  0.50 0.18 0.26 ( 0 )    27.21 ( 1.58 ) 0 ( 0 )       32.21     \n",
+       "13   50 1000 20.00 0.10 2.5 ( 0.22 )  36.6 ( 2.03 )  1.12 ( 0.09 ) 40.48     \n",
+       "14  100 1000 10.00 0.13 0.56 ( 0.02 ) 40.61 ( 1.71 ) 0 ( 0 )       45.61     \n",
+       "15  500 1000  2.00 0.14 0.27 ( 0 )    35.98 ( 2.2 )  0 ( 0 )       40.98     \n",
+       "16 1000 1000  1.00 0.15 0.27 ( 0 )    34.65 ( 2.02 ) 0 ( 0 )       39.65     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.67 0.53     14.9    0.00   \n",
+       "2  0.65 0.34     13.4    0.00   \n",
+       "3  0.64 0.26     13.6    0.00   \n",
+       "4  0.6  0.25     11.7    0.00   \n",
+       "5  0.74 0.74     19.4    0.04   \n",
+       "6  0.71 0.38     19.2    0.00   \n",
+       "7  0.67 0.27     16.2    0.00   \n",
+       "8  0.69 0.26     17.0    0.00   \n",
+       "9  0.83 1.65     30.3    0.58   \n",
+       "10 0.81 0.50     32.1    0.00   \n",
+       "11 0.78 0.28     29.7    0.00   \n",
+       "12 0.77 0.26     27.2    0.00   \n",
+       "13 0.86 2.50     36.6    1.12   \n",
+       "14 0.85 0.56     40.6    0.00   \n",
+       "15 0.8  0.27     35.9    0.00   \n",
+       "16 0.79 0.27     34.6    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/0.3_sim_indepdent_rf_cts.ipynb b/simulations/notebooks_sim_cts/0.3_sim_indepdent_rf_cts.ipynb
new file mode 100755
index 0000000..337fe47
--- /dev/null
+++ b/simulations/notebooks_sim_cts/0.3_sim_indepdent_rf_cts.ipynb
@@ -0,0 +1,517 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_rf))\n",
+    "for (i in 1:length(results_ind_rf)){\n",
+    "    results_ind_rf[[i]]$OOB = paste(round(mean(results_ind_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_ind_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_ind = rbind(table_ind, results_ind_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.31 ( 0.06 )</td><td>NaN          </td><td>2.09 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>1.85 ( 0.11 )</td><td>4.11 ( 0.1 ) </td><td>0.85 ( 0.02 )</td><td>0.18         </td><td>1.79 ( 0.01 )</td><td>3.74         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>0.3 ( 0.05 ) </td><td>1.61 ( 0.08 )</td><td>0.47 ( 0.01 )</td><td>0.8          </td><td>1.31 ( 0 )   </td><td>4.69         </td><td>0.05         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>0.07 ( 0.03 )</td><td>0.78 ( 0.07 )</td><td>0.35 ( 0 )   </td><td>0.88         </td><td>1.17 ( 0 )   </td><td>5.29         </td><td>0.01         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.32 ( 0.05 )</td><td>NaN          </td><td>2.23 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>4.33 ( 0.22 )</td><td>4.45 ( 0.09 )</td><td>0.91 ( 0.03 )</td><td>0.08         </td><td>2.02 ( 0.01 )</td><td>5.88         </td><td>0.72         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 1 ( 0 )       & 6 ( 0 )       & 1.31 ( 0.06 ) & NaN           & 2.09 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0             & 1.85 ( 0.11 ) & 4.11 ( 0.1 )  & 0.85 ( 0.02 ) & 0.18          & 1.79 ( 0.01 ) & 3.74          & 0.48         \\\\\n",
+       "\t 500           & 50            & 0             & 0.3 ( 0.05 )  & 1.61 ( 0.08 ) & 0.47 ( 0.01 ) & 0.8           & 1.31 ( 0 )    & 4.69          & 0.05         \\\\\n",
+       "\t 1000          & 50            & 0             & 0.07 ( 0.03 ) & 0.78 ( 0.07 ) & 0.35 ( 0 )    & 0.88          & 1.17 ( 0 )    & 5.29          & 0.01         \\\\\n",
+       "\t 50            & 100           & 0             & 1 ( 0 )       & 6 ( 0 )       & 1.32 ( 0.05 ) & NaN           & 2.23 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 100           & 0             & 4.33 ( 0.22 ) & 4.45 ( 0.09 ) & 0.91 ( 0.03 ) & 0.08          & 2.02 ( 0.01 ) & 5.88          & 0.72         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 1 ( 0 )       | 6 ( 0 )       | 1.31 ( 0.06 ) | NaN           | 2.09 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0             | 1.85 ( 0.11 ) | 4.11 ( 0.1 )  | 0.85 ( 0.02 ) | 0.18          | 1.79 ( 0.01 ) | 3.74          | 0.48          |\n",
+       "| 500           | 50            | 0             | 0.3 ( 0.05 )  | 1.61 ( 0.08 ) | 0.47 ( 0.01 ) | 0.8           | 1.31 ( 0 )    | 4.69          | 0.05          |\n",
+       "| 1000          | 50            | 0             | 0.07 ( 0.03 ) | 0.78 ( 0.07 ) | 0.35 ( 0 )    | 0.88          | 1.17 ( 0 )    | 5.29          | 0.01          |\n",
+       "| 50            | 100           | 0             | 1 ( 0 )       | 6 ( 0 )       | 1.32 ( 0.05 ) | NaN           | 2.23 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 100           | 0             | 4.33 ( 0.22 ) | 4.45 ( 0.09 ) | 0.91 ( 0.03 ) | 0.08          | 2.02 ( 0.01 ) | 5.88          | 0.72          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab OOB          \n",
+       "1 50   50  0   1 ( 0 )       6 ( 0 )       1.31 ( 0.06 ) NaN  2.09 ( 0.02 )\n",
+       "2 100  50  0   1.85 ( 0.11 ) 4.11 ( 0.1 )  0.85 ( 0.02 ) 0.18 1.79 ( 0.01 )\n",
+       "3 500  50  0   0.3 ( 0.05 )  1.61 ( 0.08 ) 0.47 ( 0.01 ) 0.8  1.31 ( 0 )   \n",
+       "4 1000 50  0   0.07 ( 0.03 ) 0.78 ( 0.07 ) 0.35 ( 0 )    0.88 1.17 ( 0 )   \n",
+       "5 50   100 0   1 ( 0 )       6 ( 0 )       1.32 ( 0.05 ) NaN  2.23 ( 0.02 )\n",
+       "6 100  100 0   4.33 ( 0.22 ) 4.45 ( 0.09 ) 0.91 ( 0.03 ) 0.08 2.02 ( 0.01 )\n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.74       0.48\n",
+       "3 4.69       0.05\n",
+       "4 5.29       0.01\n",
+       "5 0.00        NaN\n",
+       "6 5.88       0.72"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')\n",
+    "result.table_ind$Index = seq(1, length(results_ind_rf), 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$OOB_mean = as.numeric(substr(result.table_ind$OOB, start=1, stop=4))\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))\n",
+    "#result.table_ind$FDR = as.numeric(as.character(result.table_ind$FDR))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>Index</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td> NaN          </td><td>1.31 ( 0.06 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.09 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 1            </td><td>1.31          </td><td>   NA         </td><td>  NA          </td><td>2.09          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.18          </td><td>0.85 ( 0.02 ) </td><td>1.85 ( 0.11 ) </td><td>4.11 ( 0.1 )  </td><td>1.79 ( 0.01 ) </td><td> 3.74         </td><td>0.48          </td><td> 2            </td><td>0.85          </td><td> 1.85         </td><td>4.11          </td><td>1.79          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.80          </td><td>0.47 ( 0.01 ) </td><td>0.3 ( 0.05 )  </td><td>1.61 ( 0.08 ) </td><td>1.31 ( 0 )    </td><td> 4.69         </td><td>0.05          </td><td> 3            </td><td>0.47          </td><td> 0.30         </td><td>1.61          </td><td>1.31          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.88          </td><td>0.35 ( 0 )    </td><td>0.07 ( 0.03 ) </td><td>0.78 ( 0.07 ) </td><td>1.17 ( 0 )    </td><td> 5.29         </td><td>0.01          </td><td> 4            </td><td>0.35          </td><td> 0.07         </td><td>0.78          </td><td>1.17          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td> NaN          </td><td>1.32 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.23 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 5            </td><td>1.32          </td><td>   NA         </td><td>  NA          </td><td>2.23          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.08          </td><td>0.91 ( 0.03 ) </td><td>4.33 ( 0.22 ) </td><td>4.45 ( 0.09 ) </td><td>2.02 ( 0.01 ) </td><td> 5.88         </td><td>0.72          </td><td> 6            </td><td>0.91          </td><td> 4.33         </td><td>4.45          </td><td>2.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.60          </td><td>0.59 ( 0.01 ) </td><td>1.91 ( 0.15 ) </td><td>1.41 ( 0.07 ) </td><td>1.54 ( 0 )    </td><td> 6.50         </td><td>0.27          </td><td> 7            </td><td>0.59          </td><td> 1.91         </td><td>1.41          </td><td>1.54          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.81          </td><td>0.47 ( 0 )    </td><td>0.71 ( 0.09 ) </td><td>0.53 ( 0.06 ) </td><td>1.39 ( 0 )    </td><td> 6.18         </td><td>0.1           </td><td> 8            </td><td>0.47          </td><td> 0.71         </td><td>0.53          </td><td>1.39          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td> NaN          </td><td>1.33 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td> 9            </td><td>1.33          </td><td>   NA         </td><td>  NA          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.02          </td><td>1.07 ( 0.03 ) </td><td>23.8 ( 0.52 ) </td><td>4.26 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>25.54         </td><td>0.93          </td><td>10            </td><td>1.07          </td><td>23.80         </td><td>4.26          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.14          </td><td>0.84 ( 0.01 ) </td><td>19.92 ( 0.41 )</td><td>1.71 ( 0.08 ) </td><td>2.14 ( 0.01 ) </td><td>24.21         </td><td>0.82          </td><td>11            </td><td>0.84          </td><td>19.90         </td><td>1.71          </td><td>2.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.19          </td><td>0.78 ( 0.01 ) </td><td>18.73 ( 0.42 )</td><td>0.84 ( 0.06 ) </td><td>2.06 ( 0 )    </td><td>23.89         </td><td>0.78          </td><td>12            </td><td>0.78          </td><td>18.70         </td><td>0.84          </td><td>2.06          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td> NaN          </td><td>1.31 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td>13            </td><td>1.31          </td><td>   NA         </td><td>  NA          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.01          </td><td>1.06 ( 0.03 ) </td><td>48.48 ( 0.63 )</td><td>4.33 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>50.15         </td><td>0.97          </td><td>14            </td><td>1.06          </td><td>48.40         </td><td>4.33          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.06          </td><td>0.89 ( 0.01 ) </td><td>44.77 ( 0.71 )</td><td>1.81 ( 0.1 )  </td><td>2.3 ( 0.01 )  </td><td>48.96         </td><td>0.91          </td><td>15            </td><td>0.89          </td><td>44.70         </td><td>1.81          </td><td>2.30          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.09          </td><td>0.85 ( 0.01 ) </td><td>41.77 ( 0.68 )</td><td>0.99 ( 0.07 ) </td><td>2.25 ( 0.01 ) </td><td>46.78         </td><td>0.89          </td><td>16            </td><td>0.85          </td><td>41.70         </td><td>0.99          </td><td>2.25          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & Index & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          &  NaN           & 1.31 ( 0.06 )  & 1 ( 0 )        & 6 ( 0 )        & 2.09 ( 0.02 )  &  0.00          & NaN            &  1             & 1.31           &    NA          &   NA           & 2.09          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.18           & 0.85 ( 0.02 )  & 1.85 ( 0.11 )  & 4.11 ( 0.1 )   & 1.79 ( 0.01 )  &  3.74          & 0.48           &  2             & 0.85           &  1.85          & 4.11           & 1.79          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.80           & 0.47 ( 0.01 )  & 0.3 ( 0.05 )   & 1.61 ( 0.08 )  & 1.31 ( 0 )     &  4.69          & 0.05           &  3             & 0.47           &  0.30          & 1.61           & 1.31          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.88           & 0.35 ( 0 )     & 0.07 ( 0.03 )  & 0.78 ( 0.07 )  & 1.17 ( 0 )     &  5.29          & 0.01           &  4             & 0.35           &  0.07          & 0.78           & 1.17          \\\\\n",
+       "\t   50           &  100           &  2.00          &  NaN           & 1.32 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.23 ( 0.02 )  &  0.00          & NaN            &  5             & 1.32           &    NA          &   NA           & 2.23          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.08           & 0.91 ( 0.03 )  & 4.33 ( 0.22 )  & 4.45 ( 0.09 )  & 2.02 ( 0.01 )  &  5.88          & 0.72           &  6             & 0.91           &  4.33          & 4.45           & 2.02          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.60           & 0.59 ( 0.01 )  & 1.91 ( 0.15 )  & 1.41 ( 0.07 )  & 1.54 ( 0 )     &  6.50          & 0.27           &  7             & 0.59           &  1.91          & 1.41           & 1.54          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.81           & 0.47 ( 0 )     & 0.71 ( 0.09 )  & 0.53 ( 0.06 )  & 1.39 ( 0 )     &  6.18          & 0.1            &  8             & 0.47           &  0.71          & 0.53           & 1.39          \\\\\n",
+       "\t   50           &  500           & 10.00          &  NaN           & 1.33 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            &  9             & 1.33           &    NA          &   NA           & 2.48          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.02           & 1.07 ( 0.03 )  & 23.8 ( 0.52 )  & 4.26 ( 0.1 )   & 2.43 ( 0.02 )  & 25.54          & 0.93           & 10             & 1.07           & 23.80          & 4.26           & 2.43          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.14           & 0.84 ( 0.01 )  & 19.92 ( 0.41 ) & 1.71 ( 0.08 )  & 2.14 ( 0.01 )  & 24.21          & 0.82           & 11             & 0.84           & 19.90          & 1.71           & 2.14          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.19           & 0.78 ( 0.01 )  & 18.73 ( 0.42 ) & 0.84 ( 0.06 )  & 2.06 ( 0 )     & 23.89          & 0.78           & 12             & 0.78           & 18.70          & 0.84           & 2.06          \\\\\n",
+       "\t   50           & 1000           & 20.00          &  NaN           & 1.31 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            & 13             & 1.31           &    NA          &   NA           & 2.48          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.01           & 1.06 ( 0.03 )  & 48.48 ( 0.63 ) & 4.33 ( 0.1 )   & 2.43 ( 0.02 )  & 50.15          & 0.97           & 14             & 1.06           & 48.40          & 4.33           & 2.43          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.06           & 0.89 ( 0.01 )  & 44.77 ( 0.71 ) & 1.81 ( 0.1 )   & 2.3 ( 0.01 )   & 48.96          & 0.91           & 15             & 0.89           & 44.70          & 1.81           & 2.30          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.09           & 0.85 ( 0.01 )  & 41.77 ( 0.68 ) & 0.99 ( 0.07 )  & 2.25 ( 0.01 )  & 46.78          & 0.89           & 16             & 0.85           & 41.70          & 0.99           & 2.25          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | Index | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          |  NaN           | 1.31 ( 0.06 )  | 1 ( 0 )        | 6 ( 0 )        | 2.09 ( 0.02 )  |  0.00          | NaN            |  1             | 1.31           |    NA          |   NA           | 2.09           |\n",
+       "|  100           |   50           |  0.50          | 0.18           | 0.85 ( 0.02 )  | 1.85 ( 0.11 )  | 4.11 ( 0.1 )   | 1.79 ( 0.01 )  |  3.74          | 0.48           |  2             | 0.85           |  1.85          | 4.11           | 1.79           |\n",
+       "|  500           |   50           |  0.10          | 0.80           | 0.47 ( 0.01 )  | 0.3 ( 0.05 )   | 1.61 ( 0.08 )  | 1.31 ( 0 )     |  4.69          | 0.05           |  3             | 0.47           |  0.30          | 1.61           | 1.31           |\n",
+       "| 1000           |   50           |  0.05          | 0.88           | 0.35 ( 0 )     | 0.07 ( 0.03 )  | 0.78 ( 0.07 )  | 1.17 ( 0 )     |  5.29          | 0.01           |  4             | 0.35           |  0.07          | 0.78           | 1.17           |\n",
+       "|   50           |  100           |  2.00          |  NaN           | 1.32 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.23 ( 0.02 )  |  0.00          | NaN            |  5             | 1.32           |    NA          |   NA           | 2.23           |\n",
+       "|  100           |  100           |  1.00          | 0.08           | 0.91 ( 0.03 )  | 4.33 ( 0.22 )  | 4.45 ( 0.09 )  | 2.02 ( 0.01 )  |  5.88          | 0.72           |  6             | 0.91           |  4.33          | 4.45           | 2.02           |\n",
+       "|  500           |  100           |  0.20          | 0.60           | 0.59 ( 0.01 )  | 1.91 ( 0.15 )  | 1.41 ( 0.07 )  | 1.54 ( 0 )     |  6.50          | 0.27           |  7             | 0.59           |  1.91          | 1.41           | 1.54           |\n",
+       "| 1000           |  100           |  0.10          | 0.81           | 0.47 ( 0 )     | 0.71 ( 0.09 )  | 0.53 ( 0.06 )  | 1.39 ( 0 )     |  6.18          | 0.1            |  8             | 0.47           |  0.71          | 0.53           | 1.39           |\n",
+       "|   50           |  500           | 10.00          |  NaN           | 1.33 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            |  9             | 1.33           |    NA          |   NA           | 2.48           |\n",
+       "|  100           |  500           |  5.00          | 0.02           | 1.07 ( 0.03 )  | 23.8 ( 0.52 )  | 4.26 ( 0.1 )   | 2.43 ( 0.02 )  | 25.54          | 0.93           | 10             | 1.07           | 23.80          | 4.26           | 2.43           |\n",
+       "|  500           |  500           |  1.00          | 0.14           | 0.84 ( 0.01 )  | 19.92 ( 0.41 ) | 1.71 ( 0.08 )  | 2.14 ( 0.01 )  | 24.21          | 0.82           | 11             | 0.84           | 19.90          | 1.71           | 2.14           |\n",
+       "| 1000           |  500           |  0.50          | 0.19           | 0.78 ( 0.01 )  | 18.73 ( 0.42 ) | 0.84 ( 0.06 )  | 2.06 ( 0 )     | 23.89          | 0.78           | 12             | 0.78           | 18.70          | 0.84           | 2.06           |\n",
+       "|   50           | 1000           | 20.00          |  NaN           | 1.31 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            | 13             | 1.31           |    NA          |   NA           | 2.48           |\n",
+       "|  100           | 1000           | 10.00          | 0.01           | 1.06 ( 0.03 )  | 48.48 ( 0.63 ) | 4.33 ( 0.1 )   | 2.43 ( 0.02 )  | 50.15          | 0.97           | 14             | 1.06           | 48.40          | 4.33           | 2.43           |\n",
+       "|  500           | 1000           |  2.00          | 0.06           | 0.89 ( 0.01 )  | 44.77 ( 0.71 ) | 1.81 ( 0.1 )   | 2.3 ( 0.01 )   | 48.96          | 0.91           | 15             | 0.89           | 44.70          | 1.81           | 2.30           |\n",
+       "| 1000           | 1000           |  1.00          | 0.09           | 0.85 ( 0.01 )  | 41.77 ( 0.68 ) | 0.99 ( 0.07 )  | 2.25 ( 0.01 )  | 46.78          | 0.89           | 16             | 0.85           | 41.70          | 0.99           | 2.25           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN           \n",
+       "1    50   50  1.00  NaN 1.31 ( 0.06 ) 1 ( 0 )        6 ( 0 )      \n",
+       "2   100   50  0.50 0.18 0.85 ( 0.02 ) 1.85 ( 0.11 )  4.11 ( 0.1 ) \n",
+       "3   500   50  0.10 0.80 0.47 ( 0.01 ) 0.3 ( 0.05 )   1.61 ( 0.08 )\n",
+       "4  1000   50  0.05 0.88 0.35 ( 0 )    0.07 ( 0.03 )  0.78 ( 0.07 )\n",
+       "5    50  100  2.00  NaN 1.32 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "6   100  100  1.00 0.08 0.91 ( 0.03 ) 4.33 ( 0.22 )  4.45 ( 0.09 )\n",
+       "7   500  100  0.20 0.60 0.59 ( 0.01 ) 1.91 ( 0.15 )  1.41 ( 0.07 )\n",
+       "8  1000  100  0.10 0.81 0.47 ( 0 )    0.71 ( 0.09 )  0.53 ( 0.06 )\n",
+       "9    50  500 10.00  NaN 1.33 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "10  100  500  5.00 0.02 1.07 ( 0.03 ) 23.8 ( 0.52 )  4.26 ( 0.1 ) \n",
+       "11  500  500  1.00 0.14 0.84 ( 0.01 ) 19.92 ( 0.41 ) 1.71 ( 0.08 )\n",
+       "12 1000  500  0.50 0.19 0.78 ( 0.01 ) 18.73 ( 0.42 ) 0.84 ( 0.06 )\n",
+       "13   50 1000 20.00  NaN 1.31 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "14  100 1000 10.00 0.01 1.06 ( 0.03 ) 48.48 ( 0.63 ) 4.33 ( 0.1 ) \n",
+       "15  500 1000  2.00 0.06 0.89 ( 0.01 ) 44.77 ( 0.71 ) 1.81 ( 0.1 ) \n",
+       "16 1000 1000  1.00 0.09 0.85 ( 0.01 ) 41.77 ( 0.68 ) 0.99 ( 0.07 )\n",
+       "   OOB           num_select FDR  Index MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  2.09 ( 0.02 )  0.00      NaN   1    1.31        NA     NA    2.09    \n",
+       "2  1.79 ( 0.01 )  3.74      0.48  2    0.85      1.85   4.11    1.79    \n",
+       "3  1.31 ( 0 )     4.69      0.05  3    0.47      0.30   1.61    1.31    \n",
+       "4  1.17 ( 0 )     5.29      0.01  4    0.35      0.07   0.78    1.17    \n",
+       "5  2.23 ( 0.02 )  0.00      NaN   5    1.32        NA     NA    2.23    \n",
+       "6  2.02 ( 0.01 )  5.88      0.72  6    0.91      4.33   4.45    2.02    \n",
+       "7  1.54 ( 0 )     6.50      0.27  7    0.59      1.91   1.41    1.54    \n",
+       "8  1.39 ( 0 )     6.18      0.1   8    0.47      0.71   0.53    1.39    \n",
+       "9  2.48 ( 0.03 )  0.00      NaN   9    1.33        NA     NA    2.48    \n",
+       "10 2.43 ( 0.02 ) 25.54      0.93 10    1.07     23.80   4.26    2.43    \n",
+       "11 2.14 ( 0.01 ) 24.21      0.82 11    0.84     19.90   1.71    2.14    \n",
+       "12 2.06 ( 0 )    23.89      0.78 12    0.78     18.70   0.84    2.06    \n",
+       "13 2.48 ( 0.03 )  0.00      NaN  13    1.31        NA     NA    2.48    \n",
+       "14 2.43 ( 0.02 ) 50.15      0.97 14    1.06     48.40   4.33    2.43    \n",
+       "15 2.3 ( 0.01 )  48.96      0.91 15    0.89     44.70   1.81    2.30    \n",
+       "16 2.25 ( 0.01 ) 46.78      0.89 16    0.85     41.70   0.99    2.25    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] 0\n",
+      "[1] 0\n",
+      "[1] 0\n",
+      "[1] 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "## nothing was selected when Stab is NaN\n",
+    "for (i in c(1,5,9,13)){\n",
+    "    print(sum(results_ind_rf[[i]]$Stab.table))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>Index</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td> 1.00         </td><td>0.00          </td><td>1.31 ( 0.06 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.09 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 1            </td><td>1.31          </td><td> 1.00         </td><td>6.00          </td><td>2.09          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td> 0.50         </td><td>0.18          </td><td>0.85 ( 0.02 ) </td><td>1.85 ( 0.11 ) </td><td>4.11 ( 0.1 )  </td><td>1.79 ( 0.01 ) </td><td> 3.74         </td><td>0.48          </td><td> 2            </td><td>0.85          </td><td> 1.85         </td><td>4.11          </td><td>1.79          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td> 0.10         </td><td>0.80          </td><td>0.47 ( 0.01 ) </td><td>0.3 ( 0.05 )  </td><td>1.61 ( 0.08 ) </td><td>1.31 ( 0 )    </td><td> 4.69         </td><td>0.05          </td><td> 3            </td><td>0.47          </td><td> 0.30         </td><td>1.61          </td><td>1.31          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td> 0.05         </td><td>0.88          </td><td>0.35 ( 0 )    </td><td>0.07 ( 0.03 ) </td><td>0.78 ( 0.07 ) </td><td>1.17 ( 0 )    </td><td> 5.29         </td><td>0.01          </td><td> 4            </td><td>0.35          </td><td> 0.07         </td><td>0.78          </td><td>1.17          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td> 2.00         </td><td>0.00          </td><td>1.32 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.23 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td> 5            </td><td>1.32          </td><td> 1.00         </td><td>6.00          </td><td>2.23          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td> 1.00         </td><td>0.08          </td><td>0.91 ( 0.03 ) </td><td>4.33 ( 0.22 ) </td><td>4.45 ( 0.09 ) </td><td>2.02 ( 0.01 ) </td><td> 5.88         </td><td>0.72          </td><td> 6            </td><td>0.91          </td><td> 4.33         </td><td>4.45          </td><td>2.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td> 0.20         </td><td>0.60          </td><td>0.59 ( 0.01 ) </td><td>1.91 ( 0.15 ) </td><td>1.41 ( 0.07 ) </td><td>1.54 ( 0 )    </td><td> 6.50         </td><td>0.27          </td><td> 7            </td><td>0.59          </td><td> 1.91         </td><td>1.41          </td><td>1.54          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td> 0.10         </td><td>0.81          </td><td>0.47 ( 0 )    </td><td>0.71 ( 0.09 ) </td><td>0.53 ( 0.06 ) </td><td>1.39 ( 0 )    </td><td> 6.18         </td><td>0.1           </td><td> 8            </td><td>0.47          </td><td> 0.71         </td><td>0.53          </td><td>1.39          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>10.00         </td><td>0.00          </td><td>1.33 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td> 9            </td><td>1.33          </td><td> 1.00         </td><td>6.00          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td> 5.00         </td><td>0.02          </td><td>1.07 ( 0.03 ) </td><td>23.8 ( 0.52 ) </td><td>4.26 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>25.54         </td><td>0.93          </td><td>10            </td><td>1.07          </td><td>23.80         </td><td>4.26          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td> 1.00         </td><td>0.14          </td><td>0.84 ( 0.01 ) </td><td>19.92 ( 0.41 )</td><td>1.71 ( 0.08 ) </td><td>2.14 ( 0.01 ) </td><td>24.21         </td><td>0.82          </td><td>11            </td><td>0.84          </td><td>19.90         </td><td>1.71          </td><td>2.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td> 0.50         </td><td>0.19          </td><td>0.78 ( 0.01 ) </td><td>18.73 ( 0.42 )</td><td>0.84 ( 0.06 ) </td><td>2.06 ( 0 )    </td><td>23.89         </td><td>0.78          </td><td>12            </td><td>0.78          </td><td>18.70         </td><td>0.84          </td><td>2.06          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>20.00         </td><td>0.00          </td><td>1.31 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.48 ( 0.03 ) </td><td> 0.00         </td><td>NaN           </td><td>13            </td><td>1.31          </td><td> 1.00         </td><td>6.00          </td><td>2.48          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>10.00         </td><td>0.01          </td><td>1.06 ( 0.03 ) </td><td>48.48 ( 0.63 )</td><td>4.33 ( 0.1 )  </td><td>2.43 ( 0.02 ) </td><td>50.15         </td><td>0.97          </td><td>14            </td><td>1.06          </td><td>48.40         </td><td>4.33          </td><td>2.43          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td> 2.00         </td><td>0.06          </td><td>0.89 ( 0.01 ) </td><td>44.77 ( 0.71 )</td><td>1.81 ( 0.1 )  </td><td>2.3 ( 0.01 )  </td><td>48.96         </td><td>0.91          </td><td>15            </td><td>0.89          </td><td>44.70         </td><td>1.81          </td><td>2.30          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td> 1.00         </td><td>0.09          </td><td>0.85 ( 0.01 ) </td><td>41.77 ( 0.68 )</td><td>0.99 ( 0.07 ) </td><td>2.25 ( 0.01 ) </td><td>46.78         </td><td>0.89          </td><td>16            </td><td>0.85          </td><td>41.70         </td><td>0.99          </td><td>2.25          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & Index & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           &  1.00          & 0.00           & 1.31 ( 0.06 )  & 1 ( 0 )        & 6 ( 0 )        & 2.09 ( 0.02 )  &  0.00          & NaN            &  1             & 1.31           &  1.00          & 6.00           & 2.09          \\\\\n",
+       "\t  100           &   50           &  0.50          & 0.18           & 0.85 ( 0.02 )  & 1.85 ( 0.11 )  & 4.11 ( 0.1 )   & 1.79 ( 0.01 )  &  3.74          & 0.48           &  2             & 0.85           &  1.85          & 4.11           & 1.79          \\\\\n",
+       "\t  500           &   50           &  0.10          & 0.80           & 0.47 ( 0.01 )  & 0.3 ( 0.05 )   & 1.61 ( 0.08 )  & 1.31 ( 0 )     &  4.69          & 0.05           &  3             & 0.47           &  0.30          & 1.61           & 1.31          \\\\\n",
+       "\t 1000           &   50           &  0.05          & 0.88           & 0.35 ( 0 )     & 0.07 ( 0.03 )  & 0.78 ( 0.07 )  & 1.17 ( 0 )     &  5.29          & 0.01           &  4             & 0.35           &  0.07          & 0.78           & 1.17          \\\\\n",
+       "\t   50           &  100           &  2.00          & 0.00           & 1.32 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.23 ( 0.02 )  &  0.00          & NaN            &  5             & 1.32           &  1.00          & 6.00           & 2.23          \\\\\n",
+       "\t  100           &  100           &  1.00          & 0.08           & 0.91 ( 0.03 )  & 4.33 ( 0.22 )  & 4.45 ( 0.09 )  & 2.02 ( 0.01 )  &  5.88          & 0.72           &  6             & 0.91           &  4.33          & 4.45           & 2.02          \\\\\n",
+       "\t  500           &  100           &  0.20          & 0.60           & 0.59 ( 0.01 )  & 1.91 ( 0.15 )  & 1.41 ( 0.07 )  & 1.54 ( 0 )     &  6.50          & 0.27           &  7             & 0.59           &  1.91          & 1.41           & 1.54          \\\\\n",
+       "\t 1000           &  100           &  0.10          & 0.81           & 0.47 ( 0 )     & 0.71 ( 0.09 )  & 0.53 ( 0.06 )  & 1.39 ( 0 )     &  6.18          & 0.1            &  8             & 0.47           &  0.71          & 0.53           & 1.39          \\\\\n",
+       "\t   50           &  500           & 10.00          & 0.00           & 1.33 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            &  9             & 1.33           &  1.00          & 6.00           & 2.48          \\\\\n",
+       "\t  100           &  500           &  5.00          & 0.02           & 1.07 ( 0.03 )  & 23.8 ( 0.52 )  & 4.26 ( 0.1 )   & 2.43 ( 0.02 )  & 25.54          & 0.93           & 10             & 1.07           & 23.80          & 4.26           & 2.43          \\\\\n",
+       "\t  500           &  500           &  1.00          & 0.14           & 0.84 ( 0.01 )  & 19.92 ( 0.41 ) & 1.71 ( 0.08 )  & 2.14 ( 0.01 )  & 24.21          & 0.82           & 11             & 0.84           & 19.90          & 1.71           & 2.14          \\\\\n",
+       "\t 1000           &  500           &  0.50          & 0.19           & 0.78 ( 0.01 )  & 18.73 ( 0.42 ) & 0.84 ( 0.06 )  & 2.06 ( 0 )     & 23.89          & 0.78           & 12             & 0.78           & 18.70          & 0.84           & 2.06          \\\\\n",
+       "\t   50           & 1000           & 20.00          & 0.00           & 1.31 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.48 ( 0.03 )  &  0.00          & NaN            & 13             & 1.31           &  1.00          & 6.00           & 2.48          \\\\\n",
+       "\t  100           & 1000           & 10.00          & 0.01           & 1.06 ( 0.03 )  & 48.48 ( 0.63 ) & 4.33 ( 0.1 )   & 2.43 ( 0.02 )  & 50.15          & 0.97           & 14             & 1.06           & 48.40          & 4.33           & 2.43          \\\\\n",
+       "\t  500           & 1000           &  2.00          & 0.06           & 0.89 ( 0.01 )  & 44.77 ( 0.71 ) & 1.81 ( 0.1 )   & 2.3 ( 0.01 )   & 48.96          & 0.91           & 15             & 0.89           & 44.70          & 1.81           & 2.30          \\\\\n",
+       "\t 1000           & 1000           &  1.00          & 0.09           & 0.85 ( 0.01 )  & 41.77 ( 0.68 ) & 0.99 ( 0.07 )  & 2.25 ( 0.01 )  & 46.78          & 0.89           & 16             & 0.85           & 41.70          & 0.99           & 2.25          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | Index | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           |  1.00          | 0.00           | 1.31 ( 0.06 )  | 1 ( 0 )        | 6 ( 0 )        | 2.09 ( 0.02 )  |  0.00          | NaN            |  1             | 1.31           |  1.00          | 6.00           | 2.09           |\n",
+       "|  100           |   50           |  0.50          | 0.18           | 0.85 ( 0.02 )  | 1.85 ( 0.11 )  | 4.11 ( 0.1 )   | 1.79 ( 0.01 )  |  3.74          | 0.48           |  2             | 0.85           |  1.85          | 4.11           | 1.79           |\n",
+       "|  500           |   50           |  0.10          | 0.80           | 0.47 ( 0.01 )  | 0.3 ( 0.05 )   | 1.61 ( 0.08 )  | 1.31 ( 0 )     |  4.69          | 0.05           |  3             | 0.47           |  0.30          | 1.61           | 1.31           |\n",
+       "| 1000           |   50           |  0.05          | 0.88           | 0.35 ( 0 )     | 0.07 ( 0.03 )  | 0.78 ( 0.07 )  | 1.17 ( 0 )     |  5.29          | 0.01           |  4             | 0.35           |  0.07          | 0.78           | 1.17           |\n",
+       "|   50           |  100           |  2.00          | 0.00           | 1.32 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.23 ( 0.02 )  |  0.00          | NaN            |  5             | 1.32           |  1.00          | 6.00           | 2.23           |\n",
+       "|  100           |  100           |  1.00          | 0.08           | 0.91 ( 0.03 )  | 4.33 ( 0.22 )  | 4.45 ( 0.09 )  | 2.02 ( 0.01 )  |  5.88          | 0.72           |  6             | 0.91           |  4.33          | 4.45           | 2.02           |\n",
+       "|  500           |  100           |  0.20          | 0.60           | 0.59 ( 0.01 )  | 1.91 ( 0.15 )  | 1.41 ( 0.07 )  | 1.54 ( 0 )     |  6.50          | 0.27           |  7             | 0.59           |  1.91          | 1.41           | 1.54           |\n",
+       "| 1000           |  100           |  0.10          | 0.81           | 0.47 ( 0 )     | 0.71 ( 0.09 )  | 0.53 ( 0.06 )  | 1.39 ( 0 )     |  6.18          | 0.1            |  8             | 0.47           |  0.71          | 0.53           | 1.39           |\n",
+       "|   50           |  500           | 10.00          | 0.00           | 1.33 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            |  9             | 1.33           |  1.00          | 6.00           | 2.48           |\n",
+       "|  100           |  500           |  5.00          | 0.02           | 1.07 ( 0.03 )  | 23.8 ( 0.52 )  | 4.26 ( 0.1 )   | 2.43 ( 0.02 )  | 25.54          | 0.93           | 10             | 1.07           | 23.80          | 4.26           | 2.43           |\n",
+       "|  500           |  500           |  1.00          | 0.14           | 0.84 ( 0.01 )  | 19.92 ( 0.41 ) | 1.71 ( 0.08 )  | 2.14 ( 0.01 )  | 24.21          | 0.82           | 11             | 0.84           | 19.90          | 1.71           | 2.14           |\n",
+       "| 1000           |  500           |  0.50          | 0.19           | 0.78 ( 0.01 )  | 18.73 ( 0.42 ) | 0.84 ( 0.06 )  | 2.06 ( 0 )     | 23.89          | 0.78           | 12             | 0.78           | 18.70          | 0.84           | 2.06           |\n",
+       "|   50           | 1000           | 20.00          | 0.00           | 1.31 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.48 ( 0.03 )  |  0.00          | NaN            | 13             | 1.31           |  1.00          | 6.00           | 2.48           |\n",
+       "|  100           | 1000           | 10.00          | 0.01           | 1.06 ( 0.03 )  | 48.48 ( 0.63 ) | 4.33 ( 0.1 )   | 2.43 ( 0.02 )  | 50.15          | 0.97           | 14             | 1.06           | 48.40          | 4.33           | 2.43           |\n",
+       "|  500           | 1000           |  2.00          | 0.06           | 0.89 ( 0.01 )  | 44.77 ( 0.71 ) | 1.81 ( 0.1 )   | 2.3 ( 0.01 )   | 48.96          | 0.91           | 15             | 0.89           | 44.70          | 1.81           | 2.30           |\n",
+       "| 1000           | 1000           |  1.00          | 0.09           | 0.85 ( 0.01 )  | 41.77 ( 0.68 ) | 0.99 ( 0.07 )  | 2.25 ( 0.01 )  | 46.78          | 0.89           | 16             | 0.85           | 41.70          | 0.99           | 2.25           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP             FN           \n",
+       "1    50   50  1.00 0.00 1.31 ( 0.06 ) 1 ( 0 )        6 ( 0 )      \n",
+       "2   100   50  0.50 0.18 0.85 ( 0.02 ) 1.85 ( 0.11 )  4.11 ( 0.1 ) \n",
+       "3   500   50  0.10 0.80 0.47 ( 0.01 ) 0.3 ( 0.05 )   1.61 ( 0.08 )\n",
+       "4  1000   50  0.05 0.88 0.35 ( 0 )    0.07 ( 0.03 )  0.78 ( 0.07 )\n",
+       "5    50  100  2.00 0.00 1.32 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "6   100  100  1.00 0.08 0.91 ( 0.03 ) 4.33 ( 0.22 )  4.45 ( 0.09 )\n",
+       "7   500  100  0.20 0.60 0.59 ( 0.01 ) 1.91 ( 0.15 )  1.41 ( 0.07 )\n",
+       "8  1000  100  0.10 0.81 0.47 ( 0 )    0.71 ( 0.09 )  0.53 ( 0.06 )\n",
+       "9    50  500 10.00 0.00 1.33 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "10  100  500  5.00 0.02 1.07 ( 0.03 ) 23.8 ( 0.52 )  4.26 ( 0.1 ) \n",
+       "11  500  500  1.00 0.14 0.84 ( 0.01 ) 19.92 ( 0.41 ) 1.71 ( 0.08 )\n",
+       "12 1000  500  0.50 0.19 0.78 ( 0.01 ) 18.73 ( 0.42 ) 0.84 ( 0.06 )\n",
+       "13   50 1000 20.00 0.00 1.31 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "14  100 1000 10.00 0.01 1.06 ( 0.03 ) 48.48 ( 0.63 ) 4.33 ( 0.1 ) \n",
+       "15  500 1000  2.00 0.06 0.89 ( 0.01 ) 44.77 ( 0.71 ) 1.81 ( 0.1 ) \n",
+       "16 1000 1000  1.00 0.09 0.85 ( 0.01 ) 41.77 ( 0.68 ) 0.99 ( 0.07 )\n",
+       "   OOB           num_select FDR  Index MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  2.09 ( 0.02 )  0.00      NaN   1    1.31      1.00   6.00    2.09    \n",
+       "2  1.79 ( 0.01 )  3.74      0.48  2    0.85      1.85   4.11    1.79    \n",
+       "3  1.31 ( 0 )     4.69      0.05  3    0.47      0.30   1.61    1.31    \n",
+       "4  1.17 ( 0 )     5.29      0.01  4    0.35      0.07   0.78    1.17    \n",
+       "5  2.23 ( 0.02 )  0.00      NaN   5    1.32      1.00   6.00    2.23    \n",
+       "6  2.02 ( 0.01 )  5.88      0.72  6    0.91      4.33   4.45    2.02    \n",
+       "7  1.54 ( 0 )     6.50      0.27  7    0.59      1.91   1.41    1.54    \n",
+       "8  1.39 ( 0 )     6.18      0.1   8    0.47      0.71   0.53    1.39    \n",
+       "9  2.48 ( 0.03 )  0.00      NaN   9    1.33      1.00   6.00    2.48    \n",
+       "10 2.43 ( 0.02 ) 25.54      0.93 10    1.07     23.80   4.26    2.43    \n",
+       "11 2.14 ( 0.01 ) 24.21      0.82 11    0.84     19.90   1.71    2.14    \n",
+       "12 2.06 ( 0 )    23.89      0.78 12    0.78     18.70   0.84    2.06    \n",
+       "13 2.48 ( 0.03 )  0.00      NaN  13    1.31      1.00   6.00    2.48    \n",
+       "14 2.43 ( 0.02 ) 50.15      0.97 14    1.06     48.40   4.33    2.43    \n",
+       "15 2.3 ( 0.01 )  48.96      0.91 15    0.89     44.70   1.81    2.30    \n",
+       "16 2.25 ( 0.01 ) 46.78      0.89 16    0.85     41.70   0.99    2.25    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## assign zero values to NA Stab; also recover missing FP values (1 in this case); missing FN values (6 in this case)\n",
+    "result.table_ind$Stab[is.na(result.table_ind$Stab)] = 0\n",
+    "result.table_ind$FP_mean[is.na(result.table_ind$FP_mean)] = 1\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 6\n",
+    "#result.table_ind$FDR[is.na(result.table_ind$FDR)] = 0\n",
+    "result.table_ind\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/0.4_sim_indepdent_compLasso_cts.ipynb b/simulations/notebooks_sim_cts/0.4_sim_indepdent_compLasso_cts.ipynb
new file mode 100755
index 0000000..9304d4e
--- /dev/null
+++ b/simulations/notebooks_sim_cts/0.4_sim_indepdent_compLasso_cts.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Independent Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/independent_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "files = NULL\n",
+    "for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_independent_', paste('P', p, 'N', n, sep='_'), '.RData'))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_ind_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_ind = NULL\n",
+    "tmp_num_select = rep(0, length(results_ind_compLasso))\n",
+    "for (i in 1:length(results_ind_compLasso)){\n",
+    "    table_ind = rbind(table_ind, results_ind_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_ind_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_ind = as.data.frame(table_ind)\n",
+    "table_ind$num_select = tmp_num_select\n",
+    "table_ind$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0            </td><td>2.99 ( 0.27 )</td><td>0.05 ( 0.02 )</td><td>0.8 ( 0.05 ) </td><td>0.61         </td><td> 8.94        </td><td>0.28         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0            </td><td>0.79 ( 0.15 )</td><td>0 ( 0 )      </td><td>0.98 ( 0.06 )</td><td>0.87         </td><td> 6.79        </td><td>0.09         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0            </td><td>0.51 ( 0.11 )</td><td>0 ( 0 )      </td><td>1.08 ( 0.05 )</td><td>0.91         </td><td> 6.51        </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0            </td><td>0.39 ( 0.15 )</td><td>0 ( 0 )      </td><td>1.18 ( 0.04 )</td><td>0.93         </td><td> 6.39        </td><td>0.04         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0            </td><td>4.3 ( 0.31 ) </td><td>0.24 ( 0.05 )</td><td>1.05 ( 0.07 )</td><td>0.52         </td><td>10.06        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0            </td><td>1.02 ( 0.19 )</td><td>0 ( 0 )      </td><td>0.94 ( 0.04 )</td><td>0.85         </td><td> 7.02        </td><td>0.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0             & 2.99 ( 0.27 ) & 0.05 ( 0.02 ) & 0.8 ( 0.05 )  & 0.61          &  8.94         & 0.28         \\\\\n",
+       "\t 100           & 50            & 0             & 0.79 ( 0.15 ) & 0 ( 0 )       & 0.98 ( 0.06 ) & 0.87          &  6.79         & 0.09         \\\\\n",
+       "\t 500           & 50            & 0             & 0.51 ( 0.11 ) & 0 ( 0 )       & 1.08 ( 0.05 ) & 0.91          &  6.51         & 0.06         \\\\\n",
+       "\t 1000          & 50            & 0             & 0.39 ( 0.15 ) & 0 ( 0 )       & 1.18 ( 0.04 ) & 0.93          &  6.39         & 0.04         \\\\\n",
+       "\t 50            & 100           & 0             & 4.3 ( 0.31 )  & 0.24 ( 0.05 ) & 1.05 ( 0.07 ) & 0.52          & 10.06         & 0.37         \\\\\n",
+       "\t 100           & 100           & 0             & 1.02 ( 0.19 ) & 0 ( 0 )       & 0.94 ( 0.04 ) & 0.85          &  7.02         & 0.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0             | 2.99 ( 0.27 ) | 0.05 ( 0.02 ) | 0.8 ( 0.05 )  | 0.61          |  8.94         | 0.28          |\n",
+       "| 100           | 50            | 0             | 0.79 ( 0.15 ) | 0 ( 0 )       | 0.98 ( 0.06 ) | 0.87          |  6.79         | 0.09          |\n",
+       "| 500           | 50            | 0             | 0.51 ( 0.11 ) | 0 ( 0 )       | 1.08 ( 0.05 ) | 0.91          |  6.51         | 0.06          |\n",
+       "| 1000          | 50            | 0             | 0.39 ( 0.15 ) | 0 ( 0 )       | 1.18 ( 0.04 ) | 0.93          |  6.39         | 0.04          |\n",
+       "| 50            | 100           | 0             | 4.3 ( 0.31 )  | 0.24 ( 0.05 ) | 1.05 ( 0.07 ) | 0.52          | 10.06         | 0.37          |\n",
+       "| 100           | 100           | 0             | 1.02 ( 0.19 ) | 0 ( 0 )       | 0.94 ( 0.04 ) | 0.85          |  7.02         | 0.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0   2.99 ( 0.27 ) 0.05 ( 0.02 ) 0.8 ( 0.05 )  0.61  8.94      0.28\n",
+       "2 100  50  0   0.79 ( 0.15 ) 0 ( 0 )       0.98 ( 0.06 ) 0.87  6.79      0.09\n",
+       "3 500  50  0   0.51 ( 0.11 ) 0 ( 0 )       1.08 ( 0.05 ) 0.91  6.51      0.06\n",
+       "4 1000 50  0   0.39 ( 0.15 ) 0 ( 0 )       1.18 ( 0.04 ) 0.93  6.39      0.04\n",
+       "5 50   100 0   4.3 ( 0.31 )  0.24 ( 0.05 ) 1.05 ( 0.07 ) 0.52 10.06      0.37\n",
+       "6 100  100 0   1.02 ( 0.19 ) 0 ( 0 )       0.94 ( 0.04 ) 0.85  7.02      0.11"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_ind <- apply(table_ind,2,as.character)\n",
+    "rownames(result.table_ind) = rownames(table_ind)\n",
+    "result.table_ind = as.data.frame(result.table_ind)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_ind$n = tidyr::extract_numeric(result.table_ind$n)\n",
+    "result.table_ind$p = tidyr::extract_numeric(result.table_ind$p)\n",
+    "result.table_ind$ratio = result.table_ind$p / result.table_ind$n\n",
+    "\n",
+    "result.table_ind = result.table_ind[c('n', 'p', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_ind)[1:3] = c('N', 'P', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_ind$Stab = as.numeric(as.character(result.table_ind$Stab))\n",
+    "result.table_ind$MSE_mean = as.numeric(substr(result.table_ind$MSE, start=1, stop=4))\n",
+    "result.table_ind$FP_mean = as.numeric(substr(result.table_ind$FP, start=1, stop=4))\n",
+    "result.table_ind$FN_mean = as.numeric(substr(result.table_ind$FN, start=1, stop=4))\n",
+    "result.table_ind$FN_mean[is.na(result.table_ind$FN_mean)] = 0\n",
+    "result.table_ind$num_select = as.numeric(as.character(result.table_ind$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td>  50         </td><td> 1.00        </td><td>0.61         </td><td>0.8 ( 0.05 ) </td><td>2.99 ( 0.27 )</td><td>0.05 ( 0.02 )</td><td> 8.94        </td><td>0.28         </td><td>0.80         </td><td>2.99         </td><td>0.05         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>  50         </td><td> 0.50        </td><td>0.87         </td><td>0.98 ( 0.06 )</td><td>0.79 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.79        </td><td>0.09         </td><td>0.98         </td><td>0.79         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td>  50         </td><td> 0.10        </td><td>0.91         </td><td>1.08 ( 0.05 )</td><td>0.51 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.51        </td><td>0.06         </td><td>1.08         </td><td>0.51         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>  50         </td><td> 0.05        </td><td>0.93         </td><td>1.18 ( 0.04 )</td><td>0.39 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.39        </td><td>0.04         </td><td>1.18         </td><td>0.39         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td> 100         </td><td> 2.00        </td><td>0.52         </td><td>1.05 ( 0.07 )</td><td>4.3 ( 0.31 ) </td><td>0.24 ( 0.05 )</td><td>10.06        </td><td>0.37         </td><td>1.05         </td><td>4.30         </td><td>0.24         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 100         </td><td> 1.00        </td><td>0.85         </td><td>0.94 ( 0.04 )</td><td>1.02 ( 0.19 )</td><td>0 ( 0 )      </td><td> 7.02        </td><td>0.11         </td><td>0.94         </td><td>1.02         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 100         </td><td> 0.20        </td><td>0.96         </td><td>1.16 ( 0.04 )</td><td>0.25 ( 0.09 )</td><td>0 ( 0 )      </td><td> 6.25        </td><td>0.03         </td><td>1.16         </td><td>0.25         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 100         </td><td> 0.10        </td><td>0.89         </td><td>1.04 ( 0.05 )</td><td>0.71 ( 0.15 )</td><td>0 ( 0 )      </td><td> 6.71        </td><td>0.08         </td><td>1.04         </td><td>0.71         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td> 500         </td><td>10.00        </td><td>0.38         </td><td>2.18 ( 0.13 )</td><td>5.53 ( 0.36 )</td><td>1.27 ( 0.11 )</td><td>10.26        </td><td>0.48         </td><td>2.18         </td><td>5.53         </td><td>1.27         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 500         </td><td> 5.00        </td><td>0.75         </td><td>1 ( 0.05 )   </td><td>1.9 ( 0.29 ) </td><td>0.03 ( 0.02 )</td><td> 7.87        </td><td>0.18         </td><td>1.00         </td><td>1.90         </td><td>0.03         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 500         </td><td> 1.00        </td><td>0.96         </td><td>1.18 ( 0.04 )</td><td>0.27 ( 0.13 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.18         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 500         </td><td> 0.50        </td><td>0.96         </td><td>1.24 ( 0.04 )</td><td>0.25 ( 0.1 ) </td><td>0 ( 0 )      </td><td> 6.25        </td><td>0.03         </td><td>1.24         </td><td>0.25         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>1000         </td><td>20.00        </td><td>0.32         </td><td>3.09 ( 0.23 )</td><td>5.56 ( 0.36 )</td><td>1.95 ( 0.14 )</td><td> 9.61        </td><td>0.51         </td><td>3.09         </td><td>5.56         </td><td>1.95         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>1000         </td><td>10.00        </td><td>0.67         </td><td>1.1 ( 0.07 ) </td><td>2.8 ( 0.37 ) </td><td>0.04 ( 0.02 )</td><td> 8.76        </td><td>0.24         </td><td>1.10         </td><td>2.80         </td><td>0.04         </td></tr>\n",
+       "\t<tr><td> 500         </td><td>1000         </td><td> 2.00        </td><td>0.96         </td><td>1.12 ( 0.04 )</td><td>0.27 ( 0.08 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.12         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>1000         </td><td> 1.00        </td><td>0.96         </td><td>1.16 ( 0.04 )</td><td>0.24 ( 0.07 )</td><td>0 ( 0 )      </td><td> 6.24        </td><td>0.03         </td><td>1.16         </td><td>0.24         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " N & P & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &   50          &  1.00         & 0.61          & 0.8 ( 0.05 )  & 2.99 ( 0.27 ) & 0.05 ( 0.02 ) &  8.94         & 0.28          & 0.80          & 2.99          & 0.05         \\\\\n",
+       "\t  100          &   50          &  0.50         & 0.87          & 0.98 ( 0.06 ) & 0.79 ( 0.15 ) & 0 ( 0 )       &  6.79         & 0.09          & 0.98          & 0.79          & 0.00         \\\\\n",
+       "\t  500          &   50          &  0.10         & 0.91          & 1.08 ( 0.05 ) & 0.51 ( 0.11 ) & 0 ( 0 )       &  6.51         & 0.06          & 1.08          & 0.51          & 0.00         \\\\\n",
+       "\t 1000          &   50          &  0.05         & 0.93          & 1.18 ( 0.04 ) & 0.39 ( 0.15 ) & 0 ( 0 )       &  6.39         & 0.04          & 1.18          & 0.39          & 0.00         \\\\\n",
+       "\t   50          &  100          &  2.00         & 0.52          & 1.05 ( 0.07 ) & 4.3 ( 0.31 )  & 0.24 ( 0.05 ) & 10.06         & 0.37          & 1.05          & 4.30          & 0.24         \\\\\n",
+       "\t  100          &  100          &  1.00         & 0.85          & 0.94 ( 0.04 ) & 1.02 ( 0.19 ) & 0 ( 0 )       &  7.02         & 0.11          & 0.94          & 1.02          & 0.00         \\\\\n",
+       "\t  500          &  100          &  0.20         & 0.96          & 1.16 ( 0.04 ) & 0.25 ( 0.09 ) & 0 ( 0 )       &  6.25         & 0.03          & 1.16          & 0.25          & 0.00         \\\\\n",
+       "\t 1000          &  100          &  0.10         & 0.89          & 1.04 ( 0.05 ) & 0.71 ( 0.15 ) & 0 ( 0 )       &  6.71         & 0.08          & 1.04          & 0.71          & 0.00         \\\\\n",
+       "\t   50          &  500          & 10.00         & 0.38          & 2.18 ( 0.13 ) & 5.53 ( 0.36 ) & 1.27 ( 0.11 ) & 10.26         & 0.48          & 2.18          & 5.53          & 1.27         \\\\\n",
+       "\t  100          &  500          &  5.00         & 0.75          & 1 ( 0.05 )    & 1.9 ( 0.29 )  & 0.03 ( 0.02 ) &  7.87         & 0.18          & 1.00          & 1.90          & 0.03         \\\\\n",
+       "\t  500          &  500          &  1.00         & 0.96          & 1.18 ( 0.04 ) & 0.27 ( 0.13 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.18          & 0.27          & 0.00         \\\\\n",
+       "\t 1000          &  500          &  0.50         & 0.96          & 1.24 ( 0.04 ) & 0.25 ( 0.1 )  & 0 ( 0 )       &  6.25         & 0.03          & 1.24          & 0.25          & 0.00         \\\\\n",
+       "\t   50          & 1000          & 20.00         & 0.32          & 3.09 ( 0.23 ) & 5.56 ( 0.36 ) & 1.95 ( 0.14 ) &  9.61         & 0.51          & 3.09          & 5.56          & 1.95         \\\\\n",
+       "\t  100          & 1000          & 10.00         & 0.67          & 1.1 ( 0.07 )  & 2.8 ( 0.37 )  & 0.04 ( 0.02 ) &  8.76         & 0.24          & 1.10          & 2.80          & 0.04         \\\\\n",
+       "\t  500          & 1000          &  2.00         & 0.96          & 1.12 ( 0.04 ) & 0.27 ( 0.08 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.12          & 0.27          & 0.00         \\\\\n",
+       "\t 1000          & 1000          &  1.00         & 0.96          & 1.16 ( 0.04 ) & 0.24 ( 0.07 ) & 0 ( 0 )       &  6.24         & 0.03          & 1.16          & 0.24          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |   50          |  1.00         | 0.61          | 0.8 ( 0.05 )  | 2.99 ( 0.27 ) | 0.05 ( 0.02 ) |  8.94         | 0.28          | 0.80          | 2.99          | 0.05          |\n",
+       "|  100          |   50          |  0.50         | 0.87          | 0.98 ( 0.06 ) | 0.79 ( 0.15 ) | 0 ( 0 )       |  6.79         | 0.09          | 0.98          | 0.79          | 0.00          |\n",
+       "|  500          |   50          |  0.10         | 0.91          | 1.08 ( 0.05 ) | 0.51 ( 0.11 ) | 0 ( 0 )       |  6.51         | 0.06          | 1.08          | 0.51          | 0.00          |\n",
+       "| 1000          |   50          |  0.05         | 0.93          | 1.18 ( 0.04 ) | 0.39 ( 0.15 ) | 0 ( 0 )       |  6.39         | 0.04          | 1.18          | 0.39          | 0.00          |\n",
+       "|   50          |  100          |  2.00         | 0.52          | 1.05 ( 0.07 ) | 4.3 ( 0.31 )  | 0.24 ( 0.05 ) | 10.06         | 0.37          | 1.05          | 4.30          | 0.24          |\n",
+       "|  100          |  100          |  1.00         | 0.85          | 0.94 ( 0.04 ) | 1.02 ( 0.19 ) | 0 ( 0 )       |  7.02         | 0.11          | 0.94          | 1.02          | 0.00          |\n",
+       "|  500          |  100          |  0.20         | 0.96          | 1.16 ( 0.04 ) | 0.25 ( 0.09 ) | 0 ( 0 )       |  6.25         | 0.03          | 1.16          | 0.25          | 0.00          |\n",
+       "| 1000          |  100          |  0.10         | 0.89          | 1.04 ( 0.05 ) | 0.71 ( 0.15 ) | 0 ( 0 )       |  6.71         | 0.08          | 1.04          | 0.71          | 0.00          |\n",
+       "|   50          |  500          | 10.00         | 0.38          | 2.18 ( 0.13 ) | 5.53 ( 0.36 ) | 1.27 ( 0.11 ) | 10.26         | 0.48          | 2.18          | 5.53          | 1.27          |\n",
+       "|  100          |  500          |  5.00         | 0.75          | 1 ( 0.05 )    | 1.9 ( 0.29 )  | 0.03 ( 0.02 ) |  7.87         | 0.18          | 1.00          | 1.90          | 0.03          |\n",
+       "|  500          |  500          |  1.00         | 0.96          | 1.18 ( 0.04 ) | 0.27 ( 0.13 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.18          | 0.27          | 0.00          |\n",
+       "| 1000          |  500          |  0.50         | 0.96          | 1.24 ( 0.04 ) | 0.25 ( 0.1 )  | 0 ( 0 )       |  6.25         | 0.03          | 1.24          | 0.25          | 0.00          |\n",
+       "|   50          | 1000          | 20.00         | 0.32          | 3.09 ( 0.23 ) | 5.56 ( 0.36 ) | 1.95 ( 0.14 ) |  9.61         | 0.51          | 3.09          | 5.56          | 1.95          |\n",
+       "|  100          | 1000          | 10.00         | 0.67          | 1.1 ( 0.07 )  | 2.8 ( 0.37 )  | 0.04 ( 0.02 ) |  8.76         | 0.24          | 1.10          | 2.80          | 0.04          |\n",
+       "|  500          | 1000          |  2.00         | 0.96          | 1.12 ( 0.04 ) | 0.27 ( 0.08 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.12          | 0.27          | 0.00          |\n",
+       "| 1000          | 1000          |  1.00         | 0.96          | 1.16 ( 0.04 ) | 0.24 ( 0.07 ) | 0 ( 0 )       |  6.24         | 0.03          | 1.16          | 0.24          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Ratio Stab MSE           FP            FN            num_select\n",
+       "1    50   50  1.00 0.61 0.8 ( 0.05 )  2.99 ( 0.27 ) 0.05 ( 0.02 )  8.94     \n",
+       "2   100   50  0.50 0.87 0.98 ( 0.06 ) 0.79 ( 0.15 ) 0 ( 0 )        6.79     \n",
+       "3   500   50  0.10 0.91 1.08 ( 0.05 ) 0.51 ( 0.11 ) 0 ( 0 )        6.51     \n",
+       "4  1000   50  0.05 0.93 1.18 ( 0.04 ) 0.39 ( 0.15 ) 0 ( 0 )        6.39     \n",
+       "5    50  100  2.00 0.52 1.05 ( 0.07 ) 4.3 ( 0.31 )  0.24 ( 0.05 ) 10.06     \n",
+       "6   100  100  1.00 0.85 0.94 ( 0.04 ) 1.02 ( 0.19 ) 0 ( 0 )        7.02     \n",
+       "7   500  100  0.20 0.96 1.16 ( 0.04 ) 0.25 ( 0.09 ) 0 ( 0 )        6.25     \n",
+       "8  1000  100  0.10 0.89 1.04 ( 0.05 ) 0.71 ( 0.15 ) 0 ( 0 )        6.71     \n",
+       "9    50  500 10.00 0.38 2.18 ( 0.13 ) 5.53 ( 0.36 ) 1.27 ( 0.11 ) 10.26     \n",
+       "10  100  500  5.00 0.75 1 ( 0.05 )    1.9 ( 0.29 )  0.03 ( 0.02 )  7.87     \n",
+       "11  500  500  1.00 0.96 1.18 ( 0.04 ) 0.27 ( 0.13 ) 0 ( 0 )        6.27     \n",
+       "12 1000  500  0.50 0.96 1.24 ( 0.04 ) 0.25 ( 0.1 )  0 ( 0 )        6.25     \n",
+       "13   50 1000 20.00 0.32 3.09 ( 0.23 ) 5.56 ( 0.36 ) 1.95 ( 0.14 )  9.61     \n",
+       "14  100 1000 10.00 0.67 1.1 ( 0.07 )  2.8 ( 0.37 )  0.04 ( 0.02 )  8.76     \n",
+       "15  500 1000  2.00 0.96 1.12 ( 0.04 ) 0.27 ( 0.08 ) 0 ( 0 )        6.27     \n",
+       "16 1000 1000  1.00 0.96 1.16 ( 0.04 ) 0.24 ( 0.07 ) 0 ( 0 )        6.24     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "1  0.28 0.80     2.99    0.05   \n",
+       "2  0.09 0.98     0.79    0.00   \n",
+       "3  0.06 1.08     0.51    0.00   \n",
+       "4  0.04 1.18     0.39    0.00   \n",
+       "5  0.37 1.05     4.30    0.24   \n",
+       "6  0.11 0.94     1.02    0.00   \n",
+       "7  0.03 1.16     0.25    0.00   \n",
+       "8  0.08 1.04     0.71    0.00   \n",
+       "9  0.48 2.18     5.53    1.27   \n",
+       "10 0.18 1.00     1.90    0.03   \n",
+       "11 0.03 1.18     0.27    0.00   \n",
+       "12 0.03 1.24     0.25    0.00   \n",
+       "13 0.51 3.09     5.56    1.95   \n",
+       "14 0.24 1.10     2.80    0.04   \n",
+       "15 0.03 1.12     0.27    0.00   \n",
+       "16 0.03 1.16     0.24    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fix missing values\n",
+    "result.table_ind$MSE_mean[is.na(result.table_ind$MSE_mean)] = 1\n",
+    "result.table_ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_ind, '../results_summary_cts/sim_ind_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/1.1_sim_toe_lasso_cts.ipynb b/simulations/notebooks_sim_cts/1.1_sim_toe_lasso_cts.ipynb
new file mode 100755
index 0000000..231c1a8
--- /dev/null
+++ b/simulations/notebooks_sim_cts/1.1_sim_toe_lasso_cts.ipynb
@@ -0,0 +1,819 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_lasso))\n",
+    "for (i in 1:length(results_toe_lasso)){\n",
+    "    table_toe = rbind(table_toe, results_toe_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>8.59 ( 0.46 )</td><td>0.02 ( 0.01 )</td><td>0.62 ( 0.04 )</td><td>0.36         </td><td>13.57        </td><td>0.51         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>6.3 ( 0.43 ) </td><td>0 ( 0 )      </td><td>0.37 ( 0.01 )</td><td>0.47         </td><td>11.30        </td><td>0.40         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>2.88 ( 0.21 )</td><td>0 ( 0 )      </td><td>0.28 ( 0 )   </td><td>0.73         </td><td> 7.88        </td><td>0.20         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>1.66 ( 0.11 )</td><td>0 ( 0 )      </td><td>0.27 ( 0 )   </td><td>0.89         </td><td> 6.66        </td><td>0.08         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>11.84 ( 0.4 )</td><td>0 ( 0 )      </td><td>0.67 ( 0.05 )</td><td>0.32         </td><td>16.84        </td><td>0.62         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>7.71 ( 0.56 )</td><td>0 ( 0 )      </td><td>0.4 ( 0.01 ) </td><td>0.44         </td><td>12.71        </td><td>0.46         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 8.59 ( 0.46 ) & 0.02 ( 0.01 ) & 0.62 ( 0.04 ) & 0.36          & 13.57         & 0.51         \\\\\n",
+       "\t 100           & 50            & 0.1           & 6.3 ( 0.43 )  & 0 ( 0 )       & 0.37 ( 0.01 ) & 0.47          & 11.30         & 0.40         \\\\\n",
+       "\t 500           & 50            & 0.1           & 2.88 ( 0.21 ) & 0 ( 0 )       & 0.28 ( 0 )    & 0.73          &  7.88         & 0.20         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 1.66 ( 0.11 ) & 0 ( 0 )       & 0.27 ( 0 )    & 0.89          &  6.66         & 0.08         \\\\\n",
+       "\t 50            & 100           & 0.1           & 11.84 ( 0.4 ) & 0 ( 0 )       & 0.67 ( 0.05 ) & 0.32          & 16.84         & 0.62         \\\\\n",
+       "\t 100           & 100           & 0.1           & 7.71 ( 0.56 ) & 0 ( 0 )       & 0.4 ( 0.01 )  & 0.44          & 12.71         & 0.46         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 8.59 ( 0.46 ) | 0.02 ( 0.01 ) | 0.62 ( 0.04 ) | 0.36          | 13.57         | 0.51          |\n",
+       "| 100           | 50            | 0.1           | 6.3 ( 0.43 )  | 0 ( 0 )       | 0.37 ( 0.01 ) | 0.47          | 11.30         | 0.40          |\n",
+       "| 500           | 50            | 0.1           | 2.88 ( 0.21 ) | 0 ( 0 )       | 0.28 ( 0 )    | 0.73          |  7.88         | 0.20          |\n",
+       "| 1000          | 50            | 0.1           | 1.66 ( 0.11 ) | 0 ( 0 )       | 0.27 ( 0 )    | 0.89          |  6.66         | 0.08          |\n",
+       "| 50            | 100           | 0.1           | 11.84 ( 0.4 ) | 0 ( 0 )       | 0.67 ( 0.05 ) | 0.32          | 16.84         | 0.62          |\n",
+       "| 100           | 100           | 0.1           | 7.71 ( 0.56 ) | 0 ( 0 )       | 0.4 ( 0.01 )  | 0.44          | 12.71         | 0.46          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 8.59 ( 0.46 ) 0.02 ( 0.01 ) 0.62 ( 0.04 ) 0.36 13.57      0.51\n",
+       "2 100  50  0.1 6.3 ( 0.43 )  0 ( 0 )       0.37 ( 0.01 ) 0.47 11.30      0.40\n",
+       "3 500  50  0.1 2.88 ( 0.21 ) 0 ( 0 )       0.28 ( 0 )    0.73  7.88      0.20\n",
+       "4 1000 50  0.1 1.66 ( 0.11 ) 0 ( 0 )       0.27 ( 0 )    0.89  6.66      0.08\n",
+       "5 50   100 0.1 11.84 ( 0.4 ) 0 ( 0 )       0.67 ( 0.05 ) 0.32 16.84      0.62\n",
+       "6 100  100 0.1 7.71 ( 0.56 ) 0 ( 0 )       0.4 ( 0.01 )  0.44 12.71      0.46"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>38</th><td>100          </td><td>100          </td><td>0.5          </td><td>1            </td><td>0.36         </td><td>0.44 ( 0.02 )</td><td>10 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>14.98        </td><td>0.56         </td><td>0.44         </td><td>NA           </td><td>0.02         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t38 & 100           & 100           & 0.5           & 1             & 0.36          & 0.44 ( 0.02 ) & 10 ( 0.48 )   & 0.02 ( 0.01 ) & 14.98         & 0.56          & 0.44          & NA            & 0.02         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 38 | 100           | 100           | 0.5           | 1             | 0.36          | 0.44 ( 0.02 ) | 10 ( 0.48 )   | 0.02 ( 0.01 ) | 14.98         | 0.56          | 0.44          | NA            | 0.02          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "38 100 100 0.5  1     0.36 0.44 ( 0.02 ) 10 ( 0.48 ) 0.02 ( 0.01 ) 14.98     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "38 0.56 0.44     NA      0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$FP_mean[is.na(result.table_toe$FP_mean)] = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>38</th><td>100          </td><td>100          </td><td>0.5          </td><td>1            </td><td>0.36         </td><td>0.44 ( 0.02 )</td><td>10 ( 0.48 )  </td><td>0.02 ( 0.01 )</td><td>14.98        </td><td>0.56         </td><td>0.44         </td><td>10           </td><td>0.02         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t38 & 100           & 100           & 0.5           & 1             & 0.36          & 0.44 ( 0.02 ) & 10 ( 0.48 )   & 0.02 ( 0.01 ) & 14.98         & 0.56          & 0.44          & 10            & 0.02         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 38 | 100           | 100           | 0.5           | 1             | 0.36          | 0.44 ( 0.02 ) | 10 ( 0.48 )   | 0.02 ( 0.01 ) | 14.98         | 0.56          | 0.44          | 10            | 0.02          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "38 100 100 0.5  1     0.36 0.44 ( 0.02 ) 10 ( 0.48 ) 0.02 ( 0.01 ) 14.98     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "38 0.56 0.44     10      0.02   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[38, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.36         </td><td>0.62 ( 0.04 )</td><td>8.59 ( 0.46 )</td><td>0.02 ( 0.01 )</td><td>13.57        </td><td>0.51         </td><td>0.62         </td><td> 8.59        </td><td>0.02         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.47         </td><td>0.37 ( 0.01 )</td><td>6.3 ( 0.43 ) </td><td>0 ( 0 )      </td><td>11.30        </td><td>0.4          </td><td>0.37         </td><td> 6.30        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.73         </td><td>0.28 ( 0 )   </td><td>2.88 ( 0.21 )</td><td>0 ( 0 )      </td><td> 7.88        </td><td>0.2          </td><td>0.28         </td><td> 2.88        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.89         </td><td>0.27 ( 0 )   </td><td>1.66 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.66        </td><td>0.08         </td><td>0.27         </td><td> 1.66        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.32         </td><td>0.67 ( 0.05 )</td><td>11.84 ( 0.4 )</td><td>0 ( 0 )      </td><td>16.84        </td><td>0.62         </td><td>0.67         </td><td>11.80        </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.44         </td><td>0.4 ( 0.01 ) </td><td>7.71 ( 0.56 )</td><td>0 ( 0 )      </td><td>12.71        </td><td>0.46         </td><td>0.40         </td><td> 7.71        </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.36          & 0.62 ( 0.04 ) & 8.59 ( 0.46 ) & 0.02 ( 0.01 ) & 13.57         & 0.51          & 0.62          &  8.59         & 0.02         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.47          & 0.37 ( 0.01 ) & 6.3 ( 0.43 )  & 0 ( 0 )       & 11.30         & 0.4           & 0.37          &  6.30         & 0.00         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.73          & 0.28 ( 0 )    & 2.88 ( 0.21 ) & 0 ( 0 )       &  7.88         & 0.2           & 0.28          &  2.88         & 0.00         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.89          & 0.27 ( 0 )    & 1.66 ( 0.11 ) & 0 ( 0 )       &  6.66         & 0.08          & 0.27          &  1.66         & 0.00         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.32          & 0.67 ( 0.05 ) & 11.84 ( 0.4 ) & 0 ( 0 )       & 16.84         & 0.62          & 0.67          & 11.80         & 0.00         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.44          & 0.4 ( 0.01 )  & 7.71 ( 0.56 ) & 0 ( 0 )       & 12.71         & 0.46          & 0.40          &  7.71         & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.36          | 0.62 ( 0.04 ) | 8.59 ( 0.46 ) | 0.02 ( 0.01 ) | 13.57         | 0.51          | 0.62          |  8.59         | 0.02          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.47          | 0.37 ( 0.01 ) | 6.3 ( 0.43 )  | 0 ( 0 )       | 11.30         | 0.4           | 0.37          |  6.30         | 0.00          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.73          | 0.28 ( 0 )    | 2.88 ( 0.21 ) | 0 ( 0 )       |  7.88         | 0.2           | 0.28          |  2.88         | 0.00          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.89          | 0.27 ( 0 )    | 1.66 ( 0.11 ) | 0 ( 0 )       |  6.66         | 0.08          | 0.27          |  1.66         | 0.00          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.32          | 0.67 ( 0.05 ) | 11.84 ( 0.4 ) | 0 ( 0 )       | 16.84         | 0.62          | 0.67          | 11.80         | 0.00          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.44          | 0.4 ( 0.01 )  | 7.71 ( 0.56 ) | 0 ( 0 )       | 12.71         | 0.46          | 0.40          |  7.71         | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.36 0.62 ( 0.04 ) 8.59 ( 0.46 ) 0.02 ( 0.01 ) 13.57     \n",
+       "2  100  50 0.1  0.50  0.47 0.37 ( 0.01 ) 6.3 ( 0.43 )  0 ( 0 )       11.30     \n",
+       "3  500  50 0.1  0.10  0.73 0.28 ( 0 )    2.88 ( 0.21 ) 0 ( 0 )        7.88     \n",
+       "4 1000  50 0.1  0.05  0.89 0.27 ( 0 )    1.66 ( 0.11 ) 0 ( 0 )        6.66     \n",
+       "5   50 100 0.1  2.00  0.32 0.67 ( 0.05 ) 11.84 ( 0.4 ) 0 ( 0 )       16.84     \n",
+       "6  100 100 0.1  1.00  0.44 0.4 ( 0.01 )  7.71 ( 0.56 ) 0 ( 0 )       12.71     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.51 0.62      8.59   0.02   \n",
+       "2 0.4  0.37      6.30   0.00   \n",
+       "3 0.2  0.28      2.88   0.00   \n",
+       "4 0.08 0.27      1.66   0.00   \n",
+       "5 0.62 0.67     11.80   0.00   \n",
+       "6 0.46 0.40      7.71   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.25          </td><td>0.3 ( 0 )     </td><td>18.18 ( 0.69 )</td><td>0.03 ( 0.02 ) </td><td>23.15         </td><td>0.72          </td><td>0.30          </td><td>18.1          </td><td>0.03          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.33          </td><td>0.28 ( 0 )    </td><td>12.68 ( 0.57 )</td><td>0 ( 0 )       </td><td>17.68         </td><td>0.63          </td><td>0.28          </td><td>12.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.08          </td><td>0.9 ( 0.05 )  </td><td>18.15 ( 0.34 )</td><td>3.92 ( 0.08 ) </td><td>19.23         </td><td>0.89          </td><td>0.90          </td><td>18.1          </td><td>3.92          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.16          </td><td>0.59 ( 0.02 ) </td><td>15.99 ( 0.66 )</td><td>2.88 ( 0.06 ) </td><td>18.11         </td><td>0.81          </td><td>0.59          </td><td>15.9          </td><td>2.88          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.20          </td><td>0.31 ( 0 )    </td><td>24.62 ( 1.01 )</td><td>0.05 ( 0.02 ) </td><td>29.57         </td><td>0.77          </td><td>0.31          </td><td>24.6          </td><td>0.05          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.25          </td><td>0.28 ( 0 )    </td><td>18.51 ( 0.86 )</td><td>0 ( 0 )       </td><td>23.51         </td><td>0.71          </td><td>0.28          </td><td>18.5          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.25           & 0.3 ( 0 )      & 18.18 ( 0.69 ) & 0.03 ( 0.02 )  & 23.15          & 0.72           & 0.30           & 18.1           & 0.03          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.33           & 0.28 ( 0 )     & 12.68 ( 0.57 ) & 0 ( 0 )        & 17.68          & 0.63           & 0.28           & 12.6           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.08           & 0.9 ( 0.05 )   & 18.15 ( 0.34 ) & 3.92 ( 0.08 )  & 19.23          & 0.89           & 0.90           & 18.1           & 3.92          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.16           & 0.59 ( 0.02 )  & 15.99 ( 0.66 ) & 2.88 ( 0.06 )  & 18.11          & 0.81           & 0.59           & 15.9           & 2.88          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.20           & 0.31 ( 0 )     & 24.62 ( 1.01 ) & 0.05 ( 0.02 )  & 29.57          & 0.77           & 0.31           & 24.6           & 0.05          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.25           & 0.28 ( 0 )     & 18.51 ( 0.86 ) & 0 ( 0 )        & 23.51          & 0.71           & 0.28           & 18.5           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.25           | 0.3 ( 0 )      | 18.18 ( 0.69 ) | 0.03 ( 0.02 )  | 23.15          | 0.72           | 0.30           | 18.1           | 0.03           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.33           | 0.28 ( 0 )     | 12.68 ( 0.57 ) | 0 ( 0 )        | 17.68          | 0.63           | 0.28           | 12.6           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.08           | 0.9 ( 0.05 )   | 18.15 ( 0.34 ) | 3.92 ( 0.08 )  | 19.23          | 0.89           | 0.90           | 18.1           | 3.92           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.16           | 0.59 ( 0.02 )  | 15.99 ( 0.66 ) | 2.88 ( 0.06 )  | 18.11          | 0.81           | 0.59           | 15.9           | 2.88           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.20           | 0.31 ( 0 )     | 24.62 ( 1.01 ) | 0.05 ( 0.02 )  | 29.57          | 0.77           | 0.31           | 24.6           | 0.05           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.25           | 0.28 ( 0 )     | 18.51 ( 0.86 ) | 0 ( 0 )        | 23.51          | 0.71           | 0.28           | 18.5           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.25 0.3 ( 0 )     18.18 ( 0.69 ) 0.03 ( 0.02 )\n",
+       "76 1000  500 0.9   0.5  0.33 0.28 ( 0 )    12.68 ( 0.57 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.08 0.9 ( 0.05 )  18.15 ( 0.34 ) 3.92 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.16 0.59 ( 0.02 ) 15.99 ( 0.66 ) 2.88 ( 0.06 )\n",
+       "79  500 1000 0.9   2.0  0.20 0.31 ( 0 )    24.62 ( 1.01 ) 0.05 ( 0.02 )\n",
+       "80 1000 1000 0.9   1.0  0.25 0.28 ( 0 )    18.51 ( 0.86 ) 0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 23.15      0.72 0.30     18.1    0.03   \n",
+       "76 17.68      0.63 0.28     12.6    0.00   \n",
+       "77 19.23      0.89 0.90     18.1    3.92   \n",
+       "78 18.11      0.81 0.59     15.9    2.88   \n",
+       "79 29.57      0.77 0.31     24.6    0.05   \n",
+       "80 23.51      0.71 0.28     18.5    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.1           </td><td> 1.00         </td><td>0.36          </td><td>0.62 ( 0.04 ) </td><td>8.59 ( 0.46 ) </td><td>0.02 ( 0.01 ) </td><td>13.57         </td><td>0.51          </td><td>0.62          </td><td> 8.59         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.1           </td><td> 0.50         </td><td>0.47          </td><td>0.37 ( 0.01 ) </td><td>6.3 ( 0.43 )  </td><td>0 ( 0 )       </td><td>11.30         </td><td>0.4           </td><td>0.37          </td><td> 6.30         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.1           </td><td> 0.10         </td><td>0.73          </td><td>0.28 ( 0 )    </td><td>2.88 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 7.88         </td><td>0.2           </td><td>0.28          </td><td> 2.88         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.1           </td><td> 0.05         </td><td>0.89          </td><td>0.27 ( 0 )    </td><td>1.66 ( 0.11 ) </td><td>0 ( 0 )       </td><td> 6.66         </td><td>0.08          </td><td>0.27          </td><td> 1.66         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.1           </td><td> 2.00         </td><td>0.32          </td><td>0.67 ( 0.05 ) </td><td>11.84 ( 0.4 ) </td><td>0 ( 0 )       </td><td>16.84         </td><td>0.62          </td><td>0.67          </td><td>11.80         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.1           </td><td> 1.00         </td><td>0.44          </td><td>0.4 ( 0.01 )  </td><td>7.71 ( 0.56 ) </td><td>0 ( 0 )       </td><td>12.71         </td><td>0.46          </td><td>0.40          </td><td> 7.71         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.1           </td><td> 0.20         </td><td>0.70          </td><td>0.27 ( 0 )    </td><td>3.35 ( 0.22 ) </td><td>0 ( 0 )       </td><td> 8.35         </td><td>0.23          </td><td>0.27          </td><td> 3.35         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.1           </td><td> 0.10         </td><td>0.81          </td><td>0.27 ( 0 )    </td><td>2.29 ( 0.23 ) </td><td>0 ( 0 )       </td><td> 7.29         </td><td>0.13          </td><td>0.27          </td><td> 2.29         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.1           </td><td>10.00         </td><td>0.18          </td><td>1.61 ( 0.1 )  </td><td>20.85 ( 0.38 )</td><td>0.69 ( 0.08 ) </td><td>25.16         </td><td>0.78          </td><td>1.61          </td><td>20.80         </td><td>0.69          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.1           </td><td> 5.00         </td><td>0.30          </td><td>0.53 ( 0.02 ) </td><td>14.69 ( 0.7 ) </td><td>0 ( 0 )       </td><td>19.69         </td><td>0.66          </td><td>0.53          </td><td>14.60         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.1           </td><td> 1.00         </td><td>0.55          </td><td>0.28 ( 0 )    </td><td>5.86 ( 0.51 ) </td><td>0 ( 0 )       </td><td>10.86         </td><td>0.36          </td><td>0.28          </td><td> 5.86         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.1           </td><td> 0.50         </td><td>0.78          </td><td>0.28 ( 0 )    </td><td>2.69 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 7.69         </td><td>0.17          </td><td>0.28          </td><td> 2.69         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.1           </td><td>20.00         </td><td>0.13          </td><td>2.23 ( 0.14 ) </td><td>24.37 ( 0.39 )</td><td>1.36 ( 0.1 )  </td><td>28.01         </td><td>0.83          </td><td>2.23          </td><td>24.30         </td><td>1.36          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.1           </td><td>10.00         </td><td>0.25          </td><td>0.57 ( 0.03 ) </td><td>18.79 ( 0.7 ) </td><td>0 ( 0 )       </td><td>23.79         </td><td>0.73          </td><td>0.57          </td><td>18.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.1           </td><td> 2.00         </td><td>0.49          </td><td>0.28 ( 0 )    </td><td>7.08 ( 0.53 ) </td><td>0 ( 0 )       </td><td>12.08         </td><td>0.41          </td><td>0.28          </td><td> 7.08         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.1           </td><td> 1.00         </td><td>0.70          </td><td>0.27 ( 0 )    </td><td>3.59 ( 0.33 ) </td><td>0 ( 0 )       </td><td> 8.59         </td><td>0.23          </td><td>0.27          </td><td> 3.59         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.3           </td><td> 1.00         </td><td>0.35          </td><td>0.57 ( 0.03 ) </td><td>8.86 ( 0.42 ) </td><td>0.05 ( 0.02 ) </td><td>13.81         </td><td>0.53          </td><td>0.57          </td><td> 8.86         </td><td>0.05          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.3           </td><td> 0.50         </td><td>0.47          </td><td>0.36 ( 0.01 ) </td><td>6.18 ( 0.36 ) </td><td>0 ( 0 )       </td><td>11.18         </td><td>0.41          </td><td>0.36          </td><td> 6.18         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.3           </td><td> 0.10         </td><td>0.70          </td><td>0.28 ( 0 )    </td><td>3.14 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 8.14         </td><td>0.22          </td><td>0.28          </td><td> 3.14         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.3           </td><td> 0.05         </td><td>0.86          </td><td>0.27 ( 0 )    </td><td>1.86 ( 0.11 ) </td><td>0 ( 0 )       </td><td> 6.86         </td><td>0.11          </td><td>0.27          </td><td> 1.86         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.3           </td><td> 2.00         </td><td>0.31          </td><td>0.76 ( 0.04 ) </td><td>11.12 ( 0.37 )</td><td>0.17 ( 0.04 ) </td><td>15.95         </td><td>0.62          </td><td>0.76          </td><td>11.10         </td><td>0.17          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.3           </td><td> 1.00         </td><td>0.38          </td><td>0.41 ( 0.01 ) </td><td>9.43 ( 0.53 ) </td><td>0 ( 0 )       </td><td>14.43         </td><td>0.54          </td><td>0.41          </td><td> 9.43         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.3           </td><td> 0.20         </td><td>0.63          </td><td>0.27 ( 0 )    </td><td>4.16 ( 0.27 ) </td><td>0 ( 0 )       </td><td> 9.16         </td><td>0.29          </td><td>0.27          </td><td> 4.16         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.3           </td><td> 0.10         </td><td>0.80          </td><td>0.27 ( 0 )    </td><td>2.36 ( 0.17 ) </td><td>0 ( 0 )       </td><td> 7.36         </td><td>0.15          </td><td>0.27          </td><td> 2.36         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.3           </td><td>10.00         </td><td>0.14          </td><td>1.78 ( 0.11 ) </td><td>21.4 ( 0.41 ) </td><td>1.39 ( 0.1 )  </td><td>25.01         </td><td>0.81          </td><td>1.78          </td><td>21.40         </td><td>1.39          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.3           </td><td> 5.00         </td><td>0.26          </td><td>0.53 ( 0.02 ) </td><td>16.81 ( 0.77 )</td><td>0.02 ( 0.01 ) </td><td>21.79         </td><td>0.7           </td><td>0.53          </td><td>16.80         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.3           </td><td> 1.00         </td><td>0.55          </td><td>0.29 ( 0 )    </td><td>5.89 ( 0.51 ) </td><td>0 ( 0 )       </td><td>10.89         </td><td>0.36          </td><td>0.29          </td><td> 5.89         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.3           </td><td> 0.50         </td><td>0.69          </td><td>0.27 ( 0 )    </td><td>3.68 ( 0.32 ) </td><td>0 ( 0 )       </td><td> 8.68         </td><td>0.25          </td><td>0.27          </td><td> 3.68         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.3           </td><td>20.00         </td><td>0.11          </td><td>2.21 ( 0.15 ) </td><td>25.14 ( 0.35 )</td><td>1.87 ( 0.1 )  </td><td>28.27         </td><td>0.85          </td><td>2.21          </td><td>25.10         </td><td>1.87          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.3           </td><td>10.00         </td><td>0.22          </td><td>0.61 ( 0.03 ) </td><td>21.11 ( 0.75 )</td><td>0.02 ( 0.01 ) </td><td>26.09         </td><td>0.75          </td><td>0.61          </td><td>21.10         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.7           </td><td> 0.10         </td><td>0.60          </td><td>0.27 ( 0 )    </td><td>4.38 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 9.38         </td><td>0.33          </td><td>0.27          </td><td> 4.38         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.7           </td><td> 0.05         </td><td>0.74          </td><td>0.27 ( 0 )    </td><td>2.84 ( 0.14 ) </td><td>0 ( 0 )       </td><td> 7.84         </td><td>0.21          </td><td>0.27          </td><td> 2.84         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.7           </td><td> 2.00         </td><td>0.23          </td><td>0.8 ( 0.04 )  </td><td>11.57 ( 0.53 )</td><td>1.26 ( 0.1 )  </td><td>15.31         </td><td>0.66          </td><td>0.80          </td><td>11.50         </td><td>1.26          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.7           </td><td> 1.00         </td><td>0.31          </td><td>0.39 ( 0.01 ) </td><td>11.68 ( 0.51 )</td><td>0.1 ( 0.04 )  </td><td>16.58         </td><td>0.61          </td><td>0.39          </td><td>11.60         </td><td>0.10          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.7           </td><td> 0.20         </td><td>0.49          </td><td>0.28 ( 0 )    </td><td>6.56 ( 0.32 ) </td><td>0 ( 0 )       </td><td>11.56         </td><td>0.44          </td><td>0.28          </td><td> 6.56         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.7           </td><td> 0.10         </td><td>0.63          </td><td>0.27 ( 0 )    </td><td>4.24 ( 0.26 ) </td><td>0 ( 0 )       </td><td> 9.24         </td><td>0.3           </td><td>0.27          </td><td> 4.24         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.7           </td><td>10.00         </td><td>0.12          </td><td>1.37 ( 0.08 ) </td><td>19.8 ( 0.33 ) </td><td>2.78 ( 0.08 ) </td><td>22.02         </td><td>0.85          </td><td>1.37          </td><td>19.80         </td><td>2.78          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.7           </td><td> 5.00         </td><td>0.19          </td><td>0.62 ( 0.03 ) </td><td>18.58 ( 0.85 )</td><td>0.88 ( 0.08 ) </td><td>22.70         </td><td>0.75          </td><td>0.62          </td><td>18.50         </td><td>0.88          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.7           </td><td> 1.00         </td><td>0.36          </td><td>0.3 ( 0 )     </td><td>11.31 ( 0.6 ) </td><td>0 ( 0 )       </td><td>16.31         </td><td>0.58          </td><td>0.30          </td><td>11.30         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.7           </td><td> 0.50         </td><td>0.46          </td><td>0.27 ( 0 )    </td><td>7.93 ( 0.49 ) </td><td>0 ( 0 )       </td><td>12.93         </td><td>0.47          </td><td>0.27          </td><td> 7.93         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.7           </td><td>20.00         </td><td>0.09          </td><td>1.5 ( 0.08 )  </td><td>22.99 ( 0.35 )</td><td>3.2 ( 0.09 )  </td><td>24.79         </td><td>0.88          </td><td>1.50          </td><td>22.90         </td><td>3.20          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.7           </td><td>10.00         </td><td>0.14          </td><td>0.77 ( 0.04 ) </td><td>22.43 ( 0.68 )</td><td>1.45 ( 0.1 )  </td><td>25.98         </td><td>0.82          </td><td>0.77          </td><td>22.40         </td><td>1.45          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.7           </td><td> 2.00         </td><td>0.30          </td><td>0.31 ( 0 )    </td><td>14.7 ( 0.84 ) </td><td>0 ( 0 )       </td><td>19.70         </td><td>0.65          </td><td>0.31          </td><td>14.70         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.7           </td><td> 1.00         </td><td>0.41          </td><td>0.28 ( 0 )    </td><td>9.63 ( 0.54 ) </td><td>0 ( 0 )       </td><td>14.63         </td><td>0.53          </td><td>0.28          </td><td> 9.63         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>  50          </td><td>0.9           </td><td> 1.00         </td><td>0.31          </td><td>0.55 ( 0.03 ) </td><td>5.65 ( 0.29 ) </td><td>2.17 ( 0.13 ) </td><td> 8.48         </td><td>0.51          </td><td>0.55          </td><td> 5.65         </td><td>2.17          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>  50          </td><td>0.9           </td><td> 0.50         </td><td>0.38          </td><td>0.38 ( 0.02 ) </td><td>5.85 ( 0.3 )  </td><td>1.03 ( 0.11 ) </td><td> 9.82         </td><td>0.45          </td><td>0.38          </td><td> 5.85         </td><td>1.03          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>  50          </td><td>0.9           </td><td> 0.10         </td><td>0.61          </td><td>0.28 ( 0 )    </td><td>4.34 ( 0.19 ) </td><td>0.01 ( 0.01 ) </td><td> 9.33         </td><td>0.33          </td><td>0.28          </td><td> 4.34         </td><td>0.01          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>  50          </td><td>0.9           </td><td> 0.05         </td><td>0.76          </td><td>0.27 ( 0 )    </td><td>2.71 ( 0.12 ) </td><td>0 ( 0 )       </td><td> 7.71         </td><td>0.2           </td><td>0.27          </td><td> 2.71         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 100          </td><td>0.9           </td><td> 2.00         </td><td>0.26          </td><td>0.6 ( 0.03 )  </td><td>7.26 ( 0.35 ) </td><td>2.69 ( 0.1 )  </td><td> 9.57         </td><td>0.62          </td><td>0.60          </td><td> 7.26         </td><td>2.69          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 100          </td><td>0.9           </td><td> 1.00         </td><td>0.31          </td><td>0.44 ( 0.01 ) </td><td>7.79 ( 0.39 ) </td><td>1.51 ( 0.11 ) </td><td>11.28         </td><td>0.57          </td><td>0.44          </td><td> 7.79         </td><td>1.51          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 100          </td><td>0.9           </td><td> 0.20         </td><td>0.51          </td><td>0.29 ( 0 )    </td><td>6.45 ( 0.27 ) </td><td>0 ( 0 )       </td><td>11.45         </td><td>0.45          </td><td>0.29          </td><td> 6.45         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 100          </td><td>0.9           </td><td> 0.10         </td><td>0.61          </td><td>0.27 ( 0 )    </td><td>4.72 ( 0.21 ) </td><td>0 ( 0 )       </td><td> 9.72         </td><td>0.35          </td><td>0.27          </td><td> 4.72         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td> 500          </td><td>0.9           </td><td>10.00         </td><td>0.13          </td><td>0.81 ( 0.04 ) </td><td>13.78 ( 0.36 )</td><td>3.54 ( 0.08 ) </td><td>15.24         </td><td>0.83          </td><td>0.81          </td><td>13.70         </td><td>3.54          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 500          </td><td>0.9           </td><td> 5.00         </td><td>0.18          </td><td>0.54 ( 0.02 ) </td><td>13.78 ( 0.72 )</td><td>2.58 ( 0.08 ) </td><td>16.20         </td><td>0.76          </td><td>0.54          </td><td>13.70         </td><td>2.58          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.00         </td><td>0.25          </td><td>0.3 ( 0 )     </td><td>18.18 ( 0.69 )</td><td>0.03 ( 0.02 ) </td><td>23.15         </td><td>0.72          </td><td>0.30          </td><td>18.10         </td><td>0.03          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.50         </td><td>0.33          </td><td>0.28 ( 0 )    </td><td>12.68 ( 0.57 )</td><td>0 ( 0 )       </td><td>17.68         </td><td>0.63          </td><td>0.28          </td><td>12.60         </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.00         </td><td>0.08          </td><td>0.9 ( 0.05 )  </td><td>18.15 ( 0.34 )</td><td>3.92 ( 0.08 ) </td><td>19.23         </td><td>0.89          </td><td>0.90          </td><td>18.10         </td><td>3.92          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.00         </td><td>0.16          </td><td>0.59 ( 0.02 ) </td><td>15.99 ( 0.66 )</td><td>2.88 ( 0.06 ) </td><td>18.11         </td><td>0.81          </td><td>0.59          </td><td>15.90         </td><td>2.88          </td></tr>\n",
+       "\t<tr><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.00         </td><td>0.20          </td><td>0.31 ( 0 )    </td><td>24.62 ( 1.01 )</td><td>0.05 ( 0.02 ) </td><td>29.57         </td><td>0.77          </td><td>0.31          </td><td>24.60         </td><td>0.05          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.00         </td><td>0.25          </td><td>0.28 ( 0 )    </td><td>18.51 ( 0.86 )</td><td>0 ( 0 )       </td><td>23.51         </td><td>0.71          </td><td>0.28          </td><td>18.50         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &   50           & 0.1            &  1.00          & 0.36           & 0.62 ( 0.04 )  & 8.59 ( 0.46 )  & 0.02 ( 0.01 )  & 13.57          & 0.51           & 0.62           &  8.59          & 0.02          \\\\\n",
+       "\t  100           &   50           & 0.1            &  0.50          & 0.47           & 0.37 ( 0.01 )  & 6.3 ( 0.43 )   & 0 ( 0 )        & 11.30          & 0.4            & 0.37           &  6.30          & 0.00          \\\\\n",
+       "\t  500           &   50           & 0.1            &  0.10          & 0.73           & 0.28 ( 0 )     & 2.88 ( 0.21 )  & 0 ( 0 )        &  7.88          & 0.2            & 0.28           &  2.88          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.1            &  0.05          & 0.89           & 0.27 ( 0 )     & 1.66 ( 0.11 )  & 0 ( 0 )        &  6.66          & 0.08           & 0.27           &  1.66          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.1            &  2.00          & 0.32           & 0.67 ( 0.05 )  & 11.84 ( 0.4 )  & 0 ( 0 )        & 16.84          & 0.62           & 0.67           & 11.80          & 0.00          \\\\\n",
+       "\t  100           &  100           & 0.1            &  1.00          & 0.44           & 0.4 ( 0.01 )   & 7.71 ( 0.56 )  & 0 ( 0 )        & 12.71          & 0.46           & 0.40           &  7.71          & 0.00          \\\\\n",
+       "\t  500           &  100           & 0.1            &  0.20          & 0.70           & 0.27 ( 0 )     & 3.35 ( 0.22 )  & 0 ( 0 )        &  8.35          & 0.23           & 0.27           &  3.35          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.1            &  0.10          & 0.81           & 0.27 ( 0 )     & 2.29 ( 0.23 )  & 0 ( 0 )        &  7.29          & 0.13           & 0.27           &  2.29          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.1            & 10.00          & 0.18           & 1.61 ( 0.1 )   & 20.85 ( 0.38 ) & 0.69 ( 0.08 )  & 25.16          & 0.78           & 1.61           & 20.80          & 0.69          \\\\\n",
+       "\t  100           &  500           & 0.1            &  5.00          & 0.30           & 0.53 ( 0.02 )  & 14.69 ( 0.7 )  & 0 ( 0 )        & 19.69          & 0.66           & 0.53           & 14.60          & 0.00          \\\\\n",
+       "\t  500           &  500           & 0.1            &  1.00          & 0.55           & 0.28 ( 0 )     & 5.86 ( 0.51 )  & 0 ( 0 )        & 10.86          & 0.36           & 0.28           &  5.86          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.1            &  0.50          & 0.78           & 0.28 ( 0 )     & 2.69 ( 0.21 )  & 0 ( 0 )        &  7.69          & 0.17           & 0.28           &  2.69          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.1            & 20.00          & 0.13           & 2.23 ( 0.14 )  & 24.37 ( 0.39 ) & 1.36 ( 0.1 )   & 28.01          & 0.83           & 2.23           & 24.30          & 1.36          \\\\\n",
+       "\t  100           & 1000           & 0.1            & 10.00          & 0.25           & 0.57 ( 0.03 )  & 18.79 ( 0.7 )  & 0 ( 0 )        & 23.79          & 0.73           & 0.57           & 18.70          & 0.00          \\\\\n",
+       "\t  500           & 1000           & 0.1            &  2.00          & 0.49           & 0.28 ( 0 )     & 7.08 ( 0.53 )  & 0 ( 0 )        & 12.08          & 0.41           & 0.28           &  7.08          & 0.00          \\\\\n",
+       "\t 1000           & 1000           & 0.1            &  1.00          & 0.70           & 0.27 ( 0 )     & 3.59 ( 0.33 )  & 0 ( 0 )        &  8.59          & 0.23           & 0.27           &  3.59          & 0.00          \\\\\n",
+       "\t   50           &   50           & 0.3            &  1.00          & 0.35           & 0.57 ( 0.03 )  & 8.86 ( 0.42 )  & 0.05 ( 0.02 )  & 13.81          & 0.53           & 0.57           &  8.86          & 0.05          \\\\\n",
+       "\t  100           &   50           & 0.3            &  0.50          & 0.47           & 0.36 ( 0.01 )  & 6.18 ( 0.36 )  & 0 ( 0 )        & 11.18          & 0.41           & 0.36           &  6.18          & 0.00          \\\\\n",
+       "\t  500           &   50           & 0.3            &  0.10          & 0.70           & 0.28 ( 0 )     & 3.14 ( 0.21 )  & 0 ( 0 )        &  8.14          & 0.22           & 0.28           &  3.14          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.3            &  0.05          & 0.86           & 0.27 ( 0 )     & 1.86 ( 0.11 )  & 0 ( 0 )        &  6.86          & 0.11           & 0.27           &  1.86          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.3            &  2.00          & 0.31           & 0.76 ( 0.04 )  & 11.12 ( 0.37 ) & 0.17 ( 0.04 )  & 15.95          & 0.62           & 0.76           & 11.10          & 0.17          \\\\\n",
+       "\t  100           &  100           & 0.3            &  1.00          & 0.38           & 0.41 ( 0.01 )  & 9.43 ( 0.53 )  & 0 ( 0 )        & 14.43          & 0.54           & 0.41           &  9.43          & 0.00          \\\\\n",
+       "\t  500           &  100           & 0.3            &  0.20          & 0.63           & 0.27 ( 0 )     & 4.16 ( 0.27 )  & 0 ( 0 )        &  9.16          & 0.29           & 0.27           &  4.16          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.3            &  0.10          & 0.80           & 0.27 ( 0 )     & 2.36 ( 0.17 )  & 0 ( 0 )        &  7.36          & 0.15           & 0.27           &  2.36          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.3            & 10.00          & 0.14           & 1.78 ( 0.11 )  & 21.4 ( 0.41 )  & 1.39 ( 0.1 )   & 25.01          & 0.81           & 1.78           & 21.40          & 1.39          \\\\\n",
+       "\t  100           &  500           & 0.3            &  5.00          & 0.26           & 0.53 ( 0.02 )  & 16.81 ( 0.77 ) & 0.02 ( 0.01 )  & 21.79          & 0.7            & 0.53           & 16.80          & 0.02          \\\\\n",
+       "\t  500           &  500           & 0.3            &  1.00          & 0.55           & 0.29 ( 0 )     & 5.89 ( 0.51 )  & 0 ( 0 )        & 10.89          & 0.36           & 0.29           &  5.89          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.3            &  0.50          & 0.69           & 0.27 ( 0 )     & 3.68 ( 0.32 )  & 0 ( 0 )        &  8.68          & 0.25           & 0.27           &  3.68          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.3            & 20.00          & 0.11           & 2.21 ( 0.15 )  & 25.14 ( 0.35 ) & 1.87 ( 0.1 )   & 28.27          & 0.85           & 2.21           & 25.10          & 1.87          \\\\\n",
+       "\t  100           & 1000           & 0.3            & 10.00          & 0.22           & 0.61 ( 0.03 )  & 21.11 ( 0.75 ) & 0.02 ( 0.01 )  & 26.09          & 0.75           & 0.61           & 21.10          & 0.02          \\\\\n",
+       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
+       "\t  500           &   50           & 0.7            &  0.10          & 0.60           & 0.27 ( 0 )     & 4.38 ( 0.21 )  & 0 ( 0 )        &  9.38          & 0.33           & 0.27           &  4.38          & 0.00          \\\\\n",
+       "\t 1000           &   50           & 0.7            &  0.05          & 0.74           & 0.27 ( 0 )     & 2.84 ( 0.14 )  & 0 ( 0 )        &  7.84          & 0.21           & 0.27           &  2.84          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.7            &  2.00          & 0.23           & 0.8 ( 0.04 )   & 11.57 ( 0.53 ) & 1.26 ( 0.1 )   & 15.31          & 0.66           & 0.80           & 11.50          & 1.26          \\\\\n",
+       "\t  100           &  100           & 0.7            &  1.00          & 0.31           & 0.39 ( 0.01 )  & 11.68 ( 0.51 ) & 0.1 ( 0.04 )   & 16.58          & 0.61           & 0.39           & 11.60          & 0.10          \\\\\n",
+       "\t  500           &  100           & 0.7            &  0.20          & 0.49           & 0.28 ( 0 )     & 6.56 ( 0.32 )  & 0 ( 0 )        & 11.56          & 0.44           & 0.28           &  6.56          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.7            &  0.10          & 0.63           & 0.27 ( 0 )     & 4.24 ( 0.26 )  & 0 ( 0 )        &  9.24          & 0.3            & 0.27           &  4.24          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.7            & 10.00          & 0.12           & 1.37 ( 0.08 )  & 19.8 ( 0.33 )  & 2.78 ( 0.08 )  & 22.02          & 0.85           & 1.37           & 19.80          & 2.78          \\\\\n",
+       "\t  100           &  500           & 0.7            &  5.00          & 0.19           & 0.62 ( 0.03 )  & 18.58 ( 0.85 ) & 0.88 ( 0.08 )  & 22.70          & 0.75           & 0.62           & 18.50          & 0.88          \\\\\n",
+       "\t  500           &  500           & 0.7            &  1.00          & 0.36           & 0.3 ( 0 )      & 11.31 ( 0.6 )  & 0 ( 0 )        & 16.31          & 0.58           & 0.30           & 11.30          & 0.00          \\\\\n",
+       "\t 1000           &  500           & 0.7            &  0.50          & 0.46           & 0.27 ( 0 )     & 7.93 ( 0.49 )  & 0 ( 0 )        & 12.93          & 0.47           & 0.27           &  7.93          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.7            & 20.00          & 0.09           & 1.5 ( 0.08 )   & 22.99 ( 0.35 ) & 3.2 ( 0.09 )   & 24.79          & 0.88           & 1.50           & 22.90          & 3.20          \\\\\n",
+       "\t  100           & 1000           & 0.7            & 10.00          & 0.14           & 0.77 ( 0.04 )  & 22.43 ( 0.68 ) & 1.45 ( 0.1 )   & 25.98          & 0.82           & 0.77           & 22.40          & 1.45          \\\\\n",
+       "\t  500           & 1000           & 0.7            &  2.00          & 0.30           & 0.31 ( 0 )     & 14.7 ( 0.84 )  & 0 ( 0 )        & 19.70          & 0.65           & 0.31           & 14.70          & 0.00          \\\\\n",
+       "\t 1000           & 1000           & 0.7            &  1.00          & 0.41           & 0.28 ( 0 )     & 9.63 ( 0.54 )  & 0 ( 0 )        & 14.63          & 0.53           & 0.28           &  9.63          & 0.00          \\\\\n",
+       "\t   50           &   50           & 0.9            &  1.00          & 0.31           & 0.55 ( 0.03 )  & 5.65 ( 0.29 )  & 2.17 ( 0.13 )  &  8.48          & 0.51           & 0.55           &  5.65          & 2.17          \\\\\n",
+       "\t  100           &   50           & 0.9            &  0.50          & 0.38           & 0.38 ( 0.02 )  & 5.85 ( 0.3 )   & 1.03 ( 0.11 )  &  9.82          & 0.45           & 0.38           &  5.85          & 1.03          \\\\\n",
+       "\t  500           &   50           & 0.9            &  0.10          & 0.61           & 0.28 ( 0 )     & 4.34 ( 0.19 )  & 0.01 ( 0.01 )  &  9.33          & 0.33           & 0.28           &  4.34          & 0.01          \\\\\n",
+       "\t 1000           &   50           & 0.9            &  0.05          & 0.76           & 0.27 ( 0 )     & 2.71 ( 0.12 )  & 0 ( 0 )        &  7.71          & 0.2            & 0.27           &  2.71          & 0.00          \\\\\n",
+       "\t   50           &  100           & 0.9            &  2.00          & 0.26           & 0.6 ( 0.03 )   & 7.26 ( 0.35 )  & 2.69 ( 0.1 )   &  9.57          & 0.62           & 0.60           &  7.26          & 2.69          \\\\\n",
+       "\t  100           &  100           & 0.9            &  1.00          & 0.31           & 0.44 ( 0.01 )  & 7.79 ( 0.39 )  & 1.51 ( 0.11 )  & 11.28          & 0.57           & 0.44           &  7.79          & 1.51          \\\\\n",
+       "\t  500           &  100           & 0.9            &  0.20          & 0.51           & 0.29 ( 0 )     & 6.45 ( 0.27 )  & 0 ( 0 )        & 11.45          & 0.45           & 0.29           &  6.45          & 0.00          \\\\\n",
+       "\t 1000           &  100           & 0.9            &  0.10          & 0.61           & 0.27 ( 0 )     & 4.72 ( 0.21 )  & 0 ( 0 )        &  9.72          & 0.35           & 0.27           &  4.72          & 0.00          \\\\\n",
+       "\t   50           &  500           & 0.9            & 10.00          & 0.13           & 0.81 ( 0.04 )  & 13.78 ( 0.36 ) & 3.54 ( 0.08 )  & 15.24          & 0.83           & 0.81           & 13.70          & 3.54          \\\\\n",
+       "\t  100           &  500           & 0.9            &  5.00          & 0.18           & 0.54 ( 0.02 )  & 13.78 ( 0.72 ) & 2.58 ( 0.08 )  & 16.20          & 0.76           & 0.54           & 13.70          & 2.58          \\\\\n",
+       "\t  500           &  500           & 0.9            &  1.00          & 0.25           & 0.3 ( 0 )      & 18.18 ( 0.69 ) & 0.03 ( 0.02 )  & 23.15          & 0.72           & 0.30           & 18.10          & 0.03          \\\\\n",
+       "\t 1000           &  500           & 0.9            &  0.50          & 0.33           & 0.28 ( 0 )     & 12.68 ( 0.57 ) & 0 ( 0 )        & 17.68          & 0.63           & 0.28           & 12.60          & 0.00          \\\\\n",
+       "\t   50           & 1000           & 0.9            & 20.00          & 0.08           & 0.9 ( 0.05 )   & 18.15 ( 0.34 ) & 3.92 ( 0.08 )  & 19.23          & 0.89           & 0.90           & 18.10          & 3.92          \\\\\n",
+       "\t  100           & 1000           & 0.9            & 10.00          & 0.16           & 0.59 ( 0.02 )  & 15.99 ( 0.66 ) & 2.88 ( 0.06 )  & 18.11          & 0.81           & 0.59           & 15.90          & 2.88          \\\\\n",
+       "\t  500           & 1000           & 0.9            &  2.00          & 0.20           & 0.31 ( 0 )     & 24.62 ( 1.01 ) & 0.05 ( 0.02 )  & 29.57          & 0.77           & 0.31           & 24.60          & 0.05          \\\\\n",
+       "\t 1000           & 1000           & 0.9            &  1.00          & 0.25           & 0.28 ( 0 )     & 18.51 ( 0.86 ) & 0 ( 0 )        & 23.51          & 0.71           & 0.28           & 18.50          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |   50           | 0.1            |  1.00          | 0.36           | 0.62 ( 0.04 )  | 8.59 ( 0.46 )  | 0.02 ( 0.01 )  | 13.57          | 0.51           | 0.62           |  8.59          | 0.02           |\n",
+       "|  100           |   50           | 0.1            |  0.50          | 0.47           | 0.37 ( 0.01 )  | 6.3 ( 0.43 )   | 0 ( 0 )        | 11.30          | 0.4            | 0.37           |  6.30          | 0.00           |\n",
+       "|  500           |   50           | 0.1            |  0.10          | 0.73           | 0.28 ( 0 )     | 2.88 ( 0.21 )  | 0 ( 0 )        |  7.88          | 0.2            | 0.28           |  2.88          | 0.00           |\n",
+       "| 1000           |   50           | 0.1            |  0.05          | 0.89           | 0.27 ( 0 )     | 1.66 ( 0.11 )  | 0 ( 0 )        |  6.66          | 0.08           | 0.27           |  1.66          | 0.00           |\n",
+       "|   50           |  100           | 0.1            |  2.00          | 0.32           | 0.67 ( 0.05 )  | 11.84 ( 0.4 )  | 0 ( 0 )        | 16.84          | 0.62           | 0.67           | 11.80          | 0.00           |\n",
+       "|  100           |  100           | 0.1            |  1.00          | 0.44           | 0.4 ( 0.01 )   | 7.71 ( 0.56 )  | 0 ( 0 )        | 12.71          | 0.46           | 0.40           |  7.71          | 0.00           |\n",
+       "|  500           |  100           | 0.1            |  0.20          | 0.70           | 0.27 ( 0 )     | 3.35 ( 0.22 )  | 0 ( 0 )        |  8.35          | 0.23           | 0.27           |  3.35          | 0.00           |\n",
+       "| 1000           |  100           | 0.1            |  0.10          | 0.81           | 0.27 ( 0 )     | 2.29 ( 0.23 )  | 0 ( 0 )        |  7.29          | 0.13           | 0.27           |  2.29          | 0.00           |\n",
+       "|   50           |  500           | 0.1            | 10.00          | 0.18           | 1.61 ( 0.1 )   | 20.85 ( 0.38 ) | 0.69 ( 0.08 )  | 25.16          | 0.78           | 1.61           | 20.80          | 0.69           |\n",
+       "|  100           |  500           | 0.1            |  5.00          | 0.30           | 0.53 ( 0.02 )  | 14.69 ( 0.7 )  | 0 ( 0 )        | 19.69          | 0.66           | 0.53           | 14.60          | 0.00           |\n",
+       "|  500           |  500           | 0.1            |  1.00          | 0.55           | 0.28 ( 0 )     | 5.86 ( 0.51 )  | 0 ( 0 )        | 10.86          | 0.36           | 0.28           |  5.86          | 0.00           |\n",
+       "| 1000           |  500           | 0.1            |  0.50          | 0.78           | 0.28 ( 0 )     | 2.69 ( 0.21 )  | 0 ( 0 )        |  7.69          | 0.17           | 0.28           |  2.69          | 0.00           |\n",
+       "|   50           | 1000           | 0.1            | 20.00          | 0.13           | 2.23 ( 0.14 )  | 24.37 ( 0.39 ) | 1.36 ( 0.1 )   | 28.01          | 0.83           | 2.23           | 24.30          | 1.36           |\n",
+       "|  100           | 1000           | 0.1            | 10.00          | 0.25           | 0.57 ( 0.03 )  | 18.79 ( 0.7 )  | 0 ( 0 )        | 23.79          | 0.73           | 0.57           | 18.70          | 0.00           |\n",
+       "|  500           | 1000           | 0.1            |  2.00          | 0.49           | 0.28 ( 0 )     | 7.08 ( 0.53 )  | 0 ( 0 )        | 12.08          | 0.41           | 0.28           |  7.08          | 0.00           |\n",
+       "| 1000           | 1000           | 0.1            |  1.00          | 0.70           | 0.27 ( 0 )     | 3.59 ( 0.33 )  | 0 ( 0 )        |  8.59          | 0.23           | 0.27           |  3.59          | 0.00           |\n",
+       "|   50           |   50           | 0.3            |  1.00          | 0.35           | 0.57 ( 0.03 )  | 8.86 ( 0.42 )  | 0.05 ( 0.02 )  | 13.81          | 0.53           | 0.57           |  8.86          | 0.05           |\n",
+       "|  100           |   50           | 0.3            |  0.50          | 0.47           | 0.36 ( 0.01 )  | 6.18 ( 0.36 )  | 0 ( 0 )        | 11.18          | 0.41           | 0.36           |  6.18          | 0.00           |\n",
+       "|  500           |   50           | 0.3            |  0.10          | 0.70           | 0.28 ( 0 )     | 3.14 ( 0.21 )  | 0 ( 0 )        |  8.14          | 0.22           | 0.28           |  3.14          | 0.00           |\n",
+       "| 1000           |   50           | 0.3            |  0.05          | 0.86           | 0.27 ( 0 )     | 1.86 ( 0.11 )  | 0 ( 0 )        |  6.86          | 0.11           | 0.27           |  1.86          | 0.00           |\n",
+       "|   50           |  100           | 0.3            |  2.00          | 0.31           | 0.76 ( 0.04 )  | 11.12 ( 0.37 ) | 0.17 ( 0.04 )  | 15.95          | 0.62           | 0.76           | 11.10          | 0.17           |\n",
+       "|  100           |  100           | 0.3            |  1.00          | 0.38           | 0.41 ( 0.01 )  | 9.43 ( 0.53 )  | 0 ( 0 )        | 14.43          | 0.54           | 0.41           |  9.43          | 0.00           |\n",
+       "|  500           |  100           | 0.3            |  0.20          | 0.63           | 0.27 ( 0 )     | 4.16 ( 0.27 )  | 0 ( 0 )        |  9.16          | 0.29           | 0.27           |  4.16          | 0.00           |\n",
+       "| 1000           |  100           | 0.3            |  0.10          | 0.80           | 0.27 ( 0 )     | 2.36 ( 0.17 )  | 0 ( 0 )        |  7.36          | 0.15           | 0.27           |  2.36          | 0.00           |\n",
+       "|   50           |  500           | 0.3            | 10.00          | 0.14           | 1.78 ( 0.11 )  | 21.4 ( 0.41 )  | 1.39 ( 0.1 )   | 25.01          | 0.81           | 1.78           | 21.40          | 1.39           |\n",
+       "|  100           |  500           | 0.3            |  5.00          | 0.26           | 0.53 ( 0.02 )  | 16.81 ( 0.77 ) | 0.02 ( 0.01 )  | 21.79          | 0.7            | 0.53           | 16.80          | 0.02           |\n",
+       "|  500           |  500           | 0.3            |  1.00          | 0.55           | 0.29 ( 0 )     | 5.89 ( 0.51 )  | 0 ( 0 )        | 10.89          | 0.36           | 0.29           |  5.89          | 0.00           |\n",
+       "| 1000           |  500           | 0.3            |  0.50          | 0.69           | 0.27 ( 0 )     | 3.68 ( 0.32 )  | 0 ( 0 )        |  8.68          | 0.25           | 0.27           |  3.68          | 0.00           |\n",
+       "|   50           | 1000           | 0.3            | 20.00          | 0.11           | 2.21 ( 0.15 )  | 25.14 ( 0.35 ) | 1.87 ( 0.1 )   | 28.27          | 0.85           | 2.21           | 25.10          | 1.87           |\n",
+       "|  100           | 1000           | 0.3            | 10.00          | 0.22           | 0.61 ( 0.03 )  | 21.11 ( 0.75 ) | 0.02 ( 0.01 )  | 26.09          | 0.75           | 0.61           | 21.10          | 0.02           |\n",
+       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
+       "|  500           |   50           | 0.7            |  0.10          | 0.60           | 0.27 ( 0 )     | 4.38 ( 0.21 )  | 0 ( 0 )        |  9.38          | 0.33           | 0.27           |  4.38          | 0.00           |\n",
+       "| 1000           |   50           | 0.7            |  0.05          | 0.74           | 0.27 ( 0 )     | 2.84 ( 0.14 )  | 0 ( 0 )        |  7.84          | 0.21           | 0.27           |  2.84          | 0.00           |\n",
+       "|   50           |  100           | 0.7            |  2.00          | 0.23           | 0.8 ( 0.04 )   | 11.57 ( 0.53 ) | 1.26 ( 0.1 )   | 15.31          | 0.66           | 0.80           | 11.50          | 1.26           |\n",
+       "|  100           |  100           | 0.7            |  1.00          | 0.31           | 0.39 ( 0.01 )  | 11.68 ( 0.51 ) | 0.1 ( 0.04 )   | 16.58          | 0.61           | 0.39           | 11.60          | 0.10           |\n",
+       "|  500           |  100           | 0.7            |  0.20          | 0.49           | 0.28 ( 0 )     | 6.56 ( 0.32 )  | 0 ( 0 )        | 11.56          | 0.44           | 0.28           |  6.56          | 0.00           |\n",
+       "| 1000           |  100           | 0.7            |  0.10          | 0.63           | 0.27 ( 0 )     | 4.24 ( 0.26 )  | 0 ( 0 )        |  9.24          | 0.3            | 0.27           |  4.24          | 0.00           |\n",
+       "|   50           |  500           | 0.7            | 10.00          | 0.12           | 1.37 ( 0.08 )  | 19.8 ( 0.33 )  | 2.78 ( 0.08 )  | 22.02          | 0.85           | 1.37           | 19.80          | 2.78           |\n",
+       "|  100           |  500           | 0.7            |  5.00          | 0.19           | 0.62 ( 0.03 )  | 18.58 ( 0.85 ) | 0.88 ( 0.08 )  | 22.70          | 0.75           | 0.62           | 18.50          | 0.88           |\n",
+       "|  500           |  500           | 0.7            |  1.00          | 0.36           | 0.3 ( 0 )      | 11.31 ( 0.6 )  | 0 ( 0 )        | 16.31          | 0.58           | 0.30           | 11.30          | 0.00           |\n",
+       "| 1000           |  500           | 0.7            |  0.50          | 0.46           | 0.27 ( 0 )     | 7.93 ( 0.49 )  | 0 ( 0 )        | 12.93          | 0.47           | 0.27           |  7.93          | 0.00           |\n",
+       "|   50           | 1000           | 0.7            | 20.00          | 0.09           | 1.5 ( 0.08 )   | 22.99 ( 0.35 ) | 3.2 ( 0.09 )   | 24.79          | 0.88           | 1.50           | 22.90          | 3.20           |\n",
+       "|  100           | 1000           | 0.7            | 10.00          | 0.14           | 0.77 ( 0.04 )  | 22.43 ( 0.68 ) | 1.45 ( 0.1 )   | 25.98          | 0.82           | 0.77           | 22.40          | 1.45           |\n",
+       "|  500           | 1000           | 0.7            |  2.00          | 0.30           | 0.31 ( 0 )     | 14.7 ( 0.84 )  | 0 ( 0 )        | 19.70          | 0.65           | 0.31           | 14.70          | 0.00           |\n",
+       "| 1000           | 1000           | 0.7            |  1.00          | 0.41           | 0.28 ( 0 )     | 9.63 ( 0.54 )  | 0 ( 0 )        | 14.63          | 0.53           | 0.28           |  9.63          | 0.00           |\n",
+       "|   50           |   50           | 0.9            |  1.00          | 0.31           | 0.55 ( 0.03 )  | 5.65 ( 0.29 )  | 2.17 ( 0.13 )  |  8.48          | 0.51           | 0.55           |  5.65          | 2.17           |\n",
+       "|  100           |   50           | 0.9            |  0.50          | 0.38           | 0.38 ( 0.02 )  | 5.85 ( 0.3 )   | 1.03 ( 0.11 )  |  9.82          | 0.45           | 0.38           |  5.85          | 1.03           |\n",
+       "|  500           |   50           | 0.9            |  0.10          | 0.61           | 0.28 ( 0 )     | 4.34 ( 0.19 )  | 0.01 ( 0.01 )  |  9.33          | 0.33           | 0.28           |  4.34          | 0.01           |\n",
+       "| 1000           |   50           | 0.9            |  0.05          | 0.76           | 0.27 ( 0 )     | 2.71 ( 0.12 )  | 0 ( 0 )        |  7.71          | 0.2            | 0.27           |  2.71          | 0.00           |\n",
+       "|   50           |  100           | 0.9            |  2.00          | 0.26           | 0.6 ( 0.03 )   | 7.26 ( 0.35 )  | 2.69 ( 0.1 )   |  9.57          | 0.62           | 0.60           |  7.26          | 2.69           |\n",
+       "|  100           |  100           | 0.9            |  1.00          | 0.31           | 0.44 ( 0.01 )  | 7.79 ( 0.39 )  | 1.51 ( 0.11 )  | 11.28          | 0.57           | 0.44           |  7.79          | 1.51           |\n",
+       "|  500           |  100           | 0.9            |  0.20          | 0.51           | 0.29 ( 0 )     | 6.45 ( 0.27 )  | 0 ( 0 )        | 11.45          | 0.45           | 0.29           |  6.45          | 0.00           |\n",
+       "| 1000           |  100           | 0.9            |  0.10          | 0.61           | 0.27 ( 0 )     | 4.72 ( 0.21 )  | 0 ( 0 )        |  9.72          | 0.35           | 0.27           |  4.72          | 0.00           |\n",
+       "|   50           |  500           | 0.9            | 10.00          | 0.13           | 0.81 ( 0.04 )  | 13.78 ( 0.36 ) | 3.54 ( 0.08 )  | 15.24          | 0.83           | 0.81           | 13.70          | 3.54           |\n",
+       "|  100           |  500           | 0.9            |  5.00          | 0.18           | 0.54 ( 0.02 )  | 13.78 ( 0.72 ) | 2.58 ( 0.08 )  | 16.20          | 0.76           | 0.54           | 13.70          | 2.58           |\n",
+       "|  500           |  500           | 0.9            |  1.00          | 0.25           | 0.3 ( 0 )      | 18.18 ( 0.69 ) | 0.03 ( 0.02 )  | 23.15          | 0.72           | 0.30           | 18.10          | 0.03           |\n",
+       "| 1000           |  500           | 0.9            |  0.50          | 0.33           | 0.28 ( 0 )     | 12.68 ( 0.57 ) | 0 ( 0 )        | 17.68          | 0.63           | 0.28           | 12.60          | 0.00           |\n",
+       "|   50           | 1000           | 0.9            | 20.00          | 0.08           | 0.9 ( 0.05 )   | 18.15 ( 0.34 ) | 3.92 ( 0.08 )  | 19.23          | 0.89           | 0.90           | 18.10          | 3.92           |\n",
+       "|  100           | 1000           | 0.9            | 10.00          | 0.16           | 0.59 ( 0.02 )  | 15.99 ( 0.66 ) | 2.88 ( 0.06 )  | 18.11          | 0.81           | 0.59           | 15.90          | 2.88           |\n",
+       "|  500           | 1000           | 0.9            |  2.00          | 0.20           | 0.31 ( 0 )     | 24.62 ( 1.01 ) | 0.05 ( 0.02 )  | 29.57          | 0.77           | 0.31           | 24.60          | 0.05           |\n",
+       "| 1000           | 1000           | 0.9            |  1.00          | 0.25           | 0.28 ( 0 )     | 18.51 ( 0.86 ) | 0 ( 0 )        | 23.51          | 0.71           | 0.28           | 18.50          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "1     50   50 0.1   1.00 0.36 0.62 ( 0.04 ) 8.59 ( 0.46 )  0.02 ( 0.01 )\n",
+       "2    100   50 0.1   0.50 0.47 0.37 ( 0.01 ) 6.3 ( 0.43 )   0 ( 0 )      \n",
+       "3    500   50 0.1   0.10 0.73 0.28 ( 0 )    2.88 ( 0.21 )  0 ( 0 )      \n",
+       "4   1000   50 0.1   0.05 0.89 0.27 ( 0 )    1.66 ( 0.11 )  0 ( 0 )      \n",
+       "5     50  100 0.1   2.00 0.32 0.67 ( 0.05 ) 11.84 ( 0.4 )  0 ( 0 )      \n",
+       "6    100  100 0.1   1.00 0.44 0.4 ( 0.01 )  7.71 ( 0.56 )  0 ( 0 )      \n",
+       "7    500  100 0.1   0.20 0.70 0.27 ( 0 )    3.35 ( 0.22 )  0 ( 0 )      \n",
+       "8   1000  100 0.1   0.10 0.81 0.27 ( 0 )    2.29 ( 0.23 )  0 ( 0 )      \n",
+       "9     50  500 0.1  10.00 0.18 1.61 ( 0.1 )  20.85 ( 0.38 ) 0.69 ( 0.08 )\n",
+       "10   100  500 0.1   5.00 0.30 0.53 ( 0.02 ) 14.69 ( 0.7 )  0 ( 0 )      \n",
+       "11   500  500 0.1   1.00 0.55 0.28 ( 0 )    5.86 ( 0.51 )  0 ( 0 )      \n",
+       "12  1000  500 0.1   0.50 0.78 0.28 ( 0 )    2.69 ( 0.21 )  0 ( 0 )      \n",
+       "13    50 1000 0.1  20.00 0.13 2.23 ( 0.14 ) 24.37 ( 0.39 ) 1.36 ( 0.1 ) \n",
+       "14   100 1000 0.1  10.00 0.25 0.57 ( 0.03 ) 18.79 ( 0.7 )  0 ( 0 )      \n",
+       "15   500 1000 0.1   2.00 0.49 0.28 ( 0 )    7.08 ( 0.53 )  0 ( 0 )      \n",
+       "16  1000 1000 0.1   1.00 0.70 0.27 ( 0 )    3.59 ( 0.33 )  0 ( 0 )      \n",
+       "17    50   50 0.3   1.00 0.35 0.57 ( 0.03 ) 8.86 ( 0.42 )  0.05 ( 0.02 )\n",
+       "18   100   50 0.3   0.50 0.47 0.36 ( 0.01 ) 6.18 ( 0.36 )  0 ( 0 )      \n",
+       "19   500   50 0.3   0.10 0.70 0.28 ( 0 )    3.14 ( 0.21 )  0 ( 0 )      \n",
+       "20  1000   50 0.3   0.05 0.86 0.27 ( 0 )    1.86 ( 0.11 )  0 ( 0 )      \n",
+       "21    50  100 0.3   2.00 0.31 0.76 ( 0.04 ) 11.12 ( 0.37 ) 0.17 ( 0.04 )\n",
+       "22   100  100 0.3   1.00 0.38 0.41 ( 0.01 ) 9.43 ( 0.53 )  0 ( 0 )      \n",
+       "23   500  100 0.3   0.20 0.63 0.27 ( 0 )    4.16 ( 0.27 )  0 ( 0 )      \n",
+       "24  1000  100 0.3   0.10 0.80 0.27 ( 0 )    2.36 ( 0.17 )  0 ( 0 )      \n",
+       "25    50  500 0.3  10.00 0.14 1.78 ( 0.11 ) 21.4 ( 0.41 )  1.39 ( 0.1 ) \n",
+       "26   100  500 0.3   5.00 0.26 0.53 ( 0.02 ) 16.81 ( 0.77 ) 0.02 ( 0.01 )\n",
+       "27   500  500 0.3   1.00 0.55 0.29 ( 0 )    5.89 ( 0.51 )  0 ( 0 )      \n",
+       "28  1000  500 0.3   0.50 0.69 0.27 ( 0 )    3.68 ( 0.32 )  0 ( 0 )      \n",
+       "29    50 1000 0.3  20.00 0.11 2.21 ( 0.15 ) 25.14 ( 0.35 ) 1.87 ( 0.1 ) \n",
+       "30   100 1000 0.3  10.00 0.22 0.61 ( 0.03 ) 21.11 ( 0.75 ) 0.02 ( 0.01 )\n",
+       "... ...  ...  ...  ...   ...  ...           ...            ...          \n",
+       "51   500   50 0.7   0.10 0.60 0.27 ( 0 )    4.38 ( 0.21 )  0 ( 0 )      \n",
+       "52  1000   50 0.7   0.05 0.74 0.27 ( 0 )    2.84 ( 0.14 )  0 ( 0 )      \n",
+       "53    50  100 0.7   2.00 0.23 0.8 ( 0.04 )  11.57 ( 0.53 ) 1.26 ( 0.1 ) \n",
+       "54   100  100 0.7   1.00 0.31 0.39 ( 0.01 ) 11.68 ( 0.51 ) 0.1 ( 0.04 ) \n",
+       "55   500  100 0.7   0.20 0.49 0.28 ( 0 )    6.56 ( 0.32 )  0 ( 0 )      \n",
+       "56  1000  100 0.7   0.10 0.63 0.27 ( 0 )    4.24 ( 0.26 )  0 ( 0 )      \n",
+       "57    50  500 0.7  10.00 0.12 1.37 ( 0.08 ) 19.8 ( 0.33 )  2.78 ( 0.08 )\n",
+       "58   100  500 0.7   5.00 0.19 0.62 ( 0.03 ) 18.58 ( 0.85 ) 0.88 ( 0.08 )\n",
+       "59   500  500 0.7   1.00 0.36 0.3 ( 0 )     11.31 ( 0.6 )  0 ( 0 )      \n",
+       "60  1000  500 0.7   0.50 0.46 0.27 ( 0 )    7.93 ( 0.49 )  0 ( 0 )      \n",
+       "61    50 1000 0.7  20.00 0.09 1.5 ( 0.08 )  22.99 ( 0.35 ) 3.2 ( 0.09 ) \n",
+       "62   100 1000 0.7  10.00 0.14 0.77 ( 0.04 ) 22.43 ( 0.68 ) 1.45 ( 0.1 ) \n",
+       "63   500 1000 0.7   2.00 0.30 0.31 ( 0 )    14.7 ( 0.84 )  0 ( 0 )      \n",
+       "64  1000 1000 0.7   1.00 0.41 0.28 ( 0 )    9.63 ( 0.54 )  0 ( 0 )      \n",
+       "65    50   50 0.9   1.00 0.31 0.55 ( 0.03 ) 5.65 ( 0.29 )  2.17 ( 0.13 )\n",
+       "66   100   50 0.9   0.50 0.38 0.38 ( 0.02 ) 5.85 ( 0.3 )   1.03 ( 0.11 )\n",
+       "67   500   50 0.9   0.10 0.61 0.28 ( 0 )    4.34 ( 0.19 )  0.01 ( 0.01 )\n",
+       "68  1000   50 0.9   0.05 0.76 0.27 ( 0 )    2.71 ( 0.12 )  0 ( 0 )      \n",
+       "69    50  100 0.9   2.00 0.26 0.6 ( 0.03 )  7.26 ( 0.35 )  2.69 ( 0.1 ) \n",
+       "70   100  100 0.9   1.00 0.31 0.44 ( 0.01 ) 7.79 ( 0.39 )  1.51 ( 0.11 )\n",
+       "71   500  100 0.9   0.20 0.51 0.29 ( 0 )    6.45 ( 0.27 )  0 ( 0 )      \n",
+       "72  1000  100 0.9   0.10 0.61 0.27 ( 0 )    4.72 ( 0.21 )  0 ( 0 )      \n",
+       "73    50  500 0.9  10.00 0.13 0.81 ( 0.04 ) 13.78 ( 0.36 ) 3.54 ( 0.08 )\n",
+       "74   100  500 0.9   5.00 0.18 0.54 ( 0.02 ) 13.78 ( 0.72 ) 2.58 ( 0.08 )\n",
+       "75   500  500 0.9   1.00 0.25 0.3 ( 0 )     18.18 ( 0.69 ) 0.03 ( 0.02 )\n",
+       "76  1000  500 0.9   0.50 0.33 0.28 ( 0 )    12.68 ( 0.57 ) 0 ( 0 )      \n",
+       "77    50 1000 0.9  20.00 0.08 0.9 ( 0.05 )  18.15 ( 0.34 ) 3.92 ( 0.08 )\n",
+       "78   100 1000 0.9  10.00 0.16 0.59 ( 0.02 ) 15.99 ( 0.66 ) 2.88 ( 0.06 )\n",
+       "79   500 1000 0.9   2.00 0.20 0.31 ( 0 )    24.62 ( 1.01 ) 0.05 ( 0.02 )\n",
+       "80  1000 1000 0.9   1.00 0.25 0.28 ( 0 )    18.51 ( 0.86 ) 0 ( 0 )      \n",
+       "    num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1   13.57      0.51 0.62      8.59   0.02   \n",
+       "2   11.30      0.4  0.37      6.30   0.00   \n",
+       "3    7.88      0.2  0.28      2.88   0.00   \n",
+       "4    6.66      0.08 0.27      1.66   0.00   \n",
+       "5   16.84      0.62 0.67     11.80   0.00   \n",
+       "6   12.71      0.46 0.40      7.71   0.00   \n",
+       "7    8.35      0.23 0.27      3.35   0.00   \n",
+       "8    7.29      0.13 0.27      2.29   0.00   \n",
+       "9   25.16      0.78 1.61     20.80   0.69   \n",
+       "10  19.69      0.66 0.53     14.60   0.00   \n",
+       "11  10.86      0.36 0.28      5.86   0.00   \n",
+       "12   7.69      0.17 0.28      2.69   0.00   \n",
+       "13  28.01      0.83 2.23     24.30   1.36   \n",
+       "14  23.79      0.73 0.57     18.70   0.00   \n",
+       "15  12.08      0.41 0.28      7.08   0.00   \n",
+       "16   8.59      0.23 0.27      3.59   0.00   \n",
+       "17  13.81      0.53 0.57      8.86   0.05   \n",
+       "18  11.18      0.41 0.36      6.18   0.00   \n",
+       "19   8.14      0.22 0.28      3.14   0.00   \n",
+       "20   6.86      0.11 0.27      1.86   0.00   \n",
+       "21  15.95      0.62 0.76     11.10   0.17   \n",
+       "22  14.43      0.54 0.41      9.43   0.00   \n",
+       "23   9.16      0.29 0.27      4.16   0.00   \n",
+       "24   7.36      0.15 0.27      2.36   0.00   \n",
+       "25  25.01      0.81 1.78     21.40   1.39   \n",
+       "26  21.79      0.7  0.53     16.80   0.02   \n",
+       "27  10.89      0.36 0.29      5.89   0.00   \n",
+       "28   8.68      0.25 0.27      3.68   0.00   \n",
+       "29  28.27      0.85 2.21     25.10   1.87   \n",
+       "30  26.09      0.75 0.61     21.10   0.02   \n",
+       "... ...        ...  ...      ...     ...    \n",
+       "51   9.38      0.33 0.27      4.38   0.00   \n",
+       "52   7.84      0.21 0.27      2.84   0.00   \n",
+       "53  15.31      0.66 0.80     11.50   1.26   \n",
+       "54  16.58      0.61 0.39     11.60   0.10   \n",
+       "55  11.56      0.44 0.28      6.56   0.00   \n",
+       "56   9.24      0.3  0.27      4.24   0.00   \n",
+       "57  22.02      0.85 1.37     19.80   2.78   \n",
+       "58  22.70      0.75 0.62     18.50   0.88   \n",
+       "59  16.31      0.58 0.30     11.30   0.00   \n",
+       "60  12.93      0.47 0.27      7.93   0.00   \n",
+       "61  24.79      0.88 1.50     22.90   3.20   \n",
+       "62  25.98      0.82 0.77     22.40   1.45   \n",
+       "63  19.70      0.65 0.31     14.70   0.00   \n",
+       "64  14.63      0.53 0.28      9.63   0.00   \n",
+       "65   8.48      0.51 0.55      5.65   2.17   \n",
+       "66   9.82      0.45 0.38      5.85   1.03   \n",
+       "67   9.33      0.33 0.28      4.34   0.01   \n",
+       "68   7.71      0.2  0.27      2.71   0.00   \n",
+       "69   9.57      0.62 0.60      7.26   2.69   \n",
+       "70  11.28      0.57 0.44      7.79   1.51   \n",
+       "71  11.45      0.45 0.29      6.45   0.00   \n",
+       "72   9.72      0.35 0.27      4.72   0.00   \n",
+       "73  15.24      0.83 0.81     13.70   3.54   \n",
+       "74  16.20      0.76 0.54     13.70   2.58   \n",
+       "75  23.15      0.72 0.30     18.10   0.03   \n",
+       "76  17.68      0.63 0.28     12.60   0.00   \n",
+       "77  19.23      0.89 0.90     18.10   3.92   \n",
+       "78  18.11      0.81 0.59     15.90   2.88   \n",
+       "79  29.57      0.77 0.31     24.60   0.05   \n",
+       "80  23.51      0.71 0.28     18.50   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe\n",
+    "\n",
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/1.2_sim_toe_elnet_cts.ipynb b/simulations/notebooks_sim_cts/1.2_sim_toe_elnet_cts.ipynb
new file mode 100755
index 0000000..445c6b6
--- /dev/null
+++ b/simulations/notebooks_sim_cts/1.2_sim_toe_elnet_cts.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_elnet))\n",
+    "for (i in 1:length(results_toe_elnet)){\n",
+    "    table_toe = rbind(table_toe, results_toe_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50            </td><td>50            </td><td>0.1           </td><td>14.72 ( 0.57 )</td><td>0.01 ( 0.01 ) </td><td>0.6 ( 0.03 )  </td><td>0.21          </td><td>19.71         </td><td>0.67          </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>13.7 ( 0.66 )</td><td>0 ( 0 )      </td><td>0.34 ( 0.01 )</td><td>0.23         </td><td>18.70        </td><td>0.64         </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0.1           </td><td>13.59 ( 0.55 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.23          </td><td>18.59         </td><td>0.65          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0.1           </td><td>12.64 ( 0.54 )</td><td>0 ( 0 )       </td><td>0.26 ( 0 )    </td><td>0.25          </td><td>17.64         </td><td>0.63          </td></tr>\n",
+       "\t<tr><td>50            </td><td>100           </td><td>0.1           </td><td>19.33 ( 0.67 )</td><td>0 ( 0 )       </td><td>0.69 ( 0.05 ) </td><td>0.2           </td><td>24.33         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>17.77 ( 0.8 )</td><td>0 ( 0 )      </td><td>0.38 ( 0.01 )</td><td>0.22         </td><td>22.77        </td><td>0.70         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50             & 50             & 0.1            & 14.72 ( 0.57 ) & 0.01 ( 0.01 )  & 0.6 ( 0.03 )   & 0.21           & 19.71          & 0.67          \\\\\n",
+       "\t 100           & 50            & 0.1           & 13.7 ( 0.66 ) & 0 ( 0 )       & 0.34 ( 0.01 ) & 0.23          & 18.70         & 0.64         \\\\\n",
+       "\t 500            & 50             & 0.1            & 13.59 ( 0.55 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.23           & 18.59          & 0.65          \\\\\n",
+       "\t 1000           & 50             & 0.1            & 12.64 ( 0.54 ) & 0 ( 0 )        & 0.26 ( 0 )     & 0.25           & 17.64          & 0.63          \\\\\n",
+       "\t 50             & 100            & 0.1            & 19.33 ( 0.67 ) & 0 ( 0 )        & 0.69 ( 0.05 )  & 0.2            & 24.33          & 0.73          \\\\\n",
+       "\t 100           & 100           & 0.1           & 17.77 ( 0.8 ) & 0 ( 0 )       & 0.38 ( 0.01 ) & 0.22          & 22.77         & 0.70         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50             | 50             | 0.1            | 14.72 ( 0.57 ) | 0.01 ( 0.01 )  | 0.6 ( 0.03 )   | 0.21           | 19.71          | 0.67           |\n",
+       "| 100           | 50            | 0.1           | 13.7 ( 0.66 ) | 0 ( 0 )       | 0.34 ( 0.01 ) | 0.23          | 18.70         | 0.64          |\n",
+       "| 500            | 50             | 0.1            | 13.59 ( 0.55 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.23           | 18.59          | 0.65           |\n",
+       "| 1000           | 50             | 0.1            | 12.64 ( 0.54 ) | 0 ( 0 )        | 0.26 ( 0 )     | 0.25           | 17.64          | 0.63           |\n",
+       "| 50             | 100            | 0.1            | 19.33 ( 0.67 ) | 0 ( 0 )        | 0.69 ( 0.05 )  | 0.2            | 24.33          | 0.73           |\n",
+       "| 100           | 100           | 0.1           | 17.77 ( 0.8 ) | 0 ( 0 )       | 0.38 ( 0.01 ) | 0.22          | 22.77         | 0.70          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 14.72 ( 0.57 ) 0.01 ( 0.01 ) 0.6 ( 0.03 )  0.21 19.71      0.67\n",
+       "2 100  50  0.1 13.7 ( 0.66 )  0 ( 0 )       0.34 ( 0.01 ) 0.23 18.70      0.64\n",
+       "3 500  50  0.1 13.59 ( 0.55 ) 0 ( 0 )       0.26 ( 0 )    0.23 18.59      0.65\n",
+       "4 1000 50  0.1 12.64 ( 0.54 ) 0 ( 0 )       0.26 ( 0 )    0.25 17.64      0.63\n",
+       "5 50   100 0.1 19.33 ( 0.67 ) 0 ( 0 )       0.69 ( 0.05 ) 0.2  24.33      0.73\n",
+       "6 100  100 0.1 17.77 ( 0.8 )  0 ( 0 )       0.38 ( 0.01 ) 0.22 22.77      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>24</th><td>1000         </td><td>100          </td><td>0.3          </td><td>0.1          </td><td>0.20         </td><td>0.26 ( 0 )   </td><td>19 ( 0.86 )  </td><td>0 ( 0 )      </td><td>24.00        </td><td>0.71         </td><td>0.26         </td><td>NA           </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100         </td><td> 50          </td><td>0.9          </td><td>0.5          </td><td>0.21         </td><td>0.35 ( 0.01 )</td><td>14 ( 0.69 )  </td><td>0.27 ( 0.06 )</td><td>18.73        </td><td>0.66         </td><td>0.35         </td><td>NA           </td><td>0.27         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t24 & 1000          & 100           & 0.3           & 0.1           & 0.20          & 0.26 ( 0 )    & 19 ( 0.86 )   & 0 ( 0 )       & 24.00         & 0.71          & 0.26          & NA            & 0.00         \\\\\n",
+       "\t66 &  100          &  50           & 0.9           & 0.5           & 0.21          & 0.35 ( 0.01 ) & 14 ( 0.69 )   & 0.27 ( 0.06 ) & 18.73         & 0.66          & 0.35          & NA            & 0.27         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 24 | 1000          | 100           | 0.3           | 0.1           | 0.20          | 0.26 ( 0 )    | 19 ( 0.86 )   | 0 ( 0 )       | 24.00         | 0.71          | 0.26          | NA            | 0.00          |\n",
+       "| 66 |  100          |  50           | 0.9           | 0.5           | 0.21          | 0.35 ( 0.01 ) | 14 ( 0.69 )   | 0.27 ( 0.06 ) | 18.73         | 0.66          | 0.35          | NA            | 0.27          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "24 1000 100 0.3  0.1   0.20 0.26 ( 0 )    19 ( 0.86 ) 0 ( 0 )       24.00     \n",
+       "66  100  50 0.9  0.5   0.21 0.35 ( 0.01 ) 14 ( 0.69 ) 0.27 ( 0.06 ) 18.73     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "24 0.71 0.26     NA      0.00   \n",
+       "66 0.66 0.35     NA      0.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$FP_mean[24] = 19\n",
+    "result.table_toe$FP_mean[66] = 14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>24</th><td>1000         </td><td>100          </td><td>0.3          </td><td>0.1          </td><td>0.20         </td><td>0.26 ( 0 )   </td><td>19 ( 0.86 )  </td><td>0 ( 0 )      </td><td>24.00        </td><td>0.71         </td><td>0.26         </td><td>19           </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>66</th><td> 100         </td><td> 50          </td><td>0.9          </td><td>0.5          </td><td>0.21         </td><td>0.35 ( 0.01 )</td><td>14 ( 0.69 )  </td><td>0.27 ( 0.06 )</td><td>18.73        </td><td>0.66         </td><td>0.35         </td><td>14           </td><td>0.27         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t24 & 1000          & 100           & 0.3           & 0.1           & 0.20          & 0.26 ( 0 )    & 19 ( 0.86 )   & 0 ( 0 )       & 24.00         & 0.71          & 0.26          & 19            & 0.00         \\\\\n",
+       "\t66 &  100          &  50           & 0.9           & 0.5           & 0.21          & 0.35 ( 0.01 ) & 14 ( 0.69 )   & 0.27 ( 0.06 ) & 18.73         & 0.66          & 0.35          & 14            & 0.27         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 24 | 1000          | 100           | 0.3           | 0.1           | 0.20          | 0.26 ( 0 )    | 19 ( 0.86 )   | 0 ( 0 )       | 24.00         | 0.71          | 0.26          | 19            | 0.00          |\n",
+       "| 66 |  100          |  50           | 0.9           | 0.5           | 0.21          | 0.35 ( 0.01 ) | 14 ( 0.69 )   | 0.27 ( 0.06 ) | 18.73         | 0.66          | 0.35          | 14            | 0.27          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P   Corr Ratio Stab MSE           FP          FN            num_select\n",
+       "24 1000 100 0.3  0.1   0.20 0.26 ( 0 )    19 ( 0.86 ) 0 ( 0 )       24.00     \n",
+       "66  100  50 0.9  0.5   0.21 0.35 ( 0.01 ) 14 ( 0.69 ) 0.27 ( 0.06 ) 18.73     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "24 0.71 0.26     19      0.00   \n",
+       "66 0.66 0.35     14      0.27   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[c(24, 66), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td> 50           </td><td>0.1           </td><td>1.00          </td><td>0.21          </td><td>0.6 ( 0.03 )  </td><td>14.72 ( 0.57 )</td><td>0.01 ( 0.01 ) </td><td>19.71         </td><td>0.67          </td><td>0.60          </td><td>14.7          </td><td>0.01          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 50           </td><td>0.1           </td><td>0.50          </td><td>0.23          </td><td>0.34 ( 0.01 ) </td><td>13.7 ( 0.66 ) </td><td>0 ( 0 )       </td><td>18.70         </td><td>0.64          </td><td>0.34          </td><td>13.7          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 50           </td><td>0.1           </td><td>0.10          </td><td>0.23          </td><td>0.26 ( 0 )    </td><td>13.59 ( 0.55 )</td><td>0 ( 0 )       </td><td>18.59         </td><td>0.65          </td><td>0.26          </td><td>13.5          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 50           </td><td>0.1           </td><td>0.05          </td><td>0.25          </td><td>0.26 ( 0 )    </td><td>12.64 ( 0.54 )</td><td>0 ( 0 )       </td><td>17.64         </td><td>0.63          </td><td>0.26          </td><td>12.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>100           </td><td>0.1           </td><td>2.00          </td><td>0.20          </td><td>0.69 ( 0.05 ) </td><td>19.33 ( 0.67 )</td><td>0 ( 0 )       </td><td>24.33         </td><td>0.73          </td><td>0.69          </td><td>19.3          </td><td>0.00          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>100           </td><td>0.1           </td><td>1.00          </td><td>0.22          </td><td>0.38 ( 0.01 ) </td><td>17.77 ( 0.8 ) </td><td>0 ( 0 )       </td><td>22.77         </td><td>0.7           </td><td>0.38          </td><td>17.7          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &  50            & 0.1            & 1.00           & 0.21           & 0.6 ( 0.03 )   & 14.72 ( 0.57 ) & 0.01 ( 0.01 )  & 19.71          & 0.67           & 0.60           & 14.7           & 0.01          \\\\\n",
+       "\t  100           &  50            & 0.1            & 0.50           & 0.23           & 0.34 ( 0.01 )  & 13.7 ( 0.66 )  & 0 ( 0 )        & 18.70          & 0.64           & 0.34           & 13.7           & 0.00          \\\\\n",
+       "\t  500           &  50            & 0.1            & 0.10           & 0.23           & 0.26 ( 0 )     & 13.59 ( 0.55 ) & 0 ( 0 )        & 18.59          & 0.65           & 0.26           & 13.5           & 0.00          \\\\\n",
+       "\t 1000           &  50            & 0.1            & 0.05           & 0.25           & 0.26 ( 0 )     & 12.64 ( 0.54 ) & 0 ( 0 )        & 17.64          & 0.63           & 0.26           & 12.6           & 0.00          \\\\\n",
+       "\t   50           & 100            & 0.1            & 2.00           & 0.20           & 0.69 ( 0.05 )  & 19.33 ( 0.67 ) & 0 ( 0 )        & 24.33          & 0.73           & 0.69           & 19.3           & 0.00          \\\\\n",
+       "\t  100           & 100            & 0.1            & 1.00           & 0.22           & 0.38 ( 0.01 )  & 17.77 ( 0.8 )  & 0 ( 0 )        & 22.77          & 0.7            & 0.38           & 17.7           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |  50            | 0.1            | 1.00           | 0.21           | 0.6 ( 0.03 )   | 14.72 ( 0.57 ) | 0.01 ( 0.01 )  | 19.71          | 0.67           | 0.60           | 14.7           | 0.01           |\n",
+       "|  100           |  50            | 0.1            | 0.50           | 0.23           | 0.34 ( 0.01 )  | 13.7 ( 0.66 )  | 0 ( 0 )        | 18.70          | 0.64           | 0.34           | 13.7           | 0.00           |\n",
+       "|  500           |  50            | 0.1            | 0.10           | 0.23           | 0.26 ( 0 )     | 13.59 ( 0.55 ) | 0 ( 0 )        | 18.59          | 0.65           | 0.26           | 13.5           | 0.00           |\n",
+       "| 1000           |  50            | 0.1            | 0.05           | 0.25           | 0.26 ( 0 )     | 12.64 ( 0.54 ) | 0 ( 0 )        | 17.64          | 0.63           | 0.26           | 12.6           | 0.00           |\n",
+       "|   50           | 100            | 0.1            | 2.00           | 0.20           | 0.69 ( 0.05 )  | 19.33 ( 0.67 ) | 0 ( 0 )        | 24.33          | 0.73           | 0.69           | 19.3           | 0.00           |\n",
+       "|  100           | 100            | 0.1            | 1.00           | 0.22           | 0.38 ( 0.01 )  | 17.77 ( 0.8 )  | 0 ( 0 )        | 22.77          | 0.7            | 0.38           | 17.7           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.21 0.6 ( 0.03 )  14.72 ( 0.57 ) 0.01 ( 0.01 )\n",
+       "2  100  50 0.1  0.50  0.23 0.34 ( 0.01 ) 13.7 ( 0.66 )  0 ( 0 )      \n",
+       "3  500  50 0.1  0.10  0.23 0.26 ( 0 )    13.59 ( 0.55 ) 0 ( 0 )      \n",
+       "4 1000  50 0.1  0.05  0.25 0.26 ( 0 )    12.64 ( 0.54 ) 0 ( 0 )      \n",
+       "5   50 100 0.1  2.00  0.20 0.69 ( 0.05 ) 19.33 ( 0.67 ) 0 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.22 0.38 ( 0.01 ) 17.77 ( 0.8 )  0 ( 0 )      \n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1 19.71      0.67 0.60     14.7    0.01   \n",
+       "2 18.70      0.64 0.34     13.7    0.00   \n",
+       "3 18.59      0.65 0.26     13.5    0.00   \n",
+       "4 17.64      0.63 0.26     12.6    0.00   \n",
+       "5 24.33      0.73 0.69     19.3    0.00   \n",
+       "6 22.77      0.7  0.38     17.7    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.12          </td><td>0.29 ( 0 )    </td><td>41.14 ( 1.02 )</td><td>0 ( 0 )       </td><td>46.14         </td><td>0.86          </td><td>0.29          </td><td>41.1          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>39.21 ( 0.84 )</td><td>0 ( 0 )       </td><td>44.21         </td><td>0.86          </td><td>0.27          </td><td>39.2          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.04          </td><td>0.94 ( 0.05 ) </td><td>36.59 ( 3.6 ) </td><td>3.72 ( 0.08 ) </td><td>37.87         </td><td>0.92          </td><td>0.94          </td><td>36.5          </td><td>3.72          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.09          </td><td>0.59 ( 0.02 ) </td><td>32.05 ( 1.85 )</td><td>2.59 ( 0.06 ) </td><td>34.46         </td><td>0.88          </td><td>0.59          </td><td>32.0          </td><td>2.59          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.09          </td><td>0.3 ( 0 )     </td><td>57.69 ( 1.57 )</td><td>0 ( 0 )       </td><td>62.69         </td><td>0.9           </td><td>0.30          </td><td>57.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.10          </td><td>0.27 ( 0 )    </td><td>52.35 ( 1.6 ) </td><td>0 ( 0 )       </td><td>57.35         </td><td>0.89          </td><td>0.27          </td><td>52.3          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.12           & 0.29 ( 0 )     & 41.14 ( 1.02 ) & 0 ( 0 )        & 46.14          & 0.86           & 0.29           & 41.1           & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.13           & 0.27 ( 0 )     & 39.21 ( 0.84 ) & 0 ( 0 )        & 44.21          & 0.86           & 0.27           & 39.2           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.04           & 0.94 ( 0.05 )  & 36.59 ( 3.6 )  & 3.72 ( 0.08 )  & 37.87          & 0.92           & 0.94           & 36.5           & 3.72          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.09           & 0.59 ( 0.02 )  & 32.05 ( 1.85 ) & 2.59 ( 0.06 )  & 34.46          & 0.88           & 0.59           & 32.0           & 2.59          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.09           & 0.3 ( 0 )      & 57.69 ( 1.57 ) & 0 ( 0 )        & 62.69          & 0.9            & 0.30           & 57.6           & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.10           & 0.27 ( 0 )     & 52.35 ( 1.6 )  & 0 ( 0 )        & 57.35          & 0.89           & 0.27           & 52.3           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.12           | 0.29 ( 0 )     | 41.14 ( 1.02 ) | 0 ( 0 )        | 46.14          | 0.86           | 0.29           | 41.1           | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.13           | 0.27 ( 0 )     | 39.21 ( 0.84 ) | 0 ( 0 )        | 44.21          | 0.86           | 0.27           | 39.2           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.04           | 0.94 ( 0.05 )  | 36.59 ( 3.6 )  | 3.72 ( 0.08 )  | 37.87          | 0.92           | 0.94           | 36.5           | 3.72           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.09           | 0.59 ( 0.02 )  | 32.05 ( 1.85 ) | 2.59 ( 0.06 )  | 34.46          | 0.88           | 0.59           | 32.0           | 2.59           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.09           | 0.3 ( 0 )      | 57.69 ( 1.57 ) | 0 ( 0 )        | 62.69          | 0.9            | 0.30           | 57.6           | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.10           | 0.27 ( 0 )     | 52.35 ( 1.6 )  | 0 ( 0 )        | 57.35          | 0.89           | 0.27           | 52.3           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.12 0.29 ( 0 )    41.14 ( 1.02 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.13 0.27 ( 0 )    39.21 ( 0.84 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.04 0.94 ( 0.05 ) 36.59 ( 3.6 )  3.72 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.09 0.59 ( 0.02 ) 32.05 ( 1.85 ) 2.59 ( 0.06 )\n",
+       "79  500 1000 0.9   2.0  0.09 0.3 ( 0 )     57.69 ( 1.57 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.10 0.27 ( 0 )    52.35 ( 1.6 )  0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 46.14      0.86 0.29     41.1    0.00   \n",
+       "76 44.21      0.86 0.27     39.2    0.00   \n",
+       "77 37.87      0.92 0.94     36.5    3.72   \n",
+       "78 34.46      0.88 0.59     32.0    2.59   \n",
+       "79 62.69      0.9  0.30     57.6    0.00   \n",
+       "80 57.35      0.89 0.27     52.3    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/1.3_sim_toe_rf_cts.ipynb b/simulations/notebooks_sim_cts/1.3_sim_toe_rf_cts.ipynb
new file mode 100755
index 0000000..38b3c7f
--- /dev/null
+++ b/simulations/notebooks_sim_cts/1.3_sim_toe_rf_cts.ipynb
@@ -0,0 +1,766 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_rf))\n",
+    "for (i in 1:length(results_toe_rf)){\n",
+    "    results_toe_rf[[i]]$OOB = paste(round(mean(results_toe_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_toe_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_toe = rbind(table_toe, results_toe_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.4 ( 0.06 ) </td><td>NaN          </td><td>1.98 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>0.86 ( 0.03 )</td><td>0.19         </td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>0.44 ( 0.01 )</td><td>0.77         </td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>0.34 ( 0 )   </td><td>0.86         </td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.36 ( 0.06 )</td><td>NaN          </td><td>2.19 ( 0.02 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>0.93 ( 0.03 )</td><td>0.12         </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 1.4 ( 0.06 )  & NaN           & 1.98 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0.1           & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 0.86 ( 0.03 ) & 0.19          & 1.76 ( 0.01 ) & 3.62          & 0.48         \\\\\n",
+       "\t 500           & 50            & 0.1           & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 0.44 ( 0.01 ) & 0.77          & 1.29 ( 0 )    & 4.51          & 0.06         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 0.34 ( 0 )    & 0.86          & 1.15 ( 0 )    & 5.22          & 0.02         \\\\\n",
+       "\t 50            & 100           & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 1.36 ( 0.06 ) & NaN           & 2.19 ( 0.02 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 100           & 0.1           & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 0.93 ( 0.03 ) & 0.12          & 1.99 ( 0.01 ) & 5.93          & 0.68         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 1.4 ( 0.06 )  | NaN           | 1.98 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0.1           | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 0.86 ( 0.03 ) | 0.19          | 1.76 ( 0.01 ) | 3.62          | 0.48          |\n",
+       "| 500           | 50            | 0.1           | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 0.44 ( 0.01 ) | 0.77          | 1.29 ( 0 )    | 4.51          | 0.06          |\n",
+       "| 1000          | 50            | 0.1           | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 0.34 ( 0 )    | 0.86          | 1.15 ( 0 )    | 5.22          | 0.02          |\n",
+       "| 50            | 100           | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 1.36 ( 0.06 ) | NaN           | 2.19 ( 0.02 ) | 0.00          |  NaN          |\n",
+       "| 100           | 100           | 0.1           | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 0.93 ( 0.03 ) | 0.12          | 1.99 ( 0.01 ) | 5.93          | 0.68          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab OOB          \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       1.4 ( 0.06 )  NaN  1.98 ( 0.02 )\n",
+       "2 100  50  0.1 1.87 ( 0.14 ) 4.25 ( 0.09 ) 0.86 ( 0.03 ) 0.19 1.76 ( 0.01 )\n",
+       "3 500  50  0.1 0.32 ( 0.05 ) 1.81 ( 0.07 ) 0.44 ( 0.01 ) 0.77 1.29 ( 0 )   \n",
+       "4 1000 50  0.1 0.15 ( 0.04 ) 0.93 ( 0.07 ) 0.34 ( 0 )    0.86 1.15 ( 0 )   \n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       1.36 ( 0.06 ) NaN  2.19 ( 0.02 )\n",
+       "6 100  100 0.1 4.11 ( 0.18 ) 4.17 ( 0.1 )  0.93 ( 0.03 ) 0.12 1.99 ( 0.01 )\n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.62       0.48\n",
+       "3 4.51       0.06\n",
+       "4 5.22       0.02\n",
+       "5 0.00        NaN\n",
+       "6 5.93       0.68"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>NaN          </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0            </td><td>NaN          </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.19         </td><td>0.86 ( 0.03 )</td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.77         </td><td>0.44 ( 0.01 )</td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.86         </td><td>0.34 ( 0 )   </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>NaN          </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td>0            </td><td>NaN          </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.12         </td><td>0.93 ( 0.03 )</td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & NaN           & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0             & NaN          \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.19          & 0.86 ( 0.03 ) & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 1.76 ( 0.01 ) & 3.62          & 0.48         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.77          & 0.44 ( 0.01 ) & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 1.29 ( 0 )    & 4.51          & 0.06         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.86          & 0.34 ( 0 )    & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 1.15 ( 0 )    & 5.22          & 0.02         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & NaN           & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) & 0             & NaN          \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.12          & 0.93 ( 0.03 ) & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 1.99 ( 0.01 ) & 5.93          & 0.68         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | NaN           | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0             | NaN           |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.19          | 0.86 ( 0.03 ) | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 1.76 ( 0.01 ) | 3.62          | 0.48          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.77          | 0.44 ( 0.01 ) | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 1.29 ( 0 )    | 4.51          | 0.06          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.86          | 0.34 ( 0 )    | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 1.15 ( 0 )    | 5.22          | 0.02          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | NaN           | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) | 0             | NaN           |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.12          | 0.93 ( 0.03 ) | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 1.99 ( 0.01 ) | 5.93          | 0.68          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50  50 0.1  1.00  NaN  1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "2  100  50 0.1  0.50  0.19 0.86 ( 0.03 ) 1.87 ( 0.14 ) 4.25 ( 0.09 )\n",
+       "3  500  50 0.1  0.10  0.77 0.44 ( 0.01 ) 0.32 ( 0.05 ) 1.81 ( 0.07 )\n",
+       "4 1000  50 0.1  0.05  0.86 0.34 ( 0 )    0.15 ( 0.04 ) 0.93 ( 0.07 )\n",
+       "5   50 100 0.1  2.00  NaN  1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.12 0.93 ( 0.03 ) 4.11 ( 0.18 ) 4.17 ( 0.1 ) \n",
+       "  OOB           num_select FDR \n",
+       "1 1.98 ( 0.02 ) 0          NaN \n",
+       "2 1.76 ( 0.01 ) 3.62       0.48\n",
+       "3 1.29 ( 0 )    4.51       0.06\n",
+       "4 1.15 ( 0 )    5.22       0.02\n",
+       "5 2.19 ( 0.02 ) 0          NaN \n",
+       "6 1.99 ( 0.01 ) 5.93       0.68"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$OOB_mean = as.numeric(substr(result.table_toe$OOB, start=1, stop=4))\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50          </td><td>  50         </td><td>0.1          </td><td> 1           </td><td> NaN         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.40         </td><td>  NA         </td><td>0.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50          </td><td> 100         </td><td>0.1          </td><td> 2           </td><td> NaN         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.36         </td><td>  NA         </td><td>0.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50          </td><td> 500         </td><td>0.1          </td><td>10           </td><td> NaN         </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.39 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.29         </td><td>  NA         </td><td>0.00         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50          </td><td>1000         </td><td>0.1          </td><td>20           </td><td> NaN         </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.42 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.35         </td><td>  NA         </td><td>0.00         </td><td>2.42         </td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100          </td><td>1000         </td><td>0.1          </td><td>10           </td><td>0.01         </td><td>1 ( 0.03 )   </td><td>49.42 ( 0.7 )</td><td>4.71 ( 0.09 )</td><td>2.39 ( 0.02 )</td><td>50.71        </td><td>0.97         </td><td>  NA         </td><td>49.4         </td><td>4.71         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50          </td><td>  50         </td><td>0.3          </td><td> 1           </td><td> NaN         </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.87 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.22         </td><td>  NA         </td><td>0.00         </td><td>1.87         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td> 100         </td><td>0.3          </td><td> 2           </td><td> NaN         </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.03 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.15         </td><td>  NA         </td><td>0.00         </td><td>2.03         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50          </td><td> 500         </td><td>0.3          </td><td>10           </td><td> NaN         </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.24 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.14         </td><td>  NA         </td><td>0.00         </td><td>2.24         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50          </td><td>1000         </td><td>0.3          </td><td>20           </td><td> NaN         </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.29 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.20         </td><td>  NA         </td><td>0.00         </td><td>2.29         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50          </td><td>  50         </td><td>0.5          </td><td> 1           </td><td> NaN         </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.99         </td><td>  NA         </td><td>0.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50          </td><td> 100         </td><td>0.5          </td><td> 2           </td><td> NaN         </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.84 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.91         </td><td>  NA         </td><td>0.00         </td><td>1.84         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50          </td><td> 500         </td><td>0.5          </td><td>10           </td><td> NaN         </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.95         </td><td>  NA         </td><td>0.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50          </td><td>1000         </td><td>0.5          </td><td>20           </td><td> NaN         </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.94         </td><td>  NA         </td><td>0.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50          </td><td>  50         </td><td>0.7          </td><td> 1           </td><td> NaN         </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.44 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.66         </td><td>  NA         </td><td>0.00         </td><td>1.44         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50          </td><td> 100         </td><td>0.7          </td><td> 2           </td><td> NaN         </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.5 ( 0.02 ) </td><td> 0.00        </td><td>NaN          </td><td>0.67         </td><td>  NA         </td><td>0.00         </td><td>1.50         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50          </td><td> 500         </td><td>0.7          </td><td>10           </td><td> NaN         </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.70         </td><td>  NA         </td><td>0.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50          </td><td>1000         </td><td>0.7          </td><td>20           </td><td> NaN         </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.69 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.74         </td><td>  NA         </td><td>0.00         </td><td>1.69         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50          </td><td>  50         </td><td>0.9          </td><td> 1           </td><td> NaN         </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.96 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.27         </td><td>  NA         </td><td>0.00         </td><td>0.96         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50          </td><td> 100         </td><td>0.9          </td><td> 2           </td><td> NaN         </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1 ( 0.01 )   </td><td> 0.00        </td><td>NaN          </td><td>0.30         </td><td>  NA         </td><td>0.00         </td><td>  NA         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50          </td><td> 500         </td><td>0.9          </td><td>10           </td><td> NaN         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.1 ( 0.01 ) </td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td>  NA         </td><td>0.00         </td><td>1.10         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50          </td><td>1000         </td><td>0.9          </td><td>20           </td><td> NaN         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.11 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td>  NA         </td><td>0.00         </td><td>1.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50           &   50          & 0.1           &  1            &  NaN          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) &  0.00         & NaN           & 1.40          &   NA          & 0.00          & 1.98         \\\\\n",
+       "\t5 &  50           &  100          & 0.1           &  2            &  NaN          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) &  0.00         & NaN           & 1.36          &   NA          & 0.00          & 2.19         \\\\\n",
+       "\t9 &  50           &  500          & 0.1           & 10            &  NaN          & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.39 ( 0.02 ) &  0.00         & NaN           & 1.29          &   NA          & 0.00          & 2.39         \\\\\n",
+       "\t13 &  50           & 1000          & 0.1           & 20            &  NaN          & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.42 ( 0.02 ) &  0.00         & NaN           & 1.35          &   NA          & 0.00          & 2.42         \\\\\n",
+       "\t14 & 100           & 1000          & 0.1           & 10            & 0.01          & 1 ( 0.03 )    & 49.42 ( 0.7 ) & 4.71 ( 0.09 ) & 2.39 ( 0.02 ) & 50.71         & 0.97          &   NA          & 49.4          & 4.71          & 2.39         \\\\\n",
+       "\t17 &  50           &   50          & 0.3           &  1            &  NaN          & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.87 ( 0.02 ) &  0.00         & NaN           & 1.22          &   NA          & 0.00          & 1.87         \\\\\n",
+       "\t21 &  50           &  100          & 0.3           &  2            &  NaN          & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.03 ( 0.02 ) &  0.00         & NaN           & 1.15          &   NA          & 0.00          & 2.03         \\\\\n",
+       "\t25 &  50           &  500          & 0.3           & 10            &  NaN          & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.24 ( 0.02 ) &  0.00         & NaN           & 1.14          &   NA          & 0.00          & 2.24         \\\\\n",
+       "\t29 &  50           & 1000          & 0.3           & 20            &  NaN          & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 2.29 ( 0.02 ) &  0.00         & NaN           & 1.20          &   NA          & 0.00          & 2.29         \\\\\n",
+       "\t33 &  50           &   50          & 0.5           &  1            &  NaN          & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.99          &   NA          & 0.00          & 1.68         \\\\\n",
+       "\t37 &  50           &  100          & 0.5           &  2            &  NaN          & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.84 ( 0.02 ) &  0.00         & NaN           & 0.91          &   NA          & 0.00          & 1.84         \\\\\n",
+       "\t41 &  50           &  500          & 0.5           & 10            &  NaN          & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.95          &   NA          & 0.00          & 2.04         \\\\\n",
+       "\t45 &  50           & 1000          & 0.5           & 20            &  NaN          & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.94          &   NA          & 0.00          & 2.04         \\\\\n",
+       "\t49 &  50           &   50          & 0.7           &  1            &  NaN          & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.44 ( 0.01 ) &  0.00         & NaN           & 0.66          &   NA          & 0.00          & 1.44         \\\\\n",
+       "\t53 &  50           &  100          & 0.7           &  2            &  NaN          & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.5 ( 0.02 )  &  0.00         & NaN           & 0.67          &   NA          & 0.00          & 1.50         \\\\\n",
+       "\t57 &  50           &  500          & 0.7           & 10            &  NaN          & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.70          &   NA          & 0.00          & 1.68         \\\\\n",
+       "\t61 &  50           & 1000          & 0.7           & 20            &  NaN          & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.69 ( 0.02 ) &  0.00         & NaN           & 0.74          &   NA          & 0.00          & 1.69         \\\\\n",
+       "\t65 &  50           &   50          & 0.9           &  1            &  NaN          & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0.96 ( 0.01 ) &  0.00         & NaN           & 0.27          &   NA          & 0.00          & 0.96         \\\\\n",
+       "\t69 &  50           &  100          & 0.9           &  2            &  NaN          & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 1 ( 0.01 )    &  0.00         & NaN           & 0.30          &   NA          & 0.00          &   NA         \\\\\n",
+       "\t73 &  50           &  500          & 0.9           & 10            &  NaN          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.1 ( 0.01 )  &  0.00         & NaN           & 0.29          &   NA          & 0.00          & 1.10         \\\\\n",
+       "\t77 &  50           & 1000          & 0.9           & 20            &  NaN          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.11 ( 0.01 ) &  0.00         & NaN           & 0.29          &   NA          & 0.00          & 1.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50           |   50          | 0.1           |  1            |  NaN          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) |  0.00         | NaN           | 1.40          |   NA          | 0.00          | 1.98          |\n",
+       "| 5 |  50           |  100          | 0.1           |  2            |  NaN          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) |  0.00         | NaN           | 1.36          |   NA          | 0.00          | 2.19          |\n",
+       "| 9 |  50           |  500          | 0.1           | 10            |  NaN          | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.39 ( 0.02 ) |  0.00         | NaN           | 1.29          |   NA          | 0.00          | 2.39          |\n",
+       "| 13 |  50           | 1000          | 0.1           | 20            |  NaN          | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.42 ( 0.02 ) |  0.00         | NaN           | 1.35          |   NA          | 0.00          | 2.42          |\n",
+       "| 14 | 100           | 1000          | 0.1           | 10            | 0.01          | 1 ( 0.03 )    | 49.42 ( 0.7 ) | 4.71 ( 0.09 ) | 2.39 ( 0.02 ) | 50.71         | 0.97          |   NA          | 49.4          | 4.71          | 2.39          |\n",
+       "| 17 |  50           |   50          | 0.3           |  1            |  NaN          | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.87 ( 0.02 ) |  0.00         | NaN           | 1.22          |   NA          | 0.00          | 1.87          |\n",
+       "| 21 |  50           |  100          | 0.3           |  2            |  NaN          | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.03 ( 0.02 ) |  0.00         | NaN           | 1.15          |   NA          | 0.00          | 2.03          |\n",
+       "| 25 |  50           |  500          | 0.3           | 10            |  NaN          | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.24 ( 0.02 ) |  0.00         | NaN           | 1.14          |   NA          | 0.00          | 2.24          |\n",
+       "| 29 |  50           | 1000          | 0.3           | 20            |  NaN          | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 2.29 ( 0.02 ) |  0.00         | NaN           | 1.20          |   NA          | 0.00          | 2.29          |\n",
+       "| 33 |  50           |   50          | 0.5           |  1            |  NaN          | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.99          |   NA          | 0.00          | 1.68          |\n",
+       "| 37 |  50           |  100          | 0.5           |  2            |  NaN          | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.84 ( 0.02 ) |  0.00         | NaN           | 0.91          |   NA          | 0.00          | 1.84          |\n",
+       "| 41 |  50           |  500          | 0.5           | 10            |  NaN          | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.95          |   NA          | 0.00          | 2.04          |\n",
+       "| 45 |  50           | 1000          | 0.5           | 20            |  NaN          | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.94          |   NA          | 0.00          | 2.04          |\n",
+       "| 49 |  50           |   50          | 0.7           |  1            |  NaN          | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.44 ( 0.01 ) |  0.00         | NaN           | 0.66          |   NA          | 0.00          | 1.44          |\n",
+       "| 53 |  50           |  100          | 0.7           |  2            |  NaN          | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.5 ( 0.02 )  |  0.00         | NaN           | 0.67          |   NA          | 0.00          | 1.50          |\n",
+       "| 57 |  50           |  500          | 0.7           | 10            |  NaN          | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.70          |   NA          | 0.00          | 1.68          |\n",
+       "| 61 |  50           | 1000          | 0.7           | 20            |  NaN          | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.69 ( 0.02 ) |  0.00         | NaN           | 0.74          |   NA          | 0.00          | 1.69          |\n",
+       "| 65 |  50           |   50          | 0.9           |  1            |  NaN          | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0.96 ( 0.01 ) |  0.00         | NaN           | 0.27          |   NA          | 0.00          | 0.96          |\n",
+       "| 69 |  50           |  100          | 0.9           |  2            |  NaN          | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 1 ( 0.01 )    |  0.00         | NaN           | 0.30          |   NA          | 0.00          |   NA          |\n",
+       "| 73 |  50           |  500          | 0.9           | 10            |  NaN          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.1 ( 0.01 )  |  0.00         | NaN           | 0.29          |   NA          | 0.00          | 1.10          |\n",
+       "| 77 |  50           | 1000          | 0.9           | 20            |  NaN          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.11 ( 0.01 ) |  0.00         | NaN           | 0.29          |   NA          | 0.00          | 1.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50   50 0.1   1     NaN 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "5   50  100 0.1   2     NaN 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "9   50  500 0.1  10     NaN 1.29 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "13  50 1000 0.1  20     NaN 1.35 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "14 100 1000 0.1  10    0.01 1 ( 0.03 )    49.42 ( 0.7 ) 4.71 ( 0.09 )\n",
+       "17  50   50 0.3   1     NaN 1.22 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "21  50  100 0.3   2     NaN 1.15 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "25  50  500 0.3  10     NaN 1.14 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "29  50 1000 0.3  20     NaN 1.2 ( 0.05 )  1 ( 0 )       6 ( 0 )      \n",
+       "33  50   50 0.5   1     NaN 0.99 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "37  50  100 0.5   2     NaN 0.91 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "41  50  500 0.5  10     NaN 0.95 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "45  50 1000 0.5  20     NaN 0.94 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "49  50   50 0.7   1     NaN 0.66 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "53  50  100 0.7   2     NaN 0.67 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "57  50  500 0.7  10     NaN 0.7 ( 0.03 )  1 ( 0 )       6 ( 0 )      \n",
+       "61  50 1000 0.7  20     NaN 0.74 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "65  50   50 0.9   1     NaN 0.27 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "69  50  100 0.9   2     NaN 0.3 ( 0.01 )  1 ( 0 )       6 ( 0 )      \n",
+       "73  50  500 0.9  10     NaN 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "77  50 1000 0.9  20     NaN 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  1.98 ( 0.02 )  0.00      NaN  1.40       NA    0.00    1.98    \n",
+       "5  2.19 ( 0.02 )  0.00      NaN  1.36       NA    0.00    2.19    \n",
+       "9  2.39 ( 0.02 )  0.00      NaN  1.29       NA    0.00    2.39    \n",
+       "13 2.42 ( 0.02 )  0.00      NaN  1.35       NA    0.00    2.42    \n",
+       "14 2.39 ( 0.02 ) 50.71      0.97   NA     49.4    4.71    2.39    \n",
+       "17 1.87 ( 0.02 )  0.00      NaN  1.22       NA    0.00    1.87    \n",
+       "21 2.03 ( 0.02 )  0.00      NaN  1.15       NA    0.00    2.03    \n",
+       "25 2.24 ( 0.02 )  0.00      NaN  1.14       NA    0.00    2.24    \n",
+       "29 2.29 ( 0.02 )  0.00      NaN  1.20       NA    0.00    2.29    \n",
+       "33 1.68 ( 0.02 )  0.00      NaN  0.99       NA    0.00    1.68    \n",
+       "37 1.84 ( 0.02 )  0.00      NaN  0.91       NA    0.00    1.84    \n",
+       "41 2.04 ( 0.02 )  0.00      NaN  0.95       NA    0.00    2.04    \n",
+       "45 2.04 ( 0.02 )  0.00      NaN  0.94       NA    0.00    2.04    \n",
+       "49 1.44 ( 0.01 )  0.00      NaN  0.66       NA    0.00    1.44    \n",
+       "53 1.5 ( 0.02 )   0.00      NaN  0.67       NA    0.00    1.50    \n",
+       "57 1.68 ( 0.02 )  0.00      NaN  0.70       NA    0.00    1.68    \n",
+       "61 1.69 ( 0.02 )  0.00      NaN  0.74       NA    0.00    1.69    \n",
+       "65 0.96 ( 0.01 )  0.00      NaN  0.27       NA    0.00    0.96    \n",
+       "69 1 ( 0.01 )     0.00      NaN  0.30       NA    0.00      NA    \n",
+       "73 1.1 ( 0.01 )   0.00      NaN  0.29       NA    0.00    1.10    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29       NA    0.00    1.11    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_toe$Stab[is.na(result.table_toe$Stab)] = 0\n",
+    "result.table_toe$MSE_mean[is.na(result.table_toe$MSE_mean)] = 1\n",
+    "result.table_toe$FP_mean[is.na(result.table_toe$FP_mean)] = 1\n",
+    "result.table_toe$FN_mean[result.table_toe$num_select == 0] = 6\n",
+    "result.table_toe$OOB_mean[is.na(result.table_toe$OOB_mean)] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50          </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0.00         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.40         </td><td> 1.0         </td><td>6.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50          </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0.00         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.36         </td><td> 1.0         </td><td>6.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50          </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0.00         </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.39 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.29         </td><td> 1.0         </td><td>6.00         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50          </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0.00         </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.42 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.35         </td><td> 1.0         </td><td>6.00         </td><td>2.42         </td></tr>\n",
+       "\t<tr><th scope=row>14</th><td>100          </td><td>1000         </td><td>0.1          </td><td>10           </td><td>0.01         </td><td>1 ( 0.03 )   </td><td>49.42 ( 0.7 )</td><td>4.71 ( 0.09 )</td><td>2.39 ( 0.02 )</td><td>50.71        </td><td>0.97         </td><td>1.00         </td><td>49.4         </td><td>4.71         </td><td>2.39         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50          </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0.00         </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.87 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.22         </td><td> 1.0         </td><td>6.00         </td><td>1.87         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0.00         </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.03 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.15         </td><td> 1.0         </td><td>6.00         </td><td>2.03         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50          </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0.00         </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.24 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.14         </td><td> 1.0         </td><td>6.00         </td><td>2.24         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50          </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0.00         </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.29 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>1.20         </td><td> 1.0         </td><td>6.00         </td><td>2.29         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50          </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0.00         </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.99         </td><td> 1.0         </td><td>6.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50          </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0.00         </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.84 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.91         </td><td> 1.0         </td><td>6.00         </td><td>1.84         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50          </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0.00         </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.95         </td><td> 1.0         </td><td>6.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50          </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0.00         </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.04 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.94         </td><td> 1.0         </td><td>6.00         </td><td>2.04         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50          </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0.00         </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.44 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.66         </td><td> 1.0         </td><td>6.00         </td><td>1.44         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50          </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0.00         </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.5 ( 0.02 ) </td><td> 0.00        </td><td>NaN          </td><td>0.67         </td><td> 1.0         </td><td>6.00         </td><td>1.50         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50          </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0.00         </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.68 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.70         </td><td> 1.0         </td><td>6.00         </td><td>1.68         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50          </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0.00         </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.69 ( 0.02 )</td><td> 0.00        </td><td>NaN          </td><td>0.74         </td><td> 1.0         </td><td>6.00         </td><td>1.69         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50          </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0.00         </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.96 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.27         </td><td> 1.0         </td><td>6.00         </td><td>0.96         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50          </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0.00         </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1 ( 0.01 )   </td><td> 0.00        </td><td>NaN          </td><td>0.30         </td><td> 1.0         </td><td>6.00         </td><td>1.00         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50          </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0.00         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.1 ( 0.01 ) </td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td> 1.0         </td><td>6.00         </td><td>1.10         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50          </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0.00         </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.11 ( 0.01 )</td><td> 0.00        </td><td>NaN          </td><td>0.29         </td><td> 1.0         </td><td>6.00         </td><td>1.11         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50           &   50          & 0.1           &  1            & 0.00          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) &  0.00         & NaN           & 1.40          &  1.0          & 6.00          & 1.98         \\\\\n",
+       "\t5 &  50           &  100          & 0.1           &  2            & 0.00          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) &  0.00         & NaN           & 1.36          &  1.0          & 6.00          & 2.19         \\\\\n",
+       "\t9 &  50           &  500          & 0.1           & 10            & 0.00          & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.39 ( 0.02 ) &  0.00         & NaN           & 1.29          &  1.0          & 6.00          & 2.39         \\\\\n",
+       "\t13 &  50           & 1000          & 0.1           & 20            & 0.00          & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.42 ( 0.02 ) &  0.00         & NaN           & 1.35          &  1.0          & 6.00          & 2.42         \\\\\n",
+       "\t14 & 100           & 1000          & 0.1           & 10            & 0.01          & 1 ( 0.03 )    & 49.42 ( 0.7 ) & 4.71 ( 0.09 ) & 2.39 ( 0.02 ) & 50.71         & 0.97          & 1.00          & 49.4          & 4.71          & 2.39         \\\\\n",
+       "\t17 &  50           &   50          & 0.3           &  1            & 0.00          & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.87 ( 0.02 ) &  0.00         & NaN           & 1.22          &  1.0          & 6.00          & 1.87         \\\\\n",
+       "\t21 &  50           &  100          & 0.3           &  2            & 0.00          & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.03 ( 0.02 ) &  0.00         & NaN           & 1.15          &  1.0          & 6.00          & 2.03         \\\\\n",
+       "\t25 &  50           &  500          & 0.3           & 10            & 0.00          & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.24 ( 0.02 ) &  0.00         & NaN           & 1.14          &  1.0          & 6.00          & 2.24         \\\\\n",
+       "\t29 &  50           & 1000          & 0.3           & 20            & 0.00          & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 2.29 ( 0.02 ) &  0.00         & NaN           & 1.20          &  1.0          & 6.00          & 2.29         \\\\\n",
+       "\t33 &  50           &   50          & 0.5           &  1            & 0.00          & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.99          &  1.0          & 6.00          & 1.68         \\\\\n",
+       "\t37 &  50           &  100          & 0.5           &  2            & 0.00          & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.84 ( 0.02 ) &  0.00         & NaN           & 0.91          &  1.0          & 6.00          & 1.84         \\\\\n",
+       "\t41 &  50           &  500          & 0.5           & 10            & 0.00          & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.95          &  1.0          & 6.00          & 2.04         \\\\\n",
+       "\t45 &  50           & 1000          & 0.5           & 20            & 0.00          & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2.04 ( 0.02 ) &  0.00         & NaN           & 0.94          &  1.0          & 6.00          & 2.04         \\\\\n",
+       "\t49 &  50           &   50          & 0.7           &  1            & 0.00          & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.44 ( 0.01 ) &  0.00         & NaN           & 0.66          &  1.0          & 6.00          & 1.44         \\\\\n",
+       "\t53 &  50           &  100          & 0.7           &  2            & 0.00          & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.5 ( 0.02 )  &  0.00         & NaN           & 0.67          &  1.0          & 6.00          & 1.50         \\\\\n",
+       "\t57 &  50           &  500          & 0.7           & 10            & 0.00          & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.68 ( 0.02 ) &  0.00         & NaN           & 0.70          &  1.0          & 6.00          & 1.68         \\\\\n",
+       "\t61 &  50           & 1000          & 0.7           & 20            & 0.00          & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 1.69 ( 0.02 ) &  0.00         & NaN           & 0.74          &  1.0          & 6.00          & 1.69         \\\\\n",
+       "\t65 &  50           &   50          & 0.9           &  1            & 0.00          & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0.96 ( 0.01 ) &  0.00         & NaN           & 0.27          &  1.0          & 6.00          & 0.96         \\\\\n",
+       "\t69 &  50           &  100          & 0.9           &  2            & 0.00          & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 1 ( 0.01 )    &  0.00         & NaN           & 0.30          &  1.0          & 6.00          & 1.00         \\\\\n",
+       "\t73 &  50           &  500          & 0.9           & 10            & 0.00          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.1 ( 0.01 )  &  0.00         & NaN           & 0.29          &  1.0          & 6.00          & 1.10         \\\\\n",
+       "\t77 &  50           & 1000          & 0.9           & 20            & 0.00          & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.11 ( 0.01 ) &  0.00         & NaN           & 0.29          &  1.0          & 6.00          & 1.11         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50           |   50          | 0.1           |  1            | 0.00          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) |  0.00         | NaN           | 1.40          |  1.0          | 6.00          | 1.98          |\n",
+       "| 5 |  50           |  100          | 0.1           |  2            | 0.00          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) |  0.00         | NaN           | 1.36          |  1.0          | 6.00          | 2.19          |\n",
+       "| 9 |  50           |  500          | 0.1           | 10            | 0.00          | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.39 ( 0.02 ) |  0.00         | NaN           | 1.29          |  1.0          | 6.00          | 2.39          |\n",
+       "| 13 |  50           | 1000          | 0.1           | 20            | 0.00          | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.42 ( 0.02 ) |  0.00         | NaN           | 1.35          |  1.0          | 6.00          | 2.42          |\n",
+       "| 14 | 100           | 1000          | 0.1           | 10            | 0.01          | 1 ( 0.03 )    | 49.42 ( 0.7 ) | 4.71 ( 0.09 ) | 2.39 ( 0.02 ) | 50.71         | 0.97          | 1.00          | 49.4          | 4.71          | 2.39          |\n",
+       "| 17 |  50           |   50          | 0.3           |  1            | 0.00          | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.87 ( 0.02 ) |  0.00         | NaN           | 1.22          |  1.0          | 6.00          | 1.87          |\n",
+       "| 21 |  50           |  100          | 0.3           |  2            | 0.00          | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.03 ( 0.02 ) |  0.00         | NaN           | 1.15          |  1.0          | 6.00          | 2.03          |\n",
+       "| 25 |  50           |  500          | 0.3           | 10            | 0.00          | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.24 ( 0.02 ) |  0.00         | NaN           | 1.14          |  1.0          | 6.00          | 2.24          |\n",
+       "| 29 |  50           | 1000          | 0.3           | 20            | 0.00          | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 2.29 ( 0.02 ) |  0.00         | NaN           | 1.20          |  1.0          | 6.00          | 2.29          |\n",
+       "| 33 |  50           |   50          | 0.5           |  1            | 0.00          | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.99          |  1.0          | 6.00          | 1.68          |\n",
+       "| 37 |  50           |  100          | 0.5           |  2            | 0.00          | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.84 ( 0.02 ) |  0.00         | NaN           | 0.91          |  1.0          | 6.00          | 1.84          |\n",
+       "| 41 |  50           |  500          | 0.5           | 10            | 0.00          | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.95          |  1.0          | 6.00          | 2.04          |\n",
+       "| 45 |  50           | 1000          | 0.5           | 20            | 0.00          | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2.04 ( 0.02 ) |  0.00         | NaN           | 0.94          |  1.0          | 6.00          | 2.04          |\n",
+       "| 49 |  50           |   50          | 0.7           |  1            | 0.00          | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.44 ( 0.01 ) |  0.00         | NaN           | 0.66          |  1.0          | 6.00          | 1.44          |\n",
+       "| 53 |  50           |  100          | 0.7           |  2            | 0.00          | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.5 ( 0.02 )  |  0.00         | NaN           | 0.67          |  1.0          | 6.00          | 1.50          |\n",
+       "| 57 |  50           |  500          | 0.7           | 10            | 0.00          | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.68 ( 0.02 ) |  0.00         | NaN           | 0.70          |  1.0          | 6.00          | 1.68          |\n",
+       "| 61 |  50           | 1000          | 0.7           | 20            | 0.00          | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 1.69 ( 0.02 ) |  0.00         | NaN           | 0.74          |  1.0          | 6.00          | 1.69          |\n",
+       "| 65 |  50           |   50          | 0.9           |  1            | 0.00          | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0.96 ( 0.01 ) |  0.00         | NaN           | 0.27          |  1.0          | 6.00          | 0.96          |\n",
+       "| 69 |  50           |  100          | 0.9           |  2            | 0.00          | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 1 ( 0.01 )    |  0.00         | NaN           | 0.30          |  1.0          | 6.00          | 1.00          |\n",
+       "| 73 |  50           |  500          | 0.9           | 10            | 0.00          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.1 ( 0.01 )  |  0.00         | NaN           | 0.29          |  1.0          | 6.00          | 1.10          |\n",
+       "| 77 |  50           | 1000          | 0.9           | 20            | 0.00          | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.11 ( 0.01 ) |  0.00         | NaN           | 0.29          |  1.0          | 6.00          | 1.11          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50   50 0.1   1    0.00 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "5   50  100 0.1   2    0.00 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "9   50  500 0.1  10    0.00 1.29 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "13  50 1000 0.1  20    0.00 1.35 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "14 100 1000 0.1  10    0.01 1 ( 0.03 )    49.42 ( 0.7 ) 4.71 ( 0.09 )\n",
+       "17  50   50 0.3   1    0.00 1.22 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "21  50  100 0.3   2    0.00 1.15 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "25  50  500 0.3  10    0.00 1.14 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "29  50 1000 0.3  20    0.00 1.2 ( 0.05 )  1 ( 0 )       6 ( 0 )      \n",
+       "33  50   50 0.5   1    0.00 0.99 ( 0.05 ) 1 ( 0 )       6 ( 0 )      \n",
+       "37  50  100 0.5   2    0.00 0.91 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "41  50  500 0.5  10    0.00 0.95 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "45  50 1000 0.5  20    0.00 0.94 ( 0.04 ) 1 ( 0 )       6 ( 0 )      \n",
+       "49  50   50 0.7   1    0.00 0.66 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "53  50  100 0.7   2    0.00 0.67 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "57  50  500 0.7  10    0.00 0.7 ( 0.03 )  1 ( 0 )       6 ( 0 )      \n",
+       "61  50 1000 0.7  20    0.00 0.74 ( 0.03 ) 1 ( 0 )       6 ( 0 )      \n",
+       "65  50   50 0.9   1    0.00 0.27 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "69  50  100 0.9   2    0.00 0.3 ( 0.01 )  1 ( 0 )       6 ( 0 )      \n",
+       "73  50  500 0.9  10    0.00 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "77  50 1000 0.9  20    0.00 0.29 ( 0.01 ) 1 ( 0 )       6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  1.98 ( 0.02 )  0.00      NaN  1.40      1.0    6.00    1.98    \n",
+       "5  2.19 ( 0.02 )  0.00      NaN  1.36      1.0    6.00    2.19    \n",
+       "9  2.39 ( 0.02 )  0.00      NaN  1.29      1.0    6.00    2.39    \n",
+       "13 2.42 ( 0.02 )  0.00      NaN  1.35      1.0    6.00    2.42    \n",
+       "14 2.39 ( 0.02 ) 50.71      0.97 1.00     49.4    4.71    2.39    \n",
+       "17 1.87 ( 0.02 )  0.00      NaN  1.22      1.0    6.00    1.87    \n",
+       "21 2.03 ( 0.02 )  0.00      NaN  1.15      1.0    6.00    2.03    \n",
+       "25 2.24 ( 0.02 )  0.00      NaN  1.14      1.0    6.00    2.24    \n",
+       "29 2.29 ( 0.02 )  0.00      NaN  1.20      1.0    6.00    2.29    \n",
+       "33 1.68 ( 0.02 )  0.00      NaN  0.99      1.0    6.00    1.68    \n",
+       "37 1.84 ( 0.02 )  0.00      NaN  0.91      1.0    6.00    1.84    \n",
+       "41 2.04 ( 0.02 )  0.00      NaN  0.95      1.0    6.00    2.04    \n",
+       "45 2.04 ( 0.02 )  0.00      NaN  0.94      1.0    6.00    2.04    \n",
+       "49 1.44 ( 0.01 )  0.00      NaN  0.66      1.0    6.00    1.44    \n",
+       "53 1.5 ( 0.02 )   0.00      NaN  0.67      1.0    6.00    1.50    \n",
+       "57 1.68 ( 0.02 )  0.00      NaN  0.70      1.0    6.00    1.68    \n",
+       "61 1.69 ( 0.02 )  0.00      NaN  0.74      1.0    6.00    1.69    \n",
+       "65 0.96 ( 0.01 )  0.00      NaN  0.27      1.0    6.00    0.96    \n",
+       "69 1 ( 0.01 )     0.00      NaN  0.30      1.0    6.00    1.00    \n",
+       "73 1.1 ( 0.01 )   0.00      NaN  0.29      1.0    6.00    1.10    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29      1.0    6.00    1.11    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_toe[c(1,5,9,13,14,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.00         </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0.00         </td><td>NaN          </td><td>1.40         </td><td>1.00         </td><td>6.00         </td><td>1.98         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.19         </td><td>0.86 ( 0.03 )</td><td>1.87 ( 0.14 )</td><td>4.25 ( 0.09 )</td><td>1.76 ( 0.01 )</td><td>3.62         </td><td>0.48         </td><td>0.86         </td><td>1.87         </td><td>4.25         </td><td>1.76         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.77         </td><td>0.44 ( 0.01 )</td><td>0.32 ( 0.05 )</td><td>1.81 ( 0.07 )</td><td>1.29 ( 0 )   </td><td>4.51         </td><td>0.06         </td><td>0.44         </td><td>0.32         </td><td>1.81         </td><td>1.29         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.86         </td><td>0.34 ( 0 )   </td><td>0.15 ( 0.04 )</td><td>0.93 ( 0.07 )</td><td>1.15 ( 0 )   </td><td>5.22         </td><td>0.02         </td><td>0.34         </td><td>0.15         </td><td>0.93         </td><td>1.15         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.00         </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.19 ( 0.02 )</td><td>0.00         </td><td>NaN          </td><td>1.36         </td><td>1.00         </td><td>6.00         </td><td>2.19         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.12         </td><td>0.93 ( 0.03 )</td><td>4.11 ( 0.18 )</td><td>4.17 ( 0.1 ) </td><td>1.99 ( 0.01 )</td><td>5.93         </td><td>0.68         </td><td>0.93         </td><td>4.11         </td><td>4.17         </td><td>1.99         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.00          & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0.00          & NaN           & 1.40          & 1.00          & 6.00          & 1.98         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.19          & 0.86 ( 0.03 ) & 1.87 ( 0.14 ) & 4.25 ( 0.09 ) & 1.76 ( 0.01 ) & 3.62          & 0.48          & 0.86          & 1.87          & 4.25          & 1.76         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.77          & 0.44 ( 0.01 ) & 0.32 ( 0.05 ) & 1.81 ( 0.07 ) & 1.29 ( 0 )    & 4.51          & 0.06          & 0.44          & 0.32          & 1.81          & 1.29         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.86          & 0.34 ( 0 )    & 0.15 ( 0.04 ) & 0.93 ( 0.07 ) & 1.15 ( 0 )    & 5.22          & 0.02          & 0.34          & 0.15          & 0.93          & 1.15         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.00          & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 2.19 ( 0.02 ) & 0.00          & NaN           & 1.36          & 1.00          & 6.00          & 2.19         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.12          & 0.93 ( 0.03 ) & 4.11 ( 0.18 ) & 4.17 ( 0.1 )  & 1.99 ( 0.01 ) & 5.93          & 0.68          & 0.93          & 4.11          & 4.17          & 1.99         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.00          | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0.00          | NaN           | 1.40          | 1.00          | 6.00          | 1.98          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.19          | 0.86 ( 0.03 ) | 1.87 ( 0.14 ) | 4.25 ( 0.09 ) | 1.76 ( 0.01 ) | 3.62          | 0.48          | 0.86          | 1.87          | 4.25          | 1.76          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.77          | 0.44 ( 0.01 ) | 0.32 ( 0.05 ) | 1.81 ( 0.07 ) | 1.29 ( 0 )    | 4.51          | 0.06          | 0.44          | 0.32          | 1.81          | 1.29          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.86          | 0.34 ( 0 )    | 0.15 ( 0.04 ) | 0.93 ( 0.07 ) | 1.15 ( 0 )    | 5.22          | 0.02          | 0.34          | 0.15          | 0.93          | 1.15          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.00          | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 2.19 ( 0.02 ) | 0.00          | NaN           | 1.36          | 1.00          | 6.00          | 2.19          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.12          | 0.93 ( 0.03 ) | 4.11 ( 0.18 ) | 4.17 ( 0.1 )  | 1.99 ( 0.01 ) | 5.93          | 0.68          | 0.93          | 4.11          | 4.17          | 1.99          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN           \n",
+       "1   50  50 0.1  1.00  0.00 1.4 ( 0.06 )  1 ( 0 )       6 ( 0 )      \n",
+       "2  100  50 0.1  0.50  0.19 0.86 ( 0.03 ) 1.87 ( 0.14 ) 4.25 ( 0.09 )\n",
+       "3  500  50 0.1  0.10  0.77 0.44 ( 0.01 ) 0.32 ( 0.05 ) 1.81 ( 0.07 )\n",
+       "4 1000  50 0.1  0.05  0.86 0.34 ( 0 )    0.15 ( 0.04 ) 0.93 ( 0.07 )\n",
+       "5   50 100 0.1  2.00  0.00 1.36 ( 0.06 ) 1 ( 0 )       6 ( 0 )      \n",
+       "6  100 100 0.1  1.00  0.12 0.93 ( 0.03 ) 4.11 ( 0.18 ) 4.17 ( 0.1 ) \n",
+       "  OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1 1.98 ( 0.02 ) 0.00       NaN  1.40     1.00    6.00    1.98    \n",
+       "2 1.76 ( 0.01 ) 3.62       0.48 0.86     1.87    4.25    1.76    \n",
+       "3 1.29 ( 0 )    4.51       0.06 0.44     0.32    1.81    1.29    \n",
+       "4 1.15 ( 0 )    5.22       0.02 0.34     0.15    0.93    1.15    \n",
+       "5 2.19 ( 0.02 ) 0.00       NaN  1.36     1.00    6.00    2.19    \n",
+       "6 1.99 ( 0.01 ) 5.93       0.68 0.93     4.11    4.17    1.99    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.14          </td><td>0.17 ( 0 )    </td><td>21.38 ( 0.6 ) </td><td>3 ( 0.07 )    </td><td>0.99 ( 0 )    </td><td>24.38         </td><td>0.87          </td><td>0.17          </td><td>21.3          </td><td>0.00          </td><td>0.99          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.20          </td><td>0.16 ( 0 )    </td><td>18.68 ( 0.53 )</td><td>2.35 ( 0.06 ) </td><td>0.96 ( 0 )    </td><td>22.33         </td><td>0.83          </td><td>0.16          </td><td>18.6          </td><td>2.35          </td><td>0.96          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.00          </td><td>0.29 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.11 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.29          </td><td> 1.0          </td><td>6.00          </td><td>1.11          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.01          </td><td>0.2 ( 0.01 )  </td><td>49.91 ( 0.83 )</td><td>4.99 ( 0.09 ) </td><td>1.09 ( 0.01 ) </td><td>50.92         </td><td>0.98          </td><td>0.20          </td><td>49.9          </td><td>4.99          </td><td>1.09          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.06          </td><td>0.19 ( 0 )    </td><td>46.34 ( 0.86 )</td><td>3.13 ( 0.08 ) </td><td>1.04 ( 0 )    </td><td>49.21         </td><td>0.94          </td><td>0.19          </td><td>46.3          </td><td>3.13          </td><td>1.04          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.09          </td><td>0.18 ( 0 )    </td><td>43.52 ( 0.89 )</td><td>2.7 ( 0.07 )  </td><td>1.02 ( 0 )    </td><td>46.82         </td><td>0.93          </td><td>0.18          </td><td>43.5          </td><td>2.70          </td><td>1.02          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.14           & 0.17 ( 0 )     & 21.38 ( 0.6 )  & 3 ( 0.07 )     & 0.99 ( 0 )     & 24.38          & 0.87           & 0.17           & 21.3           & 0.00           & 0.99          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.20           & 0.16 ( 0 )     & 18.68 ( 0.53 ) & 2.35 ( 0.06 )  & 0.96 ( 0 )     & 22.33          & 0.83           & 0.16           & 18.6           & 2.35           & 0.96          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.00           & 0.29 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.11 ( 0.01 )  &  0.00          & NaN            & 0.29           &  1.0           & 6.00           & 1.11          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.01           & 0.2 ( 0.01 )   & 49.91 ( 0.83 ) & 4.99 ( 0.09 )  & 1.09 ( 0.01 )  & 50.92          & 0.98           & 0.20           & 49.9           & 4.99           & 1.09          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.06           & 0.19 ( 0 )     & 46.34 ( 0.86 ) & 3.13 ( 0.08 )  & 1.04 ( 0 )     & 49.21          & 0.94           & 0.19           & 46.3           & 3.13           & 1.04          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.09           & 0.18 ( 0 )     & 43.52 ( 0.89 ) & 2.7 ( 0.07 )   & 1.02 ( 0 )     & 46.82          & 0.93           & 0.18           & 43.5           & 2.70           & 1.02          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.14           | 0.17 ( 0 )     | 21.38 ( 0.6 )  | 3 ( 0.07 )     | 0.99 ( 0 )     | 24.38          | 0.87           | 0.17           | 21.3           | 0.00           | 0.99           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.20           | 0.16 ( 0 )     | 18.68 ( 0.53 ) | 2.35 ( 0.06 )  | 0.96 ( 0 )     | 22.33          | 0.83           | 0.16           | 18.6           | 2.35           | 0.96           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.00           | 0.29 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.11 ( 0.01 )  |  0.00          | NaN            | 0.29           |  1.0           | 6.00           | 1.11           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.01           | 0.2 ( 0.01 )   | 49.91 ( 0.83 ) | 4.99 ( 0.09 )  | 1.09 ( 0.01 )  | 50.92          | 0.98           | 0.20           | 49.9           | 4.99           | 1.09           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.06           | 0.19 ( 0 )     | 46.34 ( 0.86 ) | 3.13 ( 0.08 )  | 1.04 ( 0 )     | 49.21          | 0.94           | 0.19           | 46.3           | 3.13           | 1.04           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.09           | 0.18 ( 0 )     | 43.52 ( 0.89 ) | 2.7 ( 0.07 )   | 1.02 ( 0 )     | 46.82          | 0.93           | 0.18           | 43.5           | 2.70           | 1.02           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.14 0.17 ( 0 )    21.38 ( 0.6 )  3 ( 0.07 )   \n",
+       "76 1000  500 0.9   0.5  0.20 0.16 ( 0 )    18.68 ( 0.53 ) 2.35 ( 0.06 )\n",
+       "77   50 1000 0.9  20.0  0.00 0.29 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "78  100 1000 0.9  10.0  0.01 0.2 ( 0.01 )  49.91 ( 0.83 ) 4.99 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.06 0.19 ( 0 )    46.34 ( 0.86 ) 3.13 ( 0.08 )\n",
+       "80 1000 1000 0.9   1.0  0.09 0.18 ( 0 )    43.52 ( 0.89 ) 2.7 ( 0.07 ) \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "75 0.99 ( 0 )    24.38      0.87 0.17     21.3    0.00    0.99    \n",
+       "76 0.96 ( 0 )    22.33      0.83 0.16     18.6    2.35    0.96    \n",
+       "77 1.11 ( 0.01 )  0.00      NaN  0.29      1.0    6.00    1.11    \n",
+       "78 1.09 ( 0.01 ) 50.92      0.98 0.20     49.9    4.99    1.09    \n",
+       "79 1.04 ( 0 )    49.21      0.94 0.19     46.3    3.13    1.04    \n",
+       "80 1.02 ( 0 )    46.82      0.93 0.18     43.5    2.70    1.02    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/1.4_sim_toe_compLasso_cts.ipynb b/simulations/notebooks_sim_cts/1.4_sim_toe_compLasso_cts.ipynb
new file mode 100755
index 0000000..a071ca2
--- /dev/null
+++ b/simulations/notebooks_sim_cts/1.4_sim_toe_compLasso_cts.ipynb
@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Toeplitz Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/toe_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_toeplitz_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_toe_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_toe = NULL\n",
+    "tmp_num_select = rep(0, length(results_toe_compLasso))\n",
+    "for (i in 1:length(results_toe_compLasso)){\n",
+    "    table_toe = rbind(table_toe, results_toe_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_toe_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_toe = as.data.frame(table_toe)\n",
+    "table_toe$num_select = tmp_num_select\n",
+    "table_toe$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>3.68 ( 0.29 )</td><td>0.06 ( 0.03 )</td><td>0.88 ( 0.05 )</td><td>0.56         </td><td> 9.62        </td><td>0.33         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>1.51 ( 0.35 )</td><td>0 ( 0 )      </td><td>0.87 ( 0.05 )</td><td>0.77         </td><td> 7.51        </td><td>0.12         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>0.73 ( 0.19 )</td><td>0 ( 0 )      </td><td>0.99 ( 0.05 )</td><td>0.88         </td><td> 6.73        </td><td>0.07         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>0.55 ( 0.17 )</td><td>0 ( 0 )      </td><td>1.09 ( 0.04 )</td><td>0.9          </td><td> 6.55        </td><td>0.05         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>4.24 ( 0.3 ) </td><td>0.23 ( 0.06 )</td><td>1.12 ( 0.1 ) </td><td>0.53         </td><td>10.01        </td><td>0.37         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>1.51 ( 0.34 )</td><td>0 ( 0 )      </td><td>0.85 ( 0.04 )</td><td>0.79         </td><td> 7.51        </td><td>0.13         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 3.68 ( 0.29 ) & 0.06 ( 0.03 ) & 0.88 ( 0.05 ) & 0.56          &  9.62         & 0.33         \\\\\n",
+       "\t 100           & 50            & 0.1           & 1.51 ( 0.35 ) & 0 ( 0 )       & 0.87 ( 0.05 ) & 0.77          &  7.51         & 0.12         \\\\\n",
+       "\t 500           & 50            & 0.1           & 0.73 ( 0.19 ) & 0 ( 0 )       & 0.99 ( 0.05 ) & 0.88          &  6.73         & 0.07         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 0.55 ( 0.17 ) & 0 ( 0 )       & 1.09 ( 0.04 ) & 0.9           &  6.55         & 0.05         \\\\\n",
+       "\t 50            & 100           & 0.1           & 4.24 ( 0.3 )  & 0.23 ( 0.06 ) & 1.12 ( 0.1 )  & 0.53          & 10.01         & 0.37         \\\\\n",
+       "\t 100           & 100           & 0.1           & 1.51 ( 0.34 ) & 0 ( 0 )       & 0.85 ( 0.04 ) & 0.79          &  7.51         & 0.13         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 3.68 ( 0.29 ) | 0.06 ( 0.03 ) | 0.88 ( 0.05 ) | 0.56          |  9.62         | 0.33          |\n",
+       "| 100           | 50            | 0.1           | 1.51 ( 0.35 ) | 0 ( 0 )       | 0.87 ( 0.05 ) | 0.77          |  7.51         | 0.12          |\n",
+       "| 500           | 50            | 0.1           | 0.73 ( 0.19 ) | 0 ( 0 )       | 0.99 ( 0.05 ) | 0.88          |  6.73         | 0.07          |\n",
+       "| 1000          | 50            | 0.1           | 0.55 ( 0.17 ) | 0 ( 0 )       | 1.09 ( 0.04 ) | 0.9           |  6.55         | 0.05          |\n",
+       "| 50            | 100           | 0.1           | 4.24 ( 0.3 )  | 0.23 ( 0.06 ) | 1.12 ( 0.1 )  | 0.53          | 10.01         | 0.37          |\n",
+       "| 100           | 100           | 0.1           | 1.51 ( 0.34 ) | 0 ( 0 )       | 0.85 ( 0.04 ) | 0.79          |  7.51         | 0.13          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 3.68 ( 0.29 ) 0.06 ( 0.03 ) 0.88 ( 0.05 ) 0.56  9.62      0.33\n",
+       "2 100  50  0.1 1.51 ( 0.35 ) 0 ( 0 )       0.87 ( 0.05 ) 0.77  7.51      0.12\n",
+       "3 500  50  0.1 0.73 ( 0.19 ) 0 ( 0 )       0.99 ( 0.05 ) 0.88  6.73      0.07\n",
+       "4 1000 50  0.1 0.55 ( 0.17 ) 0 ( 0 )       1.09 ( 0.04 ) 0.9   6.55      0.05\n",
+       "5 50   100 0.1 4.24 ( 0.3 )  0.23 ( 0.06 ) 1.12 ( 0.1 )  0.53 10.01      0.37\n",
+       "6 100  100 0.1 1.51 ( 0.34 ) 0 ( 0 )       0.85 ( 0.04 ) 0.79  7.51      0.13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_toe <- apply(table_toe,2,as.character)\n",
+    "rownames(result.table_toe) = rownames(table_toe)\n",
+    "result.table_toe = as.data.frame(result.table_toe)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_toe$n = tidyr::extract_numeric(result.table_toe$n)\n",
+    "result.table_toe$p = tidyr::extract_numeric(result.table_toe$p)\n",
+    "result.table_toe$ratio = result.table_toe$p / result.table_toe$n\n",
+    "\n",
+    "result.table_toe = result.table_toe[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_toe)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_toe$Stab = as.numeric(as.character(result.table_toe$Stab))\n",
+    "result.table_toe$MSE_mean = as.numeric(substr(result.table_toe$MSE, start=1, stop=4))\n",
+    "result.table_toe$FP_mean = as.numeric(substr(result.table_toe$FP, start=1, stop=4))\n",
+    "result.table_toe$FN_mean = as.numeric(substr(result.table_toe$FN, start=1, stop=4))\n",
+    "result.table_toe$FN_mean[is.na(result.table_toe$FN_mean)] = 0\n",
+    "result.table_toe$num_select = as.numeric(as.character(result.table_toe$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "\n"
+      ],
+      "text/plain": [
+       "     N P Corr Ratio Stab MSE FP FN num_select FDR MSE_mean FP_mean FN_mean"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_toe[rowSums(is.na(result.table_toe)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.56         </td><td>0.88 ( 0.05 )</td><td>3.68 ( 0.29 )</td><td>0.06 ( 0.03 )</td><td> 9.62        </td><td>0.33         </td><td>0.88         </td><td>3.68         </td><td>0.06         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.77         </td><td>0.87 ( 0.05 )</td><td>1.51 ( 0.35 )</td><td>0 ( 0 )      </td><td> 7.51        </td><td>0.12         </td><td>0.87         </td><td>1.51         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.88         </td><td>0.99 ( 0.05 )</td><td>0.73 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.73        </td><td>0.07         </td><td>0.99         </td><td>0.73         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.90         </td><td>1.09 ( 0.04 )</td><td>0.55 ( 0.17 )</td><td>0 ( 0 )      </td><td> 6.55        </td><td>0.05         </td><td>1.09         </td><td>0.55         </td><td>0.00         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.53         </td><td>1.12 ( 0.1 ) </td><td>4.24 ( 0.3 ) </td><td>0.23 ( 0.06 )</td><td>10.01        </td><td>0.37         </td><td>1.12         </td><td>4.24         </td><td>0.23         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.79         </td><td>0.85 ( 0.04 )</td><td>1.51 ( 0.34 )</td><td>0 ( 0 )      </td><td> 7.51        </td><td>0.13         </td><td>0.85         </td><td>1.51         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.56          & 0.88 ( 0.05 ) & 3.68 ( 0.29 ) & 0.06 ( 0.03 ) &  9.62         & 0.33          & 0.88          & 3.68          & 0.06         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.77          & 0.87 ( 0.05 ) & 1.51 ( 0.35 ) & 0 ( 0 )       &  7.51         & 0.12          & 0.87          & 1.51          & 0.00         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.88          & 0.99 ( 0.05 ) & 0.73 ( 0.19 ) & 0 ( 0 )       &  6.73         & 0.07          & 0.99          & 0.73          & 0.00         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.90          & 1.09 ( 0.04 ) & 0.55 ( 0.17 ) & 0 ( 0 )       &  6.55         & 0.05          & 1.09          & 0.55          & 0.00         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.53          & 1.12 ( 0.1 )  & 4.24 ( 0.3 )  & 0.23 ( 0.06 ) & 10.01         & 0.37          & 1.12          & 4.24          & 0.23         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.79          & 0.85 ( 0.04 ) & 1.51 ( 0.34 ) & 0 ( 0 )       &  7.51         & 0.13          & 0.85          & 1.51          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.56          | 0.88 ( 0.05 ) | 3.68 ( 0.29 ) | 0.06 ( 0.03 ) |  9.62         | 0.33          | 0.88          | 3.68          | 0.06          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.77          | 0.87 ( 0.05 ) | 1.51 ( 0.35 ) | 0 ( 0 )       |  7.51         | 0.12          | 0.87          | 1.51          | 0.00          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.88          | 0.99 ( 0.05 ) | 0.73 ( 0.19 ) | 0 ( 0 )       |  6.73         | 0.07          | 0.99          | 0.73          | 0.00          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.90          | 1.09 ( 0.04 ) | 0.55 ( 0.17 ) | 0 ( 0 )       |  6.55         | 0.05          | 1.09          | 0.55          | 0.00          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.53          | 1.12 ( 0.1 )  | 4.24 ( 0.3 )  | 0.23 ( 0.06 ) | 10.01         | 0.37          | 1.12          | 4.24          | 0.23          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.79          | 0.85 ( 0.04 ) | 1.51 ( 0.34 ) | 0 ( 0 )       |  7.51         | 0.13          | 0.85          | 1.51          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.56 0.88 ( 0.05 ) 3.68 ( 0.29 ) 0.06 ( 0.03 )  9.62     \n",
+       "2  100  50 0.1  0.50  0.77 0.87 ( 0.05 ) 1.51 ( 0.35 ) 0 ( 0 )        7.51     \n",
+       "3  500  50 0.1  0.10  0.88 0.99 ( 0.05 ) 0.73 ( 0.19 ) 0 ( 0 )        6.73     \n",
+       "4 1000  50 0.1  0.05  0.90 1.09 ( 0.04 ) 0.55 ( 0.17 ) 0 ( 0 )        6.55     \n",
+       "5   50 100 0.1  2.00  0.53 1.12 ( 0.1 )  4.24 ( 0.3 )  0.23 ( 0.06 ) 10.01     \n",
+       "6  100 100 0.1  1.00  0.79 0.85 ( 0.04 ) 1.51 ( 0.34 ) 0 ( 0 )        7.51     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.33 0.88     3.68    0.06   \n",
+       "2 0.12 0.87     1.51    0.00   \n",
+       "3 0.07 0.99     0.73    0.00   \n",
+       "4 0.05 1.09     0.55    0.00   \n",
+       "5 0.37 1.12     4.24    0.23   \n",
+       "6 0.13 0.85     1.51    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.41          </td><td>0.34 ( 0.01 ) </td><td>8.17 ( 0.48 ) </td><td>0.17 ( 0.04 ) </td><td>14.00         </td><td>0.54          </td><td>0.34          </td><td> 8.17         </td><td>0.17          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.58          </td><td>0.32 ( 0 )    </td><td>4.24 ( 0.3 )  </td><td>0.01 ( 0.01 ) </td><td>10.23         </td><td>0.37          </td><td>0.32          </td><td> 4.24         </td><td>0.01          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.19          </td><td>1.04 ( 0.06 ) </td><td>5.24 ( 0.46 ) </td><td>4.23 ( 0.08 ) </td><td> 7.01         </td><td>0.6           </td><td>1.04          </td><td> 5.24         </td><td>4.23          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.22          </td><td>0.67 ( 0.02 ) </td><td>9.03 ( 0.73 ) </td><td>3.02 ( 0.07 ) </td><td>12.01         </td><td>0.66          </td><td>0.67          </td><td> 9.03         </td><td>3.02          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.34          </td><td>0.35 ( 0.01 ) </td><td>10.52 ( 0.68 )</td><td>0.32 ( 0.05 ) </td><td>16.20         </td><td>0.59          </td><td>0.35          </td><td>10.50         </td><td>0.32          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.57          </td><td>0.33 ( 0 )    </td><td>4.48 ( 0.3 )  </td><td>0.01 ( 0.01 ) </td><td>10.47         </td><td>0.39          </td><td>0.33          </td><td> 4.48         </td><td>0.01          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.41           & 0.34 ( 0.01 )  & 8.17 ( 0.48 )  & 0.17 ( 0.04 )  & 14.00          & 0.54           & 0.34           &  8.17          & 0.17          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.58           & 0.32 ( 0 )     & 4.24 ( 0.3 )   & 0.01 ( 0.01 )  & 10.23          & 0.37           & 0.32           &  4.24          & 0.01          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.19           & 1.04 ( 0.06 )  & 5.24 ( 0.46 )  & 4.23 ( 0.08 )  &  7.01          & 0.6            & 1.04           &  5.24          & 4.23          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.22           & 0.67 ( 0.02 )  & 9.03 ( 0.73 )  & 3.02 ( 0.07 )  & 12.01          & 0.66           & 0.67           &  9.03          & 3.02          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.34           & 0.35 ( 0.01 )  & 10.52 ( 0.68 ) & 0.32 ( 0.05 )  & 16.20          & 0.59           & 0.35           & 10.50          & 0.32          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.57           & 0.33 ( 0 )     & 4.48 ( 0.3 )   & 0.01 ( 0.01 )  & 10.47          & 0.39           & 0.33           &  4.48          & 0.01          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.41           | 0.34 ( 0.01 )  | 8.17 ( 0.48 )  | 0.17 ( 0.04 )  | 14.00          | 0.54           | 0.34           |  8.17          | 0.17           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.58           | 0.32 ( 0 )     | 4.24 ( 0.3 )   | 0.01 ( 0.01 )  | 10.23          | 0.37           | 0.32           |  4.24          | 0.01           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.19           | 1.04 ( 0.06 )  | 5.24 ( 0.46 )  | 4.23 ( 0.08 )  |  7.01          | 0.6            | 1.04           |  5.24          | 4.23           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.22           | 0.67 ( 0.02 )  | 9.03 ( 0.73 )  | 3.02 ( 0.07 )  | 12.01          | 0.66           | 0.67           |  9.03          | 3.02           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.34           | 0.35 ( 0.01 )  | 10.52 ( 0.68 ) | 0.32 ( 0.05 )  | 16.20          | 0.59           | 0.35           | 10.50          | 0.32           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.57           | 0.33 ( 0 )     | 4.48 ( 0.3 )   | 0.01 ( 0.01 )  | 10.47          | 0.39           | 0.33           |  4.48          | 0.01           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.41 0.34 ( 0.01 ) 8.17 ( 0.48 )  0.17 ( 0.04 )\n",
+       "76 1000  500 0.9   0.5  0.58 0.32 ( 0 )    4.24 ( 0.3 )   0.01 ( 0.01 )\n",
+       "77   50 1000 0.9  20.0  0.19 1.04 ( 0.06 ) 5.24 ( 0.46 )  4.23 ( 0.08 )\n",
+       "78  100 1000 0.9  10.0  0.22 0.67 ( 0.02 ) 9.03 ( 0.73 )  3.02 ( 0.07 )\n",
+       "79  500 1000 0.9   2.0  0.34 0.35 ( 0.01 ) 10.52 ( 0.68 ) 0.32 ( 0.05 )\n",
+       "80 1000 1000 0.9   1.0  0.57 0.33 ( 0 )    4.48 ( 0.3 )   0.01 ( 0.01 )\n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 14.00      0.54 0.34      8.17   0.17   \n",
+       "76 10.23      0.37 0.32      4.24   0.01   \n",
+       "77  7.01      0.6  1.04      5.24   4.23   \n",
+       "78 12.01      0.66 0.67      9.03   3.02   \n",
+       "79 16.20      0.59 0.35     10.50   0.32   \n",
+       "80 10.47      0.39 0.33      4.48   0.01   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_toe, '../results_summary_cts/sim_toe_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/2.1_sim_block_lasso_cts.ipynb b/simulations/notebooks_sim_cts/2.1_sim_block_lasso_cts.ipynb
new file mode 100755
index 0000000..427211a
--- /dev/null
+++ b/simulations/notebooks_sim_cts/2.1_sim_block_lasso_cts.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize lasso results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_Lasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_lasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_lasso))\n",
+    "for (i in 1:length(results_block_lasso)){\n",
+    "    table_block = rbind(table_block, results_block_lasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_lasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>3.52 ( 0.22 )</td><td>4.92 ( 0.08 )</td><td>0.36 ( 0.01 )</td><td>0.05         </td><td>3.60         </td><td>0.65         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>2.71 ( 0.25 )</td><td>4.4 ( 0.1 )  </td><td>0.31 ( 0.01 )</td><td>0.18         </td><td>3.31         </td><td>0.42         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>5.42 ( 0.25 )</td><td>1.97 ( 0.11 )</td><td>0.29 ( 0 )   </td><td>0.35         </td><td>8.45         </td><td>0.48         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>4.91 ( 0.2 ) </td><td>1.57 ( 0.09 )</td><td>0.28 ( 0 )   </td><td>0.44         </td><td>8.34         </td><td>0.44         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>4.73 ( 0.22 )</td><td>4.79 ( 0.08 )</td><td>0.37 ( 0.02 )</td><td>0.06         </td><td>4.94         </td><td>0.72         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>0.34 ( 0.01 )</td><td>0.13         </td><td>3.25         </td><td>0.57         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 3.52 ( 0.22 ) & 4.92 ( 0.08 ) & 0.36 ( 0.01 ) & 0.05          & 3.60          & 0.65         \\\\\n",
+       "\t 100           & 50            & 0.1           & 2.71 ( 0.25 ) & 4.4 ( 0.1 )   & 0.31 ( 0.01 ) & 0.18          & 3.31          & 0.42         \\\\\n",
+       "\t 500           & 50            & 0.1           & 5.42 ( 0.25 ) & 1.97 ( 0.11 ) & 0.29 ( 0 )    & 0.35          & 8.45          & 0.48         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 4.91 ( 0.2 )  & 1.57 ( 0.09 ) & 0.28 ( 0 )    & 0.44          & 8.34          & 0.44         \\\\\n",
+       "\t 50            & 100           & 0.1           & 4.73 ( 0.22 ) & 4.79 ( 0.08 ) & 0.37 ( 0.02 ) & 0.06          & 4.94          & 0.72         \\\\\n",
+       "\t 100           & 100           & 0.1           & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 0.34 ( 0.01 ) & 0.13          & 3.25          & 0.57         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 3.52 ( 0.22 ) | 4.92 ( 0.08 ) | 0.36 ( 0.01 ) | 0.05          | 3.60          | 0.65          |\n",
+       "| 100           | 50            | 0.1           | 2.71 ( 0.25 ) | 4.4 ( 0.1 )   | 0.31 ( 0.01 ) | 0.18          | 3.31          | 0.42          |\n",
+       "| 500           | 50            | 0.1           | 5.42 ( 0.25 ) | 1.97 ( 0.11 ) | 0.29 ( 0 )    | 0.35          | 8.45          | 0.48          |\n",
+       "| 1000          | 50            | 0.1           | 4.91 ( 0.2 )  | 1.57 ( 0.09 ) | 0.28 ( 0 )    | 0.44          | 8.34          | 0.44          |\n",
+       "| 50            | 100           | 0.1           | 4.73 ( 0.22 ) | 4.79 ( 0.08 ) | 0.37 ( 0.02 ) | 0.06          | 4.94          | 0.72          |\n",
+       "| 100           | 100           | 0.1           | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 0.34 ( 0.01 ) | 0.13          | 3.25          | 0.57          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 3.52 ( 0.22 ) 4.92 ( 0.08 ) 0.36 ( 0.01 ) 0.05 3.60       0.65\n",
+       "2 100  50  0.1 2.71 ( 0.25 ) 4.4 ( 0.1 )   0.31 ( 0.01 ) 0.18 3.31       0.42\n",
+       "3 500  50  0.1 5.42 ( 0.25 ) 1.97 ( 0.11 ) 0.29 ( 0 )    0.35 8.45       0.48\n",
+       "4 1000 50  0.1 4.91 ( 0.2 )  1.57 ( 0.09 ) 0.28 ( 0 )    0.44 8.34       0.44\n",
+       "5 50   100 0.1 4.73 ( 0.22 ) 4.79 ( 0.08 ) 0.37 ( 0.02 ) 0.06 4.94       0.72\n",
+       "6 100  100 0.1 3 ( 0.15 )    4.75 ( 0.09 ) 0.34 ( 0.01 ) 0.13 3.25       0.57"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>6</th><td>100          </td><td>100          </td><td>0.1          </td><td>1            </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>NA           </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t6 & 100           & 100           & 0.1           & 1             & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & NA            & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 6 | 100           | 100           | 0.1           | 1             | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | NA            | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N   P   Corr Ratio Stab MSE           FP         FN            num_select\n",
+       "6 100 100 0.1  1     0.13 0.34 ( 0.01 ) 3 ( 0.15 ) 4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "6 0.57 0.34     NA      4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>6</th><td>100          </td><td>100          </td><td>0.1          </td><td>1            </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>3            </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t6 & 100           & 100           & 0.1           & 1             & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & 3             & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 6 | 100           | 100           | 0.1           | 1             | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | 3             | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N   P   Corr Ratio Stab MSE           FP         FN            num_select\n",
+       "6 100 100 0.1  1     0.13 0.34 ( 0.01 ) 3 ( 0.15 ) 4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "6 0.57 0.34     3       4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[6, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.05         </td><td>0.36 ( 0.01 )</td><td>3.52 ( 0.22 )</td><td>4.92 ( 0.08 )</td><td>3.60         </td><td>0.65         </td><td>0.36         </td><td>3.52         </td><td>4.92         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.18         </td><td>0.31 ( 0.01 )</td><td>2.71 ( 0.25 )</td><td>4.4 ( 0.1 )  </td><td>3.31         </td><td>0.42         </td><td>0.31         </td><td>2.71         </td><td>4.40         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.35         </td><td>0.29 ( 0 )   </td><td>5.42 ( 0.25 )</td><td>1.97 ( 0.11 )</td><td>8.45         </td><td>0.48         </td><td>0.29         </td><td>5.42         </td><td>1.97         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.44         </td><td>0.28 ( 0 )   </td><td>4.91 ( 0.2 ) </td><td>1.57 ( 0.09 )</td><td>8.34         </td><td>0.44         </td><td>0.28         </td><td>4.91         </td><td>1.57         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.06         </td><td>0.37 ( 0.02 )</td><td>4.73 ( 0.22 )</td><td>4.79 ( 0.08 )</td><td>4.94         </td><td>0.72         </td><td>0.37         </td><td>4.73         </td><td>4.79         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.13         </td><td>0.34 ( 0.01 )</td><td>3 ( 0.15 )   </td><td>4.75 ( 0.09 )</td><td>3.25         </td><td>0.57         </td><td>0.34         </td><td>3.00         </td><td>4.75         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.05          & 0.36 ( 0.01 ) & 3.52 ( 0.22 ) & 4.92 ( 0.08 ) & 3.60          & 0.65          & 0.36          & 3.52          & 4.92         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.18          & 0.31 ( 0.01 ) & 2.71 ( 0.25 ) & 4.4 ( 0.1 )   & 3.31          & 0.42          & 0.31          & 2.71          & 4.40         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.35          & 0.29 ( 0 )    & 5.42 ( 0.25 ) & 1.97 ( 0.11 ) & 8.45          & 0.48          & 0.29          & 5.42          & 1.97         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.44          & 0.28 ( 0 )    & 4.91 ( 0.2 )  & 1.57 ( 0.09 ) & 8.34          & 0.44          & 0.28          & 4.91          & 1.57         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.06          & 0.37 ( 0.02 ) & 4.73 ( 0.22 ) & 4.79 ( 0.08 ) & 4.94          & 0.72          & 0.37          & 4.73          & 4.79         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.13          & 0.34 ( 0.01 ) & 3 ( 0.15 )    & 4.75 ( 0.09 ) & 3.25          & 0.57          & 0.34          & 3.00          & 4.75         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.05          | 0.36 ( 0.01 ) | 3.52 ( 0.22 ) | 4.92 ( 0.08 ) | 3.60          | 0.65          | 0.36          | 3.52          | 4.92          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.18          | 0.31 ( 0.01 ) | 2.71 ( 0.25 ) | 4.4 ( 0.1 )   | 3.31          | 0.42          | 0.31          | 2.71          | 4.40          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.35          | 0.29 ( 0 )    | 5.42 ( 0.25 ) | 1.97 ( 0.11 ) | 8.45          | 0.48          | 0.29          | 5.42          | 1.97          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.44          | 0.28 ( 0 )    | 4.91 ( 0.2 )  | 1.57 ( 0.09 ) | 8.34          | 0.44          | 0.28          | 4.91          | 1.57          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.06          | 0.37 ( 0.02 ) | 4.73 ( 0.22 ) | 4.79 ( 0.08 ) | 4.94          | 0.72          | 0.37          | 4.73          | 4.79          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.13          | 0.34 ( 0.01 ) | 3 ( 0.15 )    | 4.75 ( 0.09 ) | 3.25          | 0.57          | 0.34          | 3.00          | 4.75          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.05 0.36 ( 0.01 ) 3.52 ( 0.22 ) 4.92 ( 0.08 ) 3.60      \n",
+       "2  100  50 0.1  0.50  0.18 0.31 ( 0.01 ) 2.71 ( 0.25 ) 4.4 ( 0.1 )   3.31      \n",
+       "3  500  50 0.1  0.10  0.35 0.29 ( 0 )    5.42 ( 0.25 ) 1.97 ( 0.11 ) 8.45      \n",
+       "4 1000  50 0.1  0.05  0.44 0.28 ( 0 )    4.91 ( 0.2 )  1.57 ( 0.09 ) 8.34      \n",
+       "5   50 100 0.1  2.00  0.06 0.37 ( 0.02 ) 4.73 ( 0.22 ) 4.79 ( 0.08 ) 4.94      \n",
+       "6  100 100 0.1  1.00  0.13 0.34 ( 0.01 ) 3 ( 0.15 )    4.75 ( 0.09 ) 3.25      \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.65 0.36     3.52    4.92   \n",
+       "2 0.42 0.31     2.71    4.40   \n",
+       "3 0.48 0.29     5.42    1.97   \n",
+       "4 0.44 0.28     4.91    1.57   \n",
+       "5 0.72 0.37     4.73    4.79   \n",
+       "6 0.57 0.34     3.00    4.75   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.50          </td><td>0.29 ( 0 )    </td><td>6.87 ( 0.54 ) </td><td>0 ( 0 )       </td><td>11.87         </td><td>0.41          </td><td>0.29          </td><td> 6.87         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.71          </td><td>0.27 ( 0 )    </td><td>3.37 ( 0.27 ) </td><td>0 ( 0 )       </td><td> 8.37         </td><td>0.23          </td><td>0.27          </td><td> 3.37         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.11          </td><td>2.46 ( 0.16 ) </td><td>25.37 ( 0.35 )</td><td>1.73 ( 0.12 ) </td><td>28.64         </td><td>0.85          </td><td>2.46          </td><td>25.30         </td><td>1.73          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.22          </td><td>0.62 ( 0.03 ) </td><td>21.43 ( 0.62 )</td><td>0 ( 0 )       </td><td>26.43         </td><td>0.76          </td><td>0.62          </td><td>21.40         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.46          </td><td>0.3 ( 0 )     </td><td>7.99 ( 0.79 ) </td><td>0 ( 0 )       </td><td>12.99         </td><td>0.42          </td><td>0.30          </td><td> 7.99         </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.60          </td><td>0.28 ( 0 )    </td><td>4.93 ( 0.43 ) </td><td>0 ( 0 )       </td><td> 9.93         </td><td>0.31          </td><td>0.28          </td><td> 4.93         </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.50           & 0.29 ( 0 )     & 6.87 ( 0.54 )  & 0 ( 0 )        & 11.87          & 0.41           & 0.29           &  6.87          & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.71           & 0.27 ( 0 )     & 3.37 ( 0.27 )  & 0 ( 0 )        &  8.37          & 0.23           & 0.27           &  3.37          & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.11           & 2.46 ( 0.16 )  & 25.37 ( 0.35 ) & 1.73 ( 0.12 )  & 28.64          & 0.85           & 2.46           & 25.30          & 1.73          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.22           & 0.62 ( 0.03 )  & 21.43 ( 0.62 ) & 0 ( 0 )        & 26.43          & 0.76           & 0.62           & 21.40          & 0.00          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.46           & 0.3 ( 0 )      & 7.99 ( 0.79 )  & 0 ( 0 )        & 12.99          & 0.42           & 0.30           &  7.99          & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.60           & 0.28 ( 0 )     & 4.93 ( 0.43 )  & 0 ( 0 )        &  9.93          & 0.31           & 0.28           &  4.93          & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.50           | 0.29 ( 0 )     | 6.87 ( 0.54 )  | 0 ( 0 )        | 11.87          | 0.41           | 0.29           |  6.87          | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.71           | 0.27 ( 0 )     | 3.37 ( 0.27 )  | 0 ( 0 )        |  8.37          | 0.23           | 0.27           |  3.37          | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.11           | 2.46 ( 0.16 )  | 25.37 ( 0.35 ) | 1.73 ( 0.12 )  | 28.64          | 0.85           | 2.46           | 25.30          | 1.73           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.22           | 0.62 ( 0.03 )  | 21.43 ( 0.62 ) | 0 ( 0 )        | 26.43          | 0.76           | 0.62           | 21.40          | 0.00           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.46           | 0.3 ( 0 )      | 7.99 ( 0.79 )  | 0 ( 0 )        | 12.99          | 0.42           | 0.30           |  7.99          | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.60           | 0.28 ( 0 )     | 4.93 ( 0.43 )  | 0 ( 0 )        |  9.93          | 0.31           | 0.28           |  4.93          | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.50 0.29 ( 0 )    6.87 ( 0.54 )  0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.71 0.27 ( 0 )    3.37 ( 0.27 )  0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.11 2.46 ( 0.16 ) 25.37 ( 0.35 ) 1.73 ( 0.12 )\n",
+       "78  100 1000 0.9  10.0  0.22 0.62 ( 0.03 ) 21.43 ( 0.62 ) 0 ( 0 )      \n",
+       "79  500 1000 0.9   2.0  0.46 0.3 ( 0 )     7.99 ( 0.79 )  0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.60 0.28 ( 0 )    4.93 ( 0.43 )  0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 11.87      0.41 0.29      6.87   0.00   \n",
+       "76  8.37      0.23 0.27      3.37   0.00   \n",
+       "77 28.64      0.85 2.46     25.30   1.73   \n",
+       "78 26.43      0.76 0.62     21.40   0.00   \n",
+       "79 12.99      0.42 0.30      7.99   0.00   \n",
+       "80  9.93      0.31 0.28      4.93   0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_lasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/2.2_sim_block_elnet_cts.ipynb b/simulations/notebooks_sim_cts/2.2_sim_block_elnet_cts.ipynb
new file mode 100755
index 0000000..c718e78
--- /dev/null
+++ b/simulations/notebooks_sim_cts/2.2_sim_block_elnet_cts.ipynb
@@ -0,0 +1,482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize elastic net results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_Elnet.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_elnet[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_elnet))\n",
+    "for (i in 1:length(results_block_elnet)){\n",
+    "    table_block = rbind(table_block, results_block_elnet[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_elnet[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>10.9 ( 0.97 )</td><td>3.91 ( 0.15 )</td><td>0.38 ( 0.02 )</td><td>0.02         </td><td>11.99        </td><td>0.77         </td></tr>\n",
+       "\t<tr><td>100           </td><td>50            </td><td>0.1           </td><td>13.39 ( 1.06 )</td><td>2.45 ( 0.16 ) </td><td>0.34 ( 0.01 ) </td><td>0.06          </td><td>15.94         </td><td>0.68          </td></tr>\n",
+       "\t<tr><td>500           </td><td>50            </td><td>0.1           </td><td>19.21 ( 0.81 )</td><td>0.14 ( 0.04 ) </td><td>0.27 ( 0 )    </td><td>0.13          </td><td>24.07         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>1000          </td><td>50            </td><td>0.1           </td><td>18.76 ( 0.63 )</td><td>0.02 ( 0.01 ) </td><td>0.26 ( 0 )    </td><td>0.15          </td><td>23.74         </td><td>0.73          </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>9.27 ( 0.76 )</td><td>4.63 ( 0.09 )</td><td>0.41 ( 0.02 )</td><td>0.03         </td><td> 9.64        </td><td>0.82         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>10.1 ( 1.04 )</td><td>3.87 ( 0.13 )</td><td>0.34 ( 0.01 )</td><td>0.07         </td><td>11.23        </td><td>0.70         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 10.9 ( 0.97 ) & 3.91 ( 0.15 ) & 0.38 ( 0.02 ) & 0.02          & 11.99         & 0.77         \\\\\n",
+       "\t 100            & 50             & 0.1            & 13.39 ( 1.06 ) & 2.45 ( 0.16 )  & 0.34 ( 0.01 )  & 0.06           & 15.94          & 0.68          \\\\\n",
+       "\t 500            & 50             & 0.1            & 19.21 ( 0.81 ) & 0.14 ( 0.04 )  & 0.27 ( 0 )     & 0.13           & 24.07          & 0.73          \\\\\n",
+       "\t 1000           & 50             & 0.1            & 18.76 ( 0.63 ) & 0.02 ( 0.01 )  & 0.26 ( 0 )     & 0.15           & 23.74          & 0.73          \\\\\n",
+       "\t 50            & 100           & 0.1           & 9.27 ( 0.76 ) & 4.63 ( 0.09 ) & 0.41 ( 0.02 ) & 0.03          &  9.64         & 0.82         \\\\\n",
+       "\t 100           & 100           & 0.1           & 10.1 ( 1.04 ) & 3.87 ( 0.13 ) & 0.34 ( 0.01 ) & 0.07          & 11.23         & 0.70         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 10.9 ( 0.97 ) | 3.91 ( 0.15 ) | 0.38 ( 0.02 ) | 0.02          | 11.99         | 0.77          |\n",
+       "| 100            | 50             | 0.1            | 13.39 ( 1.06 ) | 2.45 ( 0.16 )  | 0.34 ( 0.01 )  | 0.06           | 15.94          | 0.68           |\n",
+       "| 500            | 50             | 0.1            | 19.21 ( 0.81 ) | 0.14 ( 0.04 )  | 0.27 ( 0 )     | 0.13           | 24.07          | 0.73           |\n",
+       "| 1000           | 50             | 0.1            | 18.76 ( 0.63 ) | 0.02 ( 0.01 )  | 0.26 ( 0 )     | 0.15           | 23.74          | 0.73           |\n",
+       "| 50            | 100           | 0.1           | 9.27 ( 0.76 ) | 4.63 ( 0.09 ) | 0.41 ( 0.02 ) | 0.03          |  9.64         | 0.82          |\n",
+       "| 100           | 100           | 0.1           | 10.1 ( 1.04 ) | 3.87 ( 0.13 ) | 0.34 ( 0.01 ) | 0.07          | 11.23         | 0.70          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP             FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 10.9 ( 0.97 )  3.91 ( 0.15 ) 0.38 ( 0.02 ) 0.02 11.99      0.77\n",
+       "2 100  50  0.1 13.39 ( 1.06 ) 2.45 ( 0.16 ) 0.34 ( 0.01 ) 0.06 15.94      0.68\n",
+       "3 500  50  0.1 19.21 ( 0.81 ) 0.14 ( 0.04 ) 0.27 ( 0 )    0.13 24.07      0.73\n",
+       "4 1000 50  0.1 18.76 ( 0.63 ) 0.02 ( 0.01 ) 0.26 ( 0 )    0.15 23.74      0.73\n",
+       "5 50   100 0.1 9.27 ( 0.76 )  4.63 ( 0.09 ) 0.41 ( 0.02 ) 0.03  9.64      0.82\n",
+       "6 100  100 0.1 10.1 ( 1.04 )  3.87 ( 0.13 ) 0.34 ( 0.01 ) 0.07 11.23      0.70"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>45</th><td>50            </td><td>1000          </td><td>0.5           </td><td>20            </td><td>0.04          </td><td>2 ( 0.1 )     </td><td>52.83 ( 3.84 )</td><td>2.29 ( 0.1 )  </td><td>55.54         </td><td>0.91          </td><td>NA            </td><td>52.8          </td><td>2.29          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t45 & 50             & 1000           & 0.5            & 20             & 0.04           & 2 ( 0.1 )      & 52.83 ( 3.84 ) & 2.29 ( 0.1 )   & 55.54          & 0.91           & NA             & 52.8           & 2.29          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 45 | 50             | 1000           | 0.5            | 20             | 0.04           | 2 ( 0.1 )      | 52.83 ( 3.84 ) | 2.29 ( 0.1 )   | 55.54          | 0.91           | NA             | 52.8           | 2.29           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE       FP             FN           num_select\n",
+       "45 50 1000 0.5  20    0.04 2 ( 0.1 ) 52.83 ( 3.84 ) 2.29 ( 0.1 ) 55.54     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "45 0.91 NA       52.8    2.29   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$MSE_mean[45] = 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>45</th><td>50            </td><td>1000          </td><td>0.5           </td><td>20            </td><td>0.04          </td><td>2 ( 0.1 )     </td><td>52.83 ( 3.84 )</td><td>2.29 ( 0.1 )  </td><td>55.54         </td><td>0.91          </td><td>2             </td><td>52.8          </td><td>2.29          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t45 & 50             & 1000           & 0.5            & 20             & 0.04           & 2 ( 0.1 )      & 52.83 ( 3.84 ) & 2.29 ( 0.1 )   & 55.54          & 0.91           & 2              & 52.8           & 2.29          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 45 | 50             | 1000           | 0.5            | 20             | 0.04           | 2 ( 0.1 )      | 52.83 ( 3.84 ) | 2.29 ( 0.1 )   | 55.54          | 0.91           | 2              | 52.8           | 2.29           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE       FP             FN           num_select\n",
+       "45 50 1000 0.5  20    0.04 2 ( 0.1 ) 52.83 ( 3.84 ) 2.29 ( 0.1 ) 55.54     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "45 0.91 2        52.8    2.29   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[45, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50          </td><td> 50           </td><td>0.1           </td><td>1.00          </td><td>0.02          </td><td>0.38 ( 0.02 ) </td><td>10.9 ( 0.97 ) </td><td>3.91 ( 0.15 ) </td><td>11.99         </td><td>0.77          </td><td>0.38          </td><td>10.90         </td><td>3.91          </td></tr>\n",
+       "\t<tr><td> 100          </td><td> 50           </td><td>0.1           </td><td>0.50          </td><td>0.06          </td><td>0.34 ( 0.01 ) </td><td>13.39 ( 1.06 )</td><td>2.45 ( 0.16 ) </td><td>15.94         </td><td>0.68          </td><td>0.34          </td><td>13.30         </td><td>2.45          </td></tr>\n",
+       "\t<tr><td> 500          </td><td> 50           </td><td>0.1           </td><td>0.10          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>19.21 ( 0.81 )</td><td>0.14 ( 0.04 ) </td><td>24.07         </td><td>0.73          </td><td>0.27          </td><td>19.20         </td><td>0.14          </td></tr>\n",
+       "\t<tr><td>1000          </td><td> 50           </td><td>0.1           </td><td>0.05          </td><td>0.15          </td><td>0.26 ( 0 )    </td><td>18.76 ( 0.63 )</td><td>0.02 ( 0.01 ) </td><td>23.74         </td><td>0.73          </td><td>0.26          </td><td>18.70         </td><td>0.02          </td></tr>\n",
+       "\t<tr><td>  50          </td><td>100           </td><td>0.1           </td><td>2.00          </td><td>0.03          </td><td>0.41 ( 0.02 ) </td><td>9.27 ( 0.76 ) </td><td>4.63 ( 0.09 ) </td><td> 9.64         </td><td>0.82          </td><td>0.41          </td><td> 9.27         </td><td>4.63          </td></tr>\n",
+       "\t<tr><td> 100          </td><td>100           </td><td>0.1           </td><td>1.00          </td><td>0.07          </td><td>0.34 ( 0.01 ) </td><td>10.1 ( 1.04 ) </td><td>3.87 ( 0.13 ) </td><td>11.23         </td><td>0.7           </td><td>0.34          </td><td>10.10         </td><td>3.87          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50           &  50            & 0.1            & 1.00           & 0.02           & 0.38 ( 0.02 )  & 10.9 ( 0.97 )  & 3.91 ( 0.15 )  & 11.99          & 0.77           & 0.38           & 10.90          & 3.91          \\\\\n",
+       "\t  100           &  50            & 0.1            & 0.50           & 0.06           & 0.34 ( 0.01 )  & 13.39 ( 1.06 ) & 2.45 ( 0.16 )  & 15.94          & 0.68           & 0.34           & 13.30          & 2.45          \\\\\n",
+       "\t  500           &  50            & 0.1            & 0.10           & 0.13           & 0.27 ( 0 )     & 19.21 ( 0.81 ) & 0.14 ( 0.04 )  & 24.07          & 0.73           & 0.27           & 19.20          & 0.14          \\\\\n",
+       "\t 1000           &  50            & 0.1            & 0.05           & 0.15           & 0.26 ( 0 )     & 18.76 ( 0.63 ) & 0.02 ( 0.01 )  & 23.74          & 0.73           & 0.26           & 18.70          & 0.02          \\\\\n",
+       "\t   50           & 100            & 0.1            & 2.00           & 0.03           & 0.41 ( 0.02 )  & 9.27 ( 0.76 )  & 4.63 ( 0.09 )  &  9.64          & 0.82           & 0.41           &  9.27          & 4.63          \\\\\n",
+       "\t  100           & 100            & 0.1            & 1.00           & 0.07           & 0.34 ( 0.01 )  & 10.1 ( 1.04 )  & 3.87 ( 0.13 )  & 11.23          & 0.7            & 0.34           & 10.10          & 3.87          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50           |  50            | 0.1            | 1.00           | 0.02           | 0.38 ( 0.02 )  | 10.9 ( 0.97 )  | 3.91 ( 0.15 )  | 11.99          | 0.77           | 0.38           | 10.90          | 3.91           |\n",
+       "|  100           |  50            | 0.1            | 0.50           | 0.06           | 0.34 ( 0.01 )  | 13.39 ( 1.06 ) | 2.45 ( 0.16 )  | 15.94          | 0.68           | 0.34           | 13.30          | 2.45           |\n",
+       "|  500           |  50            | 0.1            | 0.10           | 0.13           | 0.27 ( 0 )     | 19.21 ( 0.81 ) | 0.14 ( 0.04 )  | 24.07          | 0.73           | 0.27           | 19.20          | 0.14           |\n",
+       "| 1000           |  50            | 0.1            | 0.05           | 0.15           | 0.26 ( 0 )     | 18.76 ( 0.63 ) | 0.02 ( 0.01 )  | 23.74          | 0.73           | 0.26           | 18.70          | 0.02           |\n",
+       "|   50           | 100            | 0.1            | 2.00           | 0.03           | 0.41 ( 0.02 )  | 9.27 ( 0.76 )  | 4.63 ( 0.09 )  |  9.64          | 0.82           | 0.41           |  9.27          | 4.63           |\n",
+       "|  100           | 100            | 0.1            | 1.00           | 0.07           | 0.34 ( 0.01 )  | 10.1 ( 1.04 )  | 3.87 ( 0.13 )  | 11.23          | 0.7            | 0.34           | 10.10          | 3.87           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50  50 0.1  1.00  0.02 0.38 ( 0.02 ) 10.9 ( 0.97 )  3.91 ( 0.15 )\n",
+       "2  100  50 0.1  0.50  0.06 0.34 ( 0.01 ) 13.39 ( 1.06 ) 2.45 ( 0.16 )\n",
+       "3  500  50 0.1  0.10  0.13 0.27 ( 0 )    19.21 ( 0.81 ) 0.14 ( 0.04 )\n",
+       "4 1000  50 0.1  0.05  0.15 0.26 ( 0 )    18.76 ( 0.63 ) 0.02 ( 0.01 )\n",
+       "5   50 100 0.1  2.00  0.03 0.41 ( 0.02 ) 9.27 ( 0.76 )  4.63 ( 0.09 )\n",
+       "6  100 100 0.1  1.00  0.07 0.34 ( 0.01 ) 10.1 ( 1.04 )  3.87 ( 0.13 )\n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "1 11.99      0.77 0.38     10.90   3.91   \n",
+       "2 15.94      0.68 0.34     13.30   2.45   \n",
+       "3 24.07      0.73 0.27     19.20   0.14   \n",
+       "4 23.74      0.73 0.26     18.70   0.02   \n",
+       "5  9.64      0.82 0.41      9.27   4.63   \n",
+       "6 11.23      0.7  0.34     10.10   3.87   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.15          </td><td>0.28 ( 0 )    </td><td>32.6 ( 1.76 ) </td><td>0 ( 0 )       </td><td>37.60         </td><td>0.81          </td><td>0.28          </td><td>32.6          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.16          </td><td>0.26 ( 0 )    </td><td>30.4 ( 1.66 ) </td><td>0 ( 0 )       </td><td>35.40         </td><td>0.79          </td><td>0.26          </td><td>30.4          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.07          </td><td>3.06 ( 0.18 ) </td><td>43.09 ( 2.82 )</td><td>1.57 ( 0.1 )  </td><td>46.52         </td><td>0.88          </td><td>3.06          </td><td>43.0          </td><td>1.57          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.12          </td><td>0.59 ( 0.03 ) </td><td>43.91 ( 1.31 )</td><td>0 ( 0 )       </td><td>48.91         </td><td>0.87          </td><td>0.59          </td><td>43.9          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.12          </td><td>0.29 ( 0 )    </td><td>41.57 ( 2.28 )</td><td>0 ( 0 )       </td><td>46.57         </td><td>0.84          </td><td>0.29          </td><td>41.5          </td><td>0.00          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.13          </td><td>0.27 ( 0 )    </td><td>38.76 ( 2.16 )</td><td>0 ( 0 )       </td><td>43.76         </td><td>0.82          </td><td>0.27          </td><td>38.7          </td><td>0.00          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.15           & 0.28 ( 0 )     & 32.6 ( 1.76 )  & 0 ( 0 )        & 37.60          & 0.81           & 0.28           & 32.6           & 0.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.16           & 0.26 ( 0 )     & 30.4 ( 1.66 )  & 0 ( 0 )        & 35.40          & 0.79           & 0.26           & 30.4           & 0.00          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.07           & 3.06 ( 0.18 )  & 43.09 ( 2.82 ) & 1.57 ( 0.1 )   & 46.52          & 0.88           & 3.06           & 43.0           & 1.57          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.12           & 0.59 ( 0.03 )  & 43.91 ( 1.31 ) & 0 ( 0 )        & 48.91          & 0.87           & 0.59           & 43.9           & 0.00          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.12           & 0.29 ( 0 )     & 41.57 ( 2.28 ) & 0 ( 0 )        & 46.57          & 0.84           & 0.29           & 41.5           & 0.00          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.13           & 0.27 ( 0 )     & 38.76 ( 2.16 ) & 0 ( 0 )        & 43.76          & 0.82           & 0.27           & 38.7           & 0.00          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.15           | 0.28 ( 0 )     | 32.6 ( 1.76 )  | 0 ( 0 )        | 37.60          | 0.81           | 0.28           | 32.6           | 0.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.16           | 0.26 ( 0 )     | 30.4 ( 1.66 )  | 0 ( 0 )        | 35.40          | 0.79           | 0.26           | 30.4           | 0.00           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.07           | 3.06 ( 0.18 )  | 43.09 ( 2.82 ) | 1.57 ( 0.1 )   | 46.52          | 0.88           | 3.06           | 43.0           | 1.57           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.12           | 0.59 ( 0.03 )  | 43.91 ( 1.31 ) | 0 ( 0 )        | 48.91          | 0.87           | 0.59           | 43.9           | 0.00           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.12           | 0.29 ( 0 )     | 41.57 ( 2.28 ) | 0 ( 0 )        | 46.57          | 0.84           | 0.29           | 41.5           | 0.00           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.13           | 0.27 ( 0 )     | 38.76 ( 2.16 ) | 0 ( 0 )        | 43.76          | 0.82           | 0.27           | 38.7           | 0.00           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN          \n",
+       "75  500  500 0.9   1.0  0.15 0.28 ( 0 )    32.6 ( 1.76 )  0 ( 0 )     \n",
+       "76 1000  500 0.9   0.5  0.16 0.26 ( 0 )    30.4 ( 1.66 )  0 ( 0 )     \n",
+       "77   50 1000 0.9  20.0  0.07 3.06 ( 0.18 ) 43.09 ( 2.82 ) 1.57 ( 0.1 )\n",
+       "78  100 1000 0.9  10.0  0.12 0.59 ( 0.03 ) 43.91 ( 1.31 ) 0 ( 0 )     \n",
+       "79  500 1000 0.9   2.0  0.12 0.29 ( 0 )    41.57 ( 2.28 ) 0 ( 0 )     \n",
+       "80 1000 1000 0.9   1.0  0.13 0.27 ( 0 )    38.76 ( 2.16 ) 0 ( 0 )     \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75 37.60      0.81 0.28     32.6    0.00   \n",
+       "76 35.40      0.79 0.26     30.4    0.00   \n",
+       "77 46.52      0.88 3.06     43.0    1.57   \n",
+       "78 48.91      0.87 0.59     43.9    0.00   \n",
+       "79 46.57      0.84 0.29     41.5    0.00   \n",
+       "80 43.76      0.82 0.27     38.7    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_elnet.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/2.3_sim_block_rf_cts.ipynb b/simulations/notebooks_sim_cts/2.3_sim_block_rf_cts.ipynb
new file mode 100755
index 0000000..669d0af
--- /dev/null
+++ b/simulations/notebooks_sim_cts/2.3_sim_block_rf_cts.ipynb
@@ -0,0 +1,690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize random forests results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_RF.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_rf[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_rf))\n",
+    "for (i in 1:length(results_block_rf)){\n",
+    "    results_block_rf[[i]]$OOB = paste(round(mean(results_block_rf[[i]]$OOB.list, na.rm=T),2),\n",
+    "                                    '(', round(FSA::se(results_block_rf[[i]]$OOB.list, na.rm=T),2), ')')\n",
+    "    table_block = rbind(table_block, results_block_rf[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab', 'OOB')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_rf[[i]]$Stab.table))\n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.08 ( 0 )   </td><td>NaN          </td><td>0.59 ( 0.01 )</td><td>0.00         </td><td> NaN         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>2.26 ( 0.17 )</td><td>5.06 ( 0.12 )</td><td>0.07 ( 0 )   </td><td>0.03         </td><td>0.58 ( 0 )   </td><td>3.04         </td><td>0.70         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>2.34 ( 0.16 )</td><td>3.77 ( 0.14 )</td><td>0.05 ( 0 )   </td><td>0.23         </td><td>0.56 ( 0 )   </td><td>4.56         </td><td>0.49         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>2.37 ( 0.16 )</td><td>2.54 ( 0.11 )</td><td>0.05 ( 0 )   </td><td>0.51         </td><td>0.55 ( 0 )   </td><td>5.83         </td><td>0.38         </td></tr>\n",
+       "\t<tr><td>50          </td><td>100         </td><td>0.1         </td><td>1 ( 0 )     </td><td>6 ( 0 )     </td><td>0.08 ( 0 )  </td><td>NaN         </td><td>0.6 ( 0.01 )</td><td>0.00        </td><td> NaN        </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>4.76 ( 0.27 )</td><td>5.51 ( 0.09 )</td><td>0.07 ( 0 )   </td><td>0.01         </td><td>0.58 ( 0 )   </td><td>5.24         </td><td>0.91         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & OOB & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 1 ( 0 )       & 6 ( 0 )       & 0.08 ( 0 )    & NaN           & 0.59 ( 0.01 ) & 0.00          &  NaN         \\\\\n",
+       "\t 100           & 50            & 0.1           & 2.26 ( 0.17 ) & 5.06 ( 0.12 ) & 0.07 ( 0 )    & 0.03          & 0.58 ( 0 )    & 3.04          & 0.70         \\\\\n",
+       "\t 500           & 50            & 0.1           & 2.34 ( 0.16 ) & 3.77 ( 0.14 ) & 0.05 ( 0 )    & 0.23          & 0.56 ( 0 )    & 4.56          & 0.49         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 2.37 ( 0.16 ) & 2.54 ( 0.11 ) & 0.05 ( 0 )    & 0.51          & 0.55 ( 0 )    & 5.83          & 0.38         \\\\\n",
+       "\t 50           & 100          & 0.1          & 1 ( 0 )      & 6 ( 0 )      & 0.08 ( 0 )   & NaN          & 0.6 ( 0.01 ) & 0.00         &  NaN        \\\\\n",
+       "\t 100           & 100           & 0.1           & 4.76 ( 0.27 ) & 5.51 ( 0.09 ) & 0.07 ( 0 )    & 0.01          & 0.58 ( 0 )    & 5.24          & 0.91         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | OOB | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 1 ( 0 )       | 6 ( 0 )       | 0.08 ( 0 )    | NaN           | 0.59 ( 0.01 ) | 0.00          |  NaN          |\n",
+       "| 100           | 50            | 0.1           | 2.26 ( 0.17 ) | 5.06 ( 0.12 ) | 0.07 ( 0 )    | 0.03          | 0.58 ( 0 )    | 3.04          | 0.70          |\n",
+       "| 500           | 50            | 0.1           | 2.34 ( 0.16 ) | 3.77 ( 0.14 ) | 0.05 ( 0 )    | 0.23          | 0.56 ( 0 )    | 4.56          | 0.49          |\n",
+       "| 1000          | 50            | 0.1           | 2.37 ( 0.16 ) | 2.54 ( 0.11 ) | 0.05 ( 0 )    | 0.51          | 0.55 ( 0 )    | 5.83          | 0.38          |\n",
+       "| 50           | 100          | 0.1          | 1 ( 0 )      | 6 ( 0 )      | 0.08 ( 0 )   | NaN          | 0.6 ( 0.01 ) | 0.00         |  NaN         |\n",
+       "| 100           | 100           | 0.1           | 4.76 ( 0.27 ) | 5.51 ( 0.09 ) | 0.07 ( 0 )    | 0.01          | 0.58 ( 0 )    | 5.24          | 0.91          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE        Stab OOB          \n",
+       "1 50   50  0.1 1 ( 0 )       6 ( 0 )       0.08 ( 0 ) NaN  0.59 ( 0.01 )\n",
+       "2 100  50  0.1 2.26 ( 0.17 ) 5.06 ( 0.12 ) 0.07 ( 0 ) 0.03 0.58 ( 0 )   \n",
+       "3 500  50  0.1 2.34 ( 0.16 ) 3.77 ( 0.14 ) 0.05 ( 0 ) 0.23 0.56 ( 0 )   \n",
+       "4 1000 50  0.1 2.37 ( 0.16 ) 2.54 ( 0.11 ) 0.05 ( 0 ) 0.51 0.55 ( 0 )   \n",
+       "5 50   100 0.1 1 ( 0 )       6 ( 0 )       0.08 ( 0 ) NaN  0.6 ( 0.01 ) \n",
+       "6 100  100 0.1 4.76 ( 0.27 ) 5.51 ( 0.09 ) 0.07 ( 0 ) 0.01 0.58 ( 0 )   \n",
+       "  num_select FDR \n",
+       "1 0.00        NaN\n",
+       "2 3.04       0.70\n",
+       "3 4.56       0.49\n",
+       "4 5.83       0.38\n",
+       "5 0.00        NaN\n",
+       "6 5.24       0.91"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'OOB', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$OOB_mean = as.numeric(substr(result.table_block$OOB, start=1, stop=4))\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td> 50           </td><td>  50          </td><td>0.1           </td><td> 1            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td> 50           </td><td> 100          </td><td>0.1           </td><td> 2            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.6 ( 0.01 )  </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.60          </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td> 50           </td><td> 500          </td><td>0.1           </td><td>10            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td> 50           </td><td>1000          </td><td>0.1           </td><td>20            </td><td> NaN          </td><td>0.08 ( 0 )    </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>0.59 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.08          </td><td>  NA          </td><td>0.00          </td><td>0.59          </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td> 50           </td><td>  50          </td><td>0.3           </td><td> 1            </td><td> NaN          </td><td>0.31 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.14 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.31          </td><td>  NA          </td><td>0.00          </td><td>1.14          </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td> 50           </td><td> 100          </td><td>0.3           </td><td> 2            </td><td> NaN          </td><td>0.32 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.2 ( 0.01 )  </td><td> 0.00         </td><td>NaN           </td><td>0.32          </td><td>  NA          </td><td>0.00          </td><td>1.20          </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td> 50           </td><td> 500          </td><td>0.3           </td><td>10            </td><td> NaN          </td><td>0.36 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.23 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.36          </td><td>  NA          </td><td>0.00          </td><td>1.23          </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td> 50           </td><td>1000          </td><td>0.3           </td><td>20            </td><td> NaN          </td><td>0.34 ( 0.01 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.26 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.34          </td><td>  NA          </td><td>0.00          </td><td>1.26          </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td> 50           </td><td>  50          </td><td>0.5           </td><td> 1            </td><td> NaN          </td><td>0.6 ( 0.03 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.46 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.60          </td><td>  NA          </td><td>0.00          </td><td>1.46          </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td> 50           </td><td> 100          </td><td>0.5           </td><td> 2            </td><td> NaN          </td><td>0.6 ( 0.02 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.56 ( 0.01 ) </td><td> 0.00         </td><td>NaN           </td><td>0.60          </td><td>  NA          </td><td>0.00          </td><td>1.56          </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td> 50           </td><td> 500          </td><td>0.5           </td><td>10            </td><td> NaN          </td><td>0.58 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.64 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.58          </td><td>  NA          </td><td>0.00          </td><td>1.64          </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td> 50           </td><td>1000          </td><td>0.5           </td><td>20            </td><td> NaN          </td><td>0.57 ( 0.02 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.64 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.57          </td><td>  NA          </td><td>0.00          </td><td>1.64          </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td> 50           </td><td>  50          </td><td>0.7           </td><td> 1            </td><td> NaN          </td><td>0.8 ( 0.04 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.72 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.80          </td><td>  NA          </td><td>0.00          </td><td>1.72          </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td> 50           </td><td> 100          </td><td>0.7           </td><td> 2            </td><td> NaN          </td><td>0.92 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.85 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.92          </td><td>  NA          </td><td>0.00          </td><td>1.85          </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td> 50           </td><td> 500          </td><td>0.7           </td><td>10            </td><td> NaN          </td><td>0.91 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2 ( 0.02 )    </td><td> 0.00         </td><td>NaN           </td><td>0.91          </td><td>  NA          </td><td>0.00          </td><td>  NA          </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td> 50           </td><td>1000          </td><td>0.7           </td><td>20            </td><td> NaN          </td><td>0.91 ( 0.04 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.98 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>0.91          </td><td>  NA          </td><td>0.00          </td><td>1.98          </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td> 50           </td><td>  50          </td><td>0.9           </td><td> 1            </td><td> NaN          </td><td>1.1 ( 0.04 )  </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>1.99 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>1.10          </td><td>  NA          </td><td>0.00          </td><td>1.99          </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td> 50           </td><td> 100          </td><td>0.9           </td><td> 2            </td><td> NaN          </td><td>1.11 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.06 ( 0.02 ) </td><td> 0.00         </td><td>NaN           </td><td>1.11          </td><td>  NA          </td><td>0.00          </td><td>2.06          </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td> 50           </td><td> 500          </td><td>0.9           </td><td>10            </td><td> NaN          </td><td>1.18 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.2 ( 0.02 )  </td><td> 0.00         </td><td>NaN           </td><td>1.18          </td><td>  NA          </td><td>0.00          </td><td>2.20          </td></tr>\n",
+       "\t<tr><th scope=row>75</th><td>500           </td><td> 500          </td><td>0.9           </td><td> 1            </td><td>0.12          </td><td>0.73 ( 0.01 ) </td><td>20.83 ( 0.45 )</td><td>2.04 ( 0.09 ) </td><td>2 ( 0.01 )    </td><td>24.79         </td><td>0.84          </td><td>0.73          </td><td>20.8          </td><td>2.04          </td><td>  NA          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td> 50           </td><td>1000          </td><td>0.9           </td><td>20            </td><td> NaN          </td><td>1.22 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.3 ( 0.03 )  </td><td> 0.00         </td><td>NaN           </td><td>1.22          </td><td>  NA          </td><td>0.00          </td><td>2.30          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 &  50            &   50           & 0.1            &  1             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t5 &  50            &  100           & 0.1            &  2             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.6 ( 0.01 )   &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.60          \\\\\n",
+       "\t9 &  50            &  500           & 0.1            & 10             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t13 &  50            & 1000           & 0.1            & 20             &  NaN           & 0.08 ( 0 )     & 1 ( 0 )        & 6 ( 0 )        & 0.59 ( 0.01 )  &  0.00          & NaN            & 0.08           &   NA           & 0.00           & 0.59          \\\\\n",
+       "\t17 &  50            &   50           & 0.3            &  1             &  NaN           & 0.31 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.14 ( 0.01 )  &  0.00          & NaN            & 0.31           &   NA           & 0.00           & 1.14          \\\\\n",
+       "\t21 &  50            &  100           & 0.3            &  2             &  NaN           & 0.32 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.2 ( 0.01 )   &  0.00          & NaN            & 0.32           &   NA           & 0.00           & 1.20          \\\\\n",
+       "\t25 &  50            &  500           & 0.3            & 10             &  NaN           & 0.36 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.23 ( 0.01 )  &  0.00          & NaN            & 0.36           &   NA           & 0.00           & 1.23          \\\\\n",
+       "\t29 &  50            & 1000           & 0.3            & 20             &  NaN           & 0.34 ( 0.01 )  & 1 ( 0 )        & 6 ( 0 )        & 1.26 ( 0.01 )  &  0.00          & NaN            & 0.34           &   NA           & 0.00           & 1.26          \\\\\n",
+       "\t33 &  50            &   50           & 0.5            &  1             &  NaN           & 0.6 ( 0.03 )   & 1 ( 0 )        & 6 ( 0 )        & 1.46 ( 0.01 )  &  0.00          & NaN            & 0.60           &   NA           & 0.00           & 1.46          \\\\\n",
+       "\t37 &  50            &  100           & 0.5            &  2             &  NaN           & 0.6 ( 0.02 )   & 1 ( 0 )        & 6 ( 0 )        & 1.56 ( 0.01 )  &  0.00          & NaN            & 0.60           &   NA           & 0.00           & 1.56          \\\\\n",
+       "\t41 &  50            &  500           & 0.5            & 10             &  NaN           & 0.58 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.64 ( 0.02 )  &  0.00          & NaN            & 0.58           &   NA           & 0.00           & 1.64          \\\\\n",
+       "\t45 &  50            & 1000           & 0.5            & 20             &  NaN           & 0.57 ( 0.02 )  & 1 ( 0 )        & 6 ( 0 )        & 1.64 ( 0.02 )  &  0.00          & NaN            & 0.57           &   NA           & 0.00           & 1.64          \\\\\n",
+       "\t49 &  50            &   50           & 0.7            &  1             &  NaN           & 0.8 ( 0.04 )   & 1 ( 0 )        & 6 ( 0 )        & 1.72 ( 0.02 )  &  0.00          & NaN            & 0.80           &   NA           & 0.00           & 1.72          \\\\\n",
+       "\t53 &  50            &  100           & 0.7            &  2             &  NaN           & 0.92 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 1.85 ( 0.02 )  &  0.00          & NaN            & 0.92           &   NA           & 0.00           & 1.85          \\\\\n",
+       "\t57 &  50            &  500           & 0.7            & 10             &  NaN           & 0.91 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 2 ( 0.02 )     &  0.00          & NaN            & 0.91           &   NA           & 0.00           &   NA          \\\\\n",
+       "\t61 &  50            & 1000           & 0.7            & 20             &  NaN           & 0.91 ( 0.04 )  & 1 ( 0 )        & 6 ( 0 )        & 1.98 ( 0.02 )  &  0.00          & NaN            & 0.91           &   NA           & 0.00           & 1.98          \\\\\n",
+       "\t65 &  50            &   50           & 0.9            &  1             &  NaN           & 1.1 ( 0.04 )   & 1 ( 0 )        & 6 ( 0 )        & 1.99 ( 0.02 )  &  0.00          & NaN            & 1.10           &   NA           & 0.00           & 1.99          \\\\\n",
+       "\t69 &  50            &  100           & 0.9            &  2             &  NaN           & 1.11 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.06 ( 0.02 )  &  0.00          & NaN            & 1.11           &   NA           & 0.00           & 2.06          \\\\\n",
+       "\t73 &  50            &  500           & 0.9            & 10             &  NaN           & 1.18 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.2 ( 0.02 )   &  0.00          & NaN            & 1.18           &   NA           & 0.00           & 2.20          \\\\\n",
+       "\t75 & 500            &  500           & 0.9            &  1             & 0.12           & 0.73 ( 0.01 )  & 20.83 ( 0.45 ) & 2.04 ( 0.09 )  & 2 ( 0.01 )     & 24.79          & 0.84           & 0.73           & 20.8           & 2.04           &   NA          \\\\\n",
+       "\t77 &  50            & 1000           & 0.9            & 20             &  NaN           & 1.22 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.3 ( 0.03 )   &  0.00          & NaN            & 1.22           &   NA           & 0.00           & 2.30          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 |  50            |   50           | 0.1            |  1             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 5 |  50            |  100           | 0.1            |  2             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.6 ( 0.01 )   |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.60           |\n",
+       "| 9 |  50            |  500           | 0.1            | 10             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 13 |  50            | 1000           | 0.1            | 20             |  NaN           | 0.08 ( 0 )     | 1 ( 0 )        | 6 ( 0 )        | 0.59 ( 0.01 )  |  0.00          | NaN            | 0.08           |   NA           | 0.00           | 0.59           |\n",
+       "| 17 |  50            |   50           | 0.3            |  1             |  NaN           | 0.31 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.14 ( 0.01 )  |  0.00          | NaN            | 0.31           |   NA           | 0.00           | 1.14           |\n",
+       "| 21 |  50            |  100           | 0.3            |  2             |  NaN           | 0.32 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.2 ( 0.01 )   |  0.00          | NaN            | 0.32           |   NA           | 0.00           | 1.20           |\n",
+       "| 25 |  50            |  500           | 0.3            | 10             |  NaN           | 0.36 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.23 ( 0.01 )  |  0.00          | NaN            | 0.36           |   NA           | 0.00           | 1.23           |\n",
+       "| 29 |  50            | 1000           | 0.3            | 20             |  NaN           | 0.34 ( 0.01 )  | 1 ( 0 )        | 6 ( 0 )        | 1.26 ( 0.01 )  |  0.00          | NaN            | 0.34           |   NA           | 0.00           | 1.26           |\n",
+       "| 33 |  50            |   50           | 0.5            |  1             |  NaN           | 0.6 ( 0.03 )   | 1 ( 0 )        | 6 ( 0 )        | 1.46 ( 0.01 )  |  0.00          | NaN            | 0.60           |   NA           | 0.00           | 1.46           |\n",
+       "| 37 |  50            |  100           | 0.5            |  2             |  NaN           | 0.6 ( 0.02 )   | 1 ( 0 )        | 6 ( 0 )        | 1.56 ( 0.01 )  |  0.00          | NaN            | 0.60           |   NA           | 0.00           | 1.56           |\n",
+       "| 41 |  50            |  500           | 0.5            | 10             |  NaN           | 0.58 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.64 ( 0.02 )  |  0.00          | NaN            | 0.58           |   NA           | 0.00           | 1.64           |\n",
+       "| 45 |  50            | 1000           | 0.5            | 20             |  NaN           | 0.57 ( 0.02 )  | 1 ( 0 )        | 6 ( 0 )        | 1.64 ( 0.02 )  |  0.00          | NaN            | 0.57           |   NA           | 0.00           | 1.64           |\n",
+       "| 49 |  50            |   50           | 0.7            |  1             |  NaN           | 0.8 ( 0.04 )   | 1 ( 0 )        | 6 ( 0 )        | 1.72 ( 0.02 )  |  0.00          | NaN            | 0.80           |   NA           | 0.00           | 1.72           |\n",
+       "| 53 |  50            |  100           | 0.7            |  2             |  NaN           | 0.92 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 1.85 ( 0.02 )  |  0.00          | NaN            | 0.92           |   NA           | 0.00           | 1.85           |\n",
+       "| 57 |  50            |  500           | 0.7            | 10             |  NaN           | 0.91 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 2 ( 0.02 )     |  0.00          | NaN            | 0.91           |   NA           | 0.00           |   NA           |\n",
+       "| 61 |  50            | 1000           | 0.7            | 20             |  NaN           | 0.91 ( 0.04 )  | 1 ( 0 )        | 6 ( 0 )        | 1.98 ( 0.02 )  |  0.00          | NaN            | 0.91           |   NA           | 0.00           | 1.98           |\n",
+       "| 65 |  50            |   50           | 0.9            |  1             |  NaN           | 1.1 ( 0.04 )   | 1 ( 0 )        | 6 ( 0 )        | 1.99 ( 0.02 )  |  0.00          | NaN            | 1.10           |   NA           | 0.00           | 1.99           |\n",
+       "| 69 |  50            |  100           | 0.9            |  2             |  NaN           | 1.11 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.06 ( 0.02 )  |  0.00          | NaN            | 1.11           |   NA           | 0.00           | 2.06           |\n",
+       "| 73 |  50            |  500           | 0.9            | 10             |  NaN           | 1.18 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.2 ( 0.02 )   |  0.00          | NaN            | 1.18           |   NA           | 0.00           | 2.20           |\n",
+       "| 75 | 500            |  500           | 0.9            |  1             | 0.12           | 0.73 ( 0.01 )  | 20.83 ( 0.45 ) | 2.04 ( 0.09 )  | 2 ( 0.01 )     | 24.79          | 0.84           | 0.73           | 20.8           | 2.04           |   NA           |\n",
+       "| 77 |  50            | 1000           | 0.9            | 20             |  NaN           | 1.22 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.3 ( 0.03 )   |  0.00          | NaN            | 1.22           |   NA           | 0.00           | 2.30           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P    Corr Ratio Stab MSE           FP             FN           \n",
+       "1   50   50 0.1   1     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "5   50  100 0.1   2     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "9   50  500 0.1  10     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "13  50 1000 0.1  20     NaN 0.08 ( 0 )    1 ( 0 )        6 ( 0 )      \n",
+       "17  50   50 0.3   1     NaN 0.31 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "21  50  100 0.3   2     NaN 0.32 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "25  50  500 0.3  10     NaN 0.36 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "29  50 1000 0.3  20     NaN 0.34 ( 0.01 ) 1 ( 0 )        6 ( 0 )      \n",
+       "33  50   50 0.5   1     NaN 0.6 ( 0.03 )  1 ( 0 )        6 ( 0 )      \n",
+       "37  50  100 0.5   2     NaN 0.6 ( 0.02 )  1 ( 0 )        6 ( 0 )      \n",
+       "41  50  500 0.5  10     NaN 0.58 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "45  50 1000 0.5  20     NaN 0.57 ( 0.02 ) 1 ( 0 )        6 ( 0 )      \n",
+       "49  50   50 0.7   1     NaN 0.8 ( 0.04 )  1 ( 0 )        6 ( 0 )      \n",
+       "53  50  100 0.7   2     NaN 0.92 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "57  50  500 0.7  10     NaN 0.91 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "61  50 1000 0.7  20     NaN 0.91 ( 0.04 ) 1 ( 0 )        6 ( 0 )      \n",
+       "65  50   50 0.9   1     NaN 1.1 ( 0.04 )  1 ( 0 )        6 ( 0 )      \n",
+       "69  50  100 0.9   2     NaN 1.11 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "73  50  500 0.9  10     NaN 1.18 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "75 500  500 0.9   1    0.12 0.73 ( 0.01 ) 20.83 ( 0.45 ) 2.04 ( 0.09 )\n",
+       "77  50 1000 0.9  20     NaN 1.22 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "5  0.6 ( 0.01 )   0.00      NaN  0.08       NA    0.00    0.60    \n",
+       "9  0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "13 0.59 ( 0.01 )  0.00      NaN  0.08       NA    0.00    0.59    \n",
+       "17 1.14 ( 0.01 )  0.00      NaN  0.31       NA    0.00    1.14    \n",
+       "21 1.2 ( 0.01 )   0.00      NaN  0.32       NA    0.00    1.20    \n",
+       "25 1.23 ( 0.01 )  0.00      NaN  0.36       NA    0.00    1.23    \n",
+       "29 1.26 ( 0.01 )  0.00      NaN  0.34       NA    0.00    1.26    \n",
+       "33 1.46 ( 0.01 )  0.00      NaN  0.60       NA    0.00    1.46    \n",
+       "37 1.56 ( 0.01 )  0.00      NaN  0.60       NA    0.00    1.56    \n",
+       "41 1.64 ( 0.02 )  0.00      NaN  0.58       NA    0.00    1.64    \n",
+       "45 1.64 ( 0.02 )  0.00      NaN  0.57       NA    0.00    1.64    \n",
+       "49 1.72 ( 0.02 )  0.00      NaN  0.80       NA    0.00    1.72    \n",
+       "53 1.85 ( 0.02 )  0.00      NaN  0.92       NA    0.00    1.85    \n",
+       "57 2 ( 0.02 )     0.00      NaN  0.91       NA    0.00      NA    \n",
+       "61 1.98 ( 0.02 )  0.00      NaN  0.91       NA    0.00    1.98    \n",
+       "65 1.99 ( 0.02 )  0.00      NaN  1.10       NA    0.00    1.99    \n",
+       "69 2.06 ( 0.02 )  0.00      NaN  1.11       NA    0.00    2.06    \n",
+       "73 2.2 ( 0.02 )   0.00      NaN  1.18       NA    0.00    2.20    \n",
+       "75 2 ( 0.01 )    24.79      0.84 0.73     20.8    2.04      NA    \n",
+       "77 2.3 ( 0.03 )   0.00      NaN  1.22       NA    0.00    2.30    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$Stab[is.na(result.table_block$Stab)] = 0\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 1\n",
+    "result.table_block$OOB_mean[is.na(result.table_block$OOB_mean)] = 2\n",
+    "result.table_block$FN_mean[result.table_block$num_select == 0] = 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>1</th><td>50           </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>5</th><td>50           </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.6 ( 0.01 ) </td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.60         </td></tr>\n",
+       "\t<tr><th scope=row>9</th><td>50           </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>13</th><td>50           </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0            </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.08         </td><td>1            </td><td>6            </td><td>0.59         </td></tr>\n",
+       "\t<tr><th scope=row>17</th><td>50           </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0            </td><td>0.31 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.14 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.31         </td><td>1            </td><td>6            </td><td>1.14         </td></tr>\n",
+       "\t<tr><th scope=row>21</th><td>50           </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0            </td><td>0.32 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.2 ( 0.01 ) </td><td>0            </td><td>NaN          </td><td>0.32         </td><td>1            </td><td>6            </td><td>1.20         </td></tr>\n",
+       "\t<tr><th scope=row>25</th><td>50           </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0            </td><td>0.36 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.23 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.36         </td><td>1            </td><td>6            </td><td>1.23         </td></tr>\n",
+       "\t<tr><th scope=row>29</th><td>50           </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0            </td><td>0.34 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.26 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.34         </td><td>1            </td><td>6            </td><td>1.26         </td></tr>\n",
+       "\t<tr><th scope=row>33</th><td>50           </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0            </td><td>0.6 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.46 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.60         </td><td>1            </td><td>6            </td><td>1.46         </td></tr>\n",
+       "\t<tr><th scope=row>37</th><td>50           </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0            </td><td>0.6 ( 0.02 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.56 ( 0.01 )</td><td>0            </td><td>NaN          </td><td>0.60         </td><td>1            </td><td>6            </td><td>1.56         </td></tr>\n",
+       "\t<tr><th scope=row>41</th><td>50           </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0            </td><td>0.58 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.64 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.58         </td><td>1            </td><td>6            </td><td>1.64         </td></tr>\n",
+       "\t<tr><th scope=row>45</th><td>50           </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0            </td><td>0.57 ( 0.02 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.64 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.57         </td><td>1            </td><td>6            </td><td>1.64         </td></tr>\n",
+       "\t<tr><th scope=row>49</th><td>50           </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0            </td><td>0.8 ( 0.04 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.72 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.80         </td><td>1            </td><td>6            </td><td>1.72         </td></tr>\n",
+       "\t<tr><th scope=row>53</th><td>50           </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0            </td><td>0.92 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.85 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.92         </td><td>1            </td><td>6            </td><td>1.85         </td></tr>\n",
+       "\t<tr><th scope=row>57</th><td>50           </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2 ( 0.02 )   </td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>2.00         </td></tr>\n",
+       "\t<tr><th scope=row>61</th><td>50           </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.98 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>1.98         </td></tr>\n",
+       "\t<tr><th scope=row>65</th><td>50           </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0            </td><td>1.1 ( 0.04 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>1.99 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>1.10         </td><td>1            </td><td>6            </td><td>1.99         </td></tr>\n",
+       "\t<tr><th scope=row>69</th><td>50           </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0            </td><td>1.11 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.06 ( 0.02 )</td><td>0            </td><td>NaN          </td><td>1.11         </td><td>1            </td><td>6            </td><td>2.06         </td></tr>\n",
+       "\t<tr><th scope=row>73</th><td>50           </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0            </td><td>1.18 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.2 ( 0.02 ) </td><td>0            </td><td>NaN          </td><td>1.18         </td><td>1            </td><td>6            </td><td>2.20         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>50           </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0            </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>2.3 ( 0.03 ) </td><td>0            </td><td>NaN          </td><td>1.22         </td><td>1            </td><td>6            </td><td>2.30         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t1 & 50            &   50          & 0.1           &  1            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t5 & 50            &  100          & 0.1           &  2            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.6 ( 0.01 )  & 0             & NaN           & 0.08          & 1             & 6             & 0.60         \\\\\n",
+       "\t9 & 50            &  500          & 0.1           & 10            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t13 & 50            & 1000          & 0.1           & 20            & 0             & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0             & NaN           & 0.08          & 1             & 6             & 0.59         \\\\\n",
+       "\t17 & 50            &   50          & 0.3           &  1            & 0             & 0.31 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.14 ( 0.01 ) & 0             & NaN           & 0.31          & 1             & 6             & 1.14         \\\\\n",
+       "\t21 & 50            &  100          & 0.3           &  2            & 0             & 0.32 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.2 ( 0.01 )  & 0             & NaN           & 0.32          & 1             & 6             & 1.20         \\\\\n",
+       "\t25 & 50            &  500          & 0.3           & 10            & 0             & 0.36 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.23 ( 0.01 ) & 0             & NaN           & 0.36          & 1             & 6             & 1.23         \\\\\n",
+       "\t29 & 50            & 1000          & 0.3           & 20            & 0             & 0.34 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 1.26 ( 0.01 ) & 0             & NaN           & 0.34          & 1             & 6             & 1.26         \\\\\n",
+       "\t33 & 50            &   50          & 0.5           &  1            & 0             & 0.6 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 1.46 ( 0.01 ) & 0             & NaN           & 0.60          & 1             & 6             & 1.46         \\\\\n",
+       "\t37 & 50            &  100          & 0.5           &  2            & 0             & 0.6 ( 0.02 )  & 1 ( 0 )       & 6 ( 0 )       & 1.56 ( 0.01 ) & 0             & NaN           & 0.60          & 1             & 6             & 1.56         \\\\\n",
+       "\t41 & 50            &  500          & 0.5           & 10            & 0             & 0.58 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.64 ( 0.02 ) & 0             & NaN           & 0.58          & 1             & 6             & 1.64         \\\\\n",
+       "\t45 & 50            & 1000          & 0.5           & 20            & 0             & 0.57 ( 0.02 ) & 1 ( 0 )       & 6 ( 0 )       & 1.64 ( 0.02 ) & 0             & NaN           & 0.57          & 1             & 6             & 1.64         \\\\\n",
+       "\t49 & 50            &   50          & 0.7           &  1            & 0             & 0.8 ( 0.04 )  & 1 ( 0 )       & 6 ( 0 )       & 1.72 ( 0.02 ) & 0             & NaN           & 0.80          & 1             & 6             & 1.72         \\\\\n",
+       "\t53 & 50            &  100          & 0.7           &  2            & 0             & 0.92 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.85 ( 0.02 ) & 0             & NaN           & 0.92          & 1             & 6             & 1.85         \\\\\n",
+       "\t57 & 50            &  500          & 0.7           & 10            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 2 ( 0.02 )    & 0             & NaN           & 0.91          & 1             & 6             & 2.00         \\\\\n",
+       "\t61 & 50            & 1000          & 0.7           & 20            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 1.98 ( 0.02 ) & 0             & NaN           & 0.91          & 1             & 6             & 1.98         \\\\\n",
+       "\t65 & 50            &   50          & 0.9           &  1            & 0             & 1.1 ( 0.04 )  & 1 ( 0 )       & 6 ( 0 )       & 1.99 ( 0.02 ) & 0             & NaN           & 1.10          & 1             & 6             & 1.99         \\\\\n",
+       "\t69 & 50            &  100          & 0.9           &  2            & 0             & 1.11 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.06 ( 0.02 ) & 0             & NaN           & 1.11          & 1             & 6             & 2.06         \\\\\n",
+       "\t73 & 50            &  500          & 0.9           & 10            & 0             & 1.18 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.2 ( 0.02 )  & 0             & NaN           & 1.18          & 1             & 6             & 2.20         \\\\\n",
+       "\t77 & 50            & 1000          & 0.9           & 20            & 0             & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 2.3 ( 0.03 )  & 0             & NaN           & 1.22          & 1             & 6             & 2.30         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 1 | 50            |   50          | 0.1           |  1            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 5 | 50            |  100          | 0.1           |  2            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.6 ( 0.01 )  | 0             | NaN           | 0.08          | 1             | 6             | 0.60          |\n",
+       "| 9 | 50            |  500          | 0.1           | 10            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 13 | 50            | 1000          | 0.1           | 20            | 0             | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0             | NaN           | 0.08          | 1             | 6             | 0.59          |\n",
+       "| 17 | 50            |   50          | 0.3           |  1            | 0             | 0.31 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.14 ( 0.01 ) | 0             | NaN           | 0.31          | 1             | 6             | 1.14          |\n",
+       "| 21 | 50            |  100          | 0.3           |  2            | 0             | 0.32 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.2 ( 0.01 )  | 0             | NaN           | 0.32          | 1             | 6             | 1.20          |\n",
+       "| 25 | 50            |  500          | 0.3           | 10            | 0             | 0.36 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.23 ( 0.01 ) | 0             | NaN           | 0.36          | 1             | 6             | 1.23          |\n",
+       "| 29 | 50            | 1000          | 0.3           | 20            | 0             | 0.34 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 1.26 ( 0.01 ) | 0             | NaN           | 0.34          | 1             | 6             | 1.26          |\n",
+       "| 33 | 50            |   50          | 0.5           |  1            | 0             | 0.6 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 1.46 ( 0.01 ) | 0             | NaN           | 0.60          | 1             | 6             | 1.46          |\n",
+       "| 37 | 50            |  100          | 0.5           |  2            | 0             | 0.6 ( 0.02 )  | 1 ( 0 )       | 6 ( 0 )       | 1.56 ( 0.01 ) | 0             | NaN           | 0.60          | 1             | 6             | 1.56          |\n",
+       "| 41 | 50            |  500          | 0.5           | 10            | 0             | 0.58 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.64 ( 0.02 ) | 0             | NaN           | 0.58          | 1             | 6             | 1.64          |\n",
+       "| 45 | 50            | 1000          | 0.5           | 20            | 0             | 0.57 ( 0.02 ) | 1 ( 0 )       | 6 ( 0 )       | 1.64 ( 0.02 ) | 0             | NaN           | 0.57          | 1             | 6             | 1.64          |\n",
+       "| 49 | 50            |   50          | 0.7           |  1            | 0             | 0.8 ( 0.04 )  | 1 ( 0 )       | 6 ( 0 )       | 1.72 ( 0.02 ) | 0             | NaN           | 0.80          | 1             | 6             | 1.72          |\n",
+       "| 53 | 50            |  100          | 0.7           |  2            | 0             | 0.92 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.85 ( 0.02 ) | 0             | NaN           | 0.92          | 1             | 6             | 1.85          |\n",
+       "| 57 | 50            |  500          | 0.7           | 10            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 2 ( 0.02 )    | 0             | NaN           | 0.91          | 1             | 6             | 2.00          |\n",
+       "| 61 | 50            | 1000          | 0.7           | 20            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 1.98 ( 0.02 ) | 0             | NaN           | 0.91          | 1             | 6             | 1.98          |\n",
+       "| 65 | 50            |   50          | 0.9           |  1            | 0             | 1.1 ( 0.04 )  | 1 ( 0 )       | 6 ( 0 )       | 1.99 ( 0.02 ) | 0             | NaN           | 1.10          | 1             | 6             | 1.99          |\n",
+       "| 69 | 50            |  100          | 0.9           |  2            | 0             | 1.11 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.06 ( 0.02 ) | 0             | NaN           | 1.11          | 1             | 6             | 2.06          |\n",
+       "| 73 | 50            |  500          | 0.9           | 10            | 0             | 1.18 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.2 ( 0.02 )  | 0             | NaN           | 1.18          | 1             | 6             | 2.20          |\n",
+       "| 77 | 50            | 1000          | 0.9           | 20            | 0             | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 2.3 ( 0.03 )  | 0             | NaN           | 1.22          | 1             | 6             | 2.30          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N  P    Corr Ratio Stab MSE           FP      FN      OOB          \n",
+       "1  50   50 0.1   1    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "5  50  100 0.1   2    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.6 ( 0.01 ) \n",
+       "9  50  500 0.1  10    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "13 50 1000 0.1  20    0    0.08 ( 0 )    1 ( 0 ) 6 ( 0 ) 0.59 ( 0.01 )\n",
+       "17 50   50 0.3   1    0    0.31 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.14 ( 0.01 )\n",
+       "21 50  100 0.3   2    0    0.32 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.2 ( 0.01 ) \n",
+       "25 50  500 0.3  10    0    0.36 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.23 ( 0.01 )\n",
+       "29 50 1000 0.3  20    0    0.34 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 1.26 ( 0.01 )\n",
+       "33 50   50 0.5   1    0    0.6 ( 0.03 )  1 ( 0 ) 6 ( 0 ) 1.46 ( 0.01 )\n",
+       "37 50  100 0.5   2    0    0.6 ( 0.02 )  1 ( 0 ) 6 ( 0 ) 1.56 ( 0.01 )\n",
+       "41 50  500 0.5  10    0    0.58 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.64 ( 0.02 )\n",
+       "45 50 1000 0.5  20    0    0.57 ( 0.02 ) 1 ( 0 ) 6 ( 0 ) 1.64 ( 0.02 )\n",
+       "49 50   50 0.7   1    0    0.8 ( 0.04 )  1 ( 0 ) 6 ( 0 ) 1.72 ( 0.02 )\n",
+       "53 50  100 0.7   2    0    0.92 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 1.85 ( 0.02 )\n",
+       "57 50  500 0.7  10    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 2 ( 0.02 )   \n",
+       "61 50 1000 0.7  20    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 1.98 ( 0.02 )\n",
+       "65 50   50 0.9   1    0    1.1 ( 0.04 )  1 ( 0 ) 6 ( 0 ) 1.99 ( 0.02 )\n",
+       "69 50  100 0.9   2    0    1.11 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.06 ( 0.02 )\n",
+       "73 50  500 0.9  10    0    1.18 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.2 ( 0.02 ) \n",
+       "77 50 1000 0.9  20    0    1.22 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 2.3 ( 0.03 ) \n",
+       "   num_select FDR MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1  0          NaN 0.08     1       6       0.59    \n",
+       "5  0          NaN 0.08     1       6       0.60    \n",
+       "9  0          NaN 0.08     1       6       0.59    \n",
+       "13 0          NaN 0.08     1       6       0.59    \n",
+       "17 0          NaN 0.31     1       6       1.14    \n",
+       "21 0          NaN 0.32     1       6       1.20    \n",
+       "25 0          NaN 0.36     1       6       1.23    \n",
+       "29 0          NaN 0.34     1       6       1.26    \n",
+       "33 0          NaN 0.60     1       6       1.46    \n",
+       "37 0          NaN 0.60     1       6       1.56    \n",
+       "41 0          NaN 0.58     1       6       1.64    \n",
+       "45 0          NaN 0.57     1       6       1.64    \n",
+       "49 0          NaN 0.80     1       6       1.72    \n",
+       "53 0          NaN 0.92     1       6       1.85    \n",
+       "57 0          NaN 0.91     1       6       2.00    \n",
+       "61 0          NaN 0.91     1       6       1.98    \n",
+       "65 0          NaN 1.10     1       6       1.99    \n",
+       "69 0          NaN 1.11     1       6       2.06    \n",
+       "73 0          NaN 1.18     1       6       2.20    \n",
+       "77 0          NaN 1.22     1       6       2.30    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[c(1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65,69,73,77), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.00         </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.59 ( 0.01 )</td><td>0.00         </td><td>NaN          </td><td>0.08         </td><td>1.00         </td><td>6.00         </td><td>0.59         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.03         </td><td>0.07 ( 0 )   </td><td>2.26 ( 0.17 )</td><td>5.06 ( 0.12 )</td><td>0.58 ( 0 )   </td><td>3.04         </td><td>0.7          </td><td>0.07         </td><td>2.26         </td><td>5.06         </td><td>0.58         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.23         </td><td>0.05 ( 0 )   </td><td>2.34 ( 0.16 )</td><td>3.77 ( 0.14 )</td><td>0.56 ( 0 )   </td><td>4.56         </td><td>0.49         </td><td>0.05         </td><td>2.34         </td><td>3.77         </td><td>0.56         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.51         </td><td>0.05 ( 0 )   </td><td>2.37 ( 0.16 )</td><td>2.54 ( 0.11 )</td><td>0.55 ( 0 )   </td><td>5.83         </td><td>0.38         </td><td>0.05         </td><td>2.37         </td><td>2.54         </td><td>0.55         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.00         </td><td>0.08 ( 0 )   </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0.6 ( 0.01 ) </td><td>0.00         </td><td>NaN          </td><td>0.08         </td><td>1.00         </td><td>6.00         </td><td>0.60         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.01         </td><td>0.07 ( 0 )   </td><td>4.76 ( 0.27 )</td><td>5.51 ( 0.09 )</td><td>0.58 ( 0 )   </td><td>5.24         </td><td>0.91         </td><td>0.07         </td><td>4.76         </td><td>5.51         </td><td>0.58         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.00          & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.59 ( 0.01 ) & 0.00          & NaN           & 0.08          & 1.00          & 6.00          & 0.59         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.03          & 0.07 ( 0 )    & 2.26 ( 0.17 ) & 5.06 ( 0.12 ) & 0.58 ( 0 )    & 3.04          & 0.7           & 0.07          & 2.26          & 5.06          & 0.58         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.23          & 0.05 ( 0 )    & 2.34 ( 0.16 ) & 3.77 ( 0.14 ) & 0.56 ( 0 )    & 4.56          & 0.49          & 0.05          & 2.34          & 3.77          & 0.56         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.51          & 0.05 ( 0 )    & 2.37 ( 0.16 ) & 2.54 ( 0.11 ) & 0.55 ( 0 )    & 5.83          & 0.38          & 0.05          & 2.37          & 2.54          & 0.55         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.00          & 0.08 ( 0 )    & 1 ( 0 )       & 6 ( 0 )       & 0.6 ( 0.01 )  & 0.00          & NaN           & 0.08          & 1.00          & 6.00          & 0.60         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.01          & 0.07 ( 0 )    & 4.76 ( 0.27 ) & 5.51 ( 0.09 ) & 0.58 ( 0 )    & 5.24          & 0.91          & 0.07          & 4.76          & 5.51          & 0.58         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.00          | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.59 ( 0.01 ) | 0.00          | NaN           | 0.08          | 1.00          | 6.00          | 0.59          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.03          | 0.07 ( 0 )    | 2.26 ( 0.17 ) | 5.06 ( 0.12 ) | 0.58 ( 0 )    | 3.04          | 0.7           | 0.07          | 2.26          | 5.06          | 0.58          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.23          | 0.05 ( 0 )    | 2.34 ( 0.16 ) | 3.77 ( 0.14 ) | 0.56 ( 0 )    | 4.56          | 0.49          | 0.05          | 2.34          | 3.77          | 0.56          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.51          | 0.05 ( 0 )    | 2.37 ( 0.16 ) | 2.54 ( 0.11 ) | 0.55 ( 0 )    | 5.83          | 0.38          | 0.05          | 2.37          | 2.54          | 0.55          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.00          | 0.08 ( 0 )    | 1 ( 0 )       | 6 ( 0 )       | 0.6 ( 0.01 )  | 0.00          | NaN           | 0.08          | 1.00          | 6.00          | 0.60          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.01          | 0.07 ( 0 )    | 4.76 ( 0.27 ) | 5.51 ( 0.09 ) | 0.58 ( 0 )    | 5.24          | 0.91          | 0.07          | 4.76          | 5.51          | 0.58          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE        FP            FN            OOB          \n",
+       "1   50  50 0.1  1.00  0.00 0.08 ( 0 ) 1 ( 0 )       6 ( 0 )       0.59 ( 0.01 )\n",
+       "2  100  50 0.1  0.50  0.03 0.07 ( 0 ) 2.26 ( 0.17 ) 5.06 ( 0.12 ) 0.58 ( 0 )   \n",
+       "3  500  50 0.1  0.10  0.23 0.05 ( 0 ) 2.34 ( 0.16 ) 3.77 ( 0.14 ) 0.56 ( 0 )   \n",
+       "4 1000  50 0.1  0.05  0.51 0.05 ( 0 ) 2.37 ( 0.16 ) 2.54 ( 0.11 ) 0.55 ( 0 )   \n",
+       "5   50 100 0.1  2.00  0.00 0.08 ( 0 ) 1 ( 0 )       6 ( 0 )       0.6 ( 0.01 ) \n",
+       "6  100 100 0.1  1.00  0.01 0.07 ( 0 ) 4.76 ( 0.27 ) 5.51 ( 0.09 ) 0.58 ( 0 )   \n",
+       "  num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "1 0.00       NaN  0.08     1.00    6.00    0.59    \n",
+       "2 3.04       0.7  0.07     2.26    5.06    0.58    \n",
+       "3 4.56       0.49 0.05     2.34    3.77    0.56    \n",
+       "4 5.83       0.38 0.05     2.37    2.54    0.55    \n",
+       "5 0.00       NaN  0.08     1.00    6.00    0.60    \n",
+       "6 5.24       0.91 0.07     4.76    5.51    0.58    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>OOB</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>OOB_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500          </td><td> 500          </td><td>0.9           </td><td> 1.0          </td><td>0.12          </td><td>0.73 ( 0.01 ) </td><td>20.83 ( 0.45 )</td><td>2.04 ( 0.09 ) </td><td>2 ( 0.01 )    </td><td>24.79         </td><td>0.84          </td><td>0.73          </td><td>20.8          </td><td>2.04          </td><td>2.00          </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000          </td><td> 500          </td><td>0.9           </td><td> 0.5          </td><td>0.18          </td><td>0.68 ( 0.01 ) </td><td>18.47 ( 0.43 )</td><td>1 ( 0.07 )    </td><td>1.92 ( 0 )    </td><td>23.47         </td><td>0.78          </td><td>0.68          </td><td>18.4          </td><td>0.00          </td><td>1.92          </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50          </td><td>1000          </td><td>0.9           </td><td>20.0          </td><td>0.00          </td><td>1.22 ( 0.05 ) </td><td>1 ( 0 )       </td><td>6 ( 0 )       </td><td>2.3 ( 0.03 )  </td><td> 0.00         </td><td>NaN           </td><td>1.22          </td><td> 1.0          </td><td>6.00          </td><td>2.30          </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100          </td><td>1000          </td><td>0.9           </td><td>10.0          </td><td>0.01          </td><td>0.93 ( 0.02 ) </td><td>49.18 ( 0.69 )</td><td>4.67 ( 0.09 ) </td><td>2.22 ( 0.02 ) </td><td>50.51         </td><td>0.97          </td><td>0.93          </td><td>49.1          </td><td>4.67          </td><td>2.22          </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500          </td><td>1000          </td><td>0.9           </td><td> 2.0          </td><td>0.06          </td><td>0.78 ( 0.01 ) </td><td>46.01 ( 0.7 ) </td><td>2.14 ( 0.09 ) </td><td>2.12 ( 0.01 ) </td><td>49.87         </td><td>0.92          </td><td>0.78          </td><td>46.0          </td><td>2.14          </td><td>2.12          </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000          </td><td>1000          </td><td>0.9           </td><td> 1.0          </td><td>0.08          </td><td>0.74 ( 0.01 ) </td><td>43.05 ( 0.72 )</td><td>1.17 ( 0.08 ) </td><td>2.07 ( 0 )    </td><td>47.88         </td><td>0.9           </td><td>0.74          </td><td>43.0          </td><td>1.17          </td><td>2.07          </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & OOB & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & OOB\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500           &  500           & 0.9            &  1.0           & 0.12           & 0.73 ( 0.01 )  & 20.83 ( 0.45 ) & 2.04 ( 0.09 )  & 2 ( 0.01 )     & 24.79          & 0.84           & 0.73           & 20.8           & 2.04           & 2.00          \\\\\n",
+       "\t76 & 1000           &  500           & 0.9            &  0.5           & 0.18           & 0.68 ( 0.01 )  & 18.47 ( 0.43 ) & 1 ( 0.07 )     & 1.92 ( 0 )     & 23.47          & 0.78           & 0.68           & 18.4           & 0.00           & 1.92          \\\\\n",
+       "\t77 &   50           & 1000           & 0.9            & 20.0           & 0.00           & 1.22 ( 0.05 )  & 1 ( 0 )        & 6 ( 0 )        & 2.3 ( 0.03 )   &  0.00          & NaN            & 1.22           &  1.0           & 6.00           & 2.30          \\\\\n",
+       "\t78 &  100           & 1000           & 0.9            & 10.0           & 0.01           & 0.93 ( 0.02 )  & 49.18 ( 0.69 ) & 4.67 ( 0.09 )  & 2.22 ( 0.02 )  & 50.51          & 0.97           & 0.93           & 49.1           & 4.67           & 2.22          \\\\\n",
+       "\t79 &  500           & 1000           & 0.9            &  2.0           & 0.06           & 0.78 ( 0.01 )  & 46.01 ( 0.7 )  & 2.14 ( 0.09 )  & 2.12 ( 0.01 )  & 49.87          & 0.92           & 0.78           & 46.0           & 2.14           & 2.12          \\\\\n",
+       "\t80 & 1000           & 1000           & 0.9            &  1.0           & 0.08           & 0.74 ( 0.01 )  & 43.05 ( 0.72 ) & 1.17 ( 0.08 )  & 2.07 ( 0 )     & 47.88          & 0.9            & 0.74           & 43.0           & 1.17           & 2.07          \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | OOB | num_select | FDR | MSE_mean | FP_mean | FN_mean | OOB_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500           |  500           | 0.9            |  1.0           | 0.12           | 0.73 ( 0.01 )  | 20.83 ( 0.45 ) | 2.04 ( 0.09 )  | 2 ( 0.01 )     | 24.79          | 0.84           | 0.73           | 20.8           | 2.04           | 2.00           |\n",
+       "| 76 | 1000           |  500           | 0.9            |  0.5           | 0.18           | 0.68 ( 0.01 )  | 18.47 ( 0.43 ) | 1 ( 0.07 )     | 1.92 ( 0 )     | 23.47          | 0.78           | 0.68           | 18.4           | 0.00           | 1.92           |\n",
+       "| 77 |   50           | 1000           | 0.9            | 20.0           | 0.00           | 1.22 ( 0.05 )  | 1 ( 0 )        | 6 ( 0 )        | 2.3 ( 0.03 )   |  0.00          | NaN            | 1.22           |  1.0           | 6.00           | 2.30           |\n",
+       "| 78 |  100           | 1000           | 0.9            | 10.0           | 0.01           | 0.93 ( 0.02 )  | 49.18 ( 0.69 ) | 4.67 ( 0.09 )  | 2.22 ( 0.02 )  | 50.51          | 0.97           | 0.93           | 49.1           | 4.67           | 2.22           |\n",
+       "| 79 |  500           | 1000           | 0.9            |  2.0           | 0.06           | 0.78 ( 0.01 )  | 46.01 ( 0.7 )  | 2.14 ( 0.09 )  | 2.12 ( 0.01 )  | 49.87          | 0.92           | 0.78           | 46.0           | 2.14           | 2.12           |\n",
+       "| 80 | 1000           | 1000           | 0.9            |  1.0           | 0.08           | 0.74 ( 0.01 )  | 43.05 ( 0.72 ) | 1.17 ( 0.08 )  | 2.07 ( 0 )     | 47.88          | 0.9            | 0.74           | 43.0           | 1.17           | 2.07           |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP             FN           \n",
+       "75  500  500 0.9   1.0  0.12 0.73 ( 0.01 ) 20.83 ( 0.45 ) 2.04 ( 0.09 )\n",
+       "76 1000  500 0.9   0.5  0.18 0.68 ( 0.01 ) 18.47 ( 0.43 ) 1 ( 0.07 )   \n",
+       "77   50 1000 0.9  20.0  0.00 1.22 ( 0.05 ) 1 ( 0 )        6 ( 0 )      \n",
+       "78  100 1000 0.9  10.0  0.01 0.93 ( 0.02 ) 49.18 ( 0.69 ) 4.67 ( 0.09 )\n",
+       "79  500 1000 0.9   2.0  0.06 0.78 ( 0.01 ) 46.01 ( 0.7 )  2.14 ( 0.09 )\n",
+       "80 1000 1000 0.9   1.0  0.08 0.74 ( 0.01 ) 43.05 ( 0.72 ) 1.17 ( 0.08 )\n",
+       "   OOB           num_select FDR  MSE_mean FP_mean FN_mean OOB_mean\n",
+       "75 2 ( 0.01 )    24.79      0.84 0.73     20.8    2.04    2.00    \n",
+       "76 1.92 ( 0 )    23.47      0.78 0.68     18.4    0.00    1.92    \n",
+       "77 2.3 ( 0.03 )   0.00      NaN  1.22      1.0    6.00    2.30    \n",
+       "78 2.22 ( 0.02 ) 50.51      0.97 0.93     49.1    4.67    2.22    \n",
+       "79 2.12 ( 0.01 ) 49.87      0.92 0.78     46.0    2.14    2.12    \n",
+       "80 2.07 ( 0 )    47.88      0.9  0.74     43.0    1.17    2.07    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_rf.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/2.4_sim_block_compLasso_cts.ipynb b/simulations/notebooks_sim_cts/2.4_sim_block_compLasso_cts.ipynb
new file mode 100755
index 0000000..a50b32e
--- /dev/null
+++ b/simulations/notebooks_sim_cts/2.4_sim_block_compLasso_cts.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize compositional lasso results on Block Simulation Scenarios for continuous outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'\n",
+    "\n",
+    "load(paste0(dir, '/block_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim.list = list()\n",
+    "size = c(50, 100, 500, 1000)\n",
+    "idx = 0\n",
+    "for (P in size){\n",
+    "    for (N in size){\n",
+    "        idx = idx + 1\n",
+    "        dim.list[[idx]] = c(P=P, N=N)\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "rou.list = seq(0.1, 0.9, 0.2)\n",
+    "\n",
+    "files = NULL\n",
+    "for (rou in rou.list){\n",
+    "  for (dim in dim.list){\n",
+    "    p = dim[1]\n",
+    "    n = dim[2]\n",
+    "    files = cbind(files, paste0(dir, '/sim_block_corr', rou, paste('P', p, 'N', n, sep='_'), '.RData', sep=''))\n",
+    "  }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_FDR = NULL\n",
+    "for (i in 1:length(files)){\n",
+    "    sim_file = files[i]\n",
+    "    load(sim_file, dat <- new.env())\n",
+    "    sub = dat$sim_array[[i]]\n",
+    "    p = sub$p # take true values from 1st replicate of each simulated data\n",
+    "    coef = sub$beta\n",
+    "    coef.true = which(coef != 0)\n",
+    "    \n",
+    "    tt = results_block_compLasso[[i]]$Stab.table\n",
+    "    \n",
+    "    FDR = NULL\n",
+    "    for (r in 1:nrow(tt)){\n",
+    "        FDR = c(FDR, length(setdiff(which(tt[r, ] !=0), coef.true))/sum(tt[r, ]))\n",
+    "\n",
+    "    }\n",
+    "    \n",
+    "    avg_FDR = c(avg_FDR, mean(FDR, na.rm=T))\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "table_block = NULL\n",
+    "tmp_num_select = rep(0, length(results_block_compLasso))\n",
+    "for (i in 1:length(results_block_compLasso)){\n",
+    "    table_block = rbind(table_block, results_block_compLasso[[i]][c('n', 'p', 'rou', 'FP', 'FN', 'MSE', 'Stab')])\n",
+    "    tmp_num_select[i] = mean(rowSums(results_block_compLasso[[i]]$Stab.table))\n",
+    "    \n",
+    "}\n",
+    "table_block = as.data.frame(table_block)\n",
+    "table_block$num_select = tmp_num_select\n",
+    "table_block$FDR = round(avg_FDR,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>MSE</th><th scope=col>Stab</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>50           </td><td>50           </td><td>0.1          </td><td>2.8 ( 0.36 ) </td><td>5.01 ( 0.09 )</td><td>0.36 ( 0.01 )</td><td>0.03         </td><td> 3.79        </td><td>0.62         </td></tr>\n",
+       "\t<tr><td>100          </td><td>50           </td><td>0.1          </td><td>4.96 ( 0.55 )</td><td>3.77 ( 0.14 )</td><td>0.32 ( 0.01 )</td><td>0.1          </td><td> 7.19        </td><td>0.55         </td></tr>\n",
+       "\t<tr><td>500          </td><td>50           </td><td>0.1          </td><td>8.81 ( 0.4 ) </td><td>0.78 ( 0.08 )</td><td>0.28 ( 0 )   </td><td>0.26         </td><td>14.03        </td><td>0.60         </td></tr>\n",
+       "\t<tr><td>1000         </td><td>50           </td><td>0.1          </td><td>8.37 ( 0.37 )</td><td>0.24 ( 0.06 )</td><td>0.27 ( 0 )   </td><td>0.31         </td><td>14.13        </td><td>0.56         </td></tr>\n",
+       "\t<tr><td>50           </td><td>100          </td><td>0.1          </td><td>2.54 ( 0.37 )</td><td>5.22 ( 0.08 )</td><td>0.37 ( 0.02 )</td><td>0.04         </td><td> 3.32        </td><td>0.63         </td></tr>\n",
+       "\t<tr><td>100          </td><td>100          </td><td>0.1          </td><td>3.99 ( 0.57 )</td><td>4.63 ( 0.1 ) </td><td>0.35 ( 0.01 )</td><td>0.07         </td><td> 5.36        </td><td>0.56         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       " n & p & rou & FP & FN & MSE & Stab & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t 50            & 50            & 0.1           & 2.8 ( 0.36 )  & 5.01 ( 0.09 ) & 0.36 ( 0.01 ) & 0.03          &  3.79         & 0.62         \\\\\n",
+       "\t 100           & 50            & 0.1           & 4.96 ( 0.55 ) & 3.77 ( 0.14 ) & 0.32 ( 0.01 ) & 0.1           &  7.19         & 0.55         \\\\\n",
+       "\t 500           & 50            & 0.1           & 8.81 ( 0.4 )  & 0.78 ( 0.08 ) & 0.28 ( 0 )    & 0.26          & 14.03         & 0.60         \\\\\n",
+       "\t 1000          & 50            & 0.1           & 8.37 ( 0.37 ) & 0.24 ( 0.06 ) & 0.27 ( 0 )    & 0.31          & 14.13         & 0.56         \\\\\n",
+       "\t 50            & 100           & 0.1           & 2.54 ( 0.37 ) & 5.22 ( 0.08 ) & 0.37 ( 0.02 ) & 0.04          &  3.32         & 0.63         \\\\\n",
+       "\t 100           & 100           & 0.1           & 3.99 ( 0.57 ) & 4.63 ( 0.1 )  & 0.35 ( 0.01 ) & 0.07          &  5.36         & 0.56         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| n | p | rou | FP | FN | MSE | Stab | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|\n",
+       "| 50            | 50            | 0.1           | 2.8 ( 0.36 )  | 5.01 ( 0.09 ) | 0.36 ( 0.01 ) | 0.03          |  3.79         | 0.62          |\n",
+       "| 100           | 50            | 0.1           | 4.96 ( 0.55 ) | 3.77 ( 0.14 ) | 0.32 ( 0.01 ) | 0.1           |  7.19         | 0.55          |\n",
+       "| 500           | 50            | 0.1           | 8.81 ( 0.4 )  | 0.78 ( 0.08 ) | 0.28 ( 0 )    | 0.26          | 14.03         | 0.60          |\n",
+       "| 1000          | 50            | 0.1           | 8.37 ( 0.37 ) | 0.24 ( 0.06 ) | 0.27 ( 0 )    | 0.31          | 14.13         | 0.56          |\n",
+       "| 50            | 100           | 0.1           | 2.54 ( 0.37 ) | 5.22 ( 0.08 ) | 0.37 ( 0.02 ) | 0.04          |  3.32         | 0.63          |\n",
+       "| 100           | 100           | 0.1           | 3.99 ( 0.57 ) | 4.63 ( 0.1 )  | 0.35 ( 0.01 ) | 0.07          |  5.36         | 0.56          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  n    p   rou FP            FN            MSE           Stab num_select FDR \n",
+       "1 50   50  0.1 2.8 ( 0.36 )  5.01 ( 0.09 ) 0.36 ( 0.01 ) 0.03  3.79      0.62\n",
+       "2 100  50  0.1 4.96 ( 0.55 ) 3.77 ( 0.14 ) 0.32 ( 0.01 ) 0.1   7.19      0.55\n",
+       "3 500  50  0.1 8.81 ( 0.4 )  0.78 ( 0.08 ) 0.28 ( 0 )    0.26 14.03      0.60\n",
+       "4 1000 50  0.1 8.37 ( 0.37 ) 0.24 ( 0.06 ) 0.27 ( 0 )    0.31 14.13      0.56\n",
+       "5 50   100 0.1 2.54 ( 0.37 ) 5.22 ( 0.08 ) 0.37 ( 0.02 ) 0.04  3.32      0.63\n",
+       "6 100  100 0.1 3.99 ( 0.57 ) 4.63 ( 0.1 )  0.35 ( 0.01 ) 0.07  5.36      0.56"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n",
+      "extract_numeric() is deprecated: please use readr::parse_number() instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# export result\n",
+    "result.table_block <- apply(table_block,2,as.character)\n",
+    "rownames(result.table_block) = rownames(table_block)\n",
+    "result.table_block = as.data.frame(result.table_block)\n",
+    "\n",
+    "# extract numbers only for 'n' & 'p'\n",
+    "result.table_block$n = tidyr::extract_numeric(result.table_block$n)\n",
+    "result.table_block$p = tidyr::extract_numeric(result.table_block$p)\n",
+    "result.table_block$ratio = result.table_block$p / result.table_block$n\n",
+    "\n",
+    "result.table_block = result.table_block[c('n', 'p', 'rou', 'ratio', 'Stab', 'MSE', 'FP', 'FN', 'num_select', 'FDR')]\n",
+    "colnames(result.table_block)[1:4] = c('N', 'P', 'Corr', 'Ratio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”Warning message in eval(expr, envir, enclos):\n",
+      "“NAs introduced by coercion”"
+     ]
+    }
+   ],
+   "source": [
+    "# convert interested measurements to be numeric\n",
+    "result.table_block$Stab = as.numeric(as.character(result.table_block$Stab))\n",
+    "result.table_block$MSE_mean = as.numeric(substr(result.table_block$MSE, start=1, stop=4))\n",
+    "result.table_block$FP_mean = as.numeric(substr(result.table_block$FP, start=1, stop=4))\n",
+    "result.table_block$FN_mean = as.numeric(substr(result.table_block$FN, start=1, stop=4))\n",
+    "result.table_block$FN_mean[is.na(result.table_block$FN_mean)] = 0\n",
+    "result.table_block$num_select = as.numeric(as.character(result.table_block$num_select))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td>100          </td><td>0.3          </td><td>2.0          </td><td>0.27         </td><td>0.94 ( 0.05 )</td><td>6 ( 0.34 )   </td><td>1.93 ( 0.14 )</td><td>10.07        </td><td>0.54         </td><td>0.94         </td><td>  NA         </td><td>1.93         </td></tr>\n",
+       "\t<tr><th scope=row>26</th><td>100          </td><td>500          </td><td>0.3          </td><td>5.0          </td><td>0.30         </td><td>0.73 ( 0.03 )</td><td>9 ( 0.76 )   </td><td>0.99 ( 0.12 )</td><td>14.01        </td><td>0.52         </td><td>0.73         </td><td>  NA         </td><td>0.99         </td></tr>\n",
+       "\t<tr><th scope=row>51</th><td>500          </td><td> 50          </td><td>0.7          </td><td>0.1          </td><td>0.90         </td><td>1 ( 0.04 )   </td><td>0.58 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.58        </td><td>0.06         </td><td>  NA         </td><td>0.58         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t21 &  50           & 100           & 0.3           & 2.0           & 0.27          & 0.94 ( 0.05 ) & 6 ( 0.34 )    & 1.93 ( 0.14 ) & 10.07         & 0.54          & 0.94          &   NA          & 1.93         \\\\\n",
+       "\t26 & 100           & 500           & 0.3           & 5.0           & 0.30          & 0.73 ( 0.03 ) & 9 ( 0.76 )    & 0.99 ( 0.12 ) & 14.01         & 0.52          & 0.73          &   NA          & 0.99         \\\\\n",
+       "\t51 & 500           &  50           & 0.7           & 0.1           & 0.90          & 1 ( 0.04 )    & 0.58 ( 0.19 ) & 0 ( 0 )       &  6.58         & 0.06          &   NA          & 0.58          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 21 |  50           | 100           | 0.3           | 2.0           | 0.27          | 0.94 ( 0.05 ) | 6 ( 0.34 )    | 1.93 ( 0.14 ) | 10.07         | 0.54          | 0.94          |   NA          | 1.93          |\n",
+       "| 26 | 100           | 500           | 0.3           | 5.0           | 0.30          | 0.73 ( 0.03 ) | 9 ( 0.76 )    | 0.99 ( 0.12 ) | 14.01         | 0.52          | 0.73          |   NA          | 0.99          |\n",
+       "| 51 | 500           |  50           | 0.7           | 0.1           | 0.90          | 1 ( 0.04 )    | 0.58 ( 0.19 ) | 0 ( 0 )       |  6.58         | 0.06          |   NA          | 0.58          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "21  50 100 0.3  2.0   0.27 0.94 ( 0.05 ) 6 ( 0.34 )    1.93 ( 0.14 ) 10.07     \n",
+       "26 100 500 0.3  5.0   0.30 0.73 ( 0.03 ) 9 ( 0.76 )    0.99 ( 0.12 ) 14.01     \n",
+       "51 500  50 0.7  0.1   0.90 1 ( 0.04 )    0.58 ( 0.19 ) 0 ( 0 )        6.58     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "21 0.54 0.94       NA    1.93   \n",
+       "26 0.52 0.73       NA    0.99   \n",
+       "51 0.06   NA     0.58    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# check whether missing values exists\n",
+    "result.table_block[rowSums(is.na(result.table_block)) > 0,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# recover values\n",
+    "result.table_block$FP_mean[is.na(result.table_block$FP_mean)] = 6\n",
+    "result.table_block$MSE_mean[is.na(result.table_block$MSE_mean)] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>21</th><td> 50          </td><td>100          </td><td>0.3          </td><td>2.0          </td><td>0.27         </td><td>0.94 ( 0.05 )</td><td>6 ( 0.34 )   </td><td>1.93 ( 0.14 )</td><td>10.07        </td><td>0.54         </td><td>0.94         </td><td>6.00         </td><td>1.93         </td></tr>\n",
+       "\t<tr><th scope=row>26</th><td>100          </td><td>500          </td><td>0.3          </td><td>5.0          </td><td>0.30         </td><td>0.73 ( 0.03 )</td><td>9 ( 0.76 )   </td><td>0.99 ( 0.12 )</td><td>14.01        </td><td>0.52         </td><td>0.73         </td><td>6.00         </td><td>0.99         </td></tr>\n",
+       "\t<tr><th scope=row>51</th><td>500          </td><td> 50          </td><td>0.7          </td><td>0.1          </td><td>0.90         </td><td>1 ( 0.04 )   </td><td>0.58 ( 0.19 )</td><td>0 ( 0 )      </td><td> 6.58        </td><td>0.06         </td><td>1.00         </td><td>0.58         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t21 &  50           & 100           & 0.3           & 2.0           & 0.27          & 0.94 ( 0.05 ) & 6 ( 0.34 )    & 1.93 ( 0.14 ) & 10.07         & 0.54          & 0.94          & 6.00          & 1.93         \\\\\n",
+       "\t26 & 100           & 500           & 0.3           & 5.0           & 0.30          & 0.73 ( 0.03 ) & 9 ( 0.76 )    & 0.99 ( 0.12 ) & 14.01         & 0.52          & 0.73          & 6.00          & 0.99         \\\\\n",
+       "\t51 & 500           &  50           & 0.7           & 0.1           & 0.90          & 1 ( 0.04 )    & 0.58 ( 0.19 ) & 0 ( 0 )       &  6.58         & 0.06          & 1.00          & 0.58          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 21 |  50           | 100           | 0.3           | 2.0           | 0.27          | 0.94 ( 0.05 ) | 6 ( 0.34 )    | 1.93 ( 0.14 ) | 10.07         | 0.54          | 0.94          | 6.00          | 1.93          |\n",
+       "| 26 | 100           | 500           | 0.3           | 5.0           | 0.30          | 0.73 ( 0.03 ) | 9 ( 0.76 )    | 0.99 ( 0.12 ) | 14.01         | 0.52          | 0.73          | 6.00          | 0.99          |\n",
+       "| 51 | 500           |  50           | 0.7           | 0.1           | 0.90          | 1 ( 0.04 )    | 0.58 ( 0.19 ) | 0 ( 0 )       |  6.58         | 0.06          | 1.00          | 0.58          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N   P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "21  50 100 0.3  2.0   0.27 0.94 ( 0.05 ) 6 ( 0.34 )    1.93 ( 0.14 ) 10.07     \n",
+       "26 100 500 0.3  5.0   0.30 0.73 ( 0.03 ) 9 ( 0.76 )    0.99 ( 0.12 ) 14.01     \n",
+       "51 500  50 0.7  0.1   0.90 1 ( 0.04 )    0.58 ( 0.19 ) 0 ( 0 )        6.58     \n",
+       "   FDR  MSE_mean FP_mean FN_mean\n",
+       "21 0.54 0.94     6.00    1.93   \n",
+       "26 0.52 0.73     6.00    0.99   \n",
+       "51 0.06 1.00     0.58    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result.table_block[c(21,26,51), ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>  50         </td><td> 50          </td><td>0.1          </td><td>1.00         </td><td>0.03         </td><td>0.36 ( 0.01 )</td><td>2.8 ( 0.36 ) </td><td>5.01 ( 0.09 )</td><td> 3.79        </td><td>0.62         </td><td>0.36         </td><td>2.80         </td><td>5.01         </td></tr>\n",
+       "\t<tr><td> 100         </td><td> 50          </td><td>0.1          </td><td>0.50         </td><td>0.10         </td><td>0.32 ( 0.01 )</td><td>4.96 ( 0.55 )</td><td>3.77 ( 0.14 )</td><td> 7.19        </td><td>0.55         </td><td>0.32         </td><td>4.96         </td><td>3.77         </td></tr>\n",
+       "\t<tr><td> 500         </td><td> 50          </td><td>0.1          </td><td>0.10         </td><td>0.26         </td><td>0.28 ( 0 )   </td><td>8.81 ( 0.4 ) </td><td>0.78 ( 0.08 )</td><td>14.03        </td><td>0.6          </td><td>0.28         </td><td>8.81         </td><td>0.78         </td></tr>\n",
+       "\t<tr><td>1000         </td><td> 50          </td><td>0.1          </td><td>0.05         </td><td>0.31         </td><td>0.27 ( 0 )   </td><td>8.37 ( 0.37 )</td><td>0.24 ( 0.06 )</td><td>14.13        </td><td>0.56         </td><td>0.27         </td><td>8.37         </td><td>0.24         </td></tr>\n",
+       "\t<tr><td>  50         </td><td>100          </td><td>0.1          </td><td>2.00         </td><td>0.04         </td><td>0.37 ( 0.02 )</td><td>2.54 ( 0.37 )</td><td>5.22 ( 0.08 )</td><td> 3.32        </td><td>0.63         </td><td>0.37         </td><td>2.54         </td><td>5.22         </td></tr>\n",
+       "\t<tr><td> 100         </td><td>100          </td><td>0.1          </td><td>1.00         </td><td>0.07         </td><td>0.35 ( 0.01 )</td><td>3.99 ( 0.57 )</td><td>4.63 ( 0.1 ) </td><td> 5.36        </td><td>0.56         </td><td>0.35         </td><td>3.99         </td><td>4.63         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t   50          &  50           & 0.1           & 1.00          & 0.03          & 0.36 ( 0.01 ) & 2.8 ( 0.36 )  & 5.01 ( 0.09 ) &  3.79         & 0.62          & 0.36          & 2.80          & 5.01         \\\\\n",
+       "\t  100          &  50           & 0.1           & 0.50          & 0.10          & 0.32 ( 0.01 ) & 4.96 ( 0.55 ) & 3.77 ( 0.14 ) &  7.19         & 0.55          & 0.32          & 4.96          & 3.77         \\\\\n",
+       "\t  500          &  50           & 0.1           & 0.10          & 0.26          & 0.28 ( 0 )    & 8.81 ( 0.4 )  & 0.78 ( 0.08 ) & 14.03         & 0.6           & 0.28          & 8.81          & 0.78         \\\\\n",
+       "\t 1000          &  50           & 0.1           & 0.05          & 0.31          & 0.27 ( 0 )    & 8.37 ( 0.37 ) & 0.24 ( 0.06 ) & 14.13         & 0.56          & 0.27          & 8.37          & 0.24         \\\\\n",
+       "\t   50          & 100           & 0.1           & 2.00          & 0.04          & 0.37 ( 0.02 ) & 2.54 ( 0.37 ) & 5.22 ( 0.08 ) &  3.32         & 0.63          & 0.37          & 2.54          & 5.22         \\\\\n",
+       "\t  100          & 100           & 0.1           & 1.00          & 0.07          & 0.35 ( 0.01 ) & 3.99 ( 0.57 ) & 4.63 ( 0.1 )  &  5.36         & 0.56          & 0.35          & 3.99          & 4.63         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "|   50          |  50           | 0.1           | 1.00          | 0.03          | 0.36 ( 0.01 ) | 2.8 ( 0.36 )  | 5.01 ( 0.09 ) |  3.79         | 0.62          | 0.36          | 2.80          | 5.01          |\n",
+       "|  100          |  50           | 0.1           | 0.50          | 0.10          | 0.32 ( 0.01 ) | 4.96 ( 0.55 ) | 3.77 ( 0.14 ) |  7.19         | 0.55          | 0.32          | 4.96          | 3.77          |\n",
+       "|  500          |  50           | 0.1           | 0.10          | 0.26          | 0.28 ( 0 )    | 8.81 ( 0.4 )  | 0.78 ( 0.08 ) | 14.03         | 0.6           | 0.28          | 8.81          | 0.78          |\n",
+       "| 1000          |  50           | 0.1           | 0.05          | 0.31          | 0.27 ( 0 )    | 8.37 ( 0.37 ) | 0.24 ( 0.06 ) | 14.13         | 0.56          | 0.27          | 8.37          | 0.24          |\n",
+       "|   50          | 100           | 0.1           | 2.00          | 0.04          | 0.37 ( 0.02 ) | 2.54 ( 0.37 ) | 5.22 ( 0.08 ) |  3.32         | 0.63          | 0.37          | 2.54          | 5.22          |\n",
+       "|  100          | 100           | 0.1           | 1.00          | 0.07          | 0.35 ( 0.01 ) | 3.99 ( 0.57 ) | 4.63 ( 0.1 )  |  5.36         | 0.56          | 0.35          | 3.99          | 4.63          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  N    P   Corr Ratio Stab MSE           FP            FN            num_select\n",
+       "1   50  50 0.1  1.00  0.03 0.36 ( 0.01 ) 2.8 ( 0.36 )  5.01 ( 0.09 )  3.79     \n",
+       "2  100  50 0.1  0.50  0.10 0.32 ( 0.01 ) 4.96 ( 0.55 ) 3.77 ( 0.14 )  7.19     \n",
+       "3  500  50 0.1  0.10  0.26 0.28 ( 0 )    8.81 ( 0.4 )  0.78 ( 0.08 ) 14.03     \n",
+       "4 1000  50 0.1  0.05  0.31 0.27 ( 0 )    8.37 ( 0.37 ) 0.24 ( 0.06 ) 14.13     \n",
+       "5   50 100 0.1  2.00  0.04 0.37 ( 0.02 ) 2.54 ( 0.37 ) 5.22 ( 0.08 )  3.32     \n",
+       "6  100 100 0.1  1.00  0.07 0.35 ( 0.01 ) 3.99 ( 0.57 ) 4.63 ( 0.1 )   5.36     \n",
+       "  FDR  MSE_mean FP_mean FN_mean\n",
+       "1 0.62 0.36     2.80    5.01   \n",
+       "2 0.55 0.32     4.96    3.77   \n",
+       "3 0.6  0.28     8.81    0.78   \n",
+       "4 0.56 0.27     8.37    0.24   \n",
+       "5 0.63 0.37     2.54    5.22   \n",
+       "6 0.56 0.35     3.99    4.63   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>75</th><td> 500         </td><td> 500         </td><td>0.9          </td><td> 1.0         </td><td>0.93         </td><td>1.27 ( 0.05 )</td><td>0.44 ( 0.18 )</td><td>0 ( 0 )      </td><td> 6.44        </td><td>0.04         </td><td>1.27         </td><td>0.44         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>76</th><td>1000         </td><td> 500         </td><td>0.9          </td><td> 0.5         </td><td>0.96         </td><td>1.31 ( 0.05 )</td><td>0.23 ( 0.06 )</td><td>0 ( 0 )      </td><td> 6.23        </td><td>0.03         </td><td>1.31         </td><td>0.23         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>77</th><td>  50         </td><td>1000         </td><td>0.9          </td><td>20.0         </td><td>0.23         </td><td>3.37 ( 0.19 )</td><td>5.29 ( 0.41 )</td><td>3.13 ( 0.15 )</td><td> 8.16        </td><td>0.57         </td><td>3.37         </td><td>5.29         </td><td>3.13         </td></tr>\n",
+       "\t<tr><th scope=row>78</th><td> 100         </td><td>1000         </td><td>0.9          </td><td>10.0         </td><td>0.56         </td><td>1.14 ( 0.05 )</td><td>4.56 ( 0.38 )</td><td>0.07 ( 0.03 )</td><td>10.49        </td><td>0.37         </td><td>1.14         </td><td>4.56         </td><td>0.07         </td></tr>\n",
+       "\t<tr><th scope=row>79</th><td> 500         </td><td>1000         </td><td>0.9          </td><td> 2.0         </td><td>0.96         </td><td>1.28 ( 0.04 )</td><td>0.27 ( 0.11 )</td><td>0 ( 0 )      </td><td> 6.27        </td><td>0.03         </td><td>1.28         </td><td>0.27         </td><td>0.00         </td></tr>\n",
+       "\t<tr><th scope=row>80</th><td>1000         </td><td>1000         </td><td>0.9          </td><td> 1.0         </td><td>0.95         </td><td>1.35 ( 0.05 )</td><td>0.29 ( 0.09 )</td><td>0 ( 0 )      </td><td> 6.29        </td><td>0.03         </td><td>1.35         </td><td>0.29         </td><td>0.00         </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean\\\\\n",
+       "\\hline\n",
+       "\t75 &  500          &  500          & 0.9           &  1.0          & 0.93          & 1.27 ( 0.05 ) & 0.44 ( 0.18 ) & 0 ( 0 )       &  6.44         & 0.04          & 1.27          & 0.44          & 0.00         \\\\\n",
+       "\t76 & 1000          &  500          & 0.9           &  0.5          & 0.96          & 1.31 ( 0.05 ) & 0.23 ( 0.06 ) & 0 ( 0 )       &  6.23         & 0.03          & 1.31          & 0.23          & 0.00         \\\\\n",
+       "\t77 &   50          & 1000          & 0.9           & 20.0          & 0.23          & 3.37 ( 0.19 ) & 5.29 ( 0.41 ) & 3.13 ( 0.15 ) &  8.16         & 0.57          & 3.37          & 5.29          & 3.13         \\\\\n",
+       "\t78 &  100          & 1000          & 0.9           & 10.0          & 0.56          & 1.14 ( 0.05 ) & 4.56 ( 0.38 ) & 0.07 ( 0.03 ) & 10.49         & 0.37          & 1.14          & 4.56          & 0.07         \\\\\n",
+       "\t79 &  500          & 1000          & 0.9           &  2.0          & 0.96          & 1.28 ( 0.04 ) & 0.27 ( 0.11 ) & 0 ( 0 )       &  6.27         & 0.03          & 1.28          & 0.27          & 0.00         \\\\\n",
+       "\t80 & 1000          & 1000          & 0.9           &  1.0          & 0.95          & 1.35 ( 0.05 ) & 0.29 ( 0.09 ) & 0 ( 0 )       &  6.29         & 0.03          & 1.35          & 0.29          & 0.00         \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 75 |  500          |  500          | 0.9           |  1.0          | 0.93          | 1.27 ( 0.05 ) | 0.44 ( 0.18 ) | 0 ( 0 )       |  6.44         | 0.04          | 1.27          | 0.44          | 0.00          |\n",
+       "| 76 | 1000          |  500          | 0.9           |  0.5          | 0.96          | 1.31 ( 0.05 ) | 0.23 ( 0.06 ) | 0 ( 0 )       |  6.23         | 0.03          | 1.31          | 0.23          | 0.00          |\n",
+       "| 77 |   50          | 1000          | 0.9           | 20.0          | 0.23          | 3.37 ( 0.19 ) | 5.29 ( 0.41 ) | 3.13 ( 0.15 ) |  8.16         | 0.57          | 3.37          | 5.29          | 3.13          |\n",
+       "| 78 |  100          | 1000          | 0.9           | 10.0          | 0.56          | 1.14 ( 0.05 ) | 4.56 ( 0.38 ) | 0.07 ( 0.03 ) | 10.49         | 0.37          | 1.14          | 4.56          | 0.07          |\n",
+       "| 79 |  500          | 1000          | 0.9           |  2.0          | 0.96          | 1.28 ( 0.04 ) | 0.27 ( 0.11 ) | 0 ( 0 )       |  6.27         | 0.03          | 1.28          | 0.27          | 0.00          |\n",
+       "| 80 | 1000          | 1000          | 0.9           |  1.0          | 0.95          | 1.35 ( 0.05 ) | 0.29 ( 0.09 ) | 0 ( 0 )       |  6.29         | 0.03          | 1.35          | 0.29          | 0.00          |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    Corr Ratio Stab MSE           FP            FN           \n",
+       "75  500  500 0.9   1.0  0.93 1.27 ( 0.05 ) 0.44 ( 0.18 ) 0 ( 0 )      \n",
+       "76 1000  500 0.9   0.5  0.96 1.31 ( 0.05 ) 0.23 ( 0.06 ) 0 ( 0 )      \n",
+       "77   50 1000 0.9  20.0  0.23 3.37 ( 0.19 ) 5.29 ( 0.41 ) 3.13 ( 0.15 )\n",
+       "78  100 1000 0.9  10.0  0.56 1.14 ( 0.05 ) 4.56 ( 0.38 ) 0.07 ( 0.03 )\n",
+       "79  500 1000 0.9   2.0  0.96 1.28 ( 0.04 ) 0.27 ( 0.11 ) 0 ( 0 )      \n",
+       "80 1000 1000 0.9   1.0  0.95 1.35 ( 0.05 ) 0.29 ( 0.09 ) 0 ( 0 )      \n",
+       "   num_select FDR  MSE_mean FP_mean FN_mean\n",
+       "75  6.44      0.04 1.27     0.44    0.00   \n",
+       "76  6.23      0.03 1.31     0.23    0.00   \n",
+       "77  8.16      0.57 3.37     5.29    3.13   \n",
+       "78 10.49      0.37 1.14     4.56    0.07   \n",
+       "79  6.27      0.03 1.28     0.27    0.00   \n",
+       "80  6.29      0.03 1.35     0.29    0.00   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tail(result.table_block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## export\n",
+    "write.table(result.table_block, '../results_summary_cts/sim_block_compLasso.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/3_sim_cts_summary.ipynb b/simulations/notebooks_sim_cts/3_sim_cts_summary.ipynb
new file mode 100755
index 0000000..5487719
--- /dev/null
+++ b/simulations/notebooks_sim_cts/3_sim_cts_summary.ipynb
@@ -0,0 +1,998 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### summarize method comparison results across 4 feature selection methods in different simulation scenarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Independent Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_lasso = read.csv('../results_summary_cts/sim_ind_lasso.txt', sep='\\t')\n",
+    "ind_elnet = read.csv('../results_summary_cts/sim_ind_elnet.txt', sep='\\t')\n",
+    "ind_rf = read.csv('../results_summary_cts/sim_ind_rf.txt', sep='\\t')\n",
+    "ind_compLasso = read.csv('../results_summary_cts/sim_ind_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "ind_lasso$method = rep('lasso', dim(ind_lasso)[1])\n",
+    "ind_elnet$method = rep('elnet', dim(ind_elnet)[1])\n",
+    "ind_rf$method = rep('rf', dim(ind_rf)[1])\n",
+    "ind_compLasso$method = rep('compLasso', dim(ind_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(ind_lasso)\n",
+    "dim(ind_elnet)\n",
+    "dim(ind_rf)\n",
+    "dim(ind_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>16</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 16\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 16\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 16 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind_rf = ind_rf[!names(ind_rf)%in% c('OOB', 'OOB_mean', 'Index')]\n",
+    "dim(ind_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>64</li>\n",
+       "\t<li>13</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 64\n",
+       "\\item 13\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 64\n",
+       "2. 13\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 64 13"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind = rbind(ind_lasso, ind_elnet)\n",
+    "ind = rbind(ind, ind_rf)\n",
+    "ind = rbind(ind, ind_compLasso)\n",
+    "dim(ind)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "ind$FP_mean[ind$num_select ==0] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "write.table(ind, '../results_summary_cts/table_ind_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>method</th><th scope=col>Stab</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>num_select</th><th scope=col>FDR</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>16</th><td>1000     </td><td>1000     </td><td>lasso    </td><td>0.64     </td><td>0.27     </td><td> 4.39    </td><td>0.00     </td><td> 9.39    </td><td>0.26     </td></tr>\n",
+       "\t<tr><th scope=row>32</th><td>1000     </td><td>1000     </td><td>elnet    </td><td>0.15     </td><td>0.27     </td><td>34.60    </td><td>0.00     </td><td>39.65    </td><td>0.79     </td></tr>\n",
+       "\t<tr><th scope=row>48</th><td>1000     </td><td>1000     </td><td>rf       </td><td>0.09     </td><td>0.85     </td><td>41.70    </td><td>0.99     </td><td>46.78    </td><td>0.89     </td></tr>\n",
+       "\t<tr><th scope=row>64</th><td>1000     </td><td>1000     </td><td>compLasso</td><td>0.96     </td><td>1.16     </td><td> 0.24    </td><td>0.00     </td><td> 6.24    </td><td>0.03     </td></tr>\n",
+       "\t<tr><th scope=row>12</th><td>1000     </td><td> 500     </td><td>lasso    </td><td>0.72     </td><td>0.27     </td><td> 3.35    </td><td>0.00     </td><td> 8.35    </td><td>0.21     </td></tr>\n",
+       "\t<tr><th scope=row>28</th><td>1000     </td><td> 500     </td><td>elnet    </td><td>0.18     </td><td>0.26     </td><td>27.20    </td><td>0.00     </td><td>32.21    </td><td>0.77     </td></tr>\n",
+       "\t<tr><th scope=row>44</th><td>1000     </td><td> 500     </td><td>rf       </td><td>0.19     </td><td>0.78     </td><td>18.70    </td><td>0.84     </td><td>23.89    </td><td>0.78     </td></tr>\n",
+       "\t<tr><th scope=row>60</th><td>1000     </td><td> 500     </td><td>compLasso</td><td>0.96     </td><td>1.24     </td><td> 0.25    </td><td>0.00     </td><td> 6.25    </td><td>0.03     </td></tr>\n",
+       "\t<tr><th scope=row>8</th><td>1000     </td><td> 100     </td><td>lasso    </td><td>0.82     </td><td>0.27     </td><td> 2.25    </td><td>0.00     </td><td> 7.25    </td><td>0.15     </td></tr>\n",
+       "\t<tr><th scope=row>24</th><td>1000     </td><td> 100     </td><td>elnet    </td><td>0.23     </td><td>0.26     </td><td>17.00    </td><td>0.00     </td><td>22.05    </td><td>0.69     </td></tr>\n",
+       "\t<tr><th scope=row>40</th><td>1000     </td><td> 100     </td><td>rf       </td><td>0.81     </td><td>0.47     </td><td> 0.71    </td><td>0.53     </td><td> 6.18    </td><td>0.10     </td></tr>\n",
+       "\t<tr><th scope=row>56</th><td>1000     </td><td> 100     </td><td>compLasso</td><td>0.89     </td><td>1.04     </td><td> 0.71    </td><td>0.00     </td><td> 6.71    </td><td>0.08     </td></tr>\n",
+       "\t<tr><th scope=row>4</th><td>1000     </td><td>  50     </td><td>lasso    </td><td>0.86     </td><td>0.26     </td><td> 1.82    </td><td>0.00     </td><td> 6.82    </td><td>0.10     </td></tr>\n",
+       "\t<tr><th scope=row>20</th><td>1000     </td><td>  50     </td><td>elnet    </td><td>0.27     </td><td>0.25     </td><td>11.70    </td><td>0.00     </td><td>16.76    </td><td>0.60     </td></tr>\n",
+       "\t<tr><th scope=row>36</th><td>1000     </td><td>  50     </td><td>rf       </td><td>0.88     </td><td>0.35     </td><td> 0.07    </td><td>0.78     </td><td> 5.29    </td><td>0.01     </td></tr>\n",
+       "\t<tr><th scope=row>52</th><td>1000     </td><td>  50     </td><td>compLasso</td><td>0.93     </td><td>1.18     </td><td> 0.39    </td><td>0.00     </td><td> 6.39    </td><td>0.04     </td></tr>\n",
+       "\t<tr><th scope=row>15</th><td> 500     </td><td>1000     </td><td>lasso    </td><td>0.52     </td><td>0.29     </td><td> 6.51    </td><td>0.00     </td><td>11.51    </td><td>0.40     </td></tr>\n",
+       "\t<tr><th scope=row>31</th><td> 500     </td><td>1000     </td><td>elnet    </td><td>0.14     </td><td>0.27     </td><td>35.90    </td><td>0.00     </td><td>40.98    </td><td>0.80     </td></tr>\n",
+       "\t<tr><th scope=row>47</th><td> 500     </td><td>1000     </td><td>rf       </td><td>0.06     </td><td>0.89     </td><td>44.70    </td><td>1.81     </td><td>48.96    </td><td>0.91     </td></tr>\n",
+       "\t<tr><th scope=row>63</th><td> 500     </td><td>1000     </td><td>compLasso</td><td>0.96     </td><td>1.12     </td><td> 0.27    </td><td>0.00     </td><td> 6.27    </td><td>0.03     </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllll}\n",
+       "  & N & P & method & Stab & MSE\\_mean & FP\\_mean & FN\\_mean & num\\_select & FDR\\\\\n",
+       "\\hline\n",
+       "\t16 & 1000      & 1000      & lasso     & 0.64      & 0.27      &  4.39     & 0.00      &  9.39     & 0.26     \\\\\n",
+       "\t32 & 1000      & 1000      & elnet     & 0.15      & 0.27      & 34.60     & 0.00      & 39.65     & 0.79     \\\\\n",
+       "\t48 & 1000      & 1000      & rf        & 0.09      & 0.85      & 41.70     & 0.99      & 46.78     & 0.89     \\\\\n",
+       "\t64 & 1000      & 1000      & compLasso & 0.96      & 1.16      &  0.24     & 0.00      &  6.24     & 0.03     \\\\\n",
+       "\t12 & 1000      &  500      & lasso     & 0.72      & 0.27      &  3.35     & 0.00      &  8.35     & 0.21     \\\\\n",
+       "\t28 & 1000      &  500      & elnet     & 0.18      & 0.26      & 27.20     & 0.00      & 32.21     & 0.77     \\\\\n",
+       "\t44 & 1000      &  500      & rf        & 0.19      & 0.78      & 18.70     & 0.84      & 23.89     & 0.78     \\\\\n",
+       "\t60 & 1000      &  500      & compLasso & 0.96      & 1.24      &  0.25     & 0.00      &  6.25     & 0.03     \\\\\n",
+       "\t8 & 1000      &  100      & lasso     & 0.82      & 0.27      &  2.25     & 0.00      &  7.25     & 0.15     \\\\\n",
+       "\t24 & 1000      &  100      & elnet     & 0.23      & 0.26      & 17.00     & 0.00      & 22.05     & 0.69     \\\\\n",
+       "\t40 & 1000      &  100      & rf        & 0.81      & 0.47      &  0.71     & 0.53      &  6.18     & 0.10     \\\\\n",
+       "\t56 & 1000      &  100      & compLasso & 0.89      & 1.04      &  0.71     & 0.00      &  6.71     & 0.08     \\\\\n",
+       "\t4 & 1000      &   50      & lasso     & 0.86      & 0.26      &  1.82     & 0.00      &  6.82     & 0.10     \\\\\n",
+       "\t20 & 1000      &   50      & elnet     & 0.27      & 0.25      & 11.70     & 0.00      & 16.76     & 0.60     \\\\\n",
+       "\t36 & 1000      &   50      & rf        & 0.88      & 0.35      &  0.07     & 0.78      &  5.29     & 0.01     \\\\\n",
+       "\t52 & 1000      &   50      & compLasso & 0.93      & 1.18      &  0.39     & 0.00      &  6.39     & 0.04     \\\\\n",
+       "\t15 &  500      & 1000      & lasso     & 0.52      & 0.29      &  6.51     & 0.00      & 11.51     & 0.40     \\\\\n",
+       "\t31 &  500      & 1000      & elnet     & 0.14      & 0.27      & 35.90     & 0.00      & 40.98     & 0.80     \\\\\n",
+       "\t47 &  500      & 1000      & rf        & 0.06      & 0.89      & 44.70     & 1.81      & 48.96     & 0.91     \\\\\n",
+       "\t63 &  500      & 1000      & compLasso & 0.96      & 1.12      &  0.27     & 0.00      &  6.27     & 0.03     \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | method | Stab | MSE_mean | FP_mean | FN_mean | num_select | FDR |\n",
+       "|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 16 | 1000      | 1000      | lasso     | 0.64      | 0.27      |  4.39     | 0.00      |  9.39     | 0.26      |\n",
+       "| 32 | 1000      | 1000      | elnet     | 0.15      | 0.27      | 34.60     | 0.00      | 39.65     | 0.79      |\n",
+       "| 48 | 1000      | 1000      | rf        | 0.09      | 0.85      | 41.70     | 0.99      | 46.78     | 0.89      |\n",
+       "| 64 | 1000      | 1000      | compLasso | 0.96      | 1.16      |  0.24     | 0.00      |  6.24     | 0.03      |\n",
+       "| 12 | 1000      |  500      | lasso     | 0.72      | 0.27      |  3.35     | 0.00      |  8.35     | 0.21      |\n",
+       "| 28 | 1000      |  500      | elnet     | 0.18      | 0.26      | 27.20     | 0.00      | 32.21     | 0.77      |\n",
+       "| 44 | 1000      |  500      | rf        | 0.19      | 0.78      | 18.70     | 0.84      | 23.89     | 0.78      |\n",
+       "| 60 | 1000      |  500      | compLasso | 0.96      | 1.24      |  0.25     | 0.00      |  6.25     | 0.03      |\n",
+       "| 8 | 1000      |  100      | lasso     | 0.82      | 0.27      |  2.25     | 0.00      |  7.25     | 0.15      |\n",
+       "| 24 | 1000      |  100      | elnet     | 0.23      | 0.26      | 17.00     | 0.00      | 22.05     | 0.69      |\n",
+       "| 40 | 1000      |  100      | rf        | 0.81      | 0.47      |  0.71     | 0.53      |  6.18     | 0.10      |\n",
+       "| 56 | 1000      |  100      | compLasso | 0.89      | 1.04      |  0.71     | 0.00      |  6.71     | 0.08      |\n",
+       "| 4 | 1000      |   50      | lasso     | 0.86      | 0.26      |  1.82     | 0.00      |  6.82     | 0.10      |\n",
+       "| 20 | 1000      |   50      | elnet     | 0.27      | 0.25      | 11.70     | 0.00      | 16.76     | 0.60      |\n",
+       "| 36 | 1000      |   50      | rf        | 0.88      | 0.35      |  0.07     | 0.78      |  5.29     | 0.01      |\n",
+       "| 52 | 1000      |   50      | compLasso | 0.93      | 1.18      |  0.39     | 0.00      |  6.39     | 0.04      |\n",
+       "| 15 |  500      | 1000      | lasso     | 0.52      | 0.29      |  6.51     | 0.00      | 11.51     | 0.40      |\n",
+       "| 31 |  500      | 1000      | elnet     | 0.14      | 0.27      | 35.90     | 0.00      | 40.98     | 0.80      |\n",
+       "| 47 |  500      | 1000      | rf        | 0.06      | 0.89      | 44.70     | 1.81      | 48.96     | 0.91      |\n",
+       "| 63 |  500      | 1000      | compLasso | 0.96      | 1.12      |  0.27     | 0.00      |  6.27     | 0.03      |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   N    P    method    Stab MSE_mean FP_mean FN_mean num_select FDR \n",
+       "16 1000 1000 lasso     0.64 0.27      4.39   0.00     9.39      0.26\n",
+       "32 1000 1000 elnet     0.15 0.27     34.60   0.00    39.65      0.79\n",
+       "48 1000 1000 rf        0.09 0.85     41.70   0.99    46.78      0.89\n",
+       "64 1000 1000 compLasso 0.96 1.16      0.24   0.00     6.24      0.03\n",
+       "12 1000  500 lasso     0.72 0.27      3.35   0.00     8.35      0.21\n",
+       "28 1000  500 elnet     0.18 0.26     27.20   0.00    32.21      0.77\n",
+       "44 1000  500 rf        0.19 0.78     18.70   0.84    23.89      0.78\n",
+       "60 1000  500 compLasso 0.96 1.24      0.25   0.00     6.25      0.03\n",
+       "8  1000  100 lasso     0.82 0.27      2.25   0.00     7.25      0.15\n",
+       "24 1000  100 elnet     0.23 0.26     17.00   0.00    22.05      0.69\n",
+       "40 1000  100 rf        0.81 0.47      0.71   0.53     6.18      0.10\n",
+       "56 1000  100 compLasso 0.89 1.04      0.71   0.00     6.71      0.08\n",
+       "4  1000   50 lasso     0.86 0.26      1.82   0.00     6.82      0.10\n",
+       "20 1000   50 elnet     0.27 0.25     11.70   0.00    16.76      0.60\n",
+       "36 1000   50 rf        0.88 0.35      0.07   0.78     5.29      0.01\n",
+       "52 1000   50 compLasso 0.93 1.18      0.39   0.00     6.39      0.04\n",
+       "15  500 1000 lasso     0.52 0.29      6.51   0.00    11.51      0.40\n",
+       "31  500 1000 elnet     0.14 0.27     35.90   0.00    40.98      0.80\n",
+       "47  500 1000 rf        0.06 0.89     44.70   1.81    48.96      0.91\n",
+       "63  500 1000 compLasso 0.96 1.12      0.27   0.00     6.27      0.03"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ind[order(ind$N, ind$P, decreasing=T), c('N', 'P', 'method', 'Stab', 'MSE_mean', 'FP_mean', 'FN_mean', 'num_select', 'FDR')][1:20, ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Toeplitz Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_lasso = read.csv('../results_summary_cts/sim_toe_lasso.txt', sep='\\t')\n",
+    "toe_elnet = read.csv('../results_summary_cts/sim_toe_elnet.txt', sep='\\t')\n",
+    "toe_rf = read.csv('../results_summary_cts/sim_toe_rf.txt', sep='\\t')\n",
+    "toe_compLasso = read.csv('../results_summary_cts/sim_toe_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "toe_lasso$method = rep('lasso', dim(toe_lasso)[1])\n",
+    "toe_elnet$method = rep('elnet', dim(toe_elnet)[1])\n",
+    "toe_rf$method = rep('rf', dim(toe_rf)[1])\n",
+    "toe_compLasso$method = rep('compLasso', dim(toe_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(toe_lasso)\n",
+    "dim(toe_elnet)\n",
+    "dim(toe_rf)\n",
+    "dim(toe_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe_rf = toe_rf[!names(toe_rf)%in% c('OOB', 'OOB_mean', 'toeex')]\n",
+    "dim(toe_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>320</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe = rbind(toe_lasso, toe_elnet)\n",
+    "toe = rbind(toe, toe_rf)\n",
+    "toe = rbind(toe, toe_compLasso)\n",
+    "dim(toe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>N</th><th scope=col>P</th><th scope=col>Corr</th><th scope=col>Ratio</th><th scope=col>Stab</th><th scope=col>MSE</th><th scope=col>FP</th><th scope=col>FN</th><th scope=col>num_select</th><th scope=col>FDR</th><th scope=col>MSE_mean</th><th scope=col>FP_mean</th><th scope=col>FN_mean</th><th scope=col>method</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>161</th><td>50           </td><td>  50         </td><td>0.1          </td><td> 1           </td><td>0            </td><td>1.4 ( 0.06 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.40         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>165</th><td>50           </td><td> 100         </td><td>0.1          </td><td> 2           </td><td>0            </td><td>1.36 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.36         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>169</th><td>50           </td><td> 500         </td><td>0.1          </td><td>10           </td><td>0            </td><td>1.29 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>173</th><td>50           </td><td>1000         </td><td>0.1          </td><td>20           </td><td>0            </td><td>1.35 ( 0.06 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.35         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>177</th><td>50           </td><td>  50         </td><td>0.3          </td><td> 1           </td><td>0            </td><td>1.22 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.22         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>181</th><td>50           </td><td> 100         </td><td>0.3          </td><td> 2           </td><td>0            </td><td>1.15 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.15         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>185</th><td>50           </td><td> 500         </td><td>0.3          </td><td>10           </td><td>0            </td><td>1.14 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.14         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>189</th><td>50           </td><td>1000         </td><td>0.3          </td><td>20           </td><td>0            </td><td>1.2 ( 0.05 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>1.20         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>193</th><td>50           </td><td>  50         </td><td>0.5          </td><td> 1           </td><td>0            </td><td>0.99 ( 0.05 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.99         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>197</th><td>50           </td><td> 100         </td><td>0.5          </td><td> 2           </td><td>0            </td><td>0.91 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.91         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>201</th><td>50           </td><td> 500         </td><td>0.5          </td><td>10           </td><td>0            </td><td>0.95 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.95         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>205</th><td>50           </td><td>1000         </td><td>0.5          </td><td>20           </td><td>0            </td><td>0.94 ( 0.04 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.94         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>209</th><td>50           </td><td>  50         </td><td>0.7          </td><td> 1           </td><td>0            </td><td>0.66 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.66         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>213</th><td>50           </td><td> 100         </td><td>0.7          </td><td> 2           </td><td>0            </td><td>0.67 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.67         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>217</th><td>50           </td><td> 500         </td><td>0.7          </td><td>10           </td><td>0            </td><td>0.7 ( 0.03 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.70         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>221</th><td>50           </td><td>1000         </td><td>0.7          </td><td>20           </td><td>0            </td><td>0.74 ( 0.03 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.74         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>225</th><td>50           </td><td>  50         </td><td>0.9          </td><td> 1           </td><td>0            </td><td>0.27 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.27         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>229</th><td>50           </td><td> 100         </td><td>0.9          </td><td> 2           </td><td>0            </td><td>0.3 ( 0.01 ) </td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.30         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>233</th><td>50           </td><td> 500         </td><td>0.9          </td><td>10           </td><td>0            </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "\t<tr><th scope=row>237</th><td>50           </td><td>1000         </td><td>0.9          </td><td>20           </td><td>0            </td><td>0.29 ( 0.01 )</td><td>1 ( 0 )      </td><td>6 ( 0 )      </td><td>0            </td><td>NaN          </td><td>0.29         </td><td>1            </td><td>6            </td><td>rf           </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllllll}\n",
+       "  & N & P & Corr & Ratio & Stab & MSE & FP & FN & num\\_select & FDR & MSE\\_mean & FP\\_mean & FN\\_mean & method\\\\\n",
+       "\\hline\n",
+       "\t161 & 50            &   50          & 0.1           &  1            & 0             & 1.4 ( 0.06 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.40          & 1             & 6             & rf           \\\\\n",
+       "\t165 & 50            &  100          & 0.1           &  2            & 0             & 1.36 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.36          & 1             & 6             & rf           \\\\\n",
+       "\t169 & 50            &  500          & 0.1           & 10            & 0             & 1.29 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.29          & 1             & 6             & rf           \\\\\n",
+       "\t173 & 50            & 1000          & 0.1           & 20            & 0             & 1.35 ( 0.06 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.35          & 1             & 6             & rf           \\\\\n",
+       "\t177 & 50            &   50          & 0.3           &  1            & 0             & 1.22 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.22          & 1             & 6             & rf           \\\\\n",
+       "\t181 & 50            &  100          & 0.3           &  2            & 0             & 1.15 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.15          & 1             & 6             & rf           \\\\\n",
+       "\t185 & 50            &  500          & 0.3           & 10            & 0             & 1.14 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.14          & 1             & 6             & rf           \\\\\n",
+       "\t189 & 50            & 1000          & 0.3           & 20            & 0             & 1.2 ( 0.05 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 1.20          & 1             & 6             & rf           \\\\\n",
+       "\t193 & 50            &   50          & 0.5           &  1            & 0             & 0.99 ( 0.05 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.99          & 1             & 6             & rf           \\\\\n",
+       "\t197 & 50            &  100          & 0.5           &  2            & 0             & 0.91 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.91          & 1             & 6             & rf           \\\\\n",
+       "\t201 & 50            &  500          & 0.5           & 10            & 0             & 0.95 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.95          & 1             & 6             & rf           \\\\\n",
+       "\t205 & 50            & 1000          & 0.5           & 20            & 0             & 0.94 ( 0.04 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.94          & 1             & 6             & rf           \\\\\n",
+       "\t209 & 50            &   50          & 0.7           &  1            & 0             & 0.66 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.66          & 1             & 6             & rf           \\\\\n",
+       "\t213 & 50            &  100          & 0.7           &  2            & 0             & 0.67 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.67          & 1             & 6             & rf           \\\\\n",
+       "\t217 & 50            &  500          & 0.7           & 10            & 0             & 0.7 ( 0.03 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.70          & 1             & 6             & rf           \\\\\n",
+       "\t221 & 50            & 1000          & 0.7           & 20            & 0             & 0.74 ( 0.03 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.74          & 1             & 6             & rf           \\\\\n",
+       "\t225 & 50            &   50          & 0.9           &  1            & 0             & 0.27 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.27          & 1             & 6             & rf           \\\\\n",
+       "\t229 & 50            &  100          & 0.9           &  2            & 0             & 0.3 ( 0.01 )  & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.30          & 1             & 6             & rf           \\\\\n",
+       "\t233 & 50            &  500          & 0.9           & 10            & 0             & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.29          & 1             & 6             & rf           \\\\\n",
+       "\t237 & 50            & 1000          & 0.9           & 20            & 0             & 0.29 ( 0.01 ) & 1 ( 0 )       & 6 ( 0 )       & 0             & NaN           & 0.29          & 1             & 6             & rf           \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | N | P | Corr | Ratio | Stab | MSE | FP | FN | num_select | FDR | MSE_mean | FP_mean | FN_mean | method |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| 161 | 50            |   50          | 0.1           |  1            | 0             | 1.4 ( 0.06 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.40          | 1             | 6             | rf            |\n",
+       "| 165 | 50            |  100          | 0.1           |  2            | 0             | 1.36 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.36          | 1             | 6             | rf            |\n",
+       "| 169 | 50            |  500          | 0.1           | 10            | 0             | 1.29 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.29          | 1             | 6             | rf            |\n",
+       "| 173 | 50            | 1000          | 0.1           | 20            | 0             | 1.35 ( 0.06 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.35          | 1             | 6             | rf            |\n",
+       "| 177 | 50            |   50          | 0.3           |  1            | 0             | 1.22 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.22          | 1             | 6             | rf            |\n",
+       "| 181 | 50            |  100          | 0.3           |  2            | 0             | 1.15 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.15          | 1             | 6             | rf            |\n",
+       "| 185 | 50            |  500          | 0.3           | 10            | 0             | 1.14 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.14          | 1             | 6             | rf            |\n",
+       "| 189 | 50            | 1000          | 0.3           | 20            | 0             | 1.2 ( 0.05 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 1.20          | 1             | 6             | rf            |\n",
+       "| 193 | 50            |   50          | 0.5           |  1            | 0             | 0.99 ( 0.05 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.99          | 1             | 6             | rf            |\n",
+       "| 197 | 50            |  100          | 0.5           |  2            | 0             | 0.91 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.91          | 1             | 6             | rf            |\n",
+       "| 201 | 50            |  500          | 0.5           | 10            | 0             | 0.95 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.95          | 1             | 6             | rf            |\n",
+       "| 205 | 50            | 1000          | 0.5           | 20            | 0             | 0.94 ( 0.04 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.94          | 1             | 6             | rf            |\n",
+       "| 209 | 50            |   50          | 0.7           |  1            | 0             | 0.66 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.66          | 1             | 6             | rf            |\n",
+       "| 213 | 50            |  100          | 0.7           |  2            | 0             | 0.67 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.67          | 1             | 6             | rf            |\n",
+       "| 217 | 50            |  500          | 0.7           | 10            | 0             | 0.7 ( 0.03 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.70          | 1             | 6             | rf            |\n",
+       "| 221 | 50            | 1000          | 0.7           | 20            | 0             | 0.74 ( 0.03 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.74          | 1             | 6             | rf            |\n",
+       "| 225 | 50            |   50          | 0.9           |  1            | 0             | 0.27 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.27          | 1             | 6             | rf            |\n",
+       "| 229 | 50            |  100          | 0.9           |  2            | 0             | 0.3 ( 0.01 )  | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.30          | 1             | 6             | rf            |\n",
+       "| 233 | 50            |  500          | 0.9           | 10            | 0             | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.29          | 1             | 6             | rf            |\n",
+       "| 237 | 50            | 1000          | 0.9           | 20            | 0             | 0.29 ( 0.01 ) | 1 ( 0 )       | 6 ( 0 )       | 0             | NaN           | 0.29          | 1             | 6             | rf            |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "    N  P    Corr Ratio Stab MSE           FP      FN      num_select FDR\n",
+       "161 50   50 0.1   1    0    1.4 ( 0.06 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "165 50  100 0.1   2    0    1.36 ( 0.06 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "169 50  500 0.1  10    0    1.29 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "173 50 1000 0.1  20    0    1.35 ( 0.06 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "177 50   50 0.3   1    0    1.22 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "181 50  100 0.3   2    0    1.15 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "185 50  500 0.3  10    0    1.14 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "189 50 1000 0.3  20    0    1.2 ( 0.05 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "193 50   50 0.5   1    0    0.99 ( 0.05 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "197 50  100 0.5   2    0    0.91 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "201 50  500 0.5  10    0    0.95 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "205 50 1000 0.5  20    0    0.94 ( 0.04 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "209 50   50 0.7   1    0    0.66 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "213 50  100 0.7   2    0    0.67 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "217 50  500 0.7  10    0    0.7 ( 0.03 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "221 50 1000 0.7  20    0    0.74 ( 0.03 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "225 50   50 0.9   1    0    0.27 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "229 50  100 0.9   2    0    0.3 ( 0.01 )  1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "233 50  500 0.9  10    0    0.29 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "237 50 1000 0.9  20    0    0.29 ( 0.01 ) 1 ( 0 ) 6 ( 0 ) 0          NaN\n",
+       "    MSE_mean FP_mean FN_mean method\n",
+       "161 1.40     1       6       rf    \n",
+       "165 1.36     1       6       rf    \n",
+       "169 1.29     1       6       rf    \n",
+       "173 1.35     1       6       rf    \n",
+       "177 1.22     1       6       rf    \n",
+       "181 1.15     1       6       rf    \n",
+       "185 1.14     1       6       rf    \n",
+       "189 1.20     1       6       rf    \n",
+       "193 0.99     1       6       rf    \n",
+       "197 0.91     1       6       rf    \n",
+       "201 0.95     1       6       rf    \n",
+       "205 0.94     1       6       rf    \n",
+       "209 0.66     1       6       rf    \n",
+       "213 0.67     1       6       rf    \n",
+       "217 0.70     1       6       rf    \n",
+       "221 0.74     1       6       rf    \n",
+       "225 0.27     1       6       rf    \n",
+       "229 0.30     1       6       rf    \n",
+       "233 0.29     1       6       rf    \n",
+       "237 0.29     1       6       rf    "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "toe[toe$num_select == 0, ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "toe$FP_mean[toe$num_select ==0] = 0\n",
+    "write.table(toe, '../results_summary_cts/table_toe_all.txt', sep='\\t', row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Block Design"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "block_lasso = read.csv('../results_summary_cts/sim_block_lasso.txt', sep='\\t')\n",
+    "block_elnet = read.csv('../results_summary_cts/sim_block_elnet.txt', sep='\\t')\n",
+    "block_rf = read.csv('../results_summary_cts/sim_block_rf.txt', sep='\\t')\n",
+    "block_compLasso = read.csv('../results_summary_cts/sim_block_compLasso.txt', sep='\\t')\n",
+    "\n",
+    "block_lasso$method = rep('lasso', dim(block_lasso)[1])\n",
+    "block_elnet$method = rep('elnet', dim(block_elnet)[1])\n",
+    "block_rf$method = rep('rf', dim(block_rf)[1])\n",
+    "block_compLasso$method = rep('compLasso', dim(block_compLasso)[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>16</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 16\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 16\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 16"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dim(block_lasso)\n",
+    "dim(block_elnet)\n",
+    "dim(block_rf)\n",
+    "dim(block_compLasso)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>80</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 80\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 80\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 80 14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block_rf = block_rf[!names(block_rf)%in% c('OOB', 'OOB_mean', 'blockex')]\n",
+    "dim(block_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>320</li>\n",
+       "\t<li>14</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 320\n",
+       "\\item 14\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 320\n",
+       "2. 14\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "[1] 320  14"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "block = rbind(block_lasso, block_elnet)\n",
+    "block = rbind(block, block_rf)\n",
+    "block = rbind(block, block_compLasso)\n",
+    "dim(block)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fix false positives: when nothing was selected, then fp should be zero\n",
+    "# fp is now 1 due to length(setdiff(0/NA)) = 1 when actually should be 0 (in cv_sim_apply.R)\n",
+    "block$FP_mean[block$num_select ==0] = 0\n",
+    "write.table(block, '../results_summary_cts/table_block_all.txt', sep='\\t', row.names=F)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/notebooks_sim_cts/4_sim_cts_bootstrap_testing.ipynb b/simulations/notebooks_sim_cts/4_sim_cts_bootstrap_testing.ipynb
new file mode 100755
index 0000000..6ed9920
--- /dev/null
+++ b/simulations/notebooks_sim_cts/4_sim_cts_bootstrap_testing.ipynb
@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hypothesis testing based on boostrap for selected simulation scenario"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = '/panfs/panfs1.ucsd.edu/panscratch/lij014/Stability_2020/sim_data'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load(paste0(dir, '/boot_toe_RF.RData'))\n",
+    "load(paste0(dir, '/boot_block_RF.RData'))\n",
+    "load(paste0(dir, '/boot_toe_compLasso.RData'))\n",
+    "load(paste0(dir, '/boot_block_compLasso.RData'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>toe_rf</th><th scope=col>toe_lin</th><th scope=col>block_rf</th><th scope=col>block_lin</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>0.01</td><td>0.25</td><td>0.01</td><td>0.26</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.01</td><td>0.23</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.28</td><td>0.00</td><td>0.20</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.25</td><td>0.01</td><td>0.26</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.01</td><td>0.25</td></tr>\n",
+       "\t<tr><td>0.01</td><td>0.23</td><td>0.00</td><td>0.26</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llll}\n",
+       " toe\\_rf & toe\\_lin & block\\_rf & block\\_lin\\\\\n",
+       "\\hline\n",
+       "\t 0.01 & 0.25 & 0.01 & 0.26\\\\\n",
+       "\t 0.01 & 0.23 & 0.01 & 0.23\\\\\n",
+       "\t 0.01 & 0.28 & 0.00 & 0.20\\\\\n",
+       "\t 0.01 & 0.25 & 0.01 & 0.26\\\\\n",
+       "\t 0.01 & 0.23 & 0.01 & 0.25\\\\\n",
+       "\t 0.01 & 0.23 & 0.00 & 0.26\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| toe_rf | toe_lin | block_rf | block_lin |\n",
+       "|---|---|---|---|\n",
+       "| 0.01 | 0.25 | 0.01 | 0.26 |\n",
+       "| 0.01 | 0.23 | 0.01 | 0.23 |\n",
+       "| 0.01 | 0.28 | 0.00 | 0.20 |\n",
+       "| 0.01 | 0.25 | 0.01 | 0.26 |\n",
+       "| 0.01 | 0.23 | 0.01 | 0.25 |\n",
+       "| 0.01 | 0.23 | 0.00 | 0.26 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  toe_rf toe_lin block_rf block_lin\n",
+       "1 0.01   0.25    0.01     0.26     \n",
+       "2 0.01   0.23    0.01     0.23     \n",
+       "3 0.01   0.28    0.00     0.20     \n",
+       "4 0.01   0.25    0.01     0.26     \n",
+       "5 0.01   0.23    0.01     0.25     \n",
+       "6 0.01   0.23    0.00     0.26     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table = as.data.frame(cbind(toe_rf$stab_index, toe_lin$stab_index, block_rf$stab_index, block_lin$stab_index))\n",
+    "colnames(table) = c('toe_rf', 'toe_lin', 'block_rf', 'block_lin')\n",
+    "head(table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two sample test based on Bootstrap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## interpretation: if confidence interval doesn't contain zero, then significant\n",
+    "## ref: correct intrepretation of confidence intervals: https://journals.sagepub.com/doi/pdf/10.1177/201010581001900316"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2247"
+      ],
+      "text/latex": [
+       "0.2247"
+      ],
+      "text/markdown": [
+       "0.2247"
+      ],
+      "text/plain": [
+       "[1] 0.2247"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.19</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.28</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.19\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.28\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1997.5%\n",
+       ":   0.28\n",
+       "\n"
+      ],
+      "text/plain": [
+       " 2.5% 97.5% \n",
+       " 0.19  0.28 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (table$toe_lin - table$toe_rf)\n",
+    "mean(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.2289"
+      ],
+      "text/latex": [
+       "0.2289"
+      ],
+      "text/markdown": [
+       "0.2289"
+      ],
+      "text/plain": [
+       "[1] 0.2289"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>0.17475</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>0.28525</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] 0.17475\n",
+       "\\item[97.5\\textbackslash{}\\%] 0.28525\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   0.1747597.5%\n",
+       ":   0.28525\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   2.5%   97.5% \n",
+       "0.17475 0.28525 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (table$block_lin - table$block_rf)\n",
+    "mean(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare based on MSE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### block 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.47 ( 0.01 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.47 ( 0.01 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 0.4923079, 0.5236572, 0.3049313, 0.4022395, 0.4116445, 0.4457436, 0.5321101, 0.3994759, 0.3889622, 0.4957112, 0.2661450, 0.5383919, 0.4251333, 0.5020789, 0.5774619, 0.3274016, 0.5765174, 0.4136176, 0.5066335, 0.7475691, 0.7385966, 0.3141407, 0.4786586, 0.4811622, 0.4128133, 0.6605431, 0.5315163, 0.4400826, 0.8595558, 0.3382323, 0.4297301, 0.4961741, 0.5237057, 0.6787550, 0.4064282, 0.4305124, 0.3051361, 0.4404218, 0.4208728, 0.5967023, 0.5111793, 0.5062338, 0.2653858, 0.5910289, 0.3166639, 0.4614773, 0.4315483, 0.6846844, 0.4522860, 0.2785640, 0.6029796, 0.4161780, 0.5801175, 0.4112313, 0.5364697, 0.4318882, 0.3655746, 0.4455715, 0.3465285, 0.7532535, 0.3877135, 0.5023844, 0.2049257, 0.4338186, 0.3111033, 0.4220746, 0.5432892, 0.7013340, 0.5247176, 0.3747088, 0.3819685, 0.4208868, 0.2586503, 0.5655622, 0.5948446, 0.3660124, 0.4598678, 0.6435442, 0.3349638, 0.4052510, 0.6205234, 0.4349098, 0.6964996, 0.4033935, 0.4308613, 0.5466399, 0.3565571, 0.5507224, 0.3275157, 0.4857403, 0.3745229, 0.4944044, 0.4354932, 0.4055464, 0.4792593, 0.5230589, 0.2415968, 0.3994462, 0.6382857, 0.5929313"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_block_rf = NULL\n",
+    "for (i in 1:length(results_block_rf)){\n",
+    "    table_block_rf = rbind(table_block_rf, results_block_rf[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_block_rf = as.data.frame(table_block_rf)\n",
+    "rf_block5_rf= table_block_rf[table_block_rf$n == 100 & table_block_rf$p == 1000 & table_block_rf$rou == 0.5, ]\n",
+    "rf_block5_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.91 ( 0.04 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.91 ( 0.04 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 1.1101125, 0.6519860, 0.7398478, 1.1498738, 0.9619897, 0.6013746, 1.3625092, 0.3473651, 1.1450087, 0.6678814, 0.2637982, 1.1272821, 0.5100440, 1.1207711, 0.6858812, 0.4732182, 0.4877071, 1.3858793, 0.9285002, 0.8910513, 0.4041991, 0.3094465, 0.7791739, 0.9177788, 1.3679422, 0.3555295, 0.6004392, 0.9110635, 0.6472957, 0.9988893, 2.1903274, 2.5255804, 0.9870283, 1.2731291, 0.6370381, 0.9076340, 0.8604679, 0.7195767, 0.8129923, 0.8024248, 0.9105749, 1.5656134, 0.4518466, 0.5868373, 0.7896528, 1.1223770, 0.2244172, 0.4527471, 1.0980113, 0.8810156, 0.9874237, 1.6133751, 1.7549198, 0.8114664, 0.6056103, 1.2937025, 0.5187529, 0.4362994, 1.0523983, 0.4049739, 0.6115282, 0.7411138, 1.0011934, 0.3509803, 0.5946993, 0.8179413, 0.8536523, 0.5527882, 0.9707325, 1.2802410, 0.9099143, 0.8149918, 1.0858406, 0.8915861, 1.1319847, 0.9534063, 1.8160839, 1.0257458, 0.9344737, 0.6119914, 0.5159076, 0.7852905, 0.4582427, 0.8083787, 1.2116205, 0.5323257, 1.1506337, 0.9291959, 1.6839768, 1.1023322, 1.4818658, 0.7335560, 1.3694255, 0.6343668, 0.5388556, 0.8552869, 2.0695142, 0.5429316, 0.7798566, 1.5607776"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_block_compLasso = NULL\n",
+    "for (i in 1:length(results_block_compLasso)){\n",
+    "    table_block_compLasso = rbind(table_block_compLasso, results_block_compLasso[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_block_compLasso = as.data.frame(table_block_compLasso)\n",
+    "rf_block5_compLasso= table_block_compLasso[table_block_compLasso$n == 100 & table_block_compLasso$p == 1000 & table_block_compLasso$rou == 0.5, ]\n",
+    "rf_block5_compLasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.439514359427722"
+      ],
+      "text/latex": [
+       "0.439514359427722"
+      ],
+      "text/markdown": [
+       "0.439514359427722"
+      ],
+      "text/plain": [
+       "[1] 0.4395144"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.454598954212658"
+      ],
+      "text/latex": [
+       "0.454598954212658"
+      ],
+      "text/markdown": [
+       "0.454598954212658"
+      ],
+      "text/plain": [
+       "[1] 0.454599"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.273304180160457</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>1.56863259003766</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.273304180160457\n",
+       "\\item[97.5\\textbackslash{}\\%] 1.56863259003766\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.27330418016045797.5%\n",
+       ":   1.56863259003766\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.2733042  1.5686326 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_block = (unlist(rf_block5_compLasso$MSE.list) - unlist(rf_block5_rf$MSE.list))\n",
+    "mean(diff_block)\n",
+    "sd(diff_block)\n",
+    "quantile(diff_block, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Toeplitz 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.72 ( 0.02 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.72 ( 0.02 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 0.5480701, 0.5456622, 0.6046593, 0.7802792, 0.5564160, 0.8468460, 0.4962066, 0.6002301, 0.7197087, 0.8322002, 0.8370241, 0.7042491, 0.7296914, 0.7883332, 0.7773170, 0.8004030, 0.5738099, 0.7080895, 0.4735798, 0.8907030, 0.4745488, 0.6972922, 0.8183826, 0.5698736, 0.6296879, 0.6939117, 0.8883939, 0.5428835, 1.1874869, 0.6937746, 0.4956598, 0.5523018, 0.8295625, 0.8923571, 1.0726882, 0.6509979, 0.5781111, 0.9521210, 0.5251293, 0.7952157, 0.7322116, 0.6871817, 0.7204011, 1.1810854, 1.1751481, 0.7082380, 0.6971877, 1.0196209, 0.7037824, 0.8105835, 1.1165501, 0.5775225, 0.5665563, 0.7273973, 1.3113648, 0.6025675, 0.8935835, 0.6165939, 0.3838047, 0.6833741, 0.5721250, 0.5478751, 0.5123701, 0.7706215, 1.0321326, 0.7021128, 0.5282792, 0.5642605, 0.7610716, 0.6069513, 0.6572387, 0.8830648, 0.7969348, 0.9412556, 0.5570120, 0.6591877, 0.6899799, 0.5051250, 0.8911872, 0.5656055, 1.0006875, 0.8468315, 0.9820390, 0.6041058, 0.9307259, 0.7710037, 0.5545953, 0.4067023, 0.5719611, 0.4234005, 0.8979186, 0.9248135, 0.5666418, 0.7263402, 0.9324326, 0.4342892, 0.7020636, 0.8808153, 0.6929102, 0.6299956"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_toe_rf = NULL\n",
+    "for (i in 1:length(results_toe_rf)){\n",
+    "    table_toe_rf = rbind(table_toe_rf, results_toe_rf[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_toe_rf = as.data.frame(table_toe_rf)\n",
+    "rf_toe5_rf= table_toe_rf[table_toe_rf$n == 100 & table_toe_rf$p == 1000 & table_toe_rf$rou == 0.5, ]\n",
+    "rf_toe5_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>n</th><th scope=col>p</th><th scope=col>rou</th><th scope=col>MSE</th><th scope=col>MSE.list</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>46</th><td>100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td><td>0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       </td><td>0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             </td><td>1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllll}\n",
+       "  & n & p & rou & MSE & MSE.list\\\\\n",
+       "\\hline\n",
+       "\t46 & 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       & 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        & 0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              & 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "| <!--/--> | n | p | rou | MSE | MSE.list |\n",
+       "|---|---|---|---|---|---|\n",
+       "| 46 | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 1000                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | 0.5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | 0.96 ( 0.05 )                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539 |\n",
+       "\n"
+      ],
+      "text/plain": [
+       "   n   p    rou MSE          \n",
+       "46 100 1000 0.5 0.96 ( 0.05 )\n",
+       "   MSE.list                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  \n",
+       "46 1.1333219, 1.2739089, 0.6735451, 0.7455676, 0.8084525, 0.9115262, 1.1253772, 0.7718239, 0.7704166, 0.9337895, 0.6251795, 1.2304850, 0.9825552, 0.6256884, 0.8465868, 0.8928493, 0.6841306, 0.7445355, 1.0251240, 1.0109804, 0.6584266, 0.6574249, 0.8796538, 0.5822056, 1.8200702, 0.3513984, 0.7348276, 1.9856867, 0.5293853, 1.2169509, 0.4462136, 1.0628981, 1.0927890, 0.7534512, 0.3504197, 0.6451335, 0.7688543, 0.4767431, 1.7155647, 1.6765981, 0.5145321, 0.4758315, 1.9340790, 0.3082418, 0.8707792, 0.8961984, 0.6985983, 1.4072453, 0.3051685, 0.5767318, 0.8742455, 1.1188925, 1.0622541, 1.1897730, 0.7308046, 1.1798550, 0.5843354, 0.6606599, 1.0553048, 1.0076178, 0.6620472, 0.5932031, 1.5098887, 0.9258426, 0.5178589, 0.8255029, 0.7817222, 1.9833525, 0.8742543, 1.0785289, 0.9291164, 0.5450408, 0.9975609, 0.8724312, 0.5304621, 1.0475676, 1.5424944, 1.3738204, 2.1881127, 1.7968121, 0.7707653, 0.5582550, 1.4977663, 0.8136983, 0.5509221, 0.5157933, 1.1241994, 0.5044806, 1.1753270, 0.4650944, 1.0019032, 0.7552821, 0.8536905, 3.1389767, 0.8944839, 0.8503332, 0.9161472, 1.7568389, 0.9580790, 1.2403539"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table_toe_compLasso = NULL\n",
+    "for (i in 1:length(results_toe_compLasso)){\n",
+    "    table_toe_compLasso = rbind(table_toe_compLasso, results_toe_compLasso[[i]][c('n', 'p', 'rou', 'MSE', 'MSE.list')])\n",
+    "}\n",
+    "table_toe_compLasso = as.data.frame(table_toe_compLasso)\n",
+    "rf_toe5_compLasso= table_toe_compLasso[table_toe_compLasso$n == 100 & table_toe_compLasso$p == 1000 & table_toe_compLasso$rou == 0.5, ]\n",
+    "rf_toe5_compLasso"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.23096420572779"
+      ],
+      "text/latex": [
+       "0.23096420572779"
+      ],
+      "text/markdown": [
+       "0.23096420572779"
+      ],
+      "text/plain": [
+       "[1] 0.2309642"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "0.521190219773319"
+      ],
+      "text/latex": [
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+      "text/plain": [
+       "[1] 0.5211902"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<dl class=dl-horizontal>\n",
+       "\t<dt>2.5%</dt>\n",
+       "\t\t<dd>-0.621269466414493</dd>\n",
+       "\t<dt>97.5%</dt>\n",
+       "\t\t<dd>1.36106289789273</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description*}\n",
+       "\\item[2.5\\textbackslash{}\\%] -0.621269466414493\n",
+       "\\item[97.5\\textbackslash{}\\%] 1.36106289789273\n",
+       "\\end{description*}\n"
+      ],
+      "text/markdown": [
+       "2.5%\n",
+       ":   -0.62126946641449397.5%\n",
+       ":   1.36106289789273\n",
+       "\n"
+      ],
+      "text/plain": [
+       "      2.5%      97.5% \n",
+       "-0.6212695  1.3610629 "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diff_toe = (unlist(rf_toe5_compLasso$MSE.list) - unlist(rf_toe5_rf$MSE.list))\n",
+    "mean(diff_toe)\n",
+    "sd(diff_toe)\n",
+    "quantile(diff_toe, probs = c(0.025, 0.975))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/simulations/results_summary/.DS_Store b/simulations/results_summary/.DS_Store
deleted file mode 100755
index b271dc6..0000000
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diff --git a/simulations/results_summary_bin/sim_block_Elnet_binary.txt b/simulations/results_summary_bin/sim_block_Elnet_binary.txt
new file mode 100644
index 0000000..d0d4d27
--- /dev/null
+++ b/simulations/results_summary_bin/sim_block_Elnet_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.01	"0.61 ( 0.01 )"	"17.05 ( 1.31 )"	"3.17 ( 0.19 )"	18.88	"0.8"	0.61	17.05	3.17
+100	50	"0.1"	0.5	0.04	"0.59 ( 0.01 )"	"13.62 ( 1.31 )"	"2.9 ( 0.17 )"	15.72	"0.64"	0.59	13.62	2.9
+500	50	"0.1"	0.1	0.11	"0.64 ( 0 )"	"16.89 ( 1.14 )"	"1.1 ( 0.13 )"	20.79	"0.68"	0.64	16.89	1.1
+1000	50	"0.1"	0.05	0.12	"0.66 ( 0 )"	"19.43 ( 1.02 )"	"0.42 ( 0.08 )"	24.01	"0.71"	0.66	19.43	0.42
+50	100	"0.1"	2	0.01	"0.63 ( 0.01 )"	"27.39 ( 2.11 )"	"3.38 ( 0.17 )"	29.01	"0.87"	0.63	27.39	3.38
+100	100	"0.1"	1	0.02	"0.59 ( 0.01 )"	"26.17 ( 2.4 )"	"3.34 ( 0.16 )"	27.83	"0.84"	0.59	26.17	3.34
+500	100	"0.1"	0.2	0.09	"0.62 ( 0 )"	"22.75 ( 1.87 )"	"1.61 ( 0.13 )"	26.14	"0.69"	0.62	22.75	1.61
+1000	100	"0.1"	0.1	0.1	"0.64 ( 0 )"	"28.32 ( 1.88 )"	"0.66 ( 0.1 )"	32.66	"0.77"	0.64	28.32	0.66
+50	500	"0.1"	10	0	"0.61 ( 0.01 )"	"69.03 ( 7.45 )"	"4.13 ( 0.19 )"	69.9	"0.96"	0.61	69.03	4.13
+100	500	"0.1"	5	0	"0.57 ( 0.01 )"	"70.64 ( 9.13 )"	"3.91 ( 0.17 )"	71.73	"0.91"	0.57	70.64	3.91
+500	500	"0.1"	1	0.1	"0.61 ( 0.01 )"	"20.55 ( 4.33 )"	"3.15 ( 0.1 )"	22.4	"0.62"	0.61	20.55	3.15
+1000	500	"0.1"	0.5	0.12	"0.63 ( 0 )"	"26.03 ( 3.45 )"	"2.38 ( 0.11 )"	28.65	"0.62"	0.63	26.03	2.38
+50	1000	"0.1"	20	0	"0.59 ( 0.01 )"	"103.61 ( 10.54 )"	"4.34 ( 0.18 )"	104.27	"0.98"	0.59	103.61	4.34
+100	1000	"0.1"	10	0	"0.56 ( 0.01 )"	"103.21 ( 12.75 )"	"4.15 ( 0.17 )"	104.06	"0.95"	0.56	103.21	4.15
+500	1000	"0.1"	2	0.07	"0.61 ( 0 )"	"29.41 ( 7.4 )"	"3.25 ( 0.09 )"	31.16	"0.62"	0.61	29.41	3.25
+1000	1000	"0.1"	1	0.18	"0.62 ( 0 )"	"16.15 ( 2.77 )"	"2.73 ( 0.09 )"	18.42	"0.53"	0.62	16.15	2.73
+50	50	"0.3"	1	0.08	"0.68 ( 0.01 )"	"14.8 ( 1.01 )"	"1.73 ( 0.15 )"	18.07	"0.69"	0.68	14.8	1.73
+100	50	"0.3"	0.5	0.15	"0.77 ( 0.01 )"	"15.15 ( 1 )"	"0.64 ( 0.09 )"	19.51	"0.65"	0.77	15.15	0.64
+500	50	"0.3"	0.1	0.35	"0.85 ( 0 )"	"8.95 ( 0.69 )"	"0 ( 0 )"	13.95	"0.46"	0.85	8.95	0
+1000	50	"0.3"	0.05	0.33	"0.86 ( 0 )"	"9.66 ( 0.93 )"	"0 ( 0 )"	14.66	"0.44"	0.86	9.66	0
+50	100	"0.3"	2	0.05	"0.67 ( 0.01 )"	"23.92 ( 2 )"	"2.13 ( 0.15 )"	26.79	"0.77"	0.67	23.92	2.13
+100	100	"0.3"	1	0.14	"0.74 ( 0.01 )"	"17.75 ( 1.51 )"	"1.13 ( 0.11 )"	21.62	"0.67"	0.74	17.75	1.13
+500	100	"0.3"	0.2	0.32	"0.86 ( 0 )"	"11.53 ( 1.02 )"	"0.01 ( 0.01 )"	16.52	"0.51"	0.86	11.53	0.01
+1000	100	"0.3"	0.1	0.33	"0.86 ( 0 )"	"11.15 ( 1.03 )"	"0 ( 0 )"	16.15	"0.49"	0.86	11.15	0
+50	500	"0.3"	10	0.01	"0.6 ( 0.01 )"	"81.07 ( 6.91 )"	"2.92 ( 0.14 )"	83.15	"0.93"	0.6	81.07	2.92
+100	500	"0.3"	5	0.06	"0.66 ( 0.01 )"	"38.14 ( 5.11 )"	"2.16 ( 0.13 )"	40.98	"0.78"	0.66	38.14	2.16
+500	500	"0.3"	1	0.22	"0.84 ( 0 )"	"20.87 ( 1.93 )"	"0.02 ( 0.01 )"	25.85	"0.63"	0.84	20.87	0.02
+1000	500	"0.3"	0.5	0.29	"0.86 ( 0 )"	"15.35 ( 1.6 )"	"0 ( 0 )"	20.35	"0.53"	0.86	15.35	0
+50	1000	"0.3"	20	0.01	"0.63 ( 0.01 )"	"104.78 ( 9.53 )"	"3.37 ( 0.14 )"	106.41	"0.96"	0.63	104.78	3.37
+100	1000	"0.3"	10	0.04	"0.64 ( 0.01 )"	"55.95 ( 7.57 )"	"2.5 ( 0.13 )"	58.45	"0.85"	0.64	55.95	2.5
+500	1000	"0.3"	2	0.23	"0.85 ( 0 )"	"20.69 ( 1.82 )"	"0.05 ( 0.02 )"	25.64	"0.64"	0.85	20.69	0.05
+1000	1000	"0.3"	1	0.29	"0.86 ( 0 )"	"15.19 ( 1.67 )"	"0 ( 0 )"	20.19	"0.51"	0.86	15.19	0
+50	50	"0.5"	1	0.11	"0.7 ( 0.01 )"	"13.99 ( 0.96 )"	"1.59 ( 0.12 )"	17.4	"0.68"	0.7	13.99	1.59
+100	50	"0.5"	0.5	0.21	"0.8 ( 0.01 )"	"11.28 ( 0.76 )"	"0.81 ( 0.1 )"	15.47	"0.57"	0.8	11.28	0.81
+500	50	"0.5"	0.1	0.35	"0.88 ( 0 )"	"9.06 ( 0.77 )"	"0 ( 0 )"	14.06	"0.46"	0.88	9.06	0
+1000	50	"0.5"	0.05	0.39	"0.9 ( 0 )"	"8.03 ( 0.81 )"	"0 ( 0 )"	13.03	"0.4"	0.9	8.03	0
+50	100	"0.5"	2	0.06	"0.68 ( 0.01 )"	"25.61 ( 2.1 )"	"1.79 ( 0.12 )"	28.82	"0.77"	0.68	25.61	1.79
+100	100	"0.5"	1	0.18	"0.79 ( 0.01 )"	"13.99 ( 1.21 )"	"1.12 ( 0.1 )"	17.87	"0.6"	0.79	13.99	1.12
+500	100	"0.5"	0.2	0.32	"0.89 ( 0 )"	"11.65 ( 1.03 )"	"0 ( 0 )"	16.65	"0.52"	0.89	11.65	0
+1000	100	"0.5"	0.1	0.38	"0.89 ( 0 )"	"9.51 ( 0.97 )"	"0 ( 0 )"	14.51	"0.43"	0.89	9.51	0
+50	500	"0.5"	10	0.02	"0.61 ( 0.01 )"	"72.53 ( 6.75 )"	"2.72 ( 0.13 )"	74.81	"0.92"	0.61	72.53	2.72
+100	500	"0.5"	5	0.08	"0.7 ( 0.01 )"	"32.65 ( 4.21 )"	"1.9 ( 0.11 )"	35.75	"0.76"	0.7	32.65	1.9
+500	500	"0.5"	1	0.23	"0.88 ( 0 )"	"20.07 ( 1.75 )"	"0 ( 0 )"	25.07	"0.63"	0.88	20.07	0
+1000	500	"0.5"	0.5	0.3	"0.89 ( 0 )"	"14.5 ( 1.43 )"	"0 ( 0 )"	19.5	"0.53"	0.89	14.5	0
+50	1000	"0.5"	20	0.01	"0.62 ( 0.01 )"	"85.83 ( 8.57 )"	"3.31 ( 0.12 )"	87.52	"0.94"	0.62	85.83	3.31
+100	1000	"0.5"	10	0.05	"0.67 ( 0.01 )"	"51.72 ( 6.91 )"	"2.12 ( 0.12 )"	54.6	"0.83"	0.67	51.72	2.12
+500	1000	"0.5"	2	0.24	"0.88 ( 0 )"	"19.39 ( 1.85 )"	"0.02 ( 0.01 )"	24.37	"0.59"	0.88	19.39	0.02
+1000	1000	"0.5"	1	0.27	"0.89 ( 0 )"	"17.02 ( 1.92 )"	"0 ( 0 )"	22.02	"0.54"	0.89	17.02	0
+50	50	"0.7"	1	0.13	"0.73 ( 0.01 )"	"12.65 ( 0.97 )"	"1.61 ( 0.12 )"	16.04	"0.63"	0.73	12.65	1.61
+100	50	"0.7"	0.5	0.22	"0.81 ( 0.01 )"	"11.53 ( 0.67 )"	"0.54 ( 0.09 )"	15.99	"0.58"	0.81	11.53	0.54
+500	50	"0.7"	0.1	0.34	"0.91 ( 0 )"	"9.34 ( 0.72 )"	"0 ( 0 )"	14.34	"0.48"	0.91	9.34	0
+1000	50	"0.7"	0.05	0.36	"0.91 ( 0 )"	"8.58 ( 0.78 )"	"0 ( 0 )"	13.58	"0.42"	0.91	8.58	0
+50	100	"0.7"	2	0.07	"0.69 ( 0.01 )"	"24.95 ( 1.99 )"	"1.47 ( 0.1 )"	28.48	"0.76"	0.69	24.95	1.47
+100	100	"0.7"	1	0.17	"0.79 ( 0.01 )"	"15.63 ( 1.26 )"	"0.96 ( 0.11 )"	19.67	"0.63"	0.79	15.63	0.96
+500	100	"0.7"	0.2	0.34	"0.9 ( 0 )"	"10.78 ( 0.81 )"	"0.01 ( 0.01 )"	15.77	"0.5"	0.9	10.78	0.01
+1000	100	"0.7"	0.1	0.38	"0.91 ( 0 )"	"9.42 ( 0.9 )"	"0 ( 0 )"	14.42	"0.44"	0.91	9.42	0
+50	500	"0.7"	10	0.02	"0.62 ( 0.01 )"	"66.01 ( 6.05 )"	"2.75 ( 0.12 )"	68.26	"0.92"	0.62	66.01	2.75
+100	500	"0.7"	5	0.1	"0.74 ( 0.01 )"	"25.15 ( 2.8 )"	"1.84 ( 0.12 )"	28.31	"0.71"	0.74	25.15	1.84
+500	500	"0.7"	1	0.25	"0.9 ( 0 )"	"17.68 ( 1.71 )"	"0 ( 0 )"	22.68	"0.59"	0.9	17.68	0
+1000	500	"0.7"	0.5	0.31	"0.91 ( 0 )"	"13.6 ( 1.43 )"	"0 ( 0 )"	18.6	"0.51"	0.91	13.6	0
+50	1000	"0.7"	20	0.01	"0.62 ( 0.01 )"	"86.38 ( 8.91 )"	"3.09 ( 0.13 )"	88.29	"0.93"	0.62	86.38	3.09
+100	1000	"0.7"	10	0.06	"0.69 ( 0.01 )"	"44.56 ( 6.27 )"	"2.12 ( 0.11 )"	47.44	"0.82"	0.69	44.56	2.12
+500	1000	"0.7"	2	0.23	"0.9 ( 0 )"	"20.2 ( 1.89 )"	"0.03 ( 0.02 )"	25.17	"0.62"	0.9	20.2	0.03
+1000	1000	"0.7"	1	0.27	"0.91 ( 0 )"	"16.62 ( 1.81 )"	"0 ( 0 )"	21.62	"0.54"	0.91	16.62	0
+50	50	"0.9"	1	0.14	"0.74 ( 0.01 )"	"12.38 ( 0.9 )"	"1.46 ( 0.1 )"	15.92	"0.63"	0.74	12.38	1.46
+100	50	"0.9"	0.5	0.21	"0.84 ( 0.01 )"	"12.36 ( 0.84 )"	"0.46 ( 0.08 )"	16.9	"0.58"	0.84	12.36	0.46
+500	50	"0.9"	0.1	0.39	"0.92 ( 0 )"	"7.89 ( 0.68 )"	"0 ( 0 )"	12.89	"0.42"	0.92	7.89	0
+1000	50	"0.9"	0.05	0.41	"0.92 ( 0 )"	"7.38 ( 0.78 )"	"0 ( 0 )"	12.38	"0.38"	0.92	7.38	0
+50	100	"0.9"	2	0.09	"0.7 ( 0.01 )"	"19.37 ( 1.75 )"	"2.08 ( 0.13 )"	22.29	"0.75"	0.7	19.37	2.08
+100	100	"0.9"	1	0.2	"0.81 ( 0.01 )"	"14.28 ( 1.07 )"	"0.93 ( 0.09 )"	18.35	"0.61"	0.81	14.28	0.93
+500	100	"0.9"	0.2	0.36	"0.92 ( 0 )"	"10.15 ( 0.82 )"	"0 ( 0 )"	15.15	"0.48"	0.92	10.15	0
+1000	100	"0.9"	0.1	0.38	"0.93 ( 0 )"	"9.4 ( 1.07 )"	"0 ( 0 )"	14.4	"0.42"	0.93	9.4	0
+50	500	"0.9"	10	0.02	"0.62 ( 0.01 )"	"62.39 ( 5.92 )"	"2.69 ( 0.14 )"	64.7	"0.9"	0.62	62.39	2.69
+100	500	"0.9"	5	0.12	"0.74 ( 0.01 )"	"21.21 ( 2.91 )"	"2.02 ( 0.12 )"	24.19	"0.69"	0.74	21.21	2.02
+500	500	"0.9"	1	0.28	"0.91 ( 0 )"	"15.52 ( 1.4 )"	"0 ( 0 )"	20.52	"0.57"	0.91	15.52	0
+1000	500	"0.9"	0.5	0.29	"0.92 ( 0 )"	"14.84 ( 1.41 )"	"0 ( 0 )"	19.84	"0.56"	0.92	14.84	0
+50	1000	"0.9"	20	0.02	"0.63 ( 0.01 )"	"78.65 ( 8.46 )"	"3.08 ( 0.12 )"	80.57	"0.92"	0.63	78.65	3.08
+100	1000	"0.9"	10	0.06	"0.74 ( 0.01 )"	"43.94 ( 6.95 )"	"2.01 ( 0.11 )"	46.93	"0.79"	0.74	43.94	2.01
+500	1000	"0.9"	2	0.25	"0.91 ( 0 )"	"18.95 ( 1.93 )"	"0 ( 0 )"	23.95	"0.6"	0.91	18.95	0
+1000	1000	"0.9"	1	0.29	"0.92 ( 0 )"	"15.24 ( 1.68 )"	"0 ( 0 )"	20.24	"0.52"	0.92	15.24	0
diff --git a/simulations/results_summary_bin/sim_block_GenCompLasso_binary.txt b/simulations/results_summary_bin/sim_block_GenCompLasso_binary.txt
new file mode 100644
index 0000000..83deb58
--- /dev/null
+++ b/simulations/results_summary_bin/sim_block_GenCompLasso_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.02	"0.58 ( 0.01 )"	"1.68 ( 0.53 )"	"5.42 ( 0.1 )"	2.26	"0.58"	0.58	1.68	5.42
+100	50	"0.1"	0.5	0.06	"0.51 ( 0 )"	"0.08 ( 0.04 )"	"5.65 ( 0.08 )"	0.43	"0.18"	0.51	0.08	5.65
+500	50	"0.1"	0.1	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+1000	50	"0.1"	0.05	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+50	100	"0.1"	2	0.04	"0.61 ( 0.01 )"	"3.43 ( 0.63 )"	"5.23 ( 0.1 )"	4.2	"0.71"	0.61	3.43	5.23
+100	100	"0.1"	1	0.03	"0.51 ( 0 )"	"0.31 ( 0.1 )"	"5.8 ( 0.05 )"	0.51	"0.53"	0.51	0.31	5.8
+500	100	"0.1"	0.2	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+1000	100	"0.1"	0.1	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+50	500	"0.1"	10	0	"0.73 ( 0.01 )"	"29.62 ( 7.94 )"	"5.17 ( 0.13 )"	30.45	"0.9"	0.73	29.62	5.17
+100	500	"0.1"	5	0.04	"0.53 ( 0.01 )"	"0.53 ( 0.1 )"	"5.63 ( 0.08 )"	0.9	"0.57"	0.53	0.53	5.63
+500	500	"0.1"	1	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+1000	500	"0.1"	0.5	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+50	1000	"0.1"	20	0.01	"0.83 ( 0.01 )"	"17.45 ( 2.89 )"	"5.37 ( 0.1 )"	18.08	"0.94"	0.83	17.45	5.37
+100	1000	"0.1"	10	0.03	"0.56 ( 0.01 )"	"1.27 ( 0.25 )"	"5.59 ( 0.08 )"	1.68	"0.73"	0.56	1.27	5.59
+500	1000	"0.1"	2	0	"0.5 ( 0 )"	"0 ( 0 )"	"5.98 ( 0.02 )"	0.02	"0"	0.5	0	5.98
+1000	1000	"0.1"	1	NA	"0.5 ( 0 )"	"0 ( 0 )"	"6 ( 0 )"	0	"NaN"	0.5	0	6
+50	50	"0.3"	1	0.14	"0.86 ( 0.01 )"	"9.81 ( 1.05 )"	"1.78 ( 0.11 )"	14.03	"0.6"	0.86	9.81	1.78
+100	50	"0.3"	0.5	0.37	"0.83 ( 0.01 )"	"3.64 ( 0.75 )"	"1.7 ( 0.09 )"	7.94	"0.27"	0.83	3.64	1.7
+500	50	"0.3"	0.1	0.81	"0.81 ( 0 )"	"0.02 ( 0.01 )"	"1.75 ( 0.09 )"	4.27	"0"	0.81	0.02	1.75
+1000	50	"0.3"	0.05	0.81	"0.8 ( 0 )"	"0.06 ( 0.05 )"	"1.7 ( 0.09 )"	4.36	"0.01"	0.8	0.06	1.7
+50	100	"0.3"	2	0.1	"0.9 ( 0 )"	"16.32 ( 1.58 )"	"2.2 ( 0.11 )"	20.12	"0.74"	0.9	16.32	2.2
+100	100	"0.3"	1	0.31	"0.84 ( 0 )"	"5.71 ( 1.02 )"	"1.72 ( 0.1 )"	9.99	"0.41"	0.84	5.71	1.72
+500	100	"0.3"	0.2	0.8	"0.81 ( 0 )"	"0.12 ( 0.06 )"	"1.7 ( 0.09 )"	4.42	"0.01"	0.81	0.12	1.7
+1000	100	"0.3"	0.1	0.74	"0.8 ( 0 )"	"0.49 ( 0.26 )"	"2.05 ( 0.08 )"	4.44	"0.04"	0.8	0.49	2.05
+50	500	"0.3"	10	0.02	"1 ( 0 )"	"105.94 ( 7.57 )"	"2.04 ( 0.11 )"	109.9	"0.95"	1	105.94	2.04
+100	500	"0.3"	5	0.12	"0.88 ( 0.01 )"	"23.82 ( 3.36 )"	"1.9 ( 0.09 )"	27.92	"0.75"	0.88	23.82	1.9
+500	500	"0.3"	1	0.79	"0.8 ( 0 )"	"0.24 ( 0.08 )"	"1.86 ( 0.09 )"	4.38	"0.03"	0.8	0.24	1.86
+1000	500	"0.3"	0.5	0.73	"0.8 ( 0 )"	"0.63 ( 0.15 )"	"1.85 ( 0.09 )"	4.78	"0.08"	0.8	0.63	1.85
+50	1000	"0.3"	20	0.02	"1 ( 0 )"	"154.21 ( 5.09 )"	"2.09 ( 0.1 )"	158.12	"0.97"	1	154.21	2.09
+100	1000	"0.3"	10	0.07	"0.91 ( 0 )"	"36.7 ( 6.19 )"	"2.26 ( 0.1 )"	40.44	"0.84"	0.91	36.7	2.26
+500	1000	"0.3"	2	0.74	"0.81 ( 0 )"	"0.57 ( 0.15 )"	"1.66 ( 0.09 )"	4.91	"0.07"	0.81	0.57	1.66
+1000	1000	"0.3"	1	0.73	"0.8 ( 0 )"	"0.7 ( 0.15 )"	"1.84 ( 0.09 )"	4.86	"0.09"	0.8	0.7	1.84
+50	50	"0.5"	1	0.2	"0.92 ( 0 )"	"9.67 ( 0.64 )"	"1.26 ( 0.09 )"	14.41	"0.63"	0.92	9.67	1.26
+100	50	"0.5"	0.5	0.38	"0.89 ( 0 )"	"5.16 ( 0.81 )"	"0.81 ( 0.08 )"	10.35	"0.36"	0.89	5.16	0.81
+500	50	"0.5"	0.1	0.95	"0.87 ( 0 )"	"0.15 ( 0.1 )"	"0.15 ( 0.04 )"	6	"0.01"	0.87	0.15	0.15
+1000	50	"0.5"	0.05	0.96	"0.87 ( 0 )"	"0.13 ( 0.07 )"	"0.09 ( 0.03 )"	6.04	"0.01"	0.87	0.13	0.09
+50	100	"0.5"	2	0.09	"0.96 ( 0.01 )"	"25 ( 2.25 )"	"1.32 ( 0.09 )"	29.68	"0.77"	0.96	25	1.32
+100	100	"0.5"	1	0.31	"0.9 ( 0 )"	"7.65 ( 0.74 )"	"1.01 ( 0.08 )"	12.64	"0.51"	0.9	7.65	1.01
+500	100	"0.5"	0.2	0.93	"0.87 ( 0 )"	"0.14 ( 0.07 )"	"0.28 ( 0.05 )"	5.86	"0.02"	0.87	0.14	0.28
+1000	100	"0.5"	0.1	0.92	"0.87 ( 0 )"	"0.34 ( 0.17 )"	"0.12 ( 0.03 )"	6.22	"0.03"	0.87	0.34	0.12
+50	500	"0.5"	10	0.02	"1 ( 0 )"	"157.13 ( 6.78 )"	"1.35 ( 0.1 )"	161.78	"0.97"	1	157.13	1.35
+100	500	"0.5"	5	0.1	"0.95 ( 0 )"	"33.42 ( 3.83 )"	"1.3 ( 0.09 )"	38.12	"0.82"	0.95	33.42	1.3
+500	500	"0.5"	1	0.86	"0.87 ( 0 )"	"0.67 ( 0.16 )"	"0.3 ( 0.05 )"	6.37	"0.07"	0.87	0.67	0.3
+1000	500	"0.5"	0.5	0.98	"0.87 ( 0 )"	"0.03 ( 0.02 )"	"0.09 ( 0.03 )"	5.94	"0"	0.87	0.03	0.09
+50	1000	"0.5"	20	0.01	"1 ( 0 )"	"246.84 ( 6.66 )"	"1.53 ( 0.09 )"	251.31	"0.98"	1	246.84	1.53
+100	1000	"0.5"	10	0.04	"0.98 ( 0 )"	"96.92 ( 8.19 )"	"1.31 ( 0.09 )"	101.61	"0.92"	0.98	96.92	1.31
+500	1000	"0.5"	2	0.87	"0.87 ( 0 )"	"0.53 ( 0.2 )"	"0.35 ( 0.06 )"	6.18	"0.04"	0.87	0.53	0.35
+1000	1000	"0.5"	1	0.93	"0.87 ( 0 )"	"0.35 ( 0.12 )"	"0.11 ( 0.03 )"	6.24	"0.03"	0.87	0.35	0.11
+50	50	"0.7"	1	0.15	"0.95 ( 0 )"	"12.8 ( 0.96 )"	"1 ( 0.09 )"	17.8	"0.66"	0.95	12.8	1
+100	50	"0.7"	0.5	0.41	"0.91 ( 0 )"	"5.79 ( 0.7 )"	"0.31 ( 0.05 )"	11.48	"0.4"	0.91	5.79	0.31
+500	50	"0.7"	0.1	0.96	"0.9 ( 0 )"	"0.17 ( 0.08 )"	"0.04 ( 0.02 )"	6.13	"0.02"	0.9	0.17	0.04
+1000	50	"0.7"	0.05	0.98	"0.89 ( 0 )"	"0.1 ( 0.06 )"	"0 ( 0 )"	6.1	"0.01"	0.89	0.1	0
+50	100	"0.7"	2	0.06	"0.99 ( 0 )"	"35.59 ( 2.41 )"	"0.94 ( 0.09 )"	40.65	"0.83"	0.99	35.59	0.94
+100	100	"0.7"	1	0.26	"0.92 ( 0 )"	"11.64 ( 1.38 )"	"0.53 ( 0.06 )"	17.11	"0.57"	0.92	11.64	0.53
+500	100	"0.7"	0.2	0.97	"0.9 ( 0 )"	"0.15 ( 0.07 )"	"0.05 ( 0.02 )"	6.1	"0.02"	0.9	0.15	0.05
+1000	100	"0.7"	0.1	0.93	"0.9 ( 0 )"	"0.41 ( 0.14 )"	"0.03 ( 0.02 )"	6.38	"0.04"	0.9	0.41	0.03
+50	500	"0.7"	10	0.02	"1 ( 0 )"	"171.29 ( 6.32 )"	"1.12 ( 0.09 )"	176.17	"0.97"	1	171.29	1.12
+100	500	"0.7"	5	0.06	"0.98 ( 0 )"	"68.31 ( 6.57 )"	"0.98 ( 0.09 )"	73.33	"0.87"	0.98	68.31	0.98
+500	500	"0.7"	1	0.93	"0.89 ( 0 )"	"0.37 ( 0.12 )"	"0.06 ( 0.02 )"	6.31	"0.04"	0.89	0.37	0.06
+1000	500	"0.7"	0.5	0.98	"0.9 ( 0 )"	"0.13 ( 0.07 )"	"0.01 ( 0.01 )"	6.12	"0.01"	0.9	0.13	0.01
+50	1000	"0.7"	20	0.01	"1 ( 0 )"	"275.88 ( 8.89 )"	"1.39 ( 0.09 )"	280.49	"0.98"	1	275.88	1.39
+100	1000	"0.7"	10	0.03	"1 ( 0 )"	"155.38 ( 7.45 )"	"0.93 ( 0.08 )"	160.45	"0.95"	1	155.38	0.93
+500	1000	"0.7"	2	0.84	"0.9 ( 0 )"	"1.07 ( 0.26 )"	"0.1 ( 0.03 )"	6.97	"0.09"	0.9	1.07	0.1
+1000	1000	"0.7"	1	0.95	"0.9 ( 0 )"	"0.3 ( 0.11 )"	"0.02 ( 0.01 )"	6.28	"0.03"	0.9	0.3	0.02
+50	50	"0.9"	1	0.13	"0.97 ( 0 )"	"15.75 ( 1.04 )"	"0.71 ( 0.07 )"	21.04	"0.7"	0.97	15.75	0.71
+100	50	"0.9"	0.5	0.44	"0.93 ( 0 )"	"5.06 ( 0.38 )"	"0.34 ( 0.05 )"	10.72	"0.42"	0.93	5.06	0.34
+500	50	"0.9"	0.1	0.98	"0.91 ( 0 )"	"0.07 ( 0.04 )"	"0.03 ( 0.02 )"	6.04	"0.01"	0.91	0.07	0.03
+1000	50	"0.9"	0.05	1	"0.91 ( 0 )"	"0 ( 0 )"	"0 ( 0 )"	6	"0"	0.91	0	0
+50	100	"0.9"	2	0.06	"0.99 ( 0 )"	"37.69 ( 2.33 )"	"0.96 ( 0.09 )"	42.73	"0.84"	0.99	37.69	0.96
+100	100	"0.9"	1	0.26	"0.94 ( 0 )"	"11.53 ( 1.15 )"	"0.52 ( 0.06 )"	17.01	"0.61"	0.94	11.53	0.52
+500	100	"0.9"	0.2	0.96	"0.91 ( 0 )"	"0.18 ( 0.06 )"	"0.03 ( 0.02 )"	6.15	"0.02"	0.91	0.18	0.03
+1000	100	"0.9"	0.1	0.97	"0.91 ( 0 )"	"0.17 ( 0.09 )"	"0 ( 0 )"	6.17	"0.02"	0.91	0.17	0
+50	500	"0.9"	10	0.01	"1 ( 0 )"	"189.13 ( 6.24 )"	"0.91 ( 0.1 )"	194.22	"0.97"	1	189.13	0.91
+100	500	"0.9"	5	0.05	"0.99 ( 0 )"	"88.92 ( 5.92 )"	"0.68 ( 0.07 )"	94.24	"0.92"	0.99	88.92	0.68
+500	500	"0.9"	1	0.85	"0.91 ( 0 )"	"1.08 ( 0.33 )"	"0.01 ( 0.01 )"	7.07	"0.08"	0.91	1.08	0.01
+1000	500	"0.9"	0.5	0.98	"0.91 ( 0 )"	"0.14 ( 0.07 )"	"0 ( 0 )"	6.14	"0.02"	0.91	0.14	0
+50	1000	"0.9"	20	0.01	"1 ( 0 )"	"330.51 ( 8.59 )"	"0.97 ( 0.08 )"	335.54	"0.98"	1	330.51	0.97
+100	1000	"0.9"	10	0.02	"1 ( 0 )"	"217.74 ( 6.88 )"	"0.67 ( 0.07 )"	223.07	"0.97"	1	217.74	0.67
+500	1000	"0.9"	2	0.76	"0.91 ( 0 )"	"1.91 ( 0.48 )"	"0.02 ( 0.01 )"	7.89	"0.12"	0.91	1.91	0.02
+1000	1000	"0.9"	1	0.95	"0.91 ( 0 )"	"0.27 ( 0.11 )"	"0.02 ( 0.01 )"	6.25	"0.03"	0.91	0.27	0.02
diff --git a/simulations/results_summary_bin/sim_block_RF_binary.txt b/simulations/results_summary_bin/sim_block_RF_binary.txt
new file mode 100644
index 0000000..40a4b03
--- /dev/null
+++ b/simulations/results_summary_bin/sim_block_RF_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.1"	0.5	0.01	"1 ( 0 )"	"2.4 ( 0.16 )"	"5.3 ( 0.11 )"	3	"0.78"	1	2.4	5.3
+500	50	"0.1"	0.1	0.12	"1 ( 0 )"	"2.96 ( 0.21 )"	"4.29 ( 0.15 )"	4.63	"0.62"	1	2.96	4.29
+1000	50	"0.1"	0.05	0.33	"1 ( 0 )"	"3.28 ( 0.21 )"	"3.04 ( 0.12 )"	6.24	"0.49"	1	3.28	3.04
+50	100	"0.1"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.1"	1	0	"1 ( 0 )"	"5.09 ( 0.21 )"	"5.6 ( 0.09 )"	5.48	"0.93"	1	5.09	5.6
+500	100	"0.1"	0.2	0.1	"1 ( 0 )"	"5.39 ( 0.29 )"	"4.05 ( 0.16 )"	7.34	"0.72"	1	5.39	4.05
+1000	100	"0.1"	0.1	0.25	"1 ( 0 )"	"5.43 ( 0.26 )"	"2.89 ( 0.14 )"	8.54	"0.62"	1	5.43	2.89
+50	500	"0.1"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.1"	5	0	"1 ( 0 )"	"24.14 ( 0.56 )"	"5.35 ( 0.11 )"	24.79	"0.97"	1	24.14	5.35
+500	500	"0.1"	1	0.03	"1 ( 0 )"	"24.48 ( 0.54 )"	"4.25 ( 0.16 )"	26.23	"0.93"	1	24.48	4.25
+1000	500	"0.1"	0.5	0.07	"1 ( 0 )"	"24.08 ( 0.54 )"	"3.1 ( 0.16 )"	26.98	"0.89"	1	24.08	3.1
+50	1000	"0.1"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.1"	10	0	"1 ( 0 )"	"48.22 ( 0.62 )"	"5.53 ( 0.09 )"	48.69	"0.99"	1	48.22	5.53
+500	1000	"0.1"	2	0.01	"1 ( 0 )"	"50.18 ( 0.78 )"	"4.63 ( 0.12 )"	51.55	"0.97"	1	50.18	4.63
+1000	1000	"0.1"	1	0.04	"1 ( 0 )"	"49.43 ( 0.66 )"	"3.23 ( 0.14 )"	52.2	"0.95"	1	49.43	3.23
+50	50	"0.3"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.3"	0.5	0.02	"1 ( 0 )"	"2.13 ( 0.12 )"	"5.14 ( 0.08 )"	2.96	"0.71"	1	2.13	5.14
+500	50	"0.3"	0.1	0.46	"1 ( 0 )"	"1.43 ( 0.11 )"	"2.59 ( 0.11 )"	4.84	"0.27"	1	1.43	2.59
+1000	50	"0.3"	0.05	0.6	"1 ( 0 )"	"1.44 ( 0.13 )"	"1.35 ( 0.08 )"	6.09	"0.21"	1	1.44	1.35
+50	100	"0.3"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.3"	1	0.01	"1 ( 0 )"	"4.84 ( 0.18 )"	"5.1 ( 0.08 )"	5.73	"0.84"	1	4.84	5.1
+500	100	"0.3"	0.2	0.28	"1 ( 0 )"	"3.77 ( 0.17 )"	"2.76 ( 0.1 )"	7.01	"0.52"	1	3.77	2.76
+1000	100	"0.3"	0.1	0.44	"1 ( 0 )"	"3.43 ( 0.18 )"	"1.5 ( 0.09 )"	7.93	"0.41"	1	3.43	1.5
+50	500	"0.3"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.3"	5	0	"1 ( 0 )"	"24.49 ( 0.47 )"	"5.11 ( 0.08 )"	25.38	"0.96"	1	24.49	5.11
+500	500	"0.3"	1	0.06	"1 ( 0 )"	"23.05 ( 0.49 )"	"3.15 ( 0.09 )"	25.9	"0.89"	1	23.05	3.15
+1000	500	"0.3"	0.5	0.11	"1 ( 0 )"	"22.18 ( 0.49 )"	"1.91 ( 0.09 )"	26.27	"0.84"	1	22.18	1.91
+50	1000	"0.3"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.3"	10	0	"1 ( 0 )"	"46.96 ( 0.66 )"	"5.18 ( 0.08 )"	47.78	"0.98"	1	46.96	5.18
+500	1000	"0.3"	2	0.02	"1 ( 0 )"	"48.39 ( 0.72 )"	"3.64 ( 0.1 )"	50.75	"0.95"	1	48.39	3.64
+1000	1000	"0.3"	1	0.06	"1 ( 0 )"	"47.08 ( 0.59 )"	"2.05 ( 0.09 )"	51.03	"0.92"	1	47.08	2.05
+50	50	"0.5"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.5"	0.5	0.04	"1 ( 0 )"	"2.2 ( 0.13 )"	"5.06 ( 0.08 )"	3.12	"0.67"	1	2.2	5.06
+500	50	"0.5"	0.1	0.49	"1 ( 0 )"	"1.41 ( 0.12 )"	"2.45 ( 0.08 )"	4.96	"0.25"	1	1.41	2.45
+1000	50	"0.5"	0.05	0.61	"1 ( 0 )"	"1.24 ( 0.11 )"	"1.46 ( 0.09 )"	5.78	"0.19"	1	1.24	1.46
+50	100	"0.5"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.5"	1	0.02	"1 ( 0 )"	"4.57 ( 0.17 )"	"4.99 ( 0.08 )"	5.58	"0.82"	1	4.57	4.99
+500	100	"0.5"	0.2	0.35	"1 ( 0 )"	"3.49 ( 0.17 )"	"2.37 ( 0.09 )"	7.12	"0.46"	1	3.49	2.37
+1000	100	"0.5"	0.1	0.51	"1 ( 0 )"	"2.74 ( 0.15 )"	"1.23 ( 0.08 )"	7.51	"0.34"	1	2.74	1.23
+50	500	"0.5"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.5"	5	0.01	"1 ( 0 )"	"23.18 ( 0.51 )"	"5.07 ( 0.08 )"	24.11	"0.96"	1	23.18	5.07
+500	500	"0.5"	1	0.08	"1 ( 0 )"	"23.59 ( 0.47 )"	"2.72 ( 0.09 )"	26.87	"0.88"	1	23.59	2.72
+1000	500	"0.5"	0.5	0.13	"1 ( 0 )"	"22.56 ( 0.46 )"	"1.56 ( 0.1 )"	27	"0.83"	1	22.56	1.56
+50	1000	"0.5"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.5"	10	0	"1 ( 0 )"	"47.59 ( 0.62 )"	"5.2 ( 0.08 )"	48.39	"0.98"	1	47.59	5.2
+500	1000	"0.5"	2	0.03	"1 ( 0 )"	"47.63 ( 0.72 )"	"3.12 ( 0.11 )"	50.51	"0.94"	1	47.63	3.12
+1000	1000	"0.5"	1	0.06	"1 ( 0 )"	"48.12 ( 0.7 )"	"1.83 ( 0.08 )"	52.29	"0.92"	1	48.12	1.83
+50	50	"0.7"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.7"	0.5	0.05	"1 ( 0 )"	"2.01 ( 0.13 )"	"5 ( 0.08 )"	2.99	"0.65"	1	2.01	5
+500	50	"0.7"	0.1	0.53	"1 ( 0 )"	"1.29 ( 0.11 )"	"2.25 ( 0.08 )"	5.04	"0.23"	1	1.29	2.25
+1000	50	"0.7"	0.05	0.63	"1 ( 0 )"	"1.29 ( 0.11 )"	"1.3 ( 0.08 )"	5.99	"0.19"	1	1.29	1.3
+50	100	"0.7"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.7"	1	0.03	"1 ( 0 )"	"4.35 ( 0.18 )"	"4.98 ( 0.07 )"	5.37	"0.8"	1	4.35	4.98
+500	100	"0.7"	0.2	0.34	"1 ( 0 )"	"3.46 ( 0.18 )"	"2.53 ( 0.09 )"	6.93	"0.47"	1	3.46	2.53
+1000	100	"0.7"	0.1	0.48	"1 ( 0 )"	"3.04 ( 0.15 )"	"1.27 ( 0.08 )"	7.77	"0.37"	1	3.04	1.27
+50	500	"0.7"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.7"	5	0.01	"1 ( 0 )"	"23.78 ( 0.44 )"	"4.93 ( 0.08 )"	24.85	"0.96"	1	23.78	4.93
+500	500	"0.7"	1	0.08	"1 ( 0 )"	"23.14 ( 0.52 )"	"2.73 ( 0.09 )"	26.41	"0.87"	1	23.14	2.73
+1000	500	"0.7"	0.5	0.13	"1 ( 0 )"	"21.58 ( 0.49 )"	"1.57 ( 0.08 )"	26.01	"0.82"	1	21.58	1.57
+50	1000	"0.7"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.7"	10	0	"1 ( 0 )"	"47.84 ( 0.72 )"	"5 ( 0.09 )"	48.84	"0.98"	1	47.84	5
+500	1000	"0.7"	2	0.04	"1 ( 0 )"	"47.31 ( 0.68 )"	"2.9 ( 0.1 )"	50.41	"0.94"	1	47.31	2.9
+1000	1000	"0.7"	1	0.06	"1 ( 0 )"	"46.57 ( 0.73 )"	"1.71 ( 0.08 )"	50.86	"0.91"	1	46.57	1.71
+50	50	"0.9"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.9"	0.5	0.05	"1 ( 0 )"	"2.2 ( 0.13 )"	"5 ( 0.08 )"	3.19	"0.66"	1	2.2	5
+500	50	"0.9"	0.1	0.53	"1 ( 0 )"	"1.28 ( 0.1 )"	"2.32 ( 0.08 )"	4.96	"0.23"	1	1.28	2.32
+1000	50	"0.9"	0.05	0.67	"1 ( 0 )"	"0.93 ( 0.09 )"	"1.31 ( 0.09 )"	5.62	"0.15"	1	0.93	1.31
+50	100	"0.9"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.9"	1	0.04	"1 ( 0 )"	"4.24 ( 0.16 )"	"4.83 ( 0.09 )"	5.4	"0.79"	1	4.24	4.83
+500	100	"0.9"	0.2	0.36	"1 ( 0 )"	"3.54 ( 0.19 )"	"2.29 ( 0.09 )"	7.25	"0.46"	1	3.54	2.29
+1000	100	"0.9"	0.1	0.53	"1 ( 0 )"	"2.53 ( 0.14 )"	"1.31 ( 0.08 )"	7.22	"0.33"	1	2.53	1.31
+50	500	"0.9"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.9"	5	0.01	"1 ( 0 )"	"23.63 ( 0.45 )"	"5.04 ( 0.08 )"	24.59	"0.96"	1	23.63	5.04
+500	500	"0.9"	1	0.07	"1 ( 0 )"	"24.29 ( 0.42 )"	"2.65 ( 0.09 )"	27.64	"0.88"	1	24.29	2.65
+1000	500	"0.9"	0.5	0.13	"1 ( 0 )"	"21.42 ( 0.42 )"	"1.49 ( 0.08 )"	25.93	"0.82"	1	21.42	1.49
+50	1000	"0.9"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.9"	10	0	"1 ( 0 )"	"47.48 ( 0.72 )"	"4.95 ( 0.08 )"	48.53	"0.98"	1	47.48	4.95
+500	1000	"0.9"	2	0.04	"1 ( 0 )"	"48.09 ( 0.7 )"	"2.94 ( 0.09 )"	51.15	"0.94"	1	48.09	2.94
+1000	1000	"0.9"	1	0.07	"1 ( 0 )"	"45.6 ( 0.72 )"	"1.63 ( 0.08 )"	49.97	"0.91"	1	45.6	1.63
diff --git a/simulations/results_summary_bin/sim_block_lasso_binary.txt b/simulations/results_summary_bin/sim_block_lasso_binary.txt
new file mode 100644
index 0000000..505ffb4
--- /dev/null
+++ b/simulations/results_summary_bin/sim_block_lasso_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.03	"0.59 ( 0.01 )"	"2.69 ( 0.14 )"	"5.3 ( 0.07 )"	2.39	"0.67"	0.59	2.69	5.3
+100	50	"0.1"	0.5	0.11	"0.56 ( 0.01 )"	"2.02 ( 0.15 )"	"5.02 ( 0.08 )"	2	"0.39"	0.56	2.02	5.02
+500	50	"0.1"	0.1	0.28	"0.63 ( 0 )"	"4.52 ( 0.29 )"	"3 ( 0.11 )"	6.52	"0.45"	0.63	4.52	3
+1000	50	"0.1"	0.05	0.39	"0.64 ( 0 )"	"4.74 ( 0.22 )"	"2.1 ( 0.09 )"	7.64	"0.44"	0.64	4.74	2.1
+50	100	"0.1"	2	0.02	"0.62 ( 0.01 )"	"3.82 ( 0.16 )"	"5.34 ( 0.07 )"	3.48	"0.78"	0.62	3.82	5.34
+100	100	"0.1"	1	0.07	"0.56 ( 0.01 )"	"2.29 ( 0.19 )"	"5.27 ( 0.08 )"	2.02	"0.57"	0.56	2.29	5.27
+500	100	"0.1"	0.2	0.23	"0.61 ( 0 )"	"5.54 ( 0.52 )"	"3.27 ( 0.12 )"	7.27	"0.47"	0.61	5.54	3.27
+1000	100	"0.1"	0.1	0.27	"0.64 ( 0 )"	"7.69 ( 0.42 )"	"2.25 ( 0.1 )"	10.44	"0.56"	0.64	7.69	2.25
+50	500	"0.1"	10	0.01	"0.6 ( 0.01 )"	"8.69 ( 0.24 )"	"5.55 ( 0.06 )"	8.14	"0.94"	0.6	8.69	5.55
+100	500	"0.1"	5	0.03	"0.56 ( 0.01 )"	"4.39 ( 0.34 )"	"5.4 ( 0.07 )"	3.99	"0.82"	0.56	4.39	5.4
+500	500	"0.1"	1	0.27	"0.61 ( 0.01 )"	"3.84 ( 0.28 )"	"3.93 ( 0.08 )"	4.91	"0.47"	0.61	3.84	3.93
+1000	500	"0.1"	0.5	0.2	"0.63 ( 0 )"	"9.87 ( 1.4 )"	"3.18 ( 0.09 )"	11.69	"0.49"	0.63	9.87	3.18
+50	1000	"0.1"	20	0	"0.62 ( 0.01 )"	"10.42 ( 0.22 )"	"5.56 ( 0.06 )"	9.86	"0.95"	0.62	10.42	5.56
+100	1000	"0.1"	10	0.01	"0.56 ( 0.01 )"	"5.93 ( 0.28 )"	"5.53 ( 0.06 )"	5.4	"0.9"	0.56	5.93	5.53
+500	1000	"0.1"	2	0.24	"0.62 ( 0.01 )"	"4.22 ( 0.48 )"	"4.05 ( 0.09 )"	5.17	"0.46"	0.62	4.22	4.05
+1000	1000	"0.1"	1	0.33	"0.63 ( 0 )"	"5.03 ( 0.78 )"	"3.35 ( 0.06 )"	6.68	"0.42"	0.63	5.03	3.35
+50	50	"0.3"	1	0.17	"0.65 ( 0.01 )"	"3.87 ( 0.23 )"	"3.68 ( 0.12 )"	5.19	"0.5"	0.65	3.87	3.68
+100	50	"0.3"	0.5	0.32	"0.78 ( 0.01 )"	"6.48 ( 0.32 )"	"1.13 ( 0.11 )"	10.35	"0.49"	0.78	6.48	1.13
+500	50	"0.3"	0.1	0.44	"0.85 ( 0 )"	"6.84 ( 0.26 )"	"0 ( 0 )"	11.84	"0.47"	0.85	6.84	0
+1000	50	"0.3"	0.05	0.65	"0.86 ( 0 )"	"3.69 ( 0.19 )"	"0 ( 0 )"	8.69	"0.28"	0.86	3.69	0
+50	100	"0.3"	2	0.14	"0.63 ( 0.01 )"	"5.12 ( 0.25 )"	"3.91 ( 0.1 )"	6.21	"0.63"	0.63	5.12	3.91
+100	100	"0.3"	1	0.26	"0.74 ( 0.01 )"	"7.62 ( 0.48 )"	"1.8 ( 0.12 )"	10.82	"0.53"	0.74	7.62	1.8
+500	100	"0.3"	0.2	0.36	"0.86 ( 0 )"	"10.19 ( 0.4 )"	"0 ( 0 )"	15.19	"0.57"	0.86	10.19	0
+1000	100	"0.3"	0.1	0.52	"0.86 ( 0 )"	"6 ( 0.27 )"	"0 ( 0 )"	11	"0.42"	0.86	6	0
+50	500	"0.3"	10	0.03	"0.61 ( 0.01 )"	"10.46 ( 0.31 )"	"4.69 ( 0.08 )"	10.77	"0.87"	0.61	10.46	4.69
+100	500	"0.3"	5	0.17	"0.66 ( 0.01 )"	"8.6 ( 0.65 )"	"3.19 ( 0.12 )"	10.41	"0.64"	0.66	8.6	3.19
+500	500	"0.3"	1	0.19	"0.85 ( 0 )"	"25.12 ( 1.08 )"	"0 ( 0 )"	30.12	"0.77"	0.85	25.12	0
+1000	500	"0.3"	0.5	0.25	"0.86 ( 0 )"	"18.48 ( 0.73 )"	"0 ( 0 )"	23.48	"0.72"	0.86	18.48	0
+50	1000	"0.3"	20	0.02	"0.6 ( 0.01 )"	"12.08 ( 0.26 )"	"4.97 ( 0.08 )"	12.11	"0.91"	0.6	12.08	4.97
+100	1000	"0.3"	10	0.13	"0.65 ( 0.01 )"	"9 ( 0.66 )"	"3.58 ( 0.12 )"	10.42	"0.72"	0.65	9	3.58
+500	1000	"0.3"	2	0.16	"0.85 ( 0 )"	"31.41 ( 1.51 )"	"0 ( 0 )"	36.41	"0.8"	0.85	31.41	0
+1000	1000	"0.3"	1	0.17	"0.86 ( 0 )"	"28.41 ( 1.2 )"	"0 ( 0 )"	33.41	"0.79"	0.86	28.41	0
+50	50	"0.5"	1	0.24	"0.69 ( 0.01 )"	"4.53 ( 0.29 )"	"2.89 ( 0.13 )"	6.64	"0.48"	0.69	4.53	2.89
+100	50	"0.5"	0.5	0.32	"0.82 ( 0.01 )"	"7.11 ( 0.4 )"	"0.91 ( 0.09 )"	11.2	"0.48"	0.82	7.11	0.91
+500	50	"0.5"	0.1	0.5	"0.89 ( 0 )"	"5.76 ( 0.24 )"	"0 ( 0 )"	10.76	"0.41"	0.89	5.76	0
+1000	50	"0.5"	0.05	0.68	"0.9 ( 0 )"	"3.3 ( 0.16 )"	"0 ( 0 )"	8.3	"0.25"	0.9	3.3	0
+50	100	"0.5"	2	0.19	"0.68 ( 0.01 )"	"5.12 ( 0.25 )"	"3.35 ( 0.1 )"	6.77	"0.57"	0.68	5.12	3.35
+100	100	"0.5"	1	0.29	"0.8 ( 0.01 )"	"8.11 ( 0.47 )"	"1.32 ( 0.1 )"	11.79	"0.54"	0.8	8.11	1.32
+500	100	"0.5"	0.2	0.34	"0.89 ( 0 )"	"10.67 ( 0.37 )"	"0 ( 0 )"	15.67	"0.59"	0.89	10.67	0
+1000	100	"0.5"	0.1	0.55	"0.9 ( 0 )"	"5.3 ( 0.23 )"	"0 ( 0 )"	10.3	"0.38"	0.9	5.3	0
+50	500	"0.5"	10	0.06	"0.63 ( 0.01 )"	"9.49 ( 0.26 )"	"4.45 ( 0.08 )"	10.04	"0.84"	0.63	9.49	4.45
+100	500	"0.5"	5	0.2	"0.72 ( 0.01 )"	"9.62 ( 0.69 )"	"2.61 ( 0.12 )"	12.01	"0.63"	0.72	9.62	2.61
+500	500	"0.5"	1	0.18	"0.89 ( 0 )"	"27.04 ( 0.99 )"	"0 ( 0 )"	32.04	"0.79"	0.89	27.04	0
+1000	500	"0.5"	0.5	0.25	"0.9 ( 0 )"	"17.99 ( 0.68 )"	"0 ( 0 )"	22.99	"0.71"	0.9	17.99	0
+50	1000	"0.5"	20	0.05	"0.63 ( 0.01 )"	"11.31 ( 0.3 )"	"4.54 ( 0.09 )"	11.77	"0.87"	0.63	11.31	4.54
+100	1000	"0.5"	10	0.16	"0.7 ( 0.01 )"	"10.35 ( 0.82 )"	"3.02 ( 0.12 )"	12.33	"0.68"	0.7	10.35	3.02
+500	1000	"0.5"	2	0.15	"0.88 ( 0 )"	"34.48 ( 1.42 )"	"0 ( 0 )"	39.48	"0.82"	0.88	34.48	0
+1000	1000	"0.5"	1	0.19	"0.89 ( 0 )"	"25.24 ( 1.1 )"	"0 ( 0 )"	30.24	"0.77"	0.89	25.24	0
+50	50	"0.7"	1	0.27	"0.72 ( 0.01 )"	"3.87 ( 0.24 )"	"2.98 ( 0.11 )"	5.89	"0.42"	0.72	3.87	2.98
+100	50	"0.7"	0.5	0.34	"0.82 ( 0.01 )"	"7.43 ( 0.37 )"	"0.6 ( 0.08 )"	11.83	"0.49"	0.82	7.43	0.6
+500	50	"0.7"	0.1	0.49	"0.91 ( 0 )"	"5.84 ( 0.26 )"	"0 ( 0 )"	10.84	"0.42"	0.91	5.84	0
+1000	50	"0.7"	0.05	0.71	"0.92 ( 0 )"	"3.03 ( 0.15 )"	"0 ( 0 )"	8.03	"0.23"	0.92	3.03	0
+50	100	"0.7"	2	0.21	"0.69 ( 0.01 )"	"5.07 ( 0.25 )"	"3.13 ( 0.1 )"	6.94	"0.55"	0.69	5.07	3.13
+100	100	"0.7"	1	0.28	"0.82 ( 0.01 )"	"9.65 ( 0.48 )"	"0.97 ( 0.1 )"	13.68	"0.59"	0.82	9.65	0.97
+500	100	"0.7"	0.2	0.36	"0.91 ( 0 )"	"10.19 ( 0.37 )"	"0 ( 0 )"	15.19	"0.58"	0.91	10.19	0
+1000	100	"0.7"	0.1	0.59	"0.92 ( 0 )"	"4.81 ( 0.19 )"	"0 ( 0 )"	9.81	"0.37"	0.92	4.81	0
+50	500	"0.7"	10	0.09	"0.66 ( 0.01 )"	"9.13 ( 0.25 )"	"4.14 ( 0.08 )"	9.99	"0.81"	0.66	9.13	4.14
+100	500	"0.7"	5	0.21	"0.75 ( 0.01 )"	"9.59 ( 0.72 )"	"2.44 ( 0.11 )"	12.15	"0.61"	0.75	9.59	2.44
+500	500	"0.7"	1	0.18	"0.9 ( 0 )"	"26.2 ( 0.86 )"	"0 ( 0 )"	31.2	"0.79"	0.9	26.2	0
+1000	500	"0.7"	0.5	0.28	"0.91 ( 0 )"	"15.6 ( 0.55 )"	"0 ( 0 )"	20.6	"0.68"	0.91	15.6	0
+50	1000	"0.7"	20	0.05	"0.64 ( 0.01 )"	"11.44 ( 0.28 )"	"4.51 ( 0.08 )"	11.93	"0.87"	0.64	11.44	4.51
+100	1000	"0.7"	10	0.18	"0.71 ( 0.01 )"	"10.03 ( 0.71 )"	"2.95 ( 0.1 )"	12.08	"0.65"	0.71	10.03	2.95
+500	1000	"0.7"	2	0.14	"0.9 ( 0 )"	"34.85 ( 1.14 )"	"0 ( 0 )"	39.85	"0.83"	0.9	34.85	0
+1000	1000	"0.7"	1	0.2	"0.91 ( 0 )"	"24.83 ( 0.82 )"	"0 ( 0 )"	29.83	"0.78"	0.91	24.83	0
+50	50	"0.9"	1	0.3	"0.73 ( 0.01 )"	"4.4 ( 0.26 )"	"2.55 ( 0.11 )"	6.85	"0.44"	0.73	4.4	2.55
+100	50	"0.9"	0.5	0.33	"0.85 ( 0.01 )"	"8.14 ( 0.35 )"	"0.42 ( 0.06 )"	12.72	"0.53"	0.85	8.14	0.42
+500	50	"0.9"	0.1	0.54	"0.92 ( 0 )"	"5.15 ( 0.22 )"	"0 ( 0 )"	10.15	"0.38"	0.92	5.15	0
+1000	50	"0.9"	0.05	0.76	"0.92 ( 0 )"	"2.61 ( 0.13 )"	"0 ( 0 )"	7.61	"0.19"	0.92	2.61	0
+50	100	"0.9"	2	0.23	"0.7 ( 0.01 )"	"4.84 ( 0.24 )"	"3.3 ( 0.09 )"	6.54	"0.54"	0.7	4.84	3.3
+100	100	"0.9"	1	0.28	"0.81 ( 0.01 )"	"9.92 ( 0.51 )"	"0.87 ( 0.08 )"	14.05	"0.57"	0.81	9.92	0.87
+500	100	"0.9"	0.2	0.37	"0.92 ( 0 )"	"9.65 ( 0.35 )"	"0 ( 0 )"	14.65	"0.57"	0.92	9.65	0
+1000	100	"0.9"	0.1	0.62	"0.93 ( 0 )"	"4.29 ( 0.18 )"	"0 ( 0 )"	9.29	"0.33"	0.93	4.29	0
+50	500	"0.9"	10	0.08	"0.62 ( 0.01 )"	"8.99 ( 0.26 )"	"4.13 ( 0.09 )"	9.86	"0.8"	0.62	8.99	4.13
+100	500	"0.9"	5	0.23	"0.74 ( 0.01 )"	"8.85 ( 0.62 )"	"2.4 ( 0.12 )"	11.45	"0.6"	0.74	8.85	2.4
+500	500	"0.9"	1	0.19	"0.91 ( 0 )"	"24.91 ( 0.78 )"	"0 ( 0 )"	29.91	"0.78"	0.91	24.91	0
+1000	500	"0.9"	0.5	0.31	"0.92 ( 0 )"	"14.12 ( 0.43 )"	"0 ( 0 )"	19.12	"0.67"	0.92	14.12	0
+50	1000	"0.9"	20	0.07	"0.64 ( 0.01 )"	"10.81 ( 0.29 )"	"4.22 ( 0.08 )"	11.59	"0.84"	0.64	10.81	4.22
+100	1000	"0.9"	10	0.19	"0.74 ( 0.01 )"	"11.15 ( 0.75 )"	"2.44 ( 0.11 )"	13.71	"0.65"	0.74	11.15	2.44
+500	1000	"0.9"	2	0.14	"0.91 ( 0 )"	"34.98 ( 1.04 )"	"0 ( 0 )"	39.98	"0.83"	0.91	34.98	0
+1000	1000	"0.9"	1	0.22	"0.92 ( 0 )"	"22.21 ( 0.73 )"	"0 ( 0 )"	27.21	"0.76"	0.92	22.21	0
diff --git a/simulations/results_summary_bin/sim_ind_Elnet_binary.txt b/simulations/results_summary_bin/sim_ind_Elnet_binary.txt
new file mode 100644
index 0000000..3b36f18
--- /dev/null
+++ b/simulations/results_summary_bin/sim_ind_Elnet_binary.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0"	1	0.15	"0.76 ( 0.01 )"	"12.04 ( 0.89 )"	"1.42 ( 0.11 )"	15.62	"0.61"	0.76	12.04	1.42
+100	50	"0"	0.5	0.23	"0.84 ( 0.01 )"	"11.44 ( 0.76 )"	"0.46 ( 0.07 )"	15.98	"0.56"	0.84	11.44	0.46
+500	50	"0"	0.1	0.36	"0.92 ( 0 )"	"8.74 ( 0.72 )"	"0 ( 0 )"	13.74	"0.45"	0.92	8.74	0
+1000	50	"0"	0.05	0.41	"0.93 ( 0 )"	"7.56 ( 0.72 )"	"0 ( 0 )"	12.56	"0.4"	0.93	7.56	0
+50	100	"0"	2	0.1	"0.72 ( 0.01 )"	"18.92 ( 1.7 )"	"1.81 ( 0.12 )"	22.11	"0.72"	0.72	18.92	1.81
+100	100	"0"	1	0.22	"0.82 ( 0.01 )"	"12.48 ( 0.9 )"	"0.96 ( 0.09 )"	16.52	"0.58"	0.82	12.48	0.96
+500	100	"0"	0.2	0.32	"0.92 ( 0 )"	"11.78 ( 0.99 )"	"0 ( 0 )"	16.78	"0.52"	0.92	11.78	0
+1000	100	"0"	0.1	0.34	"0.93 ( 0 )"	"10.71 ( 1.03 )"	"0 ( 0 )"	15.71	"0.47"	0.93	10.71	0
+50	500	"0"	10	0.03	"0.68 ( 0.01 )"	"57.17 ( 5.72 )"	"2.27 ( 0.13 )"	59.9	"0.88"	0.68	57.17	2.27
+100	500	"0"	5	0.15	"0.77 ( 0.01 )"	"20.84 ( 2.08 )"	"1.49 ( 0.1 )"	24.35	"0.69"	0.77	20.84	1.49
+500	500	"0"	1	0.27	"0.92 ( 0 )"	"16.79 ( 1.47 )"	"0 ( 0 )"	21.79	"0.61"	0.92	16.79	0
+1000	500	"0"	0.5	0.36	"0.93 ( 0 )"	"11.47 ( 1.14 )"	"0 ( 0 )"	16.47	"0.48"	0.93	11.47	0
+50	1000	"0"	20	0.02	"0.67 ( 0.01 )"	"72.88 ( 8.55 )"	"2.89 ( 0.13 )"	74.99	"0.91"	0.67	72.88	2.89
+100	1000	"0"	10	0.09	"0.76 ( 0.01 )"	"29.95 ( 4.05 )"	"2 ( 0.1 )"	32.95	"0.77"	0.76	29.95	2
+500	1000	"0"	2	0.24	"0.91 ( 0 )"	"19.47 ( 1.76 )"	"0 ( 0 )"	24.47	"0.62"	0.91	19.47	0
+1000	1000	"0"	1	0.25	"0.93 ( 0 )"	"18.47 ( 1.76 )"	"0 ( 0 )"	23.47	"0.58"	0.93	18.47	0
diff --git a/simulations/results_summary_bin/sim_ind_GencompLasso_binary.txt b/simulations/results_summary_bin/sim_ind_GencompLasso_binary.txt
new file mode 100644
index 0000000..8ba39e0
--- /dev/null
+++ b/simulations/results_summary_bin/sim_ind_GencompLasso_binary.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0"	1	0.13	"0.98 ( 0 )"	"16.38 ( 0.96 )"	"0.61 ( 0.06 )"	21.77	"0.71"	0.98	16.38	0.61
+100	50	"0"	0.5	0.32	"0.93 ( 0 )"	"7.94 ( 0.54 )"	"0.34 ( 0.06 )"	13.6	"0.54"	0.93	7.94	0.34
+500	50	"0"	0.1	0.63	"0.91 ( 0 )"	"2.67 ( 0.6 )"	"0.1 ( 0.03 )"	8.57	"0.19"	0.91	2.67	0.1
+1000	50	"0"	0.05	0.75	"0.91 ( 0 )"	"1.61 ( 0.48 )"	"0.04 ( 0.02 )"	7.57	"0.09"	0.91	1.61	0.04
+50	100	"0"	2	0.06	"0.99 ( 0 )"	"39.18 ( 2.14 )"	"0.63 ( 0.07 )"	44.55	"0.85"	0.99	39.18	0.63
+100	100	"0"	1	0.2	"0.94 ( 0 )"	"16.29 ( 1.16 )"	"0.47 ( 0.06 )"	21.82	"0.7"	0.94	16.29	0.47
+500	100	"0"	0.2	0.47	"0.91 ( 0 )"	"5.63 ( 0.84 )"	"0.13 ( 0.03 )"	11.5	"0.3"	0.91	5.63	0.13
+1000	100	"0"	0.1	0.43	"0.91 ( 0 )"	"6.87 ( 1.8 )"	"0 ( 0 )"	12.87	"0.17"	0.91	6.87	0
+50	500	"0"	10	0.01	"1 ( 0 )"	"251.02 ( 6.8 )"	"0.36 ( 0.06 )"	256.66	"0.98"	1	251.02	0.36
+100	500	"0"	5	0.03	"1 ( 0 )"	"134.32 ( 6.63 )"	"0.33 ( 0.05 )"	139.99	"0.95"	1	134.32	0.33
+500	500	"0"	1	0.43	"0.91 ( 0 )"	"8.32 ( 1.15 )"	"0.1 ( 0.03 )"	14.22	"0.41"	0.91	8.32	0.1
+1000	500	"0"	0.5	0.6	"0.91 ( 0 )"	"4.63 ( 1.57 )"	"0 ( 0 )"	10.63	"0.21"	0.91	4.63	0
+50	1000	"0"	20	0.01	"1 ( 0 )"	"406.32 ( 7.33 )"	"0.73 ( 0.07 )"	411.59	"0.99"	1	406.32	0.73
+100	1000	"0"	10	0.02	"1 ( 0 )"	"283.24 ( 7.31 )"	"0.49 ( 0.06 )"	288.75	"0.98"	1	283.24	0.49
+500	1000	"0"	2	0.4	"0.91 ( 0 )"	"9.4 ( 1.07 )"	"0.07 ( 0.03 )"	15.33	"0.47"	0.91	9.4	0.07
+1000	1000	"0"	1	0.82	"0.91 ( 0 )"	"1.91 ( 0.29 )"	"0.01 ( 0.01 )"	7.9	"0.18"	0.91	1.91	0.01
diff --git a/simulations/results_summary_bin/sim_ind_RF_binary.txt b/simulations/results_summary_bin/sim_ind_RF_binary.txt
new file mode 100644
index 0000000..d18c003
--- /dev/null
+++ b/simulations/results_summary_bin/sim_ind_RF_binary.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0"	0.5	0.1	"1 ( 0 )"	"1.97 ( 0.13 )"	"4.62 ( 0.08 )"	3.34	"0.55"	1	1.97	4.62
+500	50	"0"	0.1	0.54	"1 ( 0 )"	"1.26 ( 0.12 )"	"2.19 ( 0.08 )"	5.07	"0.22"	1	1.26	2.19
+1000	50	"0"	0.05	0.73	"1 ( 0 )"	"0.86 ( 0.09 )"	"1.12 ( 0.08 )"	5.74	"0.13"	1	0.86	1.12
+50	100	"0"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0"	1	0.05	"1 ( 0 )"	"4.29 ( 0.17 )"	"4.79 ( 0.09 )"	5.5	"0.77"	1	4.29	4.79
+500	100	"0"	0.2	0.4	"1 ( 0 )"	"3.42 ( 0.16 )"	"2.01 ( 0.09 )"	7.41	"0.44"	1	3.42	2.01
+1000	100	"0"	0.1	0.49	"1 ( 0 )"	"3.39 ( 0.19 )"	"1.14 ( 0.07 )"	8.25	"0.38"	1	3.39	1.14
+50	500	"0"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0"	5	0.01	"1 ( 0 )"	"24.68 ( 0.48 )"	"4.5 ( 0.09 )"	26.18	"0.94"	1	24.68	4.5
+500	500	"0"	1	0.09	"1 ( 0 )"	"23.08 ( 0.51 )"	"2.34 ( 0.1 )"	26.74	"0.86"	1	23.08	2.34
+1000	500	"0"	0.5	0.14	"1 ( 0 )"	"22.72 ( 0.46 )"	"1.34 ( 0.08 )"	27.38	"0.83"	1	22.72	1.34
+50	1000	"0"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0"	10	0.01	"1 ( 0 )"	"46.76 ( 0.67 )"	"4.86 ( 0.09 )"	47.9	"0.98"	1	46.76	4.86
+500	1000	"0"	2	0.04	"1 ( 0 )"	"47.82 ( 0.66 )"	"2.5 ( 0.1 )"	51.32	"0.93"	1	47.82	2.5
+1000	1000	"0"	1	0.07	"1 ( 0 )"	"47.14 ( 0.77 )"	"1.46 ( 0.08 )"	51.68	"0.91"	1	47.14	1.46
diff --git a/simulations/results_summary_bin/sim_ind_lasso_binary.txt b/simulations/results_summary_bin/sim_ind_lasso_binary.txt
new file mode 100644
index 0000000..be7c40a
--- /dev/null
+++ b/simulations/results_summary_bin/sim_ind_lasso_binary.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0"	1	0.33	"0.74 ( 0.01 )"	"3.69 ( 0.25 )"	"2.5 ( 0.12 )"	6.19	"0.37"	0.74	3.69	2.5
+100	50	"0"	0.5	0.35	"0.85 ( 0.01 )"	"7.16 ( 0.37 )"	"0.64 ( 0.08 )"	11.52	"0.48"	0.85	7.16	0.64
+500	50	"0"	0.1	0.53	"0.93 ( 0 )"	"5.21 ( 0.21 )"	"0 ( 0 )"	10.21	"0.39"	0.93	5.21	0
+1000	50	"0"	0.05	0.8	"0.93 ( 0 )"	"2.25 ( 0.11 )"	"0 ( 0 )"	7.25	"0.15"	0.93	2.25	0
+50	100	"0"	2	0.25	"0.73 ( 0.01 )"	"5.13 ( 0.34 )"	"2.93 ( 0.1 )"	7.2	"0.5"	0.73	5.13	2.93
+100	100	"0"	1	0.32	"0.83 ( 0.01 )"	"8.22 ( 0.44 )"	"1.03 ( 0.09 )"	12.19	"0.54"	0.83	8.22	1.03
+500	100	"0"	0.2	0.4	"0.92 ( 0 )"	"8.89 ( 0.29 )"	"0 ( 0 )"	13.89	"0.55"	0.92	8.89	0
+1000	100	"0"	0.1	0.69	"0.93 ( 0 )"	"3.53 ( 0.16 )"	"0 ( 0 )"	8.53	"0.27"	0.93	3.53	0
+50	500	"0"	10	0.13	"0.7 ( 0.01 )"	"8.05 ( 0.28 )"	"3.74 ( 0.09 )"	9.31	"0.74"	0.7	8.05	3.74
+100	500	"0"	5	0.24	"0.79 ( 0.01 )"	"11.14 ( 0.64 )"	"1.71 ( 0.1 )"	14.43	"0.63"	0.79	11.14	1.71
+500	500	"0"	1	0.2	"0.92 ( 0 )"	"23.83 ( 0.69 )"	"0 ( 0 )"	28.83	"0.78"	0.92	23.83	0
+1000	500	"0"	0.5	0.36	"0.93 ( 0 )"	"11.38 ( 0.41 )"	"0 ( 0 )"	16.38	"0.6"	0.93	11.38	0
+50	1000	"0"	20	0.09	"0.67 ( 0.01 )"	"9.77 ( 0.24 )"	"4.03 ( 0.08 )"	10.74	"0.81"	0.67	9.77	4.03
+100	1000	"0"	10	0.2	"0.78 ( 0.01 )"	"11.91 ( 0.8 )"	"2.32 ( 0.1 )"	14.59	"0.66"	0.78	11.91	2.32
+500	1000	"0"	2	0.16	"0.91 ( 0 )"	"30.84 ( 0.92 )"	"0 ( 0 )"	35.84	"0.82"	0.91	30.84	0
+1000	1000	"0"	1	0.24	"0.93 ( 0 )"	"19.96 ( 0.61 )"	"0 ( 0 )"	24.96	"0.74"	0.93	19.96	0
diff --git a/simulations/results_summary_bin/sim_toe_Elnet_binary.txt b/simulations/results_summary_bin/sim_toe_Elnet_binary.txt
new file mode 100644
index 0000000..aea74dc
--- /dev/null
+++ b/simulations/results_summary_bin/sim_toe_Elnet_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.13	"0.77 ( 0.01 )"	"13.02 ( 1.02 )"	"1.51 ( 0.11 )"	16.51	"0.63"	0.77	13.02	1.51
+100	50	"0.1"	0.5	0.24	"0.85 ( 0.01 )"	"10.71 ( 0.62 )"	"0.56 ( 0.07 )"	15.15	"0.57"	0.85	10.71	0.56
+500	50	"0.1"	0.1	0.41	"0.92 ( 0 )"	"7.43 ( 0.6 )"	"0.02 ( 0.01 )"	12.41	"0.42"	0.92	7.43	0.02
+1000	50	"0.1"	0.05	0.39	"0.92 ( 0 )"	"7.82 ( 0.76 )"	"0 ( 0 )"	12.82	"0.4"	0.92	7.82	0
+50	100	"0.1"	2	0.09	"0.73 ( 0.01 )"	"21.55 ( 1.94 )"	"1.64 ( 0.11 )"	24.91	"0.73"	0.73	21.55	1.64
+100	100	"0.1"	1	0.18	"0.83 ( 0.01 )"	"15.59 ( 1.32 )"	"0.84 ( 0.1 )"	19.75	"0.62"	0.83	15.59	0.84
+500	100	"0.1"	0.2	0.32	"0.92 ( 0 )"	"11.5 ( 0.9 )"	"0 ( 0 )"	16.5	"0.54"	0.92	11.5	0
+1000	100	"0.1"	0.1	0.34	"0.92 ( 0 )"	"11.02 ( 1.05 )"	"0 ( 0 )"	16.02	"0.49"	0.92	11.02	0
+50	500	"0.1"	10	0.03	"0.66 ( 0.01 )"	"58.7 ( 5.86 )"	"2.64 ( 0.12 )"	61.06	"0.89"	0.66	58.7	2.64
+100	500	"0.1"	5	0.14	"0.78 ( 0.01 )"	"19.29 ( 1.92 )"	"1.76 ( 0.1 )"	22.53	"0.68"	0.78	19.29	1.76
+500	500	"0.1"	1	0.26	"0.91 ( 0 )"	"17.05 ( 1.43 )"	"0 ( 0 )"	22.05	"0.62"	0.91	17.05	0
+1000	500	"0.1"	0.5	0.29	"0.93 ( 0 )"	"15.14 ( 1.39 )"	"0 ( 0 )"	20.14	"0.54"	0.93	15.14	0
+50	1000	"0.1"	20	0.02	"0.65 ( 0.01 )"	"85.97 ( 9 )"	"3.03 ( 0.13 )"	87.94	"0.92"	0.65	85.97	3.03
+100	1000	"0.1"	10	0.12	"0.76 ( 0.01 )"	"22.02 ( 1.97 )"	"2.07 ( 0.1 )"	24.95	"0.75"	0.76	22.02	2.07
+500	1000	"0.1"	2	0.23	"0.91 ( 0 )"	"21.07 ( 1.75 )"	"0 ( 0 )"	26.07	"0.66"	0.91	21.07	0
+1000	1000	"0.1"	1	0.24	"0.92 ( 0 )"	"19.56 ( 2.29 )"	"0 ( 0 )"	24.56	"0.57"	0.92	19.56	0
+50	50	"0.3"	1	0.13	"0.76 ( 0.01 )"	"11.32 ( 0.99 )"	"1.87 ( 0.14 )"	14.45	"0.59"	0.76	11.32	1.87
+100	50	"0.3"	0.5	0.18	"0.83 ( 0.01 )"	"12.96 ( 0.86 )"	"0.75 ( 0.09 )"	17.21	"0.61"	0.83	12.96	0.75
+500	50	"0.3"	0.1	0.33	"0.91 ( 0 )"	"9.89 ( 0.75 )"	"0 ( 0 )"	14.89	"0.51"	0.91	9.89	0
+1000	50	"0.3"	0.05	0.38	"0.92 ( 0 )"	"8.56 ( 0.69 )"	"0 ( 0 )"	13.56	"0.44"	0.92	8.56	0
+50	100	"0.3"	2	0.08	"0.74 ( 0.01 )"	"17.99 ( 1.78 )"	"2.32 ( 0.14 )"	20.67	"0.72"	0.74	17.99	2.32
+100	100	"0.3"	1	0.17	"0.82 ( 0.01 )"	"14.36 ( 1.25 )"	"1.36 ( 0.12 )"	18	"0.6"	0.82	14.36	1.36
+500	100	"0.3"	0.2	0.29	"0.91 ( 0 )"	"13.08 ( 1 )"	"0 ( 0 )"	18.08	"0.56"	0.91	13.08	0
+1000	100	"0.3"	0.1	0.38	"0.92 ( 0 )"	"9.67 ( 0.76 )"	"0 ( 0 )"	14.67	"0.49"	0.92	9.67	0
+50	500	"0.3"	10	0.03	"0.66 ( 0.01 )"	"49.2 ( 5.65 )"	"3.15 ( 0.13 )"	51.05	"0.89"	0.66	49.2	3.15
+100	500	"0.3"	5	0.11	"0.77 ( 0.01 )"	"23.84 ( 2.37 )"	"2.05 ( 0.11 )"	26.79	"0.73"	0.77	23.84	2.05
+500	500	"0.3"	1	0.19	"0.91 ( 0 )"	"25.04 ( 1.69 )"	"0 ( 0 )"	30.04	"0.72"	0.91	25.04	0
+1000	500	"0.3"	0.5	0.25	"0.92 ( 0 )"	"18.51 ( 1.25 )"	"0 ( 0 )"	23.51	"0.66"	0.92	18.51	0
+50	1000	"0.3"	20	0.02	"0.67 ( 0.01 )"	"54.74 ( 6.8 )"	"3.54 ( 0.12 )"	56.2	"0.91"	0.67	54.74	3.54
+100	1000	"0.3"	10	0.08	"0.75 ( 0.01 )"	"28.55 ( 5.11 )"	"2.58 ( 0.11 )"	30.97	"0.75"	0.75	28.55	2.58
+500	1000	"0.3"	2	0.17	"0.9 ( 0 )"	"29.91 ( 1.92 )"	"0 ( 0 )"	34.91	"0.76"	0.9	29.91	0
+1000	1000	"0.3"	1	0.19	"0.92 ( 0 )"	"25.69 ( 2.26 )"	"0 ( 0 )"	30.69	"0.66"	0.92	25.69	0
+50	50	"0.5"	1	0.12	"0.74 ( 0.01 )"	"11.42 ( 0.91 )"	"2.04 ( 0.13 )"	14.38	"0.64"	0.74	11.42	2.04
+100	50	"0.5"	0.5	0.19	"0.83 ( 0.01 )"	"11.74 ( 0.82 )"	"1.01 ( 0.11 )"	15.73	"0.6"	0.83	11.74	1.01
+500	50	"0.5"	0.1	0.31	"0.91 ( 0 )"	"10.82 ( 0.72 )"	"0 ( 0 )"	15.82	"0.54"	0.91	10.82	0
+1000	50	"0.5"	0.05	0.36	"0.91 ( 0 )"	"9.23 ( 0.76 )"	"0 ( 0 )"	14.23	"0.46"	0.91	9.23	0
+50	100	"0.5"	2	0.08	"0.72 ( 0.01 )"	"18.36 ( 1.76 )"	"2.53 ( 0.12 )"	20.83	"0.72"	0.72	18.36	2.53
+100	100	"0.5"	1	0.16	"0.81 ( 0.01 )"	"15.05 ( 1.14 )"	"1.47 ( 0.12 )"	18.58	"0.67"	0.81	15.05	1.47
+500	100	"0.5"	0.2	0.27	"0.91 ( 0 )"	"14.93 ( 0.96 )"	"0 ( 0 )"	19.93	"0.64"	0.91	14.93	0
+1000	100	"0.5"	0.1	0.38	"0.91 ( 0 )"	"10.11 ( 0.76 )"	"0 ( 0 )"	15.11	"0.52"	0.91	10.11	0
+50	500	"0.5"	10	0.03	"0.69 ( 0.01 )"	"40.87 ( 5.37 )"	"3.42 ( 0.13 )"	42.45	"0.87"	0.69	40.87	3.42
+100	500	"0.5"	5	0.09	"0.76 ( 0.01 )"	"22.4 ( 3.42 )"	"2.91 ( 0.1 )"	24.49	"0.72"	0.76	22.4	2.91
+500	500	"0.5"	1	0.19	"0.89 ( 0 )"	"26.07 ( 1.56 )"	"0.01 ( 0.01 )"	31.06	"0.74"	0.89	26.07	0.01
+1000	500	"0.5"	0.5	0.21	"0.91 ( 0 )"	"22.64 ( 1.75 )"	"0 ( 0 )"	27.64	"0.69"	0.91	22.64	0
+50	1000	"0.5"	20	0.02	"0.66 ( 0.01 )"	"61.74 ( 7.68 )"	"3.7 ( 0.11 )"	63.04	"0.91"	0.66	61.74	3.7
+100	1000	"0.5"	10	0.07	"0.75 ( 0.01 )"	"27.52 ( 4.1 )"	"3.1 ( 0.09 )"	29.42	"0.8"	0.75	27.52	3.1
+500	1000	"0.5"	2	0.16	"0.89 ( 0 )"	"31.54 ( 2.49 )"	"0.05 ( 0.03 )"	36.49	"0.74"	0.89	31.54	0.05
+1000	1000	"0.5"	1	0.17	"0.91 ( 0 )"	"29.65 ( 2.06 )"	"0 ( 0 )"	34.65	"0.75"	0.91	29.65	0
+50	50	"0.7"	1	0.1	"0.75 ( 0.01 )"	"12.33 ( 0.98 )"	"2.23 ( 0.15 )"	15.1	"0.64"	0.75	12.33	2.23
+100	50	"0.7"	0.5	0.18	"0.81 ( 0.01 )"	"11.18 ( 0.86 )"	"1.42 ( 0.12 )"	14.76	"0.59"	0.81	11.18	1.42
+500	50	"0.7"	0.1	0.31	"0.89 ( 0 )"	"11.14 ( 0.74 )"	"0.02 ( 0.02 )"	16.12	"0.56"	0.89	11.14	0.02
+1000	50	"0.7"	0.05	0.37	"0.89 ( 0 )"	"9.65 ( 0.77 )"	"0 ( 0 )"	14.65	"0.49"	0.89	9.65	0
+50	100	"0.7"	2	0.08	"0.71 ( 0.01 )"	"17.76 ( 1.65 )"	"2.81 ( 0.14 )"	19.95	"0.74"	0.71	17.76	2.81
+100	100	"0.7"	1	0.13	"0.79 ( 0.01 )"	"16.43 ( 1.31 )"	"1.79 ( 0.13 )"	19.64	"0.68"	0.79	16.43	1.79
+500	100	"0.7"	0.2	0.25	"0.89 ( 0 )"	"16.01 ( 0.82 )"	"0.03 ( 0.03 )"	20.98	"0.67"	0.89	16.01	0.03
+1000	100	"0.7"	0.1	0.33	"0.89 ( 0 )"	"12.77 ( 0.94 )"	"0 ( 0 )"	17.77	"0.58"	0.89	12.77	0
+50	500	"0.7"	10	0.03	"0.67 ( 0.01 )"	"43.83 ( 5.17 )"	"3.75 ( 0.12 )"	45.08	"0.9"	0.67	43.83	3.75
+100	500	"0.7"	5	0.1	"0.74 ( 0.01 )"	"19.1 ( 1.88 )"	"3.08 ( 0.1 )"	21.02	"0.73"	0.74	19.1	3.08
+500	500	"0.7"	1	0.15	"0.87 ( 0 )"	"31.23 ( 1.88 )"	"0.17 ( 0.04 )"	36.06	"0.78"	0.87	31.23	0.17
+1000	500	"0.7"	0.5	0.22	"0.89 ( 0 )"	"22.46 ( 1.45 )"	"0 ( 0 )"	27.46	"0.71"	0.89	22.46	0
+50	1000	"0.7"	20	0.02	"0.64 ( 0.01 )"	"47.17 ( 5.85 )"	"4.12 ( 0.09 )"	48.05	"0.92"	0.64	47.17	4.12
+100	1000	"0.7"	10	0.08	"0.75 ( 0.01 )"	"20.86 ( 2.56 )"	"3.59 ( 0.09 )"	22.27	"0.77"	0.75	20.86	3.59
+500	1000	"0.7"	2	0.13	"0.87 ( 0 )"	"36.32 ( 2.18 )"	"0.23 ( 0.05 )"	41.09	"0.81"	0.87	36.32	0.23
+1000	1000	"0.7"	1	0.16	"0.89 ( 0 )"	"32.22 ( 2.11 )"	"0.01 ( 0.01 )"	37.21	"0.8"	0.89	32.22	0.01
+50	50	"0.9"	1	0.09	"0.71 ( 0.01 )"	"14.15 ( 1 )"	"2.3 ( 0.14 )"	16.85	"0.68"	0.71	14.15	2.3
+100	50	"0.9"	0.5	0.15	"0.77 ( 0.01 )"	"11.74 ( 0.88 )"	"1.74 ( 0.13 )"	15	"0.64"	0.77	11.74	1.74
+500	50	"0.9"	0.1	0.27	"0.83 ( 0 )"	"12.28 ( 0.9 )"	"0.18 ( 0.06 )"	17.1	"0.57"	0.83	12.28	0.18
+1000	50	"0.9"	0.05	0.39	"0.84 ( 0 )"	"9.58 ( 0.68 )"	"0.01 ( 0.01 )"	14.57	"0.51"	0.84	9.58	0.01
+50	100	"0.9"	2	0.08	"0.7 ( 0.01 )"	"15.1 ( 1.65 )"	"3.16 ( 0.13 )"	16.94	"0.7"	0.7	15.1	3.16
+100	100	"0.9"	1	0.15	"0.75 ( 0.01 )"	"14.19 ( 1.38 )"	"2.23 ( 0.13 )"	16.96	"0.64"	0.75	14.19	2.23
+500	100	"0.9"	0.2	0.25	"0.83 ( 0 )"	"16.33 ( 1.01 )"	"0.2 ( 0.06 )"	21.13	"0.67"	0.83	16.33	0.2
+1000	100	"0.9"	0.1	0.32	"0.84 ( 0 )"	"13.82 ( 1.17 )"	"0.03 ( 0.02 )"	18.79	"0.6"	0.84	13.82	0.03
+50	500	"0.9"	10	0.05	"0.66 ( 0.02 )"	"28.64 ( 3.14 )"	"4.13 ( 0.07 )"	29.51	"0.88"	0.66	28.64	4.13
+100	500	"0.9"	5	0.07	"0.7 ( 0.01 )"	"25.71 ( 4.03 )"	"3.42 ( 0.1 )"	27.29	"0.78"	0.7	25.71	3.42
+500	500	"0.9"	1	0.14	"0.81 ( 0 )"	"25.68 ( 1.61 )"	"1.07 ( 0.11 )"	29.61	"0.76"	0.81	25.68	1.07
+1000	500	"0.9"	0.5	0.21	"0.83 ( 0 )"	"23.49 ( 1.26 )"	"0.15 ( 0.05 )"	28.34	"0.75"	0.83	23.49	0.15
+50	1000	"0.9"	20	0.02	"0.65 ( 0.01 )"	"49.08 ( 6.86 )"	"4.29 ( 0.09 )"	49.79	"0.93"	0.65	49.08	4.29
+100	1000	"0.9"	10	0.05	"0.66 ( 0.01 )"	"32.99 ( 5.22 )"	"3.81 ( 0.09 )"	34.18	"0.8"	0.66	32.99	3.81
+500	1000	"0.9"	2	0.11	"0.8 ( 0 )"	"30.91 ( 2.22 )"	"1.52 ( 0.12 )"	34.39	"0.78"	0.8	30.91	1.52
+1000	1000	"0.9"	1	0.14	"0.83 ( 0 )"	"35.66 ( 1.98 )"	"0.23 ( 0.06 )"	40.43	"0.82"	0.83	35.66	0.23
diff --git a/simulations/results_summary_bin/sim_toe_GencompLasso_binary.txt b/simulations/results_summary_bin/sim_toe_GencompLasso_binary.txt
new file mode 100644
index 0000000..5cb08ae
--- /dev/null
+++ b/simulations/results_summary_bin/sim_toe_GencompLasso_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.13	"0.97 ( 0 )"	"14.84 ( 0.9 )"	"0.93 ( 0.09 )"	19.91	"0.7"	0.97	14.84	0.93
+100	50	"0.1"	0.5	0.33	"0.93 ( 0 )"	"7.19 ( 0.43 )"	"0.49 ( 0.07 )"	12.7	"0.53"	0.93	7.19	0.49
+500	50	"0.1"	0.1	0.7	"0.9 ( 0 )"	"1.93 ( 0.34 )"	"0.16 ( 0.04 )"	7.77	"0.16"	0.9	1.93	0.16
+1000	50	"0.1"	0.05	0.79	"0.9 ( 0 )"	"1.37 ( 0.42 )"	"0.01 ( 0.01 )"	7.36	"0.1"	0.9	1.37	0.01
+50	100	"0.1"	2	0.07	"0.99 ( 0 )"	"36.64 ( 2.16 )"	"0.65 ( 0.07 )"	41.99	"0.84"	0.99	36.64	0.65
+100	100	"0.1"	1	0.2	"0.94 ( 0 )"	"16.16 ( 1.22 )"	"0.47 ( 0.06 )"	21.69	"0.7"	0.94	16.16	0.47
+500	100	"0.1"	0.2	0.36	"0.9 ( 0 )"	"8.64 ( 1.35 )"	"0.08 ( 0.03 )"	14.56	"0.37"	0.9	8.64	0.08
+1000	100	"0.1"	0.1	0.47	"0.9 ( 0 )"	"5.83 ( 1.4 )"	"0.02 ( 0.01 )"	11.81	"0.22"	0.9	5.83	0.02
+50	500	"0.1"	10	0.01	"1 ( 0 )"	"248.06 ( 7.56 )"	"0.56 ( 0.07 )"	253.5	"0.98"	1	248.06	0.56
+100	500	"0.1"	5	0.03	"1 ( 0 )"	"132.04 ( 7.47 )"	"0.33 ( 0.05 )"	137.71	"0.95"	1	132.04	0.33
+500	500	"0.1"	1	0.34	"0.91 ( 0 )"	"12.55 ( 1.81 )"	"0.04 ( 0.02 )"	18.51	"0.52"	0.91	12.55	0.04
+1000	500	"0.1"	0.5	0.69	"0.9 ( 0 )"	"3.02 ( 0.6 )"	"0.01 ( 0.01 )"	9.01	"0.22"	0.9	3.02	0.01
+50	1000	"0.1"	20	0.01	"1 ( 0 )"	"398.98 ( 6.9 )"	"0.8 ( 0.08 )"	404.18	"0.99"	1	398.98	0.8
+100	1000	"0.1"	10	0.02	"1 ( 0 )"	"296.19 ( 8.29 )"	"0.35 ( 0.05 )"	301.84	"0.98"	1	296.19	0.35
+500	1000	"0.1"	2	0.39	"0.9 ( 0 )"	"10.2 ( 0.98 )"	"0.1 ( 0.03 )"	16.1	"0.52"	0.9	10.2	0.1
+1000	1000	"0.1"	1	0.83	"0.9 ( 0 )"	"1.76 ( 0.26 )"	"0.02 ( 0.01 )"	7.74	"0.18"	0.9	1.76	0.02
+50	50	"0.3"	1	0.14	"0.96 ( 0 )"	"13.96 ( 0.83 )"	"1.01 ( 0.09 )"	18.95	"0.7"	0.96	13.96	1.01
+100	50	"0.3"	0.5	0.25	"0.92 ( 0 )"	"9.38 ( 0.67 )"	"0.68 ( 0.07 )"	14.7	"0.58"	0.92	9.38	0.68
+500	50	"0.3"	0.1	0.44	"0.89 ( 0 )"	"5.43 ( 0.9 )"	"0.14 ( 0.03 )"	11.29	"0.3"	0.89	5.43	0.14
+1000	50	"0.3"	0.05	0.48	"0.88 ( 0 )"	"5.03 ( 1 )"	"0.09 ( 0.03 )"	10.94	"0.23"	0.88	5.03	0.09
+50	100	"0.3"	2	0.06	"0.99 ( 0 )"	"36.33 ( 2.24 )"	"0.9 ( 0.09 )"	41.43	"0.85"	0.99	36.33	0.9
+100	100	"0.3"	1	0.19	"0.93 ( 0 )"	"16.08 ( 0.95 )"	"0.75 ( 0.07 )"	21.33	"0.72"	0.93	16.08	0.75
+500	100	"0.3"	0.2	0.37	"0.89 ( 0 )"	"8.66 ( 1.15 )"	"0.11 ( 0.03 )"	14.55	"0.42"	0.89	8.66	0.11
+1000	100	"0.3"	0.1	0.39	"0.88 ( 0 )"	"8.45 ( 1.17 )"	"0.12 ( 0.03 )"	14.33	"0.39"	0.88	8.45	0.12
+50	500	"0.3"	10	0.01	"1 ( 0 )"	"228.37 ( 7.07 )"	"0.6 ( 0.08 )"	233.77	"0.97"	1	228.37	0.6
+100	500	"0.3"	5	0.04	"0.99 ( 0 )"	"114.53 ( 5.78 )"	"0.43 ( 0.06 )"	120.1	"0.94"	0.99	114.53	0.43
+500	500	"0.3"	1	0.31	"0.89 ( 0 )"	"13.64 ( 2.08 )"	"0.2 ( 0.04 )"	19.44	"0.57"	0.89	13.64	0.2
+1000	500	"0.3"	0.5	0.54	"0.88 ( 0 )"	"6.3 ( 1.15 )"	"0.09 ( 0.04 )"	12.21	"0.38"	0.88	6.3	0.09
+50	1000	"0.3"	20	0.01	"1 ( 0 )"	"412.73 ( 7.91 )"	"0.88 ( 0.1 )"	417.85	"0.99"	1	412.73	0.88
+100	1000	"0.3"	10	0.02	"1 ( 0 )"	"279.68 ( 7.94 )"	"0.45 ( 0.07 )"	285.23	"0.98"	1	279.68	0.45
+500	1000	"0.3"	2	0.3	"0.89 ( 0 )"	"14.7 ( 1.84 )"	"0.26 ( 0.05 )"	20.44	"0.6"	0.89	14.7	0.26
+1000	1000	"0.3"	1	0.73	"0.88 ( 0 )"	"3.45 ( 0.41 )"	"0.18 ( 0.04 )"	9.27	"0.3"	0.88	3.45	0.18
+50	50	"0.5"	1	0.13	"0.94 ( 0 )"	"12.95 ( 0.79 )"	"1.38 ( 0.1 )"	17.57	"0.7"	0.94	12.95	1.38
+100	50	"0.5"	0.5	0.23	"0.9 ( 0 )"	"8.65 ( 0.57 )"	"1.15 ( 0.08 )"	13.5	"0.59"	0.9	8.65	1.15
+500	50	"0.5"	0.1	0.39	"0.86 ( 0 )"	"4.81 ( 0.77 )"	"1.01 ( 0.1 )"	9.8	"0.3"	0.86	4.81	1.01
+1000	50	"0.5"	0.05	0.37	"0.86 ( 0 )"	"4.69 ( 1.01 )"	"1.17 ( 0.11 )"	9.52	"0.22"	0.86	4.69	1.17
+50	100	"0.5"	2	0.07	"0.98 ( 0 )"	"30.24 ( 1.94 )"	"1.32 ( 0.11 )"	34.92	"0.84"	0.98	30.24	1.32
+100	100	"0.5"	1	0.17	"0.91 ( 0 )"	"15.59 ( 1.05 )"	"1.24 ( 0.08 )"	20.35	"0.73"	0.91	15.59	1.24
+500	100	"0.5"	0.2	0.34	"0.87 ( 0 )"	"6.45 ( 1.28 )"	"1.24 ( 0.1 )"	11.21	"0.29"	0.87	6.45	1.24
+1000	100	"0.5"	0.1	0.28	"0.86 ( 0 )"	"8.33 ( 1.58 )"	"1.21 ( 0.1 )"	13.12	"0.3"	0.86	8.33	1.21
+50	500	"0.5"	10	0.01	"1 ( 0 )"	"211.18 ( 7.59 )"	"0.85 ( 0.09 )"	216.33	"0.97"	1	211.18	0.85
+100	500	"0.5"	5	0.05	"0.98 ( 0 )"	"84.58 ( 6.44 )"	"0.94 ( 0.1 )"	89.64	"0.93"	0.98	84.58	0.94
+500	500	"0.5"	1	0.27	"0.86 ( 0 )"	"13.6 ( 1.48 )"	"0.91 ( 0.09 )"	18.69	"0.58"	0.86	13.6	0.91
+1000	500	"0.5"	0.5	0.51	"0.86 ( 0 )"	"3.77 ( 0.58 )"	"1.52 ( 0.12 )"	8.25	"0.28"	0.86	3.77	1.52
+50	1000	"0.5"	20	0.01	"1 ( 0 )"	"394.36 ( 8.82 )"	"1.1 ( 0.1 )"	399.26	"0.99"	1	394.36	1.1
+100	1000	"0.5"	10	0.02	"1 ( 0 )"	"223.93 ( 9.26 )"	"0.61 ( 0.08 )"	229.32	"0.97"	1	223.93	0.61
+500	1000	"0.5"	2	0.3	"0.87 ( 0 )"	"12.27 ( 1.53 )"	"0.94 ( 0.1 )"	17.33	"0.54"	0.87	12.27	0.94
+1000	1000	"0.5"	1	0.59	"0.86 ( 0 )"	"3.92 ( 0.7 )"	"1.07 ( 0.11 )"	8.85	"0.3"	0.86	3.92	1.07
+50	50	"0.7"	1	0.16	"0.92 ( 0 )"	"10.22 ( 0.71 )"	"1.97 ( 0.1 )"	14.25	"0.68"	0.92	10.22	1.97
+100	50	"0.7"	0.5	0.23	"0.88 ( 0 )"	"7.69 ( 0.73 )"	"2.02 ( 0.08 )"	11.67	"0.58"	0.88	7.69	2.02
+500	50	"0.7"	0.1	0.67	"0.86 ( 0 )"	"1.32 ( 0.25 )"	"2.42 ( 0.06 )"	4.9	"0.19"	0.86	1.32	2.42
+1000	50	"0.7"	0.05	0.74	"0.85 ( 0 )"	"0.84 ( 0.14 )"	"2.48 ( 0.06 )"	4.36	"0.13"	0.85	0.84	2.48
+50	100	"0.7"	2	0.07	"0.96 ( 0 )"	"24.54 ( 1.62 )"	"2.08 ( 0.11 )"	28.46	"0.84"	0.96	24.54	2.08
+100	100	"0.7"	1	0.16	"0.89 ( 0 )"	"14.69 ( 1.28 )"	"1.85 ( 0.08 )"	18.84	"0.71"	0.89	14.69	1.85
+500	100	"0.7"	0.2	0.55	"0.86 ( 0 )"	"2.19 ( 0.64 )"	"2.33 ( 0.07 )"	5.86	"0.18"	0.86	2.19	2.33
+1000	100	"0.7"	0.1	0.81	"0.85 ( 0 )"	"0.51 ( 0.1 )"	"2.56 ( 0.05 )"	3.95	"0.09"	0.85	0.51	2.56
+50	500	"0.7"	10	0.02	"1 ( 0 )"	"161.23 ( 7.83 )"	"1.29 ( 0.1 )"	165.94	"0.96"	1	161.23	1.29
+100	500	"0.7"	5	0.06	"0.96 ( 0 )"	"60.05 ( 4.29 )"	"1.63 ( 0.12 )"	64.42	"0.92"	0.96	60.05	1.63
+500	500	"0.7"	1	0.5	"0.85 ( 0 )"	"3.12 ( 1.31 )"	"2.68 ( 0.07 )"	6.44	"0.19"	0.85	3.12	2.68
+1000	500	"0.7"	0.5	0.89	"0.85 ( 0 )"	"0.39 ( 0.06 )"	"2.88 ( 0.04 )"	3.51	"0.09"	0.85	0.39	2.88
+50	1000	"0.7"	20	0.01	"1 ( 0 )"	"363.91 ( 11.45 )"	"1.39 ( 0.1 )"	368.52	"0.99"	1	363.91	1.39
+100	1000	"0.7"	10	0.03	"0.99 ( 0 )"	"145.9 ( 7.62 )"	"1.24 ( 0.11 )"	150.66	"0.96"	0.99	145.9	1.24
+500	1000	"0.7"	2	0.61	"0.85 ( 0 )"	"2.38 ( 0.4 )"	"2.7 ( 0.06 )"	5.68	"0.27"	0.85	2.38	2.7
+1000	1000	"0.7"	1	0.87	"0.85 ( 0 )"	"0.65 ( 0.08 )"	"2.87 ( 0.04 )"	3.78	"0.14"	0.85	0.65	2.87
+50	50	"0.9"	1	0.14	"0.84 ( 0.01 )"	"9.98 ( 0.78 )"	"2.68 ( 0.11 )"	13.3	"0.7"	0.84	9.98	2.68
+100	50	"0.9"	0.5	0.25	"0.8 ( 0 )"	"6.57 ( 0.7 )"	"2.73 ( 0.09 )"	9.84	"0.58"	0.8	6.57	2.73
+500	50	"0.9"	0.1	0.52	"0.8 ( 0 )"	"2.9 ( 0.5 )"	"2.75 ( 0.07 )"	6.15	"0.33"	0.8	2.9	2.75
+1000	50	"0.9"	0.05	0.7	"0.8 ( 0 )"	"1.88 ( 0.19 )"	"2.79 ( 0.05 )"	5.09	"0.3"	0.8	1.88	2.79
+50	100	"0.9"	2	0.1	"0.87 ( 0.01 )"	"15.93 ( 1 )"	"2.88 ( 0.1 )"	19.05	"0.81"	0.87	15.93	2.88
+100	100	"0.9"	1	0.14	"0.81 ( 0 )"	"14.34 ( 1.4 )"	"2.75 ( 0.1 )"	17.59	"0.73"	0.81	14.34	2.75
+500	100	"0.9"	0.2	0.56	"0.8 ( 0 )"	"3.51 ( 0.44 )"	"2.86 ( 0.04 )"	6.65	"0.4"	0.8	3.51	2.86
+1000	100	"0.9"	0.1	0.57	"0.8 ( 0 )"	"3.18 ( 0.59 )"	"2.86 ( 0.04 )"	6.32	"0.34"	0.8	3.18	2.86
+50	500	"0.9"	10	0.02	"0.99 ( 0 )"	"95.52 ( 6.42 )"	"2.37 ( 0.15 )"	99.15	"0.95"	0.99	95.52	2.37
+100	500	"0.9"	5	0.04	"0.88 ( 0 )"	"53.08 ( 3.93 )"	"2.8 ( 0.12 )"	56.28	"0.93"	0.88	53.08	2.8
+500	500	"0.9"	1	0.4	"0.79 ( 0 )"	"6.75 ( 0.73 )"	"2.76 ( 0.07 )"	9.99	"0.56"	0.79	6.75	2.76
+1000	500	"0.9"	0.5	0.73	"0.8 ( 0 )"	"2.48 ( 0.28 )"	"2.79 ( 0.05 )"	5.69	"0.37"	0.8	2.48	2.79
+50	1000	"0.9"	20	0.01	"1 ( 0 )"	"201.98 ( 10.15 )"	"2.34 ( 0.16 )"	205.64	"0.98"	1	201.98	2.34
+100	1000	"0.9"	10	0.03	"0.95 ( 0 )"	"70.81 ( 4.4 )"	"3.05 ( 0.12 )"	73.76	"0.96"	0.95	70.81	3.05
+500	1000	"0.9"	2	0.38	"0.79 ( 0 )"	"7.71 ( 1.09 )"	"2.85 ( 0.06 )"	10.86	"0.59"	0.79	7.71	2.85
+1000	1000	"0.9"	1	0.76	"0.8 ( 0 )"	"2.41 ( 0.17 )"	"2.77 ( 0.07 )"	5.64	"0.39"	0.8	2.41	2.77
diff --git a/simulations/results_summary_bin/sim_toe_RF_binary.txt b/simulations/results_summary_bin/sim_toe_RF_binary.txt
new file mode 100644
index 0000000..8cd0e12
--- /dev/null
+++ b/simulations/results_summary_bin/sim_toe_RF_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.1"	0.5	0.1	"1 ( 0 )"	"2.05 ( 0.14 )"	"4.71 ( 0.08 )"	3.31	"0.56"	1	2.05	4.71
+500	50	"0.1"	0.1	0.5	"1 ( 0 )"	"1.4 ( 0.12 )"	"2.4 ( 0.08 )"	5	"0.25"	1	1.4	2.4
+1000	50	"0.1"	0.05	0.69	"1 ( 0 )"	"0.98 ( 0.09 )"	"1.2 ( 0.08 )"	5.78	"0.15"	1	0.98	1.2
+50	100	"0.1"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.1"	1	0.06	"1 ( 0 )"	"3.95 ( 0.21 )"	"4.66 ( 0.08 )"	5.29	"0.72"	1	3.95	4.66
+500	100	"0.1"	0.2	0.35	"1 ( 0 )"	"3.91 ( 0.21 )"	"2.17 ( 0.08 )"	7.74	"0.48"	1	3.91	2.17
+1000	100	"0.1"	0.1	0.51	"1 ( 0 )"	"2.97 ( 0.15 )"	"1.29 ( 0.07 )"	7.68	"0.36"	1	2.97	1.29
+50	500	"0.1"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.1"	5	0.01	"1 ( 0 )"	"24.57 ( 0.55 )"	"4.72 ( 0.08 )"	25.85	"0.95"	1	24.57	4.72
+500	500	"0.1"	1	0.09	"1 ( 0 )"	"23.57 ( 0.51 )"	"2.43 ( 0.1 )"	27.14	"0.86"	1	23.57	2.43
+1000	500	"0.1"	0.5	0.14	"1 ( 0 )"	"22.61 ( 0.5 )"	"1.31 ( 0.08 )"	27.3	"0.82"	1	22.61	1.31
+50	1000	"0.1"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.1"	10	0.01	"1 ( 0 )"	"48.35 ( 0.66 )"	"4.88 ( 0.08 )"	49.47	"0.98"	1	48.35	4.88
+500	1000	"0.1"	2	0.04	"1 ( 0 )"	"48.59 ( 0.73 )"	"2.71 ( 0.1 )"	51.88	"0.94"	1	48.59	2.71
+1000	1000	"0.1"	1	0.06	"1 ( 0 )"	"46.74 ( 0.64 )"	"1.75 ( 0.09 )"	50.99	"0.92"	1	46.74	1.75
+50	50	"0.3"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.3"	0.5	0.1	"1 ( 0 )"	"1.68 ( 0.12 )"	"4.86 ( 0.08 )"	2.81	"0.54"	1	1.68	4.86
+500	50	"0.3"	0.1	0.44	"1 ( 0 )"	"1.57 ( 0.12 )"	"2.74 ( 0.08 )"	4.83	"0.3"	1	1.57	2.74
+1000	50	"0.3"	0.05	0.62	"1 ( 0 )"	"1.33 ( 0.1 )"	"1.44 ( 0.08 )"	5.89	"0.21"	1	1.33	1.44
+50	100	"0.3"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.3"	1	0.05	"1 ( 0 )"	"4.45 ( 0.2 )"	"4.85 ( 0.08 )"	5.6	"0.78"	1	4.45	4.85
+500	100	"0.3"	0.2	0.33	"1 ( 0 )"	"3.36 ( 0.19 )"	"2.57 ( 0.09 )"	6.79	"0.46"	1	3.36	2.57
+1000	100	"0.3"	0.1	0.47	"1 ( 0 )"	"3.43 ( 0.17 )"	"1.61 ( 0.09 )"	7.82	"0.41"	1	3.43	1.61
+50	500	"0.3"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.3"	5	0.01	"1 ( 0 )"	"23.69 ( 0.53 )"	"4.87 ( 0.08 )"	24.82	"0.95"	1	23.69	4.87
+500	500	"0.3"	1	0.07	"1 ( 0 )"	"22.84 ( 0.44 )"	"3.11 ( 0.09 )"	25.73	"0.89"	1	22.84	3.11
+1000	500	"0.3"	0.5	0.13	"1 ( 0 )"	"22.24 ( 0.43 )"	"1.77 ( 0.09 )"	26.47	"0.84"	1	22.24	1.77
+50	1000	"0.3"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.3"	10	0.01	"1 ( 0 )"	"48.06 ( 0.76 )"	"5.02 ( 0.08 )"	49.04	"0.98"	1	48.06	5.02
+500	1000	"0.3"	2	0.04	"1 ( 0 )"	"47.71 ( 0.73 )"	"2.98 ( 0.09 )"	50.73	"0.94"	1	47.71	2.98
+1000	1000	"0.3"	1	0.06	"1 ( 0 )"	"47.11 ( 0.84 )"	"2.03 ( 0.09 )"	51.08	"0.92"	1	47.11	2.03
+50	50	"0.5"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.5"	0.5	0.15	"1 ( 0 )"	"1.88 ( 0.15 )"	"4.77 ( 0.08 )"	3.1	"0.54"	1	1.88	4.77
+500	50	"0.5"	0.1	0.44	"1 ( 0 )"	"1.83 ( 0.11 )"	"2.9 ( 0.09 )"	4.93	"0.35"	1	1.83	2.9
+1000	50	"0.5"	0.05	0.61	"1 ( 0 )"	"1.9 ( 0.11 )"	"2.06 ( 0.08 )"	5.84	"0.31"	1	1.9	2.06
+50	100	"0.5"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.5"	1	0.08	"1 ( 0 )"	"4.6 ( 0.23 )"	"4.71 ( 0.07 )"	5.89	"0.75"	1	4.6	4.71
+500	100	"0.5"	0.2	0.31	"1 ( 0 )"	"4.2 ( 0.22 )"	"3.01 ( 0.08 )"	7.19	"0.55"	1	4.2	3.01
+1000	100	"0.5"	0.1	0.5	"1 ( 0 )"	"3.78 ( 0.18 )"	"1.84 ( 0.08 )"	7.94	"0.46"	1	3.78	1.84
+50	500	"0.5"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.5"	5	0.01	"1 ( 0 )"	"23.59 ( 0.56 )"	"4.98 ( 0.08 )"	24.61	"0.96"	1	23.59	4.98
+500	500	"0.5"	1	0.07	"1 ( 0 )"	"23.16 ( 0.45 )"	"3.28 ( 0.08 )"	25.88	"0.89"	1	23.16	3.28
+1000	500	"0.5"	0.5	0.13	"1 ( 0 )"	"22.83 ( 0.49 )"	"2.29 ( 0.08 )"	26.54	"0.86"	1	22.83	2.29
+50	1000	"0.5"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.5"	10	0.01	"1 ( 0 )"	"46.65 ( 0.77 )"	"4.98 ( 0.09 )"	47.67	"0.98"	1	46.65	4.98
+500	1000	"0.5"	2	0.04	"1 ( 0 )"	"47.46 ( 0.83 )"	"3.51 ( 0.08 )"	49.95	"0.95"	1	47.46	3.51
+1000	1000	"0.5"	1	0.06	"1 ( 0 )"	"47.83 ( 0.8 )"	"2.59 ( 0.08 )"	51.24	"0.93"	1	47.83	2.59
+50	50	"0.7"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.7"	0.5	0.16	"1 ( 0 )"	"1.87 ( 0.14 )"	"4.88 ( 0.08 )"	2.96	"0.57"	1	1.87	4.88
+500	50	"0.7"	0.1	0.45	"1 ( 0 )"	"2.14 ( 0.13 )"	"3.36 ( 0.08 )"	4.78	"0.43"	1	2.14	3.36
+1000	50	"0.7"	0.05	0.6	"1 ( 0 )"	"2.66 ( 0.14 )"	"2.27 ( 0.08 )"	6.39	"0.39"	1	2.66	2.27
+50	100	"0.7"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.7"	1	0.08	"1 ( 0 )"	"4.36 ( 0.22 )"	"4.8 ( 0.09 )"	5.56	"0.77"	1	4.36	4.8
+500	100	"0.7"	0.2	0.38	"1 ( 0 )"	"4.03 ( 0.18 )"	"3.1 ( 0.09 )"	6.93	"0.56"	1	4.03	3.1
+1000	100	"0.7"	0.1	0.5	"1 ( 0 )"	"4.65 ( 0.22 )"	"2 ( 0.07 )"	8.65	"0.51"	1	4.65	2
+50	500	"0.7"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.7"	5	0.02	"1 ( 0 )"	"23.58 ( 0.53 )"	"4.72 ( 0.09 )"	24.86	"0.95"	1	23.58	4.72
+500	500	"0.7"	1	0.1	"1 ( 0 )"	"23.09 ( 0.56 )"	"3.38 ( 0.09 )"	25.71	"0.89"	1	23.09	3.38
+1000	500	"0.7"	0.5	0.14	"1 ( 0 )"	"23.33 ( 0.53 )"	"2.58 ( 0.07 )"	26.75	"0.87"	1	23.33	2.58
+50	1000	"0.7"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.7"	10	0.01	"1 ( 0 )"	"46.8 ( 0.69 )"	"5.13 ( 0.07 )"	47.67	"0.98"	1	46.8	5.13
+500	1000	"0.7"	2	0.05	"1 ( 0 )"	"48.68 ( 0.77 )"	"3.54 ( 0.08 )"	51.14	"0.95"	1	48.68	3.54
+1000	1000	"0.7"	1	0.07	"1 ( 0 )"	"47.14 ( 0.7 )"	"2.73 ( 0.07 )"	50.41	"0.93"	1	47.14	2.73
+50	50	"0.9"	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	50	"0.9"	0.5	0.12	"1 ( 0 )"	"2.09 ( 0.16 )"	"4.98 ( 0.09 )"	3.06	"0.62"	1	2.09	4.98
+500	50	"0.9"	0.1	0.45	"1 ( 0 )"	"2.6 ( 0.13 )"	"3.33 ( 0.08 )"	5.27	"0.47"	1	2.6	3.33
+1000	50	"0.9"	0.05	0.51	"1 ( 0 )"	"3.48 ( 0.17 )"	"2.35 ( 0.08 )"	7.13	"0.47"	1	3.48	2.35
+50	100	"0.9"	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	100	"0.9"	1	0.07	"1 ( 0 )"	"4.4 ( 0.23 )"	"4.96 ( 0.09 )"	5.43	"0.81"	1	4.4	4.96
+500	100	"0.9"	0.2	0.35	"1 ( 0 )"	"4.57 ( 0.24 )"	"3.5 ( 0.08 )"	7.07	"0.62"	1	4.57	3.5
+1000	100	"0.9"	0.1	0.37	"1 ( 0 )"	"6.43 ( 0.28 )"	"2.57 ( 0.08 )"	9.86	"0.63"	1	6.43	2.57
+50	500	"0.9"	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	500	"0.9"	5	0.01	"1 ( 0 )"	"24.58 ( 0.66 )"	"5.1 ( 0.09 )"	25.48	"0.96"	1	24.58	5.1
+500	500	"0.9"	1	0.08	"1 ( 0 )"	"24.91 ( 0.57 )"	"3.53 ( 0.09 )"	27.38	"0.91"	1	24.91	3.53
+1000	500	"0.9"	0.5	0.13	"1 ( 0 )"	"24.53 ( 0.53 )"	"2.84 ( 0.08 )"	27.69	"0.88"	1	24.53	2.84
+50	1000	"0.9"	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	"NaN"	1	1	6
+100	1000	"0.9"	10	0	"1 ( 0 )"	"47.19 ( 0.96 )"	"5.2 ( 0.08 )"	47.99	"0.98"	1	47.19	5.2
+500	1000	"0.9"	2	0.04	"1 ( 0 )"	"48.11 ( 0.92 )"	"3.78 ( 0.08 )"	50.33	"0.95"	1	48.11	3.78
+1000	1000	"0.9"	1	0.07	"1 ( 0 )"	"49.47 ( 0.78 )"	"2.97 ( 0.07 )"	52.5	"0.94"	1	49.47	2.97
diff --git a/simulations/results_summary_bin/sim_toe_lasso_binary.txt b/simulations/results_summary_bin/sim_toe_lasso_binary.txt
new file mode 100644
index 0000000..b18a769
--- /dev/null
+++ b/simulations/results_summary_bin/sim_toe_lasso_binary.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.32	"0.78 ( 0.01 )"	"3.65 ( 0.23 )"	"2.83 ( 0.11 )"	5.82	"0.39"	0.78	3.65	2.83
+100	50	"0.1"	0.5	0.34	"0.85 ( 0.01 )"	"7.35 ( 0.34 )"	"0.68 ( 0.09 )"	11.67	"0.5"	0.85	7.35	0.68
+500	50	"0.1"	0.1	0.55	"0.92 ( 0 )"	"4.99 ( 0.22 )"	"0 ( 0 )"	9.99	"0.37"	0.92	4.99	0
+1000	50	"0.1"	0.05	0.78	"0.93 ( 0 )"	"2.42 ( 0.12 )"	"0 ( 0 )"	7.42	"0.17"	0.93	2.42	0
+50	100	"0.1"	2	0.26	"0.74 ( 0.01 )"	"4.65 ( 0.29 )"	"2.93 ( 0.12 )"	6.72	"0.48"	0.74	4.65	2.93
+100	100	"0.1"	1	0.33	"0.85 ( 0.01 )"	"8.11 ( 0.41 )"	"0.86 ( 0.09 )"	12.25	"0.52"	0.85	8.11	0.86
+500	100	"0.1"	0.2	0.4	"0.92 ( 0 )"	"8.63 ( 0.29 )"	"0 ( 0 )"	13.63	"0.54"	0.92	8.63	0
+1000	100	"0.1"	0.1	0.64	"0.92 ( 0 )"	"4.02 ( 0.18 )"	"0 ( 0 )"	9.02	"0.31"	0.92	4.02	0
+50	500	"0.1"	10	0.12	"0.71 ( 0.01 )"	"8.29 ( 0.25 )"	"3.89 ( 0.1 )"	9.4	"0.77"	0.71	8.29	3.89
+100	500	"0.1"	5	0.24	"0.8 ( 0.01 )"	"8.84 ( 0.61 )"	"2.19 ( 0.12 )"	11.65	"0.57"	0.8	8.84	2.19
+500	500	"0.1"	1	0.21	"0.92 ( 0 )"	"22.86 ( 0.75 )"	"0 ( 0 )"	27.86	"0.77"	0.92	22.86	0
+1000	500	"0.1"	0.5	0.33	"0.93 ( 0 )"	"12.73 ( 0.35 )"	"0 ( 0 )"	17.73	"0.65"	0.93	12.73	0
+50	1000	"0.1"	20	0.09	"0.68 ( 0.01 )"	"10.35 ( 0.33 )"	"4.14 ( 0.09 )"	11.21	"0.82"	0.68	10.35	4.14
+100	1000	"0.1"	10	0.19	"0.77 ( 0.01 )"	"10.84 ( 0.75 )"	"2.46 ( 0.1 )"	13.38	"0.63"	0.77	10.84	2.46
+500	1000	"0.1"	2	0.16	"0.92 ( 0 )"	"30.83 ( 1.05 )"	"0 ( 0 )"	35.83	"0.81"	0.92	30.83	0
+1000	1000	"0.1"	1	0.23	"0.93 ( 0 )"	"20.39 ( 0.59 )"	"0 ( 0 )"	25.39	"0.75"	0.93	20.39	0
+50	50	"0.3"	1	0.28	"0.75 ( 0.01 )"	"3.69 ( 0.22 )"	"3.04 ( 0.1 )"	5.65	"0.4"	0.75	3.69	3.04
+100	50	"0.3"	0.5	0.32	"0.84 ( 0.01 )"	"7.11 ( 0.33 )"	"0.93 ( 0.1 )"	11.18	"0.5"	0.84	7.11	0.93
+500	50	"0.3"	0.1	0.51	"0.92 ( 0 )"	"5.67 ( 0.22 )"	"0 ( 0 )"	10.67	"0.41"	0.92	5.67	0
+1000	50	"0.3"	0.05	0.78	"0.92 ( 0 )"	"2.48 ( 0.13 )"	"0 ( 0 )"	7.48	"0.18"	0.92	2.48	0
+50	100	"0.3"	2	0.26	"0.75 ( 0.01 )"	"4.41 ( 0.27 )"	"3.47 ( 0.09 )"	5.94	"0.49"	0.75	4.41	3.47
+100	100	"0.3"	1	0.28	"0.82 ( 0.01 )"	"8.16 ( 0.53 )"	"1.46 ( 0.11 )"	11.7	"0.52"	0.82	8.16	1.46
+500	100	"0.3"	0.2	0.41	"0.92 ( 0 )"	"8.48 ( 0.34 )"	"0 ( 0 )"	13.48	"0.53"	0.92	8.48	0
+1000	100	"0.3"	0.1	0.67	"0.92 ( 0 )"	"3.71 ( 0.15 )"	"0 ( 0 )"	8.71	"0.29"	0.92	3.71	0
+50	500	"0.3"	10	0.13	"0.69 ( 0.01 )"	"7.94 ( 0.3 )"	"4.11 ( 0.08 )"	8.83	"0.77"	0.69	7.94	4.11
+100	500	"0.3"	5	0.2	"0.78 ( 0.01 )"	"10.16 ( 0.68 )"	"2.49 ( 0.11 )"	12.67	"0.63"	0.78	10.16	2.49
+500	500	"0.3"	1	0.2	"0.91 ( 0 )"	"23.65 ( 0.7 )"	"0 ( 0 )"	28.65	"0.78"	0.91	23.65	0
+1000	500	"0.3"	0.5	0.34	"0.92 ( 0 )"	"12.28 ( 0.34 )"	"0 ( 0 )"	17.28	"0.64"	0.92	12.28	0
+50	1000	"0.3"	20	0.1	"0.68 ( 0.01 )"	"9.77 ( 0.31 )"	"4.3 ( 0.06 )"	10.47	"0.83"	0.68	9.77	4.3
+100	1000	"0.3"	10	0.17	"0.77 ( 0.01 )"	"10.68 ( 0.8 )"	"3.02 ( 0.11 )"	12.66	"0.67"	0.77	10.68	3.02
+500	1000	"0.3"	2	0.17	"0.9 ( 0 )"	"30.07 ( 1.09 )"	"0 ( 0 )"	35.07	"0.81"	0.9	30.07	0
+1000	1000	"0.3"	1	0.24	"0.92 ( 0 )"	"19.7 ( 0.7 )"	"0 ( 0 )"	24.7	"0.73"	0.92	19.7	0
+50	50	"0.5"	1	0.25	"0.76 ( 0.01 )"	"3.86 ( 0.22 )"	"3.4 ( 0.12 )"	5.46	"0.47"	0.76	3.86	3.4
+100	50	"0.5"	0.5	0.31	"0.84 ( 0.01 )"	"6.67 ( 0.32 )"	"1.32 ( 0.1 )"	10.35	"0.51"	0.84	6.67	1.32
+500	50	"0.5"	0.1	0.55	"0.91 ( 0 )"	"4.95 ( 0.22 )"	"0 ( 0 )"	9.95	"0.37"	0.91	4.95	0
+1000	50	"0.5"	0.05	0.77	"0.91 ( 0 )"	"2.5 ( 0.14 )"	"0 ( 0 )"	7.5	"0.17"	0.91	2.5	0
+50	100	"0.5"	2	0.25	"0.72 ( 0.01 )"	"4.08 ( 0.28 )"	"3.81 ( 0.08 )"	5.27	"0.49"	0.72	4.08	3.81
+100	100	"0.5"	1	0.26	"0.8 ( 0.01 )"	"7.61 ( 0.43 )"	"2.03 ( 0.12 )"	10.58	"0.55"	0.8	7.61	2.03
+500	100	"0.5"	0.2	0.43	"0.91 ( 0 )"	"8.05 ( 0.31 )"	"0 ( 0 )"	13.05	"0.51"	0.91	8.05	0
+1000	100	"0.5"	0.1	0.65	"0.91 ( 0 )"	"3.9 ( 0.16 )"	"0 ( 0 )"	8.9	"0.3"	0.91	3.9	0
+50	500	"0.5"	10	0.14	"0.7 ( 0.01 )"	"7.23 ( 0.28 )"	"4.26 ( 0.07 )"	7.97	"0.76"	0.7	7.23	4.26
+100	500	"0.5"	5	0.18	"0.77 ( 0.01 )"	"8.77 ( 0.71 )"	"3.31 ( 0.1 )"	10.46	"0.61"	0.77	8.77	3.31
+500	500	"0.5"	1	0.2	"0.9 ( 0 )"	"23.13 ( 0.75 )"	"0.01 ( 0.01 )"	28.12	"0.77"	0.9	23.13	0.01
+1000	500	"0.5"	0.5	0.32	"0.91 ( 0 )"	"13.23 ( 0.36 )"	"0.01 ( 0.01 )"	18.22	"0.66"	0.91	13.23	0.01
+50	1000	"0.5"	20	0.11	"0.68 ( 0.01 )"	"9.13 ( 0.25 )"	"4.4 ( 0.06 )"	9.73	"0.83"	0.68	9.13	4.4
+100	1000	"0.5"	10	0.19	"0.77 ( 0.01 )"	"8.39 ( 0.65 )"	"3.48 ( 0.08 )"	9.91	"0.64"	0.77	8.39	3.48
+500	1000	"0.5"	2	0.16	"0.89 ( 0 )"	"30.97 ( 0.97 )"	"0 ( 0 )"	35.97	"0.82"	0.89	30.97	0
+1000	1000	"0.5"	1	0.23	"0.91 ( 0 )"	"21.03 ( 0.66 )"	"0 ( 0 )"	26.03	"0.75"	0.91	21.03	0
+50	50	"0.7"	1	0.28	"0.77 ( 0.01 )"	"3.76 ( 0.26 )"	"3.51 ( 0.11 )"	5.25	"0.42"	0.77	3.76	3.51
+100	50	"0.7"	0.5	0.33	"0.81 ( 0.01 )"	"5.27 ( 0.32 )"	"2.04 ( 0.12 )"	8.23	"0.44"	0.81	5.27	2.04
+500	50	"0.7"	0.1	0.57	"0.89 ( 0 )"	"4.61 ( 0.17 )"	"0.04 ( 0.02 )"	9.57	"0.36"	0.89	4.61	0.04
+1000	50	"0.7"	0.05	0.74	"0.89 ( 0 )"	"2.8 ( 0.15 )"	"0 ( 0 )"	7.8	"0.21"	0.89	2.8	0
+50	100	"0.7"	2	0.24	"0.71 ( 0.01 )"	"4.31 ( 0.28 )"	"4.05 ( 0.09 )"	5.26	"0.55"	0.71	4.31	4.05
+100	100	"0.7"	1	0.27	"0.79 ( 0.01 )"	"6.34 ( 0.4 )"	"2.67 ( 0.12 )"	8.67	"0.53"	0.79	6.34	2.67
+500	100	"0.7"	0.2	0.43	"0.89 ( 0 )"	"7.92 ( 0.24 )"	"0.04 ( 0.02 )"	12.88	"0.52"	0.89	7.92	0.04
+1000	100	"0.7"	0.1	0.66	"0.9 ( 0 )"	"3.85 ( 0.16 )"	"0 ( 0 )"	8.85	"0.3"	0.9	3.85	0
+50	500	"0.7"	10	0.14	"0.7 ( 0.01 )"	"6.5 ( 0.24 )"	"4.63 ( 0.06 )"	6.87	"0.77"	0.7	6.5	4.63
+100	500	"0.7"	5	0.21	"0.76 ( 0.01 )"	"6.98 ( 0.58 )"	"3.65 ( 0.08 )"	8.33	"0.56"	0.76	6.98	3.65
+500	500	"0.7"	1	0.2	"0.88 ( 0 )"	"22.04 ( 0.74 )"	"0.14 ( 0.04 )"	26.9	"0.76"	0.88	22.04	0.14
+1000	500	"0.7"	0.5	0.33	"0.89 ( 0 )"	"13.15 ( 0.38 )"	"0 ( 0 )"	18.15	"0.65"	0.89	13.15	0
+50	1000	"0.7"	20	0.1	"0.66 ( 0.01 )"	"8.82 ( 0.28 )"	"4.72 ( 0.05 )"	9.1	"0.85"	0.66	8.82	4.72
+100	1000	"0.7"	10	0.18	"0.76 ( 0.01 )"	"6.93 ( 0.59 )"	"4 ( 0.08 )"	7.93	"0.62"	0.76	6.93	4
+500	1000	"0.7"	2	0.16	"0.87 ( 0 )"	"29.07 ( 1 )"	"0.16 ( 0.04 )"	33.91	"0.81"	0.87	29.07	0.16
+1000	1000	"0.7"	1	0.22	"0.89 ( 0 )"	"21.8 ( 0.63 )"	"0 ( 0 )"	26.8	"0.76"	0.89	21.8	0
+50	50	"0.9"	1	0.3	"0.69 ( 0.01 )"	"2.9 ( 0.2 )"	"4.22 ( 0.09 )"	3.68	"0.41"	0.69	2.9	4.22
+100	50	"0.9"	0.5	0.37	"0.76 ( 0.01 )"	"3.93 ( 0.21 )"	"2.74 ( 0.11 )"	6.19	"0.41"	0.76	3.93	2.74
+500	50	"0.9"	0.1	0.57	"0.83 ( 0 )"	"3.65 ( 0.16 )"	"0.56 ( 0.08 )"	8.09	"0.31"	0.83	3.65	0.56
+1000	50	"0.9"	0.05	0.76	"0.83 ( 0 )"	"2.41 ( 0.12 )"	"0.23 ( 0.04 )"	7.18	"0.18"	0.83	2.41	0.23
+50	100	"0.9"	2	0.26	"0.7 ( 0.01 )"	"3.29 ( 0.23 )"	"4.29 ( 0.09 )"	4	"0.45"	0.7	3.29	4.29
+100	100	"0.9"	1	0.32	"0.76 ( 0.01 )"	"4.77 ( 0.33 )"	"3.26 ( 0.09 )"	6.51	"0.46"	0.76	4.77	3.26
+500	100	"0.9"	0.2	0.45	"0.82 ( 0 )"	"5.65 ( 0.21 )"	"0.92 ( 0.09 )"	9.73	"0.45"	0.82	5.65	0.92
+1000	100	"0.9"	0.1	0.63	"0.83 ( 0 )"	"3.87 ( 0.18 )"	"0.24 ( 0.05 )"	8.63	"0.3"	0.83	3.87	0.24
+50	500	"0.9"	10	0.15	"0.66 ( 0.01 )"	"5.8 ( 0.24 )"	"4.65 ( 0.07 )"	6.15	"0.75"	0.66	5.8	4.65
+100	500	"0.9"	5	0.21	"0.7 ( 0.01 )"	"5.55 ( 0.55 )"	"4.14 ( 0.09 )"	6.41	"0.51"	0.7	5.55	4.14
+500	500	"0.9"	1	0.19	"0.81 ( 0 )"	"15.93 ( 0.73 )"	"1.42 ( 0.1 )"	19.51	"0.73"	0.81	15.93	1.42
+1000	500	"0.9"	0.5	0.31	"0.83 ( 0 )"	"11.96 ( 0.46 )"	"0.42 ( 0.06 )"	16.54	"0.63"	0.83	11.96	0.42
+50	1000	"0.9"	20	0.08	"0.66 ( 0.01 )"	"8.02 ( 0.28 )"	"4.9 ( 0.07 )"	8.12	"0.85"	0.66	8.02	4.9
+100	1000	"0.9"	10	0.18	"0.69 ( 0.01 )"	"5.85 ( 0.58 )"	"4.37 ( 0.07 )"	6.48	"0.61"	0.69	5.85	4.37
+500	1000	"0.9"	2	0.16	"0.8 ( 0 )"	"18.7 ( 0.93 )"	"1.88 ( 0.1 )"	21.82	"0.77"	0.8	18.7	1.88
+1000	1000	"0.9"	1	0.21	"0.83 ( 0 )"	"20.07 ( 0.72 )"	"0.41 ( 0.05 )"	24.66	"0.75"	0.83	20.07	0.41
diff --git a/simulations/results_summary_bin/table_block_binary_all.txt b/simulations/results_summary_bin/table_block_binary_all.txt
new file mode 100644
index 0000000..957463e
--- /dev/null
+++ b/simulations/results_summary_bin/table_block_binary_all.txt
@@ -0,0 +1,321 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	0.1	1	0.03	"0.59 ( 0.01 )"	"2.69 ( 0.14 )"	"5.3 ( 0.07 )"	2.39	0.67	0.59	2.69	5.3	"lasso"
+100	50	0.1	0.5	0.11	"0.56 ( 0.01 )"	"2.02 ( 0.15 )"	"5.02 ( 0.08 )"	2	0.39	0.56	2.02	5.02	"lasso"
+500	50	0.1	0.1	0.28	"0.63 ( 0 )"	"4.52 ( 0.29 )"	"3 ( 0.11 )"	6.52	0.45	0.63	4.52	3	"lasso"
+1000	50	0.1	0.05	0.39	"0.64 ( 0 )"	"4.74 ( 0.22 )"	"2.1 ( 0.09 )"	7.64	0.44	0.64	4.74	2.1	"lasso"
+50	100	0.1	2	0.02	"0.62 ( 0.01 )"	"3.82 ( 0.16 )"	"5.34 ( 0.07 )"	3.48	0.78	0.62	3.82	5.34	"lasso"
+100	100	0.1	1	0.07	"0.56 ( 0.01 )"	"2.29 ( 0.19 )"	"5.27 ( 0.08 )"	2.02	0.57	0.56	2.29	5.27	"lasso"
+500	100	0.1	0.2	0.23	"0.61 ( 0 )"	"5.54 ( 0.52 )"	"3.27 ( 0.12 )"	7.27	0.47	0.61	5.54	3.27	"lasso"
+1000	100	0.1	0.1	0.27	"0.64 ( 0 )"	"7.69 ( 0.42 )"	"2.25 ( 0.1 )"	10.44	0.56	0.64	7.69	2.25	"lasso"
+50	500	0.1	10	0.01	"0.6 ( 0.01 )"	"8.69 ( 0.24 )"	"5.55 ( 0.06 )"	8.14	0.94	0.6	8.69	5.55	"lasso"
+100	500	0.1	5	0.03	"0.56 ( 0.01 )"	"4.39 ( 0.34 )"	"5.4 ( 0.07 )"	3.99	0.82	0.56	4.39	5.4	"lasso"
+500	500	0.1	1	0.27	"0.61 ( 0.01 )"	"3.84 ( 0.28 )"	"3.93 ( 0.08 )"	4.91	0.47	0.61	3.84	3.93	"lasso"
+1000	500	0.1	0.5	0.2	"0.63 ( 0 )"	"9.87 ( 1.4 )"	"3.18 ( 0.09 )"	11.69	0.49	0.63	9.87	3.18	"lasso"
+50	1000	0.1	20	0	"0.62 ( 0.01 )"	"10.42 ( 0.22 )"	"5.56 ( 0.06 )"	9.86	0.95	0.62	10.42	5.56	"lasso"
+100	1000	0.1	10	0.01	"0.56 ( 0.01 )"	"5.93 ( 0.28 )"	"5.53 ( 0.06 )"	5.4	0.9	0.56	5.93	5.53	"lasso"
+500	1000	0.1	2	0.24	"0.62 ( 0.01 )"	"4.22 ( 0.48 )"	"4.05 ( 0.09 )"	5.17	0.46	0.62	4.22	4.05	"lasso"
+1000	1000	0.1	1	0.33	"0.63 ( 0 )"	"5.03 ( 0.78 )"	"3.35 ( 0.06 )"	6.68	0.42	0.63	5.03	3.35	"lasso"
+50	50	0.3	1	0.17	"0.65 ( 0.01 )"	"3.87 ( 0.23 )"	"3.68 ( 0.12 )"	5.19	0.5	0.65	3.87	3.68	"lasso"
+100	50	0.3	0.5	0.32	"0.78 ( 0.01 )"	"6.48 ( 0.32 )"	"1.13 ( 0.11 )"	10.35	0.49	0.78	6.48	1.13	"lasso"
+500	50	0.3	0.1	0.44	"0.85 ( 0 )"	"6.84 ( 0.26 )"	"0 ( 0 )"	11.84	0.47	0.85	6.84	0	"lasso"
+1000	50	0.3	0.05	0.65	"0.86 ( 0 )"	"3.69 ( 0.19 )"	"0 ( 0 )"	8.69	0.28	0.86	3.69	0	"lasso"
+50	100	0.3	2	0.14	"0.63 ( 0.01 )"	"5.12 ( 0.25 )"	"3.91 ( 0.1 )"	6.21	0.63	0.63	5.12	3.91	"lasso"
+100	100	0.3	1	0.26	"0.74 ( 0.01 )"	"7.62 ( 0.48 )"	"1.8 ( 0.12 )"	10.82	0.53	0.74	7.62	1.8	"lasso"
+500	100	0.3	0.2	0.36	"0.86 ( 0 )"	"10.19 ( 0.4 )"	"0 ( 0 )"	15.19	0.57	0.86	10.19	0	"lasso"
+1000	100	0.3	0.1	0.52	"0.86 ( 0 )"	"6 ( 0.27 )"	"0 ( 0 )"	11	0.42	0.86	6	0	"lasso"
+50	500	0.3	10	0.03	"0.61 ( 0.01 )"	"10.46 ( 0.31 )"	"4.69 ( 0.08 )"	10.77	0.87	0.61	10.46	4.69	"lasso"
+100	500	0.3	5	0.17	"0.66 ( 0.01 )"	"8.6 ( 0.65 )"	"3.19 ( 0.12 )"	10.41	0.64	0.66	8.6	3.19	"lasso"
+500	500	0.3	1	0.19	"0.85 ( 0 )"	"25.12 ( 1.08 )"	"0 ( 0 )"	30.12	0.77	0.85	25.12	0	"lasso"
+1000	500	0.3	0.5	0.25	"0.86 ( 0 )"	"18.48 ( 0.73 )"	"0 ( 0 )"	23.48	0.72	0.86	18.48	0	"lasso"
+50	1000	0.3	20	0.02	"0.6 ( 0.01 )"	"12.08 ( 0.26 )"	"4.97 ( 0.08 )"	12.11	0.91	0.6	12.08	4.97	"lasso"
+100	1000	0.3	10	0.13	"0.65 ( 0.01 )"	"9 ( 0.66 )"	"3.58 ( 0.12 )"	10.42	0.72	0.65	9	3.58	"lasso"
+500	1000	0.3	2	0.16	"0.85 ( 0 )"	"31.41 ( 1.51 )"	"0 ( 0 )"	36.41	0.8	0.85	31.41	0	"lasso"
+1000	1000	0.3	1	0.17	"0.86 ( 0 )"	"28.41 ( 1.2 )"	"0 ( 0 )"	33.41	0.79	0.86	28.41	0	"lasso"
+50	50	0.5	1	0.24	"0.69 ( 0.01 )"	"4.53 ( 0.29 )"	"2.89 ( 0.13 )"	6.64	0.48	0.69	4.53	2.89	"lasso"
+100	50	0.5	0.5	0.32	"0.82 ( 0.01 )"	"7.11 ( 0.4 )"	"0.91 ( 0.09 )"	11.2	0.48	0.82	7.11	0.91	"lasso"
+500	50	0.5	0.1	0.5	"0.89 ( 0 )"	"5.76 ( 0.24 )"	"0 ( 0 )"	10.76	0.41	0.89	5.76	0	"lasso"
+1000	50	0.5	0.05	0.68	"0.9 ( 0 )"	"3.3 ( 0.16 )"	"0 ( 0 )"	8.3	0.25	0.9	3.3	0	"lasso"
+50	100	0.5	2	0.19	"0.68 ( 0.01 )"	"5.12 ( 0.25 )"	"3.35 ( 0.1 )"	6.77	0.57	0.68	5.12	3.35	"lasso"
+100	100	0.5	1	0.29	"0.8 ( 0.01 )"	"8.11 ( 0.47 )"	"1.32 ( 0.1 )"	11.79	0.54	0.8	8.11	1.32	"lasso"
+500	100	0.5	0.2	0.34	"0.89 ( 0 )"	"10.67 ( 0.37 )"	"0 ( 0 )"	15.67	0.59	0.89	10.67	0	"lasso"
+1000	100	0.5	0.1	0.55	"0.9 ( 0 )"	"5.3 ( 0.23 )"	"0 ( 0 )"	10.3	0.38	0.9	5.3	0	"lasso"
+50	500	0.5	10	0.06	"0.63 ( 0.01 )"	"9.49 ( 0.26 )"	"4.45 ( 0.08 )"	10.04	0.84	0.63	9.49	4.45	"lasso"
+100	500	0.5	5	0.2	"0.72 ( 0.01 )"	"9.62 ( 0.69 )"	"2.61 ( 0.12 )"	12.01	0.63	0.72	9.62	2.61	"lasso"
+500	500	0.5	1	0.18	"0.89 ( 0 )"	"27.04 ( 0.99 )"	"0 ( 0 )"	32.04	0.79	0.89	27.04	0	"lasso"
+1000	500	0.5	0.5	0.25	"0.9 ( 0 )"	"17.99 ( 0.68 )"	"0 ( 0 )"	22.99	0.71	0.9	17.99	0	"lasso"
+50	1000	0.5	20	0.05	"0.63 ( 0.01 )"	"11.31 ( 0.3 )"	"4.54 ( 0.09 )"	11.77	0.87	0.63	11.31	4.54	"lasso"
+100	1000	0.5	10	0.16	"0.7 ( 0.01 )"	"10.35 ( 0.82 )"	"3.02 ( 0.12 )"	12.33	0.68	0.7	10.35	3.02	"lasso"
+500	1000	0.5	2	0.15	"0.88 ( 0 )"	"34.48 ( 1.42 )"	"0 ( 0 )"	39.48	0.82	0.88	34.48	0	"lasso"
+1000	1000	0.5	1	0.19	"0.89 ( 0 )"	"25.24 ( 1.1 )"	"0 ( 0 )"	30.24	0.77	0.89	25.24	0	"lasso"
+50	50	0.7	1	0.27	"0.72 ( 0.01 )"	"3.87 ( 0.24 )"	"2.98 ( 0.11 )"	5.89	0.42	0.72	3.87	2.98	"lasso"
+100	50	0.7	0.5	0.34	"0.82 ( 0.01 )"	"7.43 ( 0.37 )"	"0.6 ( 0.08 )"	11.83	0.49	0.82	7.43	0.6	"lasso"
+500	50	0.7	0.1	0.49	"0.91 ( 0 )"	"5.84 ( 0.26 )"	"0 ( 0 )"	10.84	0.42	0.91	5.84	0	"lasso"
+1000	50	0.7	0.05	0.71	"0.92 ( 0 )"	"3.03 ( 0.15 )"	"0 ( 0 )"	8.03	0.23	0.92	3.03	0	"lasso"
+50	100	0.7	2	0.21	"0.69 ( 0.01 )"	"5.07 ( 0.25 )"	"3.13 ( 0.1 )"	6.94	0.55	0.69	5.07	3.13	"lasso"
+100	100	0.7	1	0.28	"0.82 ( 0.01 )"	"9.65 ( 0.48 )"	"0.97 ( 0.1 )"	13.68	0.59	0.82	9.65	0.97	"lasso"
+500	100	0.7	0.2	0.36	"0.91 ( 0 )"	"10.19 ( 0.37 )"	"0 ( 0 )"	15.19	0.58	0.91	10.19	0	"lasso"
+1000	100	0.7	0.1	0.59	"0.92 ( 0 )"	"4.81 ( 0.19 )"	"0 ( 0 )"	9.81	0.37	0.92	4.81	0	"lasso"
+50	500	0.7	10	0.09	"0.66 ( 0.01 )"	"9.13 ( 0.25 )"	"4.14 ( 0.08 )"	9.99	0.81	0.66	9.13	4.14	"lasso"
+100	500	0.7	5	0.21	"0.75 ( 0.01 )"	"9.59 ( 0.72 )"	"2.44 ( 0.11 )"	12.15	0.61	0.75	9.59	2.44	"lasso"
+500	500	0.7	1	0.18	"0.9 ( 0 )"	"26.2 ( 0.86 )"	"0 ( 0 )"	31.2	0.79	0.9	26.2	0	"lasso"
+1000	500	0.7	0.5	0.28	"0.91 ( 0 )"	"15.6 ( 0.55 )"	"0 ( 0 )"	20.6	0.68	0.91	15.6	0	"lasso"
+50	1000	0.7	20	0.05	"0.64 ( 0.01 )"	"11.44 ( 0.28 )"	"4.51 ( 0.08 )"	11.93	0.87	0.64	11.44	4.51	"lasso"
+100	1000	0.7	10	0.18	"0.71 ( 0.01 )"	"10.03 ( 0.71 )"	"2.95 ( 0.1 )"	12.08	0.65	0.71	10.03	2.95	"lasso"
+500	1000	0.7	2	0.14	"0.9 ( 0 )"	"34.85 ( 1.14 )"	"0 ( 0 )"	39.85	0.83	0.9	34.85	0	"lasso"
+1000	1000	0.7	1	0.2	"0.91 ( 0 )"	"24.83 ( 0.82 )"	"0 ( 0 )"	29.83	0.78	0.91	24.83	0	"lasso"
+50	50	0.9	1	0.3	"0.73 ( 0.01 )"	"4.4 ( 0.26 )"	"2.55 ( 0.11 )"	6.85	0.44	0.73	4.4	2.55	"lasso"
+100	50	0.9	0.5	0.33	"0.85 ( 0.01 )"	"8.14 ( 0.35 )"	"0.42 ( 0.06 )"	12.72	0.53	0.85	8.14	0.42	"lasso"
+500	50	0.9	0.1	0.54	"0.92 ( 0 )"	"5.15 ( 0.22 )"	"0 ( 0 )"	10.15	0.38	0.92	5.15	0	"lasso"
+1000	50	0.9	0.05	0.76	"0.92 ( 0 )"	"2.61 ( 0.13 )"	"0 ( 0 )"	7.61	0.19	0.92	2.61	0	"lasso"
+50	100	0.9	2	0.23	"0.7 ( 0.01 )"	"4.84 ( 0.24 )"	"3.3 ( 0.09 )"	6.54	0.54	0.7	4.84	3.3	"lasso"
+100	100	0.9	1	0.28	"0.81 ( 0.01 )"	"9.92 ( 0.51 )"	"0.87 ( 0.08 )"	14.05	0.57	0.81	9.92	0.87	"lasso"
+500	100	0.9	0.2	0.37	"0.92 ( 0 )"	"9.65 ( 0.35 )"	"0 ( 0 )"	14.65	0.57	0.92	9.65	0	"lasso"
+1000	100	0.9	0.1	0.62	"0.93 ( 0 )"	"4.29 ( 0.18 )"	"0 ( 0 )"	9.29	0.33	0.93	4.29	0	"lasso"
+50	500	0.9	10	0.08	"0.62 ( 0.01 )"	"8.99 ( 0.26 )"	"4.13 ( 0.09 )"	9.86	0.8	0.62	8.99	4.13	"lasso"
+100	500	0.9	5	0.23	"0.74 ( 0.01 )"	"8.85 ( 0.62 )"	"2.4 ( 0.12 )"	11.45	0.6	0.74	8.85	2.4	"lasso"
+500	500	0.9	1	0.19	"0.91 ( 0 )"	"24.91 ( 0.78 )"	"0 ( 0 )"	29.91	0.78	0.91	24.91	0	"lasso"
+1000	500	0.9	0.5	0.31	"0.92 ( 0 )"	"14.12 ( 0.43 )"	"0 ( 0 )"	19.12	0.67	0.92	14.12	0	"lasso"
+50	1000	0.9	20	0.07	"0.64 ( 0.01 )"	"10.81 ( 0.29 )"	"4.22 ( 0.08 )"	11.59	0.84	0.64	10.81	4.22	"lasso"
+100	1000	0.9	10	0.19	"0.74 ( 0.01 )"	"11.15 ( 0.75 )"	"2.44 ( 0.11 )"	13.71	0.65	0.74	11.15	2.44	"lasso"
+500	1000	0.9	2	0.14	"0.91 ( 0 )"	"34.98 ( 1.04 )"	"0 ( 0 )"	39.98	0.83	0.91	34.98	0	"lasso"
+1000	1000	0.9	1	0.22	"0.92 ( 0 )"	"22.21 ( 0.73 )"	"0 ( 0 )"	27.21	0.76	0.92	22.21	0	"lasso"
+50	50	0.1	1	0.01	"0.61 ( 0.01 )"	"17.05 ( 1.31 )"	"3.17 ( 0.19 )"	18.88	0.8	0.61	17.05	3.17	"elnet"
+100	50	0.1	0.5	0.04	"0.59 ( 0.01 )"	"13.62 ( 1.31 )"	"2.9 ( 0.17 )"	15.72	0.64	0.59	13.62	2.9	"elnet"
+500	50	0.1	0.1	0.11	"0.64 ( 0 )"	"16.89 ( 1.14 )"	"1.1 ( 0.13 )"	20.79	0.68	0.64	16.89	1.1	"elnet"
+1000	50	0.1	0.05	0.12	"0.66 ( 0 )"	"19.43 ( 1.02 )"	"0.42 ( 0.08 )"	24.01	0.71	0.66	19.43	0.42	"elnet"
+50	100	0.1	2	0.01	"0.63 ( 0.01 )"	"27.39 ( 2.11 )"	"3.38 ( 0.17 )"	29.01	0.87	0.63	27.39	3.38	"elnet"
+100	100	0.1	1	0.02	"0.59 ( 0.01 )"	"26.17 ( 2.4 )"	"3.34 ( 0.16 )"	27.83	0.84	0.59	26.17	3.34	"elnet"
+500	100	0.1	0.2	0.09	"0.62 ( 0 )"	"22.75 ( 1.87 )"	"1.61 ( 0.13 )"	26.14	0.69	0.62	22.75	1.61	"elnet"
+1000	100	0.1	0.1	0.1	"0.64 ( 0 )"	"28.32 ( 1.88 )"	"0.66 ( 0.1 )"	32.66	0.77	0.64	28.32	0.66	"elnet"
+50	500	0.1	10	0	"0.61 ( 0.01 )"	"69.03 ( 7.45 )"	"4.13 ( 0.19 )"	69.9	0.96	0.61	69.03	4.13	"elnet"
+100	500	0.1	5	0	"0.57 ( 0.01 )"	"70.64 ( 9.13 )"	"3.91 ( 0.17 )"	71.73	0.91	0.57	70.64	3.91	"elnet"
+500	500	0.1	1	0.1	"0.61 ( 0.01 )"	"20.55 ( 4.33 )"	"3.15 ( 0.1 )"	22.4	0.62	0.61	20.55	3.15	"elnet"
+1000	500	0.1	0.5	0.12	"0.63 ( 0 )"	"26.03 ( 3.45 )"	"2.38 ( 0.11 )"	28.65	0.62	0.63	26.03	2.38	"elnet"
+50	1000	0.1	20	0	"0.59 ( 0.01 )"	"103.61 ( 10.54 )"	"4.34 ( 0.18 )"	104.27	0.98	0.59	103.61	4.34	"elnet"
+100	1000	0.1	10	0	"0.56 ( 0.01 )"	"103.21 ( 12.75 )"	"4.15 ( 0.17 )"	104.06	0.95	0.56	103.21	4.15	"elnet"
+500	1000	0.1	2	0.07	"0.61 ( 0 )"	"29.41 ( 7.4 )"	"3.25 ( 0.09 )"	31.16	0.62	0.61	29.41	3.25	"elnet"
+1000	1000	0.1	1	0.18	"0.62 ( 0 )"	"16.15 ( 2.77 )"	"2.73 ( 0.09 )"	18.42	0.53	0.62	16.15	2.73	"elnet"
+50	50	0.3	1	0.08	"0.68 ( 0.01 )"	"14.8 ( 1.01 )"	"1.73 ( 0.15 )"	18.07	0.69	0.68	14.8	1.73	"elnet"
+100	50	0.3	0.5	0.15	"0.77 ( 0.01 )"	"15.15 ( 1 )"	"0.64 ( 0.09 )"	19.51	0.65	0.77	15.15	0.64	"elnet"
+500	50	0.3	0.1	0.35	"0.85 ( 0 )"	"8.95 ( 0.69 )"	"0 ( 0 )"	13.95	0.46	0.85	8.95	0	"elnet"
+1000	50	0.3	0.05	0.33	"0.86 ( 0 )"	"9.66 ( 0.93 )"	"0 ( 0 )"	14.66	0.44	0.86	9.66	0	"elnet"
+50	100	0.3	2	0.05	"0.67 ( 0.01 )"	"23.92 ( 2 )"	"2.13 ( 0.15 )"	26.79	0.77	0.67	23.92	2.13	"elnet"
+100	100	0.3	1	0.14	"0.74 ( 0.01 )"	"17.75 ( 1.51 )"	"1.13 ( 0.11 )"	21.62	0.67	0.74	17.75	1.13	"elnet"
+500	100	0.3	0.2	0.32	"0.86 ( 0 )"	"11.53 ( 1.02 )"	"0.01 ( 0.01 )"	16.52	0.51	0.86	11.53	0.01	"elnet"
+1000	100	0.3	0.1	0.33	"0.86 ( 0 )"	"11.15 ( 1.03 )"	"0 ( 0 )"	16.15	0.49	0.86	11.15	0	"elnet"
+50	500	0.3	10	0.01	"0.6 ( 0.01 )"	"81.07 ( 6.91 )"	"2.92 ( 0.14 )"	83.15	0.93	0.6	81.07	2.92	"elnet"
+100	500	0.3	5	0.06	"0.66 ( 0.01 )"	"38.14 ( 5.11 )"	"2.16 ( 0.13 )"	40.98	0.78	0.66	38.14	2.16	"elnet"
+500	500	0.3	1	0.22	"0.84 ( 0 )"	"20.87 ( 1.93 )"	"0.02 ( 0.01 )"	25.85	0.63	0.84	20.87	0.02	"elnet"
+1000	500	0.3	0.5	0.29	"0.86 ( 0 )"	"15.35 ( 1.6 )"	"0 ( 0 )"	20.35	0.53	0.86	15.35	0	"elnet"
+50	1000	0.3	20	0.01	"0.63 ( 0.01 )"	"104.78 ( 9.53 )"	"3.37 ( 0.14 )"	106.41	0.96	0.63	104.78	3.37	"elnet"
+100	1000	0.3	10	0.04	"0.64 ( 0.01 )"	"55.95 ( 7.57 )"	"2.5 ( 0.13 )"	58.45	0.85	0.64	55.95	2.5	"elnet"
+500	1000	0.3	2	0.23	"0.85 ( 0 )"	"20.69 ( 1.82 )"	"0.05 ( 0.02 )"	25.64	0.64	0.85	20.69	0.05	"elnet"
+1000	1000	0.3	1	0.29	"0.86 ( 0 )"	"15.19 ( 1.67 )"	"0 ( 0 )"	20.19	0.51	0.86	15.19	0	"elnet"
+50	50	0.5	1	0.11	"0.7 ( 0.01 )"	"13.99 ( 0.96 )"	"1.59 ( 0.12 )"	17.4	0.68	0.7	13.99	1.59	"elnet"
+100	50	0.5	0.5	0.21	"0.8 ( 0.01 )"	"11.28 ( 0.76 )"	"0.81 ( 0.1 )"	15.47	0.57	0.8	11.28	0.81	"elnet"
+500	50	0.5	0.1	0.35	"0.88 ( 0 )"	"9.06 ( 0.77 )"	"0 ( 0 )"	14.06	0.46	0.88	9.06	0	"elnet"
+1000	50	0.5	0.05	0.39	"0.9 ( 0 )"	"8.03 ( 0.81 )"	"0 ( 0 )"	13.03	0.4	0.9	8.03	0	"elnet"
+50	100	0.5	2	0.06	"0.68 ( 0.01 )"	"25.61 ( 2.1 )"	"1.79 ( 0.12 )"	28.82	0.77	0.68	25.61	1.79	"elnet"
+100	100	0.5	1	0.18	"0.79 ( 0.01 )"	"13.99 ( 1.21 )"	"1.12 ( 0.1 )"	17.87	0.6	0.79	13.99	1.12	"elnet"
+500	100	0.5	0.2	0.32	"0.89 ( 0 )"	"11.65 ( 1.03 )"	"0 ( 0 )"	16.65	0.52	0.89	11.65	0	"elnet"
+1000	100	0.5	0.1	0.38	"0.89 ( 0 )"	"9.51 ( 0.97 )"	"0 ( 0 )"	14.51	0.43	0.89	9.51	0	"elnet"
+50	500	0.5	10	0.02	"0.61 ( 0.01 )"	"72.53 ( 6.75 )"	"2.72 ( 0.13 )"	74.81	0.92	0.61	72.53	2.72	"elnet"
+100	500	0.5	5	0.08	"0.7 ( 0.01 )"	"32.65 ( 4.21 )"	"1.9 ( 0.11 )"	35.75	0.76	0.7	32.65	1.9	"elnet"
+500	500	0.5	1	0.23	"0.88 ( 0 )"	"20.07 ( 1.75 )"	"0 ( 0 )"	25.07	0.63	0.88	20.07	0	"elnet"
+1000	500	0.5	0.5	0.3	"0.89 ( 0 )"	"14.5 ( 1.43 )"	"0 ( 0 )"	19.5	0.53	0.89	14.5	0	"elnet"
+50	1000	0.5	20	0.01	"0.62 ( 0.01 )"	"85.83 ( 8.57 )"	"3.31 ( 0.12 )"	87.52	0.94	0.62	85.83	3.31	"elnet"
+100	1000	0.5	10	0.05	"0.67 ( 0.01 )"	"51.72 ( 6.91 )"	"2.12 ( 0.12 )"	54.6	0.83	0.67	51.72	2.12	"elnet"
+500	1000	0.5	2	0.24	"0.88 ( 0 )"	"19.39 ( 1.85 )"	"0.02 ( 0.01 )"	24.37	0.59	0.88	19.39	0.02	"elnet"
+1000	1000	0.5	1	0.27	"0.89 ( 0 )"	"17.02 ( 1.92 )"	"0 ( 0 )"	22.02	0.54	0.89	17.02	0	"elnet"
+50	50	0.7	1	0.13	"0.73 ( 0.01 )"	"12.65 ( 0.97 )"	"1.61 ( 0.12 )"	16.04	0.63	0.73	12.65	1.61	"elnet"
+100	50	0.7	0.5	0.22	"0.81 ( 0.01 )"	"11.53 ( 0.67 )"	"0.54 ( 0.09 )"	15.99	0.58	0.81	11.53	0.54	"elnet"
+500	50	0.7	0.1	0.34	"0.91 ( 0 )"	"9.34 ( 0.72 )"	"0 ( 0 )"	14.34	0.48	0.91	9.34	0	"elnet"
+1000	50	0.7	0.05	0.36	"0.91 ( 0 )"	"8.58 ( 0.78 )"	"0 ( 0 )"	13.58	0.42	0.91	8.58	0	"elnet"
+50	100	0.7	2	0.07	"0.69 ( 0.01 )"	"24.95 ( 1.99 )"	"1.47 ( 0.1 )"	28.48	0.76	0.69	24.95	1.47	"elnet"
+100	100	0.7	1	0.17	"0.79 ( 0.01 )"	"15.63 ( 1.26 )"	"0.96 ( 0.11 )"	19.67	0.63	0.79	15.63	0.96	"elnet"
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+1000	100	0.7	0.1	0.38	"0.91 ( 0 )"	"9.42 ( 0.9 )"	"0 ( 0 )"	14.42	0.44	0.91	9.42	0	"elnet"
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+500	100	0.3	0.2	0.8	"0.81 ( 0 )"	"0.12 ( 0.06 )"	"1.7 ( 0.09 )"	4.42	0.01	0.81	0.12	1.7	"compLasso"
+1000	100	0.3	0.1	0.74	"0.8 ( 0 )"	"0.49 ( 0.26 )"	"2.05 ( 0.08 )"	4.44	0.04	0.8	0.49	2.05	"compLasso"
+50	500	0.3	10	0.02	"1 ( 0 )"	"105.94 ( 7.57 )"	"2.04 ( 0.11 )"	109.9	0.95	1	105.94	2.04	"compLasso"
+100	500	0.3	5	0.12	"0.88 ( 0.01 )"	"23.82 ( 3.36 )"	"1.9 ( 0.09 )"	27.92	0.75	0.88	23.82	1.9	"compLasso"
+500	500	0.3	1	0.79	"0.8 ( 0 )"	"0.24 ( 0.08 )"	"1.86 ( 0.09 )"	4.38	0.03	0.8	0.24	1.86	"compLasso"
+1000	500	0.3	0.5	0.73	"0.8 ( 0 )"	"0.63 ( 0.15 )"	"1.85 ( 0.09 )"	4.78	0.08	0.8	0.63	1.85	"compLasso"
+50	1000	0.3	20	0.02	"1 ( 0 )"	"154.21 ( 5.09 )"	"2.09 ( 0.1 )"	158.12	0.97	1	154.21	2.09	"compLasso"
+100	1000	0.3	10	0.07	"0.91 ( 0 )"	"36.7 ( 6.19 )"	"2.26 ( 0.1 )"	40.44	0.84	0.91	36.7	2.26	"compLasso"
+500	1000	0.3	2	0.74	"0.81 ( 0 )"	"0.57 ( 0.15 )"	"1.66 ( 0.09 )"	4.91	0.07	0.81	0.57	1.66	"compLasso"
+1000	1000	0.3	1	0.73	"0.8 ( 0 )"	"0.7 ( 0.15 )"	"1.84 ( 0.09 )"	4.86	0.09	0.8	0.7	1.84	"compLasso"
+50	50	0.5	1	0.2	"0.92 ( 0 )"	"9.67 ( 0.64 )"	"1.26 ( 0.09 )"	14.41	0.63	0.92	9.67	1.26	"compLasso"
+100	50	0.5	0.5	0.38	"0.89 ( 0 )"	"5.16 ( 0.81 )"	"0.81 ( 0.08 )"	10.35	0.36	0.89	5.16	0.81	"compLasso"
+500	50	0.5	0.1	0.95	"0.87 ( 0 )"	"0.15 ( 0.1 )"	"0.15 ( 0.04 )"	6	0.01	0.87	0.15	0.15	"compLasso"
+1000	50	0.5	0.05	0.96	"0.87 ( 0 )"	"0.13 ( 0.07 )"	"0.09 ( 0.03 )"	6.04	0.01	0.87	0.13	0.09	"compLasso"
+50	100	0.5	2	0.09	"0.96 ( 0.01 )"	"25 ( 2.25 )"	"1.32 ( 0.09 )"	29.68	0.77	0.96	25	1.32	"compLasso"
+100	100	0.5	1	0.31	"0.9 ( 0 )"	"7.65 ( 0.74 )"	"1.01 ( 0.08 )"	12.64	0.51	0.9	7.65	1.01	"compLasso"
+500	100	0.5	0.2	0.93	"0.87 ( 0 )"	"0.14 ( 0.07 )"	"0.28 ( 0.05 )"	5.86	0.02	0.87	0.14	0.28	"compLasso"
+1000	100	0.5	0.1	0.92	"0.87 ( 0 )"	"0.34 ( 0.17 )"	"0.12 ( 0.03 )"	6.22	0.03	0.87	0.34	0.12	"compLasso"
+50	500	0.5	10	0.02	"1 ( 0 )"	"157.13 ( 6.78 )"	"1.35 ( 0.1 )"	161.78	0.97	1	157.13	1.35	"compLasso"
+100	500	0.5	5	0.1	"0.95 ( 0 )"	"33.42 ( 3.83 )"	"1.3 ( 0.09 )"	38.12	0.82	0.95	33.42	1.3	"compLasso"
+500	500	0.5	1	0.86	"0.87 ( 0 )"	"0.67 ( 0.16 )"	"0.3 ( 0.05 )"	6.37	0.07	0.87	0.67	0.3	"compLasso"
+1000	500	0.5	0.5	0.98	"0.87 ( 0 )"	"0.03 ( 0.02 )"	"0.09 ( 0.03 )"	5.94	0	0.87	0.03	0.09	"compLasso"
+50	1000	0.5	20	0.01	"1 ( 0 )"	"246.84 ( 6.66 )"	"1.53 ( 0.09 )"	251.31	0.98	1	246.84	1.53	"compLasso"
+100	1000	0.5	10	0.04	"0.98 ( 0 )"	"96.92 ( 8.19 )"	"1.31 ( 0.09 )"	101.61	0.92	0.98	96.92	1.31	"compLasso"
+500	1000	0.5	2	0.87	"0.87 ( 0 )"	"0.53 ( 0.2 )"	"0.35 ( 0.06 )"	6.18	0.04	0.87	0.53	0.35	"compLasso"
+1000	1000	0.5	1	0.93	"0.87 ( 0 )"	"0.35 ( 0.12 )"	"0.11 ( 0.03 )"	6.24	0.03	0.87	0.35	0.11	"compLasso"
+50	50	0.7	1	0.15	"0.95 ( 0 )"	"12.8 ( 0.96 )"	"1 ( 0.09 )"	17.8	0.66	0.95	12.8	1	"compLasso"
+100	50	0.7	0.5	0.41	"0.91 ( 0 )"	"5.79 ( 0.7 )"	"0.31 ( 0.05 )"	11.48	0.4	0.91	5.79	0.31	"compLasso"
+500	50	0.7	0.1	0.96	"0.9 ( 0 )"	"0.17 ( 0.08 )"	"0.04 ( 0.02 )"	6.13	0.02	0.9	0.17	0.04	"compLasso"
+1000	50	0.7	0.05	0.98	"0.89 ( 0 )"	"0.1 ( 0.06 )"	"0 ( 0 )"	6.1	0.01	0.89	0.1	0	"compLasso"
+50	100	0.7	2	0.06	"0.99 ( 0 )"	"35.59 ( 2.41 )"	"0.94 ( 0.09 )"	40.65	0.83	0.99	35.59	0.94	"compLasso"
+100	100	0.7	1	0.26	"0.92 ( 0 )"	"11.64 ( 1.38 )"	"0.53 ( 0.06 )"	17.11	0.57	0.92	11.64	0.53	"compLasso"
+500	100	0.7	0.2	0.97	"0.9 ( 0 )"	"0.15 ( 0.07 )"	"0.05 ( 0.02 )"	6.1	0.02	0.9	0.15	0.05	"compLasso"
+1000	100	0.7	0.1	0.93	"0.9 ( 0 )"	"0.41 ( 0.14 )"	"0.03 ( 0.02 )"	6.38	0.04	0.9	0.41	0.03	"compLasso"
+50	500	0.7	10	0.02	"1 ( 0 )"	"171.29 ( 6.32 )"	"1.12 ( 0.09 )"	176.17	0.97	1	171.29	1.12	"compLasso"
+100	500	0.7	5	0.06	"0.98 ( 0 )"	"68.31 ( 6.57 )"	"0.98 ( 0.09 )"	73.33	0.87	0.98	68.31	0.98	"compLasso"
+500	500	0.7	1	0.93	"0.89 ( 0 )"	"0.37 ( 0.12 )"	"0.06 ( 0.02 )"	6.31	0.04	0.89	0.37	0.06	"compLasso"
+1000	500	0.7	0.5	0.98	"0.9 ( 0 )"	"0.13 ( 0.07 )"	"0.01 ( 0.01 )"	6.12	0.01	0.9	0.13	0.01	"compLasso"
+50	1000	0.7	20	0.01	"1 ( 0 )"	"275.88 ( 8.89 )"	"1.39 ( 0.09 )"	280.49	0.98	1	275.88	1.39	"compLasso"
+100	1000	0.7	10	0.03	"1 ( 0 )"	"155.38 ( 7.45 )"	"0.93 ( 0.08 )"	160.45	0.95	1	155.38	0.93	"compLasso"
+500	1000	0.7	2	0.84	"0.9 ( 0 )"	"1.07 ( 0.26 )"	"0.1 ( 0.03 )"	6.97	0.09	0.9	1.07	0.1	"compLasso"
+1000	1000	0.7	1	0.95	"0.9 ( 0 )"	"0.3 ( 0.11 )"	"0.02 ( 0.01 )"	6.28	0.03	0.9	0.3	0.02	"compLasso"
+50	50	0.9	1	0.13	"0.97 ( 0 )"	"15.75 ( 1.04 )"	"0.71 ( 0.07 )"	21.04	0.7	0.97	15.75	0.71	"compLasso"
+100	50	0.9	0.5	0.44	"0.93 ( 0 )"	"5.06 ( 0.38 )"	"0.34 ( 0.05 )"	10.72	0.42	0.93	5.06	0.34	"compLasso"
+500	50	0.9	0.1	0.98	"0.91 ( 0 )"	"0.07 ( 0.04 )"	"0.03 ( 0.02 )"	6.04	0.01	0.91	0.07	0.03	"compLasso"
+1000	50	0.9	0.05	1	"0.91 ( 0 )"	"0 ( 0 )"	"0 ( 0 )"	6	0	0.91	0	0	"compLasso"
+50	100	0.9	2	0.06	"0.99 ( 0 )"	"37.69 ( 2.33 )"	"0.96 ( 0.09 )"	42.73	0.84	0.99	37.69	0.96	"compLasso"
+100	100	0.9	1	0.26	"0.94 ( 0 )"	"11.53 ( 1.15 )"	"0.52 ( 0.06 )"	17.01	0.61	0.94	11.53	0.52	"compLasso"
+500	100	0.9	0.2	0.96	"0.91 ( 0 )"	"0.18 ( 0.06 )"	"0.03 ( 0.02 )"	6.15	0.02	0.91	0.18	0.03	"compLasso"
+1000	100	0.9	0.1	0.97	"0.91 ( 0 )"	"0.17 ( 0.09 )"	"0 ( 0 )"	6.17	0.02	0.91	0.17	0	"compLasso"
+50	500	0.9	10	0.01	"1 ( 0 )"	"189.13 ( 6.24 )"	"0.91 ( 0.1 )"	194.22	0.97	1	189.13	0.91	"compLasso"
+100	500	0.9	5	0.05	"0.99 ( 0 )"	"88.92 ( 5.92 )"	"0.68 ( 0.07 )"	94.24	0.92	0.99	88.92	0.68	"compLasso"
+500	500	0.9	1	0.85	"0.91 ( 0 )"	"1.08 ( 0.33 )"	"0.01 ( 0.01 )"	7.07	0.08	0.91	1.08	0.01	"compLasso"
+1000	500	0.9	0.5	0.98	"0.91 ( 0 )"	"0.14 ( 0.07 )"	"0 ( 0 )"	6.14	0.02	0.91	0.14	0	"compLasso"
+50	1000	0.9	20	0.01	"1 ( 0 )"	"330.51 ( 8.59 )"	"0.97 ( 0.08 )"	335.54	0.98	1	330.51	0.97	"compLasso"
+100	1000	0.9	10	0.02	"1 ( 0 )"	"217.74 ( 6.88 )"	"0.67 ( 0.07 )"	223.07	0.97	1	217.74	0.67	"compLasso"
+500	1000	0.9	2	0.76	"0.91 ( 0 )"	"1.91 ( 0.48 )"	"0.02 ( 0.01 )"	7.89	0.12	0.91	1.91	0.02	"compLasso"
+1000	1000	0.9	1	0.95	"0.91 ( 0 )"	"0.27 ( 0.11 )"	"0.02 ( 0.01 )"	6.25	0.03	0.91	0.27	0.02	"compLasso"
diff --git a/simulations/results_summary_bin/table_ind_binary_all.txt b/simulations/results_summary_bin/table_ind_binary_all.txt
new file mode 100644
index 0000000..9ba1348
--- /dev/null
+++ b/simulations/results_summary_bin/table_ind_binary_all.txt
@@ -0,0 +1,65 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	0	1	0.33	"0.74 ( 0.01 )"	"3.69 ( 0.25 )"	"2.5 ( 0.12 )"	6.19	0.37	0.74	3.69	2.5	"lasso"
+100	50	0	0.5	0.35	"0.85 ( 0.01 )"	"7.16 ( 0.37 )"	"0.64 ( 0.08 )"	11.52	0.48	0.85	7.16	0.64	"lasso"
+500	50	0	0.1	0.53	"0.93 ( 0 )"	"5.21 ( 0.21 )"	"0 ( 0 )"	10.21	0.39	0.93	5.21	0	"lasso"
+1000	50	0	0.05	0.8	"0.93 ( 0 )"	"2.25 ( 0.11 )"	"0 ( 0 )"	7.25	0.15	0.93	2.25	0	"lasso"
+50	100	0	2	0.25	"0.73 ( 0.01 )"	"5.13 ( 0.34 )"	"2.93 ( 0.1 )"	7.2	0.5	0.73	5.13	2.93	"lasso"
+100	100	0	1	0.32	"0.83 ( 0.01 )"	"8.22 ( 0.44 )"	"1.03 ( 0.09 )"	12.19	0.54	0.83	8.22	1.03	"lasso"
+500	100	0	0.2	0.4	"0.92 ( 0 )"	"8.89 ( 0.29 )"	"0 ( 0 )"	13.89	0.55	0.92	8.89	0	"lasso"
+1000	100	0	0.1	0.69	"0.93 ( 0 )"	"3.53 ( 0.16 )"	"0 ( 0 )"	8.53	0.27	0.93	3.53	0	"lasso"
+50	500	0	10	0.13	"0.7 ( 0.01 )"	"8.05 ( 0.28 )"	"3.74 ( 0.09 )"	9.31	0.74	0.7	8.05	3.74	"lasso"
+100	500	0	5	0.24	"0.79 ( 0.01 )"	"11.14 ( 0.64 )"	"1.71 ( 0.1 )"	14.43	0.63	0.79	11.14	1.71	"lasso"
+500	500	0	1	0.2	"0.92 ( 0 )"	"23.83 ( 0.69 )"	"0 ( 0 )"	28.83	0.78	0.92	23.83	0	"lasso"
+1000	500	0	0.5	0.36	"0.93 ( 0 )"	"11.38 ( 0.41 )"	"0 ( 0 )"	16.38	0.6	0.93	11.38	0	"lasso"
+50	1000	0	20	0.09	"0.67 ( 0.01 )"	"9.77 ( 0.24 )"	"4.03 ( 0.08 )"	10.74	0.81	0.67	9.77	4.03	"lasso"
+100	1000	0	10	0.2	"0.78 ( 0.01 )"	"11.91 ( 0.8 )"	"2.32 ( 0.1 )"	14.59	0.66	0.78	11.91	2.32	"lasso"
+500	1000	0	2	0.16	"0.91 ( 0 )"	"30.84 ( 0.92 )"	"0 ( 0 )"	35.84	0.82	0.91	30.84	0	"lasso"
+1000	1000	0	1	0.24	"0.93 ( 0 )"	"19.96 ( 0.61 )"	"0 ( 0 )"	24.96	0.74	0.93	19.96	0	"lasso"
+50	50	0	1	0.15	"0.76 ( 0.01 )"	"12.04 ( 0.89 )"	"1.42 ( 0.11 )"	15.62	0.61	0.76	12.04	1.42	"elnet"
+100	50	0	0.5	0.23	"0.84 ( 0.01 )"	"11.44 ( 0.76 )"	"0.46 ( 0.07 )"	15.98	0.56	0.84	11.44	0.46	"elnet"
+500	50	0	0.1	0.36	"0.92 ( 0 )"	"8.74 ( 0.72 )"	"0 ( 0 )"	13.74	0.45	0.92	8.74	0	"elnet"
+1000	50	0	0.05	0.41	"0.93 ( 0 )"	"7.56 ( 0.72 )"	"0 ( 0 )"	12.56	0.4	0.93	7.56	0	"elnet"
+50	100	0	2	0.1	"0.72 ( 0.01 )"	"18.92 ( 1.7 )"	"1.81 ( 0.12 )"	22.11	0.72	0.72	18.92	1.81	"elnet"
+100	100	0	1	0.22	"0.82 ( 0.01 )"	"12.48 ( 0.9 )"	"0.96 ( 0.09 )"	16.52	0.58	0.82	12.48	0.96	"elnet"
+500	100	0	0.2	0.32	"0.92 ( 0 )"	"11.78 ( 0.99 )"	"0 ( 0 )"	16.78	0.52	0.92	11.78	0	"elnet"
+1000	100	0	0.1	0.34	"0.93 ( 0 )"	"10.71 ( 1.03 )"	"0 ( 0 )"	15.71	0.47	0.93	10.71	0	"elnet"
+50	500	0	10	0.03	"0.68 ( 0.01 )"	"57.17 ( 5.72 )"	"2.27 ( 0.13 )"	59.9	0.88	0.68	57.17	2.27	"elnet"
+100	500	0	5	0.15	"0.77 ( 0.01 )"	"20.84 ( 2.08 )"	"1.49 ( 0.1 )"	24.35	0.69	0.77	20.84	1.49	"elnet"
+500	500	0	1	0.27	"0.92 ( 0 )"	"16.79 ( 1.47 )"	"0 ( 0 )"	21.79	0.61	0.92	16.79	0	"elnet"
+1000	500	0	0.5	0.36	"0.93 ( 0 )"	"11.47 ( 1.14 )"	"0 ( 0 )"	16.47	0.48	0.93	11.47	0	"elnet"
+50	1000	0	20	0.02	"0.67 ( 0.01 )"	"72.88 ( 8.55 )"	"2.89 ( 0.13 )"	74.99	0.91	0.67	72.88	2.89	"elnet"
+100	1000	0	10	0.09	"0.76 ( 0.01 )"	"29.95 ( 4.05 )"	"2 ( 0.1 )"	32.95	0.77	0.76	29.95	2	"elnet"
+500	1000	0	2	0.24	"0.91 ( 0 )"	"19.47 ( 1.76 )"	"0 ( 0 )"	24.47	0.62	0.91	19.47	0	"elnet"
+1000	1000	0	1	0.25	"0.93 ( 0 )"	"18.47 ( 1.76 )"	"0 ( 0 )"	23.47	0.58	0.93	18.47	0	"elnet"
+50	50	0	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0	0.5	0.1	"1 ( 0 )"	"1.97 ( 0.13 )"	"4.62 ( 0.08 )"	3.34	0.55	1	1.97	4.62	"rf"
+500	50	0	0.1	0.54	"1 ( 0 )"	"1.26 ( 0.12 )"	"2.19 ( 0.08 )"	5.07	0.22	1	1.26	2.19	"rf"
+1000	50	0	0.05	0.73	"1 ( 0 )"	"0.86 ( 0.09 )"	"1.12 ( 0.08 )"	5.74	0.13	1	0.86	1.12	"rf"
+50	100	0	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0	1	0.05	"1 ( 0 )"	"4.29 ( 0.17 )"	"4.79 ( 0.09 )"	5.5	0.77	1	4.29	4.79	"rf"
+500	100	0	0.2	0.4	"1 ( 0 )"	"3.42 ( 0.16 )"	"2.01 ( 0.09 )"	7.41	0.44	1	3.42	2.01	"rf"
+1000	100	0	0.1	0.49	"1 ( 0 )"	"3.39 ( 0.19 )"	"1.14 ( 0.07 )"	8.25	0.38	1	3.39	1.14	"rf"
+50	500	0	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0	5	0.01	"1 ( 0 )"	"24.68 ( 0.48 )"	"4.5 ( 0.09 )"	26.18	0.94	1	24.68	4.5	"rf"
+500	500	0	1	0.09	"1 ( 0 )"	"23.08 ( 0.51 )"	"2.34 ( 0.1 )"	26.74	0.86	1	23.08	2.34	"rf"
+1000	500	0	0.5	0.14	"1 ( 0 )"	"22.72 ( 0.46 )"	"1.34 ( 0.08 )"	27.38	0.83	1	22.72	1.34	"rf"
+50	1000	0	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0	10	0.01	"1 ( 0 )"	"46.76 ( 0.67 )"	"4.86 ( 0.09 )"	47.9	0.98	1	46.76	4.86	"rf"
+500	1000	0	2	0.04	"1 ( 0 )"	"47.82 ( 0.66 )"	"2.5 ( 0.1 )"	51.32	0.93	1	47.82	2.5	"rf"
+1000	1000	0	1	0.07	"1 ( 0 )"	"47.14 ( 0.77 )"	"1.46 ( 0.08 )"	51.68	0.91	1	47.14	1.46	"rf"
+50	50	0	1	0.13	"0.98 ( 0 )"	"16.38 ( 0.96 )"	"0.61 ( 0.06 )"	21.77	0.71	0.98	16.38	0.61	"compLasso"
+100	50	0	0.5	0.32	"0.93 ( 0 )"	"7.94 ( 0.54 )"	"0.34 ( 0.06 )"	13.6	0.54	0.93	7.94	0.34	"compLasso"
+500	50	0	0.1	0.63	"0.91 ( 0 )"	"2.67 ( 0.6 )"	"0.1 ( 0.03 )"	8.57	0.19	0.91	2.67	0.1	"compLasso"
+1000	50	0	0.05	0.75	"0.91 ( 0 )"	"1.61 ( 0.48 )"	"0.04 ( 0.02 )"	7.57	0.09	0.91	1.61	0.04	"compLasso"
+50	100	0	2	0.06	"0.99 ( 0 )"	"39.18 ( 2.14 )"	"0.63 ( 0.07 )"	44.55	0.85	0.99	39.18	0.63	"compLasso"
+100	100	0	1	0.2	"0.94 ( 0 )"	"16.29 ( 1.16 )"	"0.47 ( 0.06 )"	21.82	0.7	0.94	16.29	0.47	"compLasso"
+500	100	0	0.2	0.47	"0.91 ( 0 )"	"5.63 ( 0.84 )"	"0.13 ( 0.03 )"	11.5	0.3	0.91	5.63	0.13	"compLasso"
+1000	100	0	0.1	0.43	"0.91 ( 0 )"	"6.87 ( 1.8 )"	"0 ( 0 )"	12.87	0.17	0.91	6.87	0	"compLasso"
+50	500	0	10	0.01	"1 ( 0 )"	"251.02 ( 6.8 )"	"0.36 ( 0.06 )"	256.66	0.98	1	251.02	0.36	"compLasso"
+100	500	0	5	0.03	"1 ( 0 )"	"134.32 ( 6.63 )"	"0.33 ( 0.05 )"	139.99	0.95	1	134.32	0.33	"compLasso"
+500	500	0	1	0.43	"0.91 ( 0 )"	"8.32 ( 1.15 )"	"0.1 ( 0.03 )"	14.22	0.41	0.91	8.32	0.1	"compLasso"
+1000	500	0	0.5	0.6	"0.91 ( 0 )"	"4.63 ( 1.57 )"	"0 ( 0 )"	10.63	0.21	0.91	4.63	0	"compLasso"
+50	1000	0	20	0.01	"1 ( 0 )"	"406.32 ( 7.33 )"	"0.73 ( 0.07 )"	411.59	0.99	1	406.32	0.73	"compLasso"
+100	1000	0	10	0.02	"1 ( 0 )"	"283.24 ( 7.31 )"	"0.49 ( 0.06 )"	288.75	0.98	1	283.24	0.49	"compLasso"
+500	1000	0	2	0.4	"0.91 ( 0 )"	"9.4 ( 1.07 )"	"0.07 ( 0.03 )"	15.33	0.47	0.91	9.4	0.07	"compLasso"
+1000	1000	0	1	0.82	"0.91 ( 0 )"	"1.91 ( 0.29 )"	"0.01 ( 0.01 )"	7.9	0.18	0.91	1.91	0.01	"compLasso"
diff --git a/simulations/results_summary_bin/table_toe_binary_all.txt b/simulations/results_summary_bin/table_toe_binary_all.txt
new file mode 100644
index 0000000..870f214
--- /dev/null
+++ b/simulations/results_summary_bin/table_toe_binary_all.txt
@@ -0,0 +1,321 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"ROC"	"FP"	"FN"	"num_select"	"FDR"	"ROC_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	0.1	1	0.32	"0.78 ( 0.01 )"	"3.65 ( 0.23 )"	"2.83 ( 0.11 )"	5.82	0.39	0.78	3.65	2.83	"lasso"
+100	50	0.1	0.5	0.34	"0.85 ( 0.01 )"	"7.35 ( 0.34 )"	"0.68 ( 0.09 )"	11.67	0.5	0.85	7.35	0.68	"lasso"
+500	50	0.1	0.1	0.55	"0.92 ( 0 )"	"4.99 ( 0.22 )"	"0 ( 0 )"	9.99	0.37	0.92	4.99	0	"lasso"
+1000	50	0.1	0.05	0.78	"0.93 ( 0 )"	"2.42 ( 0.12 )"	"0 ( 0 )"	7.42	0.17	0.93	2.42	0	"lasso"
+50	100	0.1	2	0.26	"0.74 ( 0.01 )"	"4.65 ( 0.29 )"	"2.93 ( 0.12 )"	6.72	0.48	0.74	4.65	2.93	"lasso"
+100	100	0.1	1	0.33	"0.85 ( 0.01 )"	"8.11 ( 0.41 )"	"0.86 ( 0.09 )"	12.25	0.52	0.85	8.11	0.86	"lasso"
+500	100	0.1	0.2	0.4	"0.92 ( 0 )"	"8.63 ( 0.29 )"	"0 ( 0 )"	13.63	0.54	0.92	8.63	0	"lasso"
+1000	100	0.1	0.1	0.64	"0.92 ( 0 )"	"4.02 ( 0.18 )"	"0 ( 0 )"	9.02	0.31	0.92	4.02	0	"lasso"
+50	500	0.1	10	0.12	"0.71 ( 0.01 )"	"8.29 ( 0.25 )"	"3.89 ( 0.1 )"	9.4	0.77	0.71	8.29	3.89	"lasso"
+100	500	0.1	5	0.24	"0.8 ( 0.01 )"	"8.84 ( 0.61 )"	"2.19 ( 0.12 )"	11.65	0.57	0.8	8.84	2.19	"lasso"
+500	500	0.1	1	0.21	"0.92 ( 0 )"	"22.86 ( 0.75 )"	"0 ( 0 )"	27.86	0.77	0.92	22.86	0	"lasso"
+1000	500	0.1	0.5	0.33	"0.93 ( 0 )"	"12.73 ( 0.35 )"	"0 ( 0 )"	17.73	0.65	0.93	12.73	0	"lasso"
+50	1000	0.1	20	0.09	"0.68 ( 0.01 )"	"10.35 ( 0.33 )"	"4.14 ( 0.09 )"	11.21	0.82	0.68	10.35	4.14	"lasso"
+100	1000	0.1	10	0.19	"0.77 ( 0.01 )"	"10.84 ( 0.75 )"	"2.46 ( 0.1 )"	13.38	0.63	0.77	10.84	2.46	"lasso"
+500	1000	0.1	2	0.16	"0.92 ( 0 )"	"30.83 ( 1.05 )"	"0 ( 0 )"	35.83	0.81	0.92	30.83	0	"lasso"
+1000	1000	0.1	1	0.23	"0.93 ( 0 )"	"20.39 ( 0.59 )"	"0 ( 0 )"	25.39	0.75	0.93	20.39	0	"lasso"
+50	50	0.3	1	0.28	"0.75 ( 0.01 )"	"3.69 ( 0.22 )"	"3.04 ( 0.1 )"	5.65	0.4	0.75	3.69	3.04	"lasso"
+100	50	0.3	0.5	0.32	"0.84 ( 0.01 )"	"7.11 ( 0.33 )"	"0.93 ( 0.1 )"	11.18	0.5	0.84	7.11	0.93	"lasso"
+500	50	0.3	0.1	0.51	"0.92 ( 0 )"	"5.67 ( 0.22 )"	"0 ( 0 )"	10.67	0.41	0.92	5.67	0	"lasso"
+1000	50	0.3	0.05	0.78	"0.92 ( 0 )"	"2.48 ( 0.13 )"	"0 ( 0 )"	7.48	0.18	0.92	2.48	0	"lasso"
+50	100	0.3	2	0.26	"0.75 ( 0.01 )"	"4.41 ( 0.27 )"	"3.47 ( 0.09 )"	5.94	0.49	0.75	4.41	3.47	"lasso"
+100	100	0.3	1	0.28	"0.82 ( 0.01 )"	"8.16 ( 0.53 )"	"1.46 ( 0.11 )"	11.7	0.52	0.82	8.16	1.46	"lasso"
+500	100	0.3	0.2	0.41	"0.92 ( 0 )"	"8.48 ( 0.34 )"	"0 ( 0 )"	13.48	0.53	0.92	8.48	0	"lasso"
+1000	100	0.3	0.1	0.67	"0.92 ( 0 )"	"3.71 ( 0.15 )"	"0 ( 0 )"	8.71	0.29	0.92	3.71	0	"lasso"
+50	500	0.3	10	0.13	"0.69 ( 0.01 )"	"7.94 ( 0.3 )"	"4.11 ( 0.08 )"	8.83	0.77	0.69	7.94	4.11	"lasso"
+100	500	0.3	5	0.2	"0.78 ( 0.01 )"	"10.16 ( 0.68 )"	"2.49 ( 0.11 )"	12.67	0.63	0.78	10.16	2.49	"lasso"
+500	500	0.3	1	0.2	"0.91 ( 0 )"	"23.65 ( 0.7 )"	"0 ( 0 )"	28.65	0.78	0.91	23.65	0	"lasso"
+1000	500	0.3	0.5	0.34	"0.92 ( 0 )"	"12.28 ( 0.34 )"	"0 ( 0 )"	17.28	0.64	0.92	12.28	0	"lasso"
+50	1000	0.3	20	0.1	"0.68 ( 0.01 )"	"9.77 ( 0.31 )"	"4.3 ( 0.06 )"	10.47	0.83	0.68	9.77	4.3	"lasso"
+100	1000	0.3	10	0.17	"0.77 ( 0.01 )"	"10.68 ( 0.8 )"	"3.02 ( 0.11 )"	12.66	0.67	0.77	10.68	3.02	"lasso"
+500	1000	0.3	2	0.17	"0.9 ( 0 )"	"30.07 ( 1.09 )"	"0 ( 0 )"	35.07	0.81	0.9	30.07	0	"lasso"
+1000	1000	0.3	1	0.24	"0.92 ( 0 )"	"19.7 ( 0.7 )"	"0 ( 0 )"	24.7	0.73	0.92	19.7	0	"lasso"
+50	50	0.5	1	0.25	"0.76 ( 0.01 )"	"3.86 ( 0.22 )"	"3.4 ( 0.12 )"	5.46	0.47	0.76	3.86	3.4	"lasso"
+100	50	0.5	0.5	0.31	"0.84 ( 0.01 )"	"6.67 ( 0.32 )"	"1.32 ( 0.1 )"	10.35	0.51	0.84	6.67	1.32	"lasso"
+500	50	0.5	0.1	0.55	"0.91 ( 0 )"	"4.95 ( 0.22 )"	"0 ( 0 )"	9.95	0.37	0.91	4.95	0	"lasso"
+1000	50	0.5	0.05	0.77	"0.91 ( 0 )"	"2.5 ( 0.14 )"	"0 ( 0 )"	7.5	0.17	0.91	2.5	0	"lasso"
+50	100	0.5	2	0.25	"0.72 ( 0.01 )"	"4.08 ( 0.28 )"	"3.81 ( 0.08 )"	5.27	0.49	0.72	4.08	3.81	"lasso"
+100	100	0.5	1	0.26	"0.8 ( 0.01 )"	"7.61 ( 0.43 )"	"2.03 ( 0.12 )"	10.58	0.55	0.8	7.61	2.03	"lasso"
+500	100	0.5	0.2	0.43	"0.91 ( 0 )"	"8.05 ( 0.31 )"	"0 ( 0 )"	13.05	0.51	0.91	8.05	0	"lasso"
+1000	100	0.5	0.1	0.65	"0.91 ( 0 )"	"3.9 ( 0.16 )"	"0 ( 0 )"	8.9	0.3	0.91	3.9	0	"lasso"
+50	500	0.5	10	0.14	"0.7 ( 0.01 )"	"7.23 ( 0.28 )"	"4.26 ( 0.07 )"	7.97	0.76	0.7	7.23	4.26	"lasso"
+100	500	0.5	5	0.18	"0.77 ( 0.01 )"	"8.77 ( 0.71 )"	"3.31 ( 0.1 )"	10.46	0.61	0.77	8.77	3.31	"lasso"
+500	500	0.5	1	0.2	"0.9 ( 0 )"	"23.13 ( 0.75 )"	"0.01 ( 0.01 )"	28.12	0.77	0.9	23.13	0.01	"lasso"
+1000	500	0.5	0.5	0.32	"0.91 ( 0 )"	"13.23 ( 0.36 )"	"0.01 ( 0.01 )"	18.22	0.66	0.91	13.23	0.01	"lasso"
+50	1000	0.5	20	0.11	"0.68 ( 0.01 )"	"9.13 ( 0.25 )"	"4.4 ( 0.06 )"	9.73	0.83	0.68	9.13	4.4	"lasso"
+100	1000	0.5	10	0.19	"0.77 ( 0.01 )"	"8.39 ( 0.65 )"	"3.48 ( 0.08 )"	9.91	0.64	0.77	8.39	3.48	"lasso"
+500	1000	0.5	2	0.16	"0.89 ( 0 )"	"30.97 ( 0.97 )"	"0 ( 0 )"	35.97	0.82	0.89	30.97	0	"lasso"
+1000	1000	0.5	1	0.23	"0.91 ( 0 )"	"21.03 ( 0.66 )"	"0 ( 0 )"	26.03	0.75	0.91	21.03	0	"lasso"
+50	50	0.7	1	0.28	"0.77 ( 0.01 )"	"3.76 ( 0.26 )"	"3.51 ( 0.11 )"	5.25	0.42	0.77	3.76	3.51	"lasso"
+100	50	0.7	0.5	0.33	"0.81 ( 0.01 )"	"5.27 ( 0.32 )"	"2.04 ( 0.12 )"	8.23	0.44	0.81	5.27	2.04	"lasso"
+500	50	0.7	0.1	0.57	"0.89 ( 0 )"	"4.61 ( 0.17 )"	"0.04 ( 0.02 )"	9.57	0.36	0.89	4.61	0.04	"lasso"
+1000	50	0.7	0.05	0.74	"0.89 ( 0 )"	"2.8 ( 0.15 )"	"0 ( 0 )"	7.8	0.21	0.89	2.8	0	"lasso"
+50	100	0.7	2	0.24	"0.71 ( 0.01 )"	"4.31 ( 0.28 )"	"4.05 ( 0.09 )"	5.26	0.55	0.71	4.31	4.05	"lasso"
+100	100	0.7	1	0.27	"0.79 ( 0.01 )"	"6.34 ( 0.4 )"	"2.67 ( 0.12 )"	8.67	0.53	0.79	6.34	2.67	"lasso"
+500	100	0.7	0.2	0.43	"0.89 ( 0 )"	"7.92 ( 0.24 )"	"0.04 ( 0.02 )"	12.88	0.52	0.89	7.92	0.04	"lasso"
+1000	100	0.7	0.1	0.66	"0.9 ( 0 )"	"3.85 ( 0.16 )"	"0 ( 0 )"	8.85	0.3	0.9	3.85	0	"lasso"
+50	500	0.7	10	0.14	"0.7 ( 0.01 )"	"6.5 ( 0.24 )"	"4.63 ( 0.06 )"	6.87	0.77	0.7	6.5	4.63	"lasso"
+100	500	0.7	5	0.21	"0.76 ( 0.01 )"	"6.98 ( 0.58 )"	"3.65 ( 0.08 )"	8.33	0.56	0.76	6.98	3.65	"lasso"
+500	500	0.7	1	0.2	"0.88 ( 0 )"	"22.04 ( 0.74 )"	"0.14 ( 0.04 )"	26.9	0.76	0.88	22.04	0.14	"lasso"
+1000	500	0.7	0.5	0.33	"0.89 ( 0 )"	"13.15 ( 0.38 )"	"0 ( 0 )"	18.15	0.65	0.89	13.15	0	"lasso"
+50	1000	0.7	20	0.1	"0.66 ( 0.01 )"	"8.82 ( 0.28 )"	"4.72 ( 0.05 )"	9.1	0.85	0.66	8.82	4.72	"lasso"
+100	1000	0.7	10	0.18	"0.76 ( 0.01 )"	"6.93 ( 0.59 )"	"4 ( 0.08 )"	7.93	0.62	0.76	6.93	4	"lasso"
+500	1000	0.7	2	0.16	"0.87 ( 0 )"	"29.07 ( 1 )"	"0.16 ( 0.04 )"	33.91	0.81	0.87	29.07	0.16	"lasso"
+1000	1000	0.7	1	0.22	"0.89 ( 0 )"	"21.8 ( 0.63 )"	"0 ( 0 )"	26.8	0.76	0.89	21.8	0	"lasso"
+50	50	0.9	1	0.3	"0.69 ( 0.01 )"	"2.9 ( 0.2 )"	"4.22 ( 0.09 )"	3.68	0.41	0.69	2.9	4.22	"lasso"
+100	50	0.9	0.5	0.37	"0.76 ( 0.01 )"	"3.93 ( 0.21 )"	"2.74 ( 0.11 )"	6.19	0.41	0.76	3.93	2.74	"lasso"
+500	50	0.9	0.1	0.57	"0.83 ( 0 )"	"3.65 ( 0.16 )"	"0.56 ( 0.08 )"	8.09	0.31	0.83	3.65	0.56	"lasso"
+1000	50	0.9	0.05	0.76	"0.83 ( 0 )"	"2.41 ( 0.12 )"	"0.23 ( 0.04 )"	7.18	0.18	0.83	2.41	0.23	"lasso"
+50	100	0.9	2	0.26	"0.7 ( 0.01 )"	"3.29 ( 0.23 )"	"4.29 ( 0.09 )"	4	0.45	0.7	3.29	4.29	"lasso"
+100	100	0.9	1	0.32	"0.76 ( 0.01 )"	"4.77 ( 0.33 )"	"3.26 ( 0.09 )"	6.51	0.46	0.76	4.77	3.26	"lasso"
+500	100	0.9	0.2	0.45	"0.82 ( 0 )"	"5.65 ( 0.21 )"	"0.92 ( 0.09 )"	9.73	0.45	0.82	5.65	0.92	"lasso"
+1000	100	0.9	0.1	0.63	"0.83 ( 0 )"	"3.87 ( 0.18 )"	"0.24 ( 0.05 )"	8.63	0.3	0.83	3.87	0.24	"lasso"
+50	500	0.9	10	0.15	"0.66 ( 0.01 )"	"5.8 ( 0.24 )"	"4.65 ( 0.07 )"	6.15	0.75	0.66	5.8	4.65	"lasso"
+100	500	0.9	5	0.21	"0.7 ( 0.01 )"	"5.55 ( 0.55 )"	"4.14 ( 0.09 )"	6.41	0.51	0.7	5.55	4.14	"lasso"
+500	500	0.9	1	0.19	"0.81 ( 0 )"	"15.93 ( 0.73 )"	"1.42 ( 0.1 )"	19.51	0.73	0.81	15.93	1.42	"lasso"
+1000	500	0.9	0.5	0.31	"0.83 ( 0 )"	"11.96 ( 0.46 )"	"0.42 ( 0.06 )"	16.54	0.63	0.83	11.96	0.42	"lasso"
+50	1000	0.9	20	0.08	"0.66 ( 0.01 )"	"8.02 ( 0.28 )"	"4.9 ( 0.07 )"	8.12	0.85	0.66	8.02	4.9	"lasso"
+100	1000	0.9	10	0.18	"0.69 ( 0.01 )"	"5.85 ( 0.58 )"	"4.37 ( 0.07 )"	6.48	0.61	0.69	5.85	4.37	"lasso"
+500	1000	0.9	2	0.16	"0.8 ( 0 )"	"18.7 ( 0.93 )"	"1.88 ( 0.1 )"	21.82	0.77	0.8	18.7	1.88	"lasso"
+1000	1000	0.9	1	0.21	"0.83 ( 0 )"	"20.07 ( 0.72 )"	"0.41 ( 0.05 )"	24.66	0.75	0.83	20.07	0.41	"lasso"
+50	50	0.1	1	0.13	"0.77 ( 0.01 )"	"13.02 ( 1.02 )"	"1.51 ( 0.11 )"	16.51	0.63	0.77	13.02	1.51	"elnet"
+100	50	0.1	0.5	0.24	"0.85 ( 0.01 )"	"10.71 ( 0.62 )"	"0.56 ( 0.07 )"	15.15	0.57	0.85	10.71	0.56	"elnet"
+500	50	0.1	0.1	0.41	"0.92 ( 0 )"	"7.43 ( 0.6 )"	"0.02 ( 0.01 )"	12.41	0.42	0.92	7.43	0.02	"elnet"
+1000	50	0.1	0.05	0.39	"0.92 ( 0 )"	"7.82 ( 0.76 )"	"0 ( 0 )"	12.82	0.4	0.92	7.82	0	"elnet"
+50	100	0.1	2	0.09	"0.73 ( 0.01 )"	"21.55 ( 1.94 )"	"1.64 ( 0.11 )"	24.91	0.73	0.73	21.55	1.64	"elnet"
+100	100	0.1	1	0.18	"0.83 ( 0.01 )"	"15.59 ( 1.32 )"	"0.84 ( 0.1 )"	19.75	0.62	0.83	15.59	0.84	"elnet"
+500	100	0.1	0.2	0.32	"0.92 ( 0 )"	"11.5 ( 0.9 )"	"0 ( 0 )"	16.5	0.54	0.92	11.5	0	"elnet"
+1000	100	0.1	0.1	0.34	"0.92 ( 0 )"	"11.02 ( 1.05 )"	"0 ( 0 )"	16.02	0.49	0.92	11.02	0	"elnet"
+50	500	0.1	10	0.03	"0.66 ( 0.01 )"	"58.7 ( 5.86 )"	"2.64 ( 0.12 )"	61.06	0.89	0.66	58.7	2.64	"elnet"
+100	500	0.1	5	0.14	"0.78 ( 0.01 )"	"19.29 ( 1.92 )"	"1.76 ( 0.1 )"	22.53	0.68	0.78	19.29	1.76	"elnet"
+500	500	0.1	1	0.26	"0.91 ( 0 )"	"17.05 ( 1.43 )"	"0 ( 0 )"	22.05	0.62	0.91	17.05	0	"elnet"
+1000	500	0.1	0.5	0.29	"0.93 ( 0 )"	"15.14 ( 1.39 )"	"0 ( 0 )"	20.14	0.54	0.93	15.14	0	"elnet"
+50	1000	0.1	20	0.02	"0.65 ( 0.01 )"	"85.97 ( 9 )"	"3.03 ( 0.13 )"	87.94	0.92	0.65	85.97	3.03	"elnet"
+100	1000	0.1	10	0.12	"0.76 ( 0.01 )"	"22.02 ( 1.97 )"	"2.07 ( 0.1 )"	24.95	0.75	0.76	22.02	2.07	"elnet"
+500	1000	0.1	2	0.23	"0.91 ( 0 )"	"21.07 ( 1.75 )"	"0 ( 0 )"	26.07	0.66	0.91	21.07	0	"elnet"
+1000	1000	0.1	1	0.24	"0.92 ( 0 )"	"19.56 ( 2.29 )"	"0 ( 0 )"	24.56	0.57	0.92	19.56	0	"elnet"
+50	50	0.3	1	0.13	"0.76 ( 0.01 )"	"11.32 ( 0.99 )"	"1.87 ( 0.14 )"	14.45	0.59	0.76	11.32	1.87	"elnet"
+100	50	0.3	0.5	0.18	"0.83 ( 0.01 )"	"12.96 ( 0.86 )"	"0.75 ( 0.09 )"	17.21	0.61	0.83	12.96	0.75	"elnet"
+500	50	0.3	0.1	0.33	"0.91 ( 0 )"	"9.89 ( 0.75 )"	"0 ( 0 )"	14.89	0.51	0.91	9.89	0	"elnet"
+1000	50	0.3	0.05	0.38	"0.92 ( 0 )"	"8.56 ( 0.69 )"	"0 ( 0 )"	13.56	0.44	0.92	8.56	0	"elnet"
+50	100	0.3	2	0.08	"0.74 ( 0.01 )"	"17.99 ( 1.78 )"	"2.32 ( 0.14 )"	20.67	0.72	0.74	17.99	2.32	"elnet"
+100	100	0.3	1	0.17	"0.82 ( 0.01 )"	"14.36 ( 1.25 )"	"1.36 ( 0.12 )"	18	0.6	0.82	14.36	1.36	"elnet"
+500	100	0.3	0.2	0.29	"0.91 ( 0 )"	"13.08 ( 1 )"	"0 ( 0 )"	18.08	0.56	0.91	13.08	0	"elnet"
+1000	100	0.3	0.1	0.38	"0.92 ( 0 )"	"9.67 ( 0.76 )"	"0 ( 0 )"	14.67	0.49	0.92	9.67	0	"elnet"
+50	500	0.3	10	0.03	"0.66 ( 0.01 )"	"49.2 ( 5.65 )"	"3.15 ( 0.13 )"	51.05	0.89	0.66	49.2	3.15	"elnet"
+100	500	0.3	5	0.11	"0.77 ( 0.01 )"	"23.84 ( 2.37 )"	"2.05 ( 0.11 )"	26.79	0.73	0.77	23.84	2.05	"elnet"
+500	500	0.3	1	0.19	"0.91 ( 0 )"	"25.04 ( 1.69 )"	"0 ( 0 )"	30.04	0.72	0.91	25.04	0	"elnet"
+1000	500	0.3	0.5	0.25	"0.92 ( 0 )"	"18.51 ( 1.25 )"	"0 ( 0 )"	23.51	0.66	0.92	18.51	0	"elnet"
+50	1000	0.3	20	0.02	"0.67 ( 0.01 )"	"54.74 ( 6.8 )"	"3.54 ( 0.12 )"	56.2	0.91	0.67	54.74	3.54	"elnet"
+100	1000	0.3	10	0.08	"0.75 ( 0.01 )"	"28.55 ( 5.11 )"	"2.58 ( 0.11 )"	30.97	0.75	0.75	28.55	2.58	"elnet"
+500	1000	0.3	2	0.17	"0.9 ( 0 )"	"29.91 ( 1.92 )"	"0 ( 0 )"	34.91	0.76	0.9	29.91	0	"elnet"
+1000	1000	0.3	1	0.19	"0.92 ( 0 )"	"25.69 ( 2.26 )"	"0 ( 0 )"	30.69	0.66	0.92	25.69	0	"elnet"
+50	50	0.5	1	0.12	"0.74 ( 0.01 )"	"11.42 ( 0.91 )"	"2.04 ( 0.13 )"	14.38	0.64	0.74	11.42	2.04	"elnet"
+100	50	0.5	0.5	0.19	"0.83 ( 0.01 )"	"11.74 ( 0.82 )"	"1.01 ( 0.11 )"	15.73	0.6	0.83	11.74	1.01	"elnet"
+500	50	0.5	0.1	0.31	"0.91 ( 0 )"	"10.82 ( 0.72 )"	"0 ( 0 )"	15.82	0.54	0.91	10.82	0	"elnet"
+1000	50	0.5	0.05	0.36	"0.91 ( 0 )"	"9.23 ( 0.76 )"	"0 ( 0 )"	14.23	0.46	0.91	9.23	0	"elnet"
+50	100	0.5	2	0.08	"0.72 ( 0.01 )"	"18.36 ( 1.76 )"	"2.53 ( 0.12 )"	20.83	0.72	0.72	18.36	2.53	"elnet"
+100	100	0.5	1	0.16	"0.81 ( 0.01 )"	"15.05 ( 1.14 )"	"1.47 ( 0.12 )"	18.58	0.67	0.81	15.05	1.47	"elnet"
+500	100	0.5	0.2	0.27	"0.91 ( 0 )"	"14.93 ( 0.96 )"	"0 ( 0 )"	19.93	0.64	0.91	14.93	0	"elnet"
+1000	100	0.5	0.1	0.38	"0.91 ( 0 )"	"10.11 ( 0.76 )"	"0 ( 0 )"	15.11	0.52	0.91	10.11	0	"elnet"
+50	500	0.5	10	0.03	"0.69 ( 0.01 )"	"40.87 ( 5.37 )"	"3.42 ( 0.13 )"	42.45	0.87	0.69	40.87	3.42	"elnet"
+100	500	0.5	5	0.09	"0.76 ( 0.01 )"	"22.4 ( 3.42 )"	"2.91 ( 0.1 )"	24.49	0.72	0.76	22.4	2.91	"elnet"
+500	500	0.5	1	0.19	"0.89 ( 0 )"	"26.07 ( 1.56 )"	"0.01 ( 0.01 )"	31.06	0.74	0.89	26.07	0.01	"elnet"
+1000	500	0.5	0.5	0.21	"0.91 ( 0 )"	"22.64 ( 1.75 )"	"0 ( 0 )"	27.64	0.69	0.91	22.64	0	"elnet"
+50	1000	0.5	20	0.02	"0.66 ( 0.01 )"	"61.74 ( 7.68 )"	"3.7 ( 0.11 )"	63.04	0.91	0.66	61.74	3.7	"elnet"
+100	1000	0.5	10	0.07	"0.75 ( 0.01 )"	"27.52 ( 4.1 )"	"3.1 ( 0.09 )"	29.42	0.8	0.75	27.52	3.1	"elnet"
+500	1000	0.5	2	0.16	"0.89 ( 0 )"	"31.54 ( 2.49 )"	"0.05 ( 0.03 )"	36.49	0.74	0.89	31.54	0.05	"elnet"
+1000	1000	0.5	1	0.17	"0.91 ( 0 )"	"29.65 ( 2.06 )"	"0 ( 0 )"	34.65	0.75	0.91	29.65	0	"elnet"
+50	50	0.7	1	0.1	"0.75 ( 0.01 )"	"12.33 ( 0.98 )"	"2.23 ( 0.15 )"	15.1	0.64	0.75	12.33	2.23	"elnet"
+100	50	0.7	0.5	0.18	"0.81 ( 0.01 )"	"11.18 ( 0.86 )"	"1.42 ( 0.12 )"	14.76	0.59	0.81	11.18	1.42	"elnet"
+500	50	0.7	0.1	0.31	"0.89 ( 0 )"	"11.14 ( 0.74 )"	"0.02 ( 0.02 )"	16.12	0.56	0.89	11.14	0.02	"elnet"
+1000	50	0.7	0.05	0.37	"0.89 ( 0 )"	"9.65 ( 0.77 )"	"0 ( 0 )"	14.65	0.49	0.89	9.65	0	"elnet"
+50	100	0.7	2	0.08	"0.71 ( 0.01 )"	"17.76 ( 1.65 )"	"2.81 ( 0.14 )"	19.95	0.74	0.71	17.76	2.81	"elnet"
+100	100	0.7	1	0.13	"0.79 ( 0.01 )"	"16.43 ( 1.31 )"	"1.79 ( 0.13 )"	19.64	0.68	0.79	16.43	1.79	"elnet"
+500	100	0.7	0.2	0.25	"0.89 ( 0 )"	"16.01 ( 0.82 )"	"0.03 ( 0.03 )"	20.98	0.67	0.89	16.01	0.03	"elnet"
+1000	100	0.7	0.1	0.33	"0.89 ( 0 )"	"12.77 ( 0.94 )"	"0 ( 0 )"	17.77	0.58	0.89	12.77	0	"elnet"
+50	500	0.7	10	0.03	"0.67 ( 0.01 )"	"43.83 ( 5.17 )"	"3.75 ( 0.12 )"	45.08	0.9	0.67	43.83	3.75	"elnet"
+100	500	0.7	5	0.1	"0.74 ( 0.01 )"	"19.1 ( 1.88 )"	"3.08 ( 0.1 )"	21.02	0.73	0.74	19.1	3.08	"elnet"
+500	500	0.7	1	0.15	"0.87 ( 0 )"	"31.23 ( 1.88 )"	"0.17 ( 0.04 )"	36.06	0.78	0.87	31.23	0.17	"elnet"
+1000	500	0.7	0.5	0.22	"0.89 ( 0 )"	"22.46 ( 1.45 )"	"0 ( 0 )"	27.46	0.71	0.89	22.46	0	"elnet"
+50	1000	0.7	20	0.02	"0.64 ( 0.01 )"	"47.17 ( 5.85 )"	"4.12 ( 0.09 )"	48.05	0.92	0.64	47.17	4.12	"elnet"
+100	1000	0.7	10	0.08	"0.75 ( 0.01 )"	"20.86 ( 2.56 )"	"3.59 ( 0.09 )"	22.27	0.77	0.75	20.86	3.59	"elnet"
+500	1000	0.7	2	0.13	"0.87 ( 0 )"	"36.32 ( 2.18 )"	"0.23 ( 0.05 )"	41.09	0.81	0.87	36.32	0.23	"elnet"
+1000	1000	0.7	1	0.16	"0.89 ( 0 )"	"32.22 ( 2.11 )"	"0.01 ( 0.01 )"	37.21	0.8	0.89	32.22	0.01	"elnet"
+50	50	0.9	1	0.09	"0.71 ( 0.01 )"	"14.15 ( 1 )"	"2.3 ( 0.14 )"	16.85	0.68	0.71	14.15	2.3	"elnet"
+100	50	0.9	0.5	0.15	"0.77 ( 0.01 )"	"11.74 ( 0.88 )"	"1.74 ( 0.13 )"	15	0.64	0.77	11.74	1.74	"elnet"
+500	50	0.9	0.1	0.27	"0.83 ( 0 )"	"12.28 ( 0.9 )"	"0.18 ( 0.06 )"	17.1	0.57	0.83	12.28	0.18	"elnet"
+1000	50	0.9	0.05	0.39	"0.84 ( 0 )"	"9.58 ( 0.68 )"	"0.01 ( 0.01 )"	14.57	0.51	0.84	9.58	0.01	"elnet"
+50	100	0.9	2	0.08	"0.7 ( 0.01 )"	"15.1 ( 1.65 )"	"3.16 ( 0.13 )"	16.94	0.7	0.7	15.1	3.16	"elnet"
+100	100	0.9	1	0.15	"0.75 ( 0.01 )"	"14.19 ( 1.38 )"	"2.23 ( 0.13 )"	16.96	0.64	0.75	14.19	2.23	"elnet"
+500	100	0.9	0.2	0.25	"0.83 ( 0 )"	"16.33 ( 1.01 )"	"0.2 ( 0.06 )"	21.13	0.67	0.83	16.33	0.2	"elnet"
+1000	100	0.9	0.1	0.32	"0.84 ( 0 )"	"13.82 ( 1.17 )"	"0.03 ( 0.02 )"	18.79	0.6	0.84	13.82	0.03	"elnet"
+50	500	0.9	10	0.05	"0.66 ( 0.02 )"	"28.64 ( 3.14 )"	"4.13 ( 0.07 )"	29.51	0.88	0.66	28.64	4.13	"elnet"
+100	500	0.9	5	0.07	"0.7 ( 0.01 )"	"25.71 ( 4.03 )"	"3.42 ( 0.1 )"	27.29	0.78	0.7	25.71	3.42	"elnet"
+500	500	0.9	1	0.14	"0.81 ( 0 )"	"25.68 ( 1.61 )"	"1.07 ( 0.11 )"	29.61	0.76	0.81	25.68	1.07	"elnet"
+1000	500	0.9	0.5	0.21	"0.83 ( 0 )"	"23.49 ( 1.26 )"	"0.15 ( 0.05 )"	28.34	0.75	0.83	23.49	0.15	"elnet"
+50	1000	0.9	20	0.02	"0.65 ( 0.01 )"	"49.08 ( 6.86 )"	"4.29 ( 0.09 )"	49.79	0.93	0.65	49.08	4.29	"elnet"
+100	1000	0.9	10	0.05	"0.66 ( 0.01 )"	"32.99 ( 5.22 )"	"3.81 ( 0.09 )"	34.18	0.8	0.66	32.99	3.81	"elnet"
+500	1000	0.9	2	0.11	"0.8 ( 0 )"	"30.91 ( 2.22 )"	"1.52 ( 0.12 )"	34.39	0.78	0.8	30.91	1.52	"elnet"
+1000	1000	0.9	1	0.14	"0.83 ( 0 )"	"35.66 ( 1.98 )"	"0.23 ( 0.06 )"	40.43	0.82	0.83	35.66	0.23	"elnet"
+50	50	0.1	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0.1	0.5	0.1	"1 ( 0 )"	"2.05 ( 0.14 )"	"4.71 ( 0.08 )"	3.31	0.56	1	2.05	4.71	"rf"
+500	50	0.1	0.1	0.5	"1 ( 0 )"	"1.4 ( 0.12 )"	"2.4 ( 0.08 )"	5	0.25	1	1.4	2.4	"rf"
+1000	50	0.1	0.05	0.69	"1 ( 0 )"	"0.98 ( 0.09 )"	"1.2 ( 0.08 )"	5.78	0.15	1	0.98	1.2	"rf"
+50	100	0.1	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0.1	1	0.06	"1 ( 0 )"	"3.95 ( 0.21 )"	"4.66 ( 0.08 )"	5.29	0.72	1	3.95	4.66	"rf"
+500	100	0.1	0.2	0.35	"1 ( 0 )"	"3.91 ( 0.21 )"	"2.17 ( 0.08 )"	7.74	0.48	1	3.91	2.17	"rf"
+1000	100	0.1	0.1	0.51	"1 ( 0 )"	"2.97 ( 0.15 )"	"1.29 ( 0.07 )"	7.68	0.36	1	2.97	1.29	"rf"
+50	500	0.1	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0.1	5	0.01	"1 ( 0 )"	"24.57 ( 0.55 )"	"4.72 ( 0.08 )"	25.85	0.95	1	24.57	4.72	"rf"
+500	500	0.1	1	0.09	"1 ( 0 )"	"23.57 ( 0.51 )"	"2.43 ( 0.1 )"	27.14	0.86	1	23.57	2.43	"rf"
+1000	500	0.1	0.5	0.14	"1 ( 0 )"	"22.61 ( 0.5 )"	"1.31 ( 0.08 )"	27.3	0.82	1	22.61	1.31	"rf"
+50	1000	0.1	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0.1	10	0.01	"1 ( 0 )"	"48.35 ( 0.66 )"	"4.88 ( 0.08 )"	49.47	0.98	1	48.35	4.88	"rf"
+500	1000	0.1	2	0.04	"1 ( 0 )"	"48.59 ( 0.73 )"	"2.71 ( 0.1 )"	51.88	0.94	1	48.59	2.71	"rf"
+1000	1000	0.1	1	0.06	"1 ( 0 )"	"46.74 ( 0.64 )"	"1.75 ( 0.09 )"	50.99	0.92	1	46.74	1.75	"rf"
+50	50	0.3	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0.3	0.5	0.1	"1 ( 0 )"	"1.68 ( 0.12 )"	"4.86 ( 0.08 )"	2.81	0.54	1	1.68	4.86	"rf"
+500	50	0.3	0.1	0.44	"1 ( 0 )"	"1.57 ( 0.12 )"	"2.74 ( 0.08 )"	4.83	0.3	1	1.57	2.74	"rf"
+1000	50	0.3	0.05	0.62	"1 ( 0 )"	"1.33 ( 0.1 )"	"1.44 ( 0.08 )"	5.89	0.21	1	1.33	1.44	"rf"
+50	100	0.3	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0.3	1	0.05	"1 ( 0 )"	"4.45 ( 0.2 )"	"4.85 ( 0.08 )"	5.6	0.78	1	4.45	4.85	"rf"
+500	100	0.3	0.2	0.33	"1 ( 0 )"	"3.36 ( 0.19 )"	"2.57 ( 0.09 )"	6.79	0.46	1	3.36	2.57	"rf"
+1000	100	0.3	0.1	0.47	"1 ( 0 )"	"3.43 ( 0.17 )"	"1.61 ( 0.09 )"	7.82	0.41	1	3.43	1.61	"rf"
+50	500	0.3	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0.3	5	0.01	"1 ( 0 )"	"23.69 ( 0.53 )"	"4.87 ( 0.08 )"	24.82	0.95	1	23.69	4.87	"rf"
+500	500	0.3	1	0.07	"1 ( 0 )"	"22.84 ( 0.44 )"	"3.11 ( 0.09 )"	25.73	0.89	1	22.84	3.11	"rf"
+1000	500	0.3	0.5	0.13	"1 ( 0 )"	"22.24 ( 0.43 )"	"1.77 ( 0.09 )"	26.47	0.84	1	22.24	1.77	"rf"
+50	1000	0.3	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0.3	10	0.01	"1 ( 0 )"	"48.06 ( 0.76 )"	"5.02 ( 0.08 )"	49.04	0.98	1	48.06	5.02	"rf"
+500	1000	0.3	2	0.04	"1 ( 0 )"	"47.71 ( 0.73 )"	"2.98 ( 0.09 )"	50.73	0.94	1	47.71	2.98	"rf"
+1000	1000	0.3	1	0.06	"1 ( 0 )"	"47.11 ( 0.84 )"	"2.03 ( 0.09 )"	51.08	0.92	1	47.11	2.03	"rf"
+50	50	0.5	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0.5	0.5	0.15	"1 ( 0 )"	"1.88 ( 0.15 )"	"4.77 ( 0.08 )"	3.1	0.54	1	1.88	4.77	"rf"
+500	50	0.5	0.1	0.44	"1 ( 0 )"	"1.83 ( 0.11 )"	"2.9 ( 0.09 )"	4.93	0.35	1	1.83	2.9	"rf"
+1000	50	0.5	0.05	0.61	"1 ( 0 )"	"1.9 ( 0.11 )"	"2.06 ( 0.08 )"	5.84	0.31	1	1.9	2.06	"rf"
+50	100	0.5	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0.5	1	0.08	"1 ( 0 )"	"4.6 ( 0.23 )"	"4.71 ( 0.07 )"	5.89	0.75	1	4.6	4.71	"rf"
+500	100	0.5	0.2	0.31	"1 ( 0 )"	"4.2 ( 0.22 )"	"3.01 ( 0.08 )"	7.19	0.55	1	4.2	3.01	"rf"
+1000	100	0.5	0.1	0.5	"1 ( 0 )"	"3.78 ( 0.18 )"	"1.84 ( 0.08 )"	7.94	0.46	1	3.78	1.84	"rf"
+50	500	0.5	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0.5	5	0.01	"1 ( 0 )"	"23.59 ( 0.56 )"	"4.98 ( 0.08 )"	24.61	0.96	1	23.59	4.98	"rf"
+500	500	0.5	1	0.07	"1 ( 0 )"	"23.16 ( 0.45 )"	"3.28 ( 0.08 )"	25.88	0.89	1	23.16	3.28	"rf"
+1000	500	0.5	0.5	0.13	"1 ( 0 )"	"22.83 ( 0.49 )"	"2.29 ( 0.08 )"	26.54	0.86	1	22.83	2.29	"rf"
+50	1000	0.5	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0.5	10	0.01	"1 ( 0 )"	"46.65 ( 0.77 )"	"4.98 ( 0.09 )"	47.67	0.98	1	46.65	4.98	"rf"
+500	1000	0.5	2	0.04	"1 ( 0 )"	"47.46 ( 0.83 )"	"3.51 ( 0.08 )"	49.95	0.95	1	47.46	3.51	"rf"
+1000	1000	0.5	1	0.06	"1 ( 0 )"	"47.83 ( 0.8 )"	"2.59 ( 0.08 )"	51.24	0.93	1	47.83	2.59	"rf"
+50	50	0.7	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0.7	0.5	0.16	"1 ( 0 )"	"1.87 ( 0.14 )"	"4.88 ( 0.08 )"	2.96	0.57	1	1.87	4.88	"rf"
+500	50	0.7	0.1	0.45	"1 ( 0 )"	"2.14 ( 0.13 )"	"3.36 ( 0.08 )"	4.78	0.43	1	2.14	3.36	"rf"
+1000	50	0.7	0.05	0.6	"1 ( 0 )"	"2.66 ( 0.14 )"	"2.27 ( 0.08 )"	6.39	0.39	1	2.66	2.27	"rf"
+50	100	0.7	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0.7	1	0.08	"1 ( 0 )"	"4.36 ( 0.22 )"	"4.8 ( 0.09 )"	5.56	0.77	1	4.36	4.8	"rf"
+500	100	0.7	0.2	0.38	"1 ( 0 )"	"4.03 ( 0.18 )"	"3.1 ( 0.09 )"	6.93	0.56	1	4.03	3.1	"rf"
+1000	100	0.7	0.1	0.5	"1 ( 0 )"	"4.65 ( 0.22 )"	"2 ( 0.07 )"	8.65	0.51	1	4.65	2	"rf"
+50	500	0.7	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0.7	5	0.02	"1 ( 0 )"	"23.58 ( 0.53 )"	"4.72 ( 0.09 )"	24.86	0.95	1	23.58	4.72	"rf"
+500	500	0.7	1	0.1	"1 ( 0 )"	"23.09 ( 0.56 )"	"3.38 ( 0.09 )"	25.71	0.89	1	23.09	3.38	"rf"
+1000	500	0.7	0.5	0.14	"1 ( 0 )"	"23.33 ( 0.53 )"	"2.58 ( 0.07 )"	26.75	0.87	1	23.33	2.58	"rf"
+50	1000	0.7	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0.7	10	0.01	"1 ( 0 )"	"46.8 ( 0.69 )"	"5.13 ( 0.07 )"	47.67	0.98	1	46.8	5.13	"rf"
+500	1000	0.7	2	0.05	"1 ( 0 )"	"48.68 ( 0.77 )"	"3.54 ( 0.08 )"	51.14	0.95	1	48.68	3.54	"rf"
+1000	1000	0.7	1	0.07	"1 ( 0 )"	"47.14 ( 0.7 )"	"2.73 ( 0.07 )"	50.41	0.93	1	47.14	2.73	"rf"
+50	50	0.9	1	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	50	0.9	0.5	0.12	"1 ( 0 )"	"2.09 ( 0.16 )"	"4.98 ( 0.09 )"	3.06	0.62	1	2.09	4.98	"rf"
+500	50	0.9	0.1	0.45	"1 ( 0 )"	"2.6 ( 0.13 )"	"3.33 ( 0.08 )"	5.27	0.47	1	2.6	3.33	"rf"
+1000	50	0.9	0.05	0.51	"1 ( 0 )"	"3.48 ( 0.17 )"	"2.35 ( 0.08 )"	7.13	0.47	1	3.48	2.35	"rf"
+50	100	0.9	2	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	100	0.9	1	0.07	"1 ( 0 )"	"4.4 ( 0.23 )"	"4.96 ( 0.09 )"	5.43	0.81	1	4.4	4.96	"rf"
+500	100	0.9	0.2	0.35	"1 ( 0 )"	"4.57 ( 0.24 )"	"3.5 ( 0.08 )"	7.07	0.62	1	4.57	3.5	"rf"
+1000	100	0.9	0.1	0.37	"1 ( 0 )"	"6.43 ( 0.28 )"	"2.57 ( 0.08 )"	9.86	0.63	1	6.43	2.57	"rf"
+50	500	0.9	10	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	500	0.9	5	0.01	"1 ( 0 )"	"24.58 ( 0.66 )"	"5.1 ( 0.09 )"	25.48	0.96	1	24.58	5.1	"rf"
+500	500	0.9	1	0.08	"1 ( 0 )"	"24.91 ( 0.57 )"	"3.53 ( 0.09 )"	27.38	0.91	1	24.91	3.53	"rf"
+1000	500	0.9	0.5	0.13	"1 ( 0 )"	"24.53 ( 0.53 )"	"2.84 ( 0.08 )"	27.69	0.88	1	24.53	2.84	"rf"
+50	1000	0.9	20	NA	"1 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1	0	6	"rf"
+100	1000	0.9	10	0	"1 ( 0 )"	"47.19 ( 0.96 )"	"5.2 ( 0.08 )"	47.99	0.98	1	47.19	5.2	"rf"
+500	1000	0.9	2	0.04	"1 ( 0 )"	"48.11 ( 0.92 )"	"3.78 ( 0.08 )"	50.33	0.95	1	48.11	3.78	"rf"
+1000	1000	0.9	1	0.07	"1 ( 0 )"	"49.47 ( 0.78 )"	"2.97 ( 0.07 )"	52.5	0.94	1	49.47	2.97	"rf"
+50	50	0.1	1	0.13	"0.97 ( 0 )"	"14.84 ( 0.9 )"	"0.93 ( 0.09 )"	19.91	0.7	0.97	14.84	0.93	"compLasso"
+100	50	0.1	0.5	0.33	"0.93 ( 0 )"	"7.19 ( 0.43 )"	"0.49 ( 0.07 )"	12.7	0.53	0.93	7.19	0.49	"compLasso"
+500	50	0.1	0.1	0.7	"0.9 ( 0 )"	"1.93 ( 0.34 )"	"0.16 ( 0.04 )"	7.77	0.16	0.9	1.93	0.16	"compLasso"
+1000	50	0.1	0.05	0.79	"0.9 ( 0 )"	"1.37 ( 0.42 )"	"0.01 ( 0.01 )"	7.36	0.1	0.9	1.37	0.01	"compLasso"
+50	100	0.1	2	0.07	"0.99 ( 0 )"	"36.64 ( 2.16 )"	"0.65 ( 0.07 )"	41.99	0.84	0.99	36.64	0.65	"compLasso"
+100	100	0.1	1	0.2	"0.94 ( 0 )"	"16.16 ( 1.22 )"	"0.47 ( 0.06 )"	21.69	0.7	0.94	16.16	0.47	"compLasso"
+500	100	0.1	0.2	0.36	"0.9 ( 0 )"	"8.64 ( 1.35 )"	"0.08 ( 0.03 )"	14.56	0.37	0.9	8.64	0.08	"compLasso"
+1000	100	0.1	0.1	0.47	"0.9 ( 0 )"	"5.83 ( 1.4 )"	"0.02 ( 0.01 )"	11.81	0.22	0.9	5.83	0.02	"compLasso"
+50	500	0.1	10	0.01	"1 ( 0 )"	"248.06 ( 7.56 )"	"0.56 ( 0.07 )"	253.5	0.98	1	248.06	0.56	"compLasso"
+100	500	0.1	5	0.03	"1 ( 0 )"	"132.04 ( 7.47 )"	"0.33 ( 0.05 )"	137.71	0.95	1	132.04	0.33	"compLasso"
+500	500	0.1	1	0.34	"0.91 ( 0 )"	"12.55 ( 1.81 )"	"0.04 ( 0.02 )"	18.51	0.52	0.91	12.55	0.04	"compLasso"
+1000	500	0.1	0.5	0.69	"0.9 ( 0 )"	"3.02 ( 0.6 )"	"0.01 ( 0.01 )"	9.01	0.22	0.9	3.02	0.01	"compLasso"
+50	1000	0.1	20	0.01	"1 ( 0 )"	"398.98 ( 6.9 )"	"0.8 ( 0.08 )"	404.18	0.99	1	398.98	0.8	"compLasso"
+100	1000	0.1	10	0.02	"1 ( 0 )"	"296.19 ( 8.29 )"	"0.35 ( 0.05 )"	301.84	0.98	1	296.19	0.35	"compLasso"
+500	1000	0.1	2	0.39	"0.9 ( 0 )"	"10.2 ( 0.98 )"	"0.1 ( 0.03 )"	16.1	0.52	0.9	10.2	0.1	"compLasso"
+1000	1000	0.1	1	0.83	"0.9 ( 0 )"	"1.76 ( 0.26 )"	"0.02 ( 0.01 )"	7.74	0.18	0.9	1.76	0.02	"compLasso"
+50	50	0.3	1	0.14	"0.96 ( 0 )"	"13.96 ( 0.83 )"	"1.01 ( 0.09 )"	18.95	0.7	0.96	13.96	1.01	"compLasso"
+100	50	0.3	0.5	0.25	"0.92 ( 0 )"	"9.38 ( 0.67 )"	"0.68 ( 0.07 )"	14.7	0.58	0.92	9.38	0.68	"compLasso"
+500	50	0.3	0.1	0.44	"0.89 ( 0 )"	"5.43 ( 0.9 )"	"0.14 ( 0.03 )"	11.29	0.3	0.89	5.43	0.14	"compLasso"
+1000	50	0.3	0.05	0.48	"0.88 ( 0 )"	"5.03 ( 1 )"	"0.09 ( 0.03 )"	10.94	0.23	0.88	5.03	0.09	"compLasso"
+50	100	0.3	2	0.06	"0.99 ( 0 )"	"36.33 ( 2.24 )"	"0.9 ( 0.09 )"	41.43	0.85	0.99	36.33	0.9	"compLasso"
+100	100	0.3	1	0.19	"0.93 ( 0 )"	"16.08 ( 0.95 )"	"0.75 ( 0.07 )"	21.33	0.72	0.93	16.08	0.75	"compLasso"
+500	100	0.3	0.2	0.37	"0.89 ( 0 )"	"8.66 ( 1.15 )"	"0.11 ( 0.03 )"	14.55	0.42	0.89	8.66	0.11	"compLasso"
+1000	100	0.3	0.1	0.39	"0.88 ( 0 )"	"8.45 ( 1.17 )"	"0.12 ( 0.03 )"	14.33	0.39	0.88	8.45	0.12	"compLasso"
+50	500	0.3	10	0.01	"1 ( 0 )"	"228.37 ( 7.07 )"	"0.6 ( 0.08 )"	233.77	0.97	1	228.37	0.6	"compLasso"
+100	500	0.3	5	0.04	"0.99 ( 0 )"	"114.53 ( 5.78 )"	"0.43 ( 0.06 )"	120.1	0.94	0.99	114.53	0.43	"compLasso"
+500	500	0.3	1	0.31	"0.89 ( 0 )"	"13.64 ( 2.08 )"	"0.2 ( 0.04 )"	19.44	0.57	0.89	13.64	0.2	"compLasso"
+1000	500	0.3	0.5	0.54	"0.88 ( 0 )"	"6.3 ( 1.15 )"	"0.09 ( 0.04 )"	12.21	0.38	0.88	6.3	0.09	"compLasso"
+50	1000	0.3	20	0.01	"1 ( 0 )"	"412.73 ( 7.91 )"	"0.88 ( 0.1 )"	417.85	0.99	1	412.73	0.88	"compLasso"
+100	1000	0.3	10	0.02	"1 ( 0 )"	"279.68 ( 7.94 )"	"0.45 ( 0.07 )"	285.23	0.98	1	279.68	0.45	"compLasso"
+500	1000	0.3	2	0.3	"0.89 ( 0 )"	"14.7 ( 1.84 )"	"0.26 ( 0.05 )"	20.44	0.6	0.89	14.7	0.26	"compLasso"
+1000	1000	0.3	1	0.73	"0.88 ( 0 )"	"3.45 ( 0.41 )"	"0.18 ( 0.04 )"	9.27	0.3	0.88	3.45	0.18	"compLasso"
+50	50	0.5	1	0.13	"0.94 ( 0 )"	"12.95 ( 0.79 )"	"1.38 ( 0.1 )"	17.57	0.7	0.94	12.95	1.38	"compLasso"
+100	50	0.5	0.5	0.23	"0.9 ( 0 )"	"8.65 ( 0.57 )"	"1.15 ( 0.08 )"	13.5	0.59	0.9	8.65	1.15	"compLasso"
+500	50	0.5	0.1	0.39	"0.86 ( 0 )"	"4.81 ( 0.77 )"	"1.01 ( 0.1 )"	9.8	0.3	0.86	4.81	1.01	"compLasso"
+1000	50	0.5	0.05	0.37	"0.86 ( 0 )"	"4.69 ( 1.01 )"	"1.17 ( 0.11 )"	9.52	0.22	0.86	4.69	1.17	"compLasso"
+50	100	0.5	2	0.07	"0.98 ( 0 )"	"30.24 ( 1.94 )"	"1.32 ( 0.11 )"	34.92	0.84	0.98	30.24	1.32	"compLasso"
+100	100	0.5	1	0.17	"0.91 ( 0 )"	"15.59 ( 1.05 )"	"1.24 ( 0.08 )"	20.35	0.73	0.91	15.59	1.24	"compLasso"
+500	100	0.5	0.2	0.34	"0.87 ( 0 )"	"6.45 ( 1.28 )"	"1.24 ( 0.1 )"	11.21	0.29	0.87	6.45	1.24	"compLasso"
+1000	100	0.5	0.1	0.28	"0.86 ( 0 )"	"8.33 ( 1.58 )"	"1.21 ( 0.1 )"	13.12	0.3	0.86	8.33	1.21	"compLasso"
+50	500	0.5	10	0.01	"1 ( 0 )"	"211.18 ( 7.59 )"	"0.85 ( 0.09 )"	216.33	0.97	1	211.18	0.85	"compLasso"
+100	500	0.5	5	0.05	"0.98 ( 0 )"	"84.58 ( 6.44 )"	"0.94 ( 0.1 )"	89.64	0.93	0.98	84.58	0.94	"compLasso"
+500	500	0.5	1	0.27	"0.86 ( 0 )"	"13.6 ( 1.48 )"	"0.91 ( 0.09 )"	18.69	0.58	0.86	13.6	0.91	"compLasso"
+1000	500	0.5	0.5	0.51	"0.86 ( 0 )"	"3.77 ( 0.58 )"	"1.52 ( 0.12 )"	8.25	0.28	0.86	3.77	1.52	"compLasso"
+50	1000	0.5	20	0.01	"1 ( 0 )"	"394.36 ( 8.82 )"	"1.1 ( 0.1 )"	399.26	0.99	1	394.36	1.1	"compLasso"
+100	1000	0.5	10	0.02	"1 ( 0 )"	"223.93 ( 9.26 )"	"0.61 ( 0.08 )"	229.32	0.97	1	223.93	0.61	"compLasso"
+500	1000	0.5	2	0.3	"0.87 ( 0 )"	"12.27 ( 1.53 )"	"0.94 ( 0.1 )"	17.33	0.54	0.87	12.27	0.94	"compLasso"
+1000	1000	0.5	1	0.59	"0.86 ( 0 )"	"3.92 ( 0.7 )"	"1.07 ( 0.11 )"	8.85	0.3	0.86	3.92	1.07	"compLasso"
+50	50	0.7	1	0.16	"0.92 ( 0 )"	"10.22 ( 0.71 )"	"1.97 ( 0.1 )"	14.25	0.68	0.92	10.22	1.97	"compLasso"
+100	50	0.7	0.5	0.23	"0.88 ( 0 )"	"7.69 ( 0.73 )"	"2.02 ( 0.08 )"	11.67	0.58	0.88	7.69	2.02	"compLasso"
+500	50	0.7	0.1	0.67	"0.86 ( 0 )"	"1.32 ( 0.25 )"	"2.42 ( 0.06 )"	4.9	0.19	0.86	1.32	2.42	"compLasso"
+1000	50	0.7	0.05	0.74	"0.85 ( 0 )"	"0.84 ( 0.14 )"	"2.48 ( 0.06 )"	4.36	0.13	0.85	0.84	2.48	"compLasso"
+50	100	0.7	2	0.07	"0.96 ( 0 )"	"24.54 ( 1.62 )"	"2.08 ( 0.11 )"	28.46	0.84	0.96	24.54	2.08	"compLasso"
+100	100	0.7	1	0.16	"0.89 ( 0 )"	"14.69 ( 1.28 )"	"1.85 ( 0.08 )"	18.84	0.71	0.89	14.69	1.85	"compLasso"
+500	100	0.7	0.2	0.55	"0.86 ( 0 )"	"2.19 ( 0.64 )"	"2.33 ( 0.07 )"	5.86	0.18	0.86	2.19	2.33	"compLasso"
+1000	100	0.7	0.1	0.81	"0.85 ( 0 )"	"0.51 ( 0.1 )"	"2.56 ( 0.05 )"	3.95	0.09	0.85	0.51	2.56	"compLasso"
+50	500	0.7	10	0.02	"1 ( 0 )"	"161.23 ( 7.83 )"	"1.29 ( 0.1 )"	165.94	0.96	1	161.23	1.29	"compLasso"
+100	500	0.7	5	0.06	"0.96 ( 0 )"	"60.05 ( 4.29 )"	"1.63 ( 0.12 )"	64.42	0.92	0.96	60.05	1.63	"compLasso"
+500	500	0.7	1	0.5	"0.85 ( 0 )"	"3.12 ( 1.31 )"	"2.68 ( 0.07 )"	6.44	0.19	0.85	3.12	2.68	"compLasso"
+1000	500	0.7	0.5	0.89	"0.85 ( 0 )"	"0.39 ( 0.06 )"	"2.88 ( 0.04 )"	3.51	0.09	0.85	0.39	2.88	"compLasso"
+50	1000	0.7	20	0.01	"1 ( 0 )"	"363.91 ( 11.45 )"	"1.39 ( 0.1 )"	368.52	0.99	1	363.91	1.39	"compLasso"
+100	1000	0.7	10	0.03	"0.99 ( 0 )"	"145.9 ( 7.62 )"	"1.24 ( 0.11 )"	150.66	0.96	0.99	145.9	1.24	"compLasso"
+500	1000	0.7	2	0.61	"0.85 ( 0 )"	"2.38 ( 0.4 )"	"2.7 ( 0.06 )"	5.68	0.27	0.85	2.38	2.7	"compLasso"
+1000	1000	0.7	1	0.87	"0.85 ( 0 )"	"0.65 ( 0.08 )"	"2.87 ( 0.04 )"	3.78	0.14	0.85	0.65	2.87	"compLasso"
+50	50	0.9	1	0.14	"0.84 ( 0.01 )"	"9.98 ( 0.78 )"	"2.68 ( 0.11 )"	13.3	0.7	0.84	9.98	2.68	"compLasso"
+100	50	0.9	0.5	0.25	"0.8 ( 0 )"	"6.57 ( 0.7 )"	"2.73 ( 0.09 )"	9.84	0.58	0.8	6.57	2.73	"compLasso"
+500	50	0.9	0.1	0.52	"0.8 ( 0 )"	"2.9 ( 0.5 )"	"2.75 ( 0.07 )"	6.15	0.33	0.8	2.9	2.75	"compLasso"
+1000	50	0.9	0.05	0.7	"0.8 ( 0 )"	"1.88 ( 0.19 )"	"2.79 ( 0.05 )"	5.09	0.3	0.8	1.88	2.79	"compLasso"
+50	100	0.9	2	0.1	"0.87 ( 0.01 )"	"15.93 ( 1 )"	"2.88 ( 0.1 )"	19.05	0.81	0.87	15.93	2.88	"compLasso"
+100	100	0.9	1	0.14	"0.81 ( 0 )"	"14.34 ( 1.4 )"	"2.75 ( 0.1 )"	17.59	0.73	0.81	14.34	2.75	"compLasso"
+500	100	0.9	0.2	0.56	"0.8 ( 0 )"	"3.51 ( 0.44 )"	"2.86 ( 0.04 )"	6.65	0.4	0.8	3.51	2.86	"compLasso"
+1000	100	0.9	0.1	0.57	"0.8 ( 0 )"	"3.18 ( 0.59 )"	"2.86 ( 0.04 )"	6.32	0.34	0.8	3.18	2.86	"compLasso"
+50	500	0.9	10	0.02	"0.99 ( 0 )"	"95.52 ( 6.42 )"	"2.37 ( 0.15 )"	99.15	0.95	0.99	95.52	2.37	"compLasso"
+100	500	0.9	5	0.04	"0.88 ( 0 )"	"53.08 ( 3.93 )"	"2.8 ( 0.12 )"	56.28	0.93	0.88	53.08	2.8	"compLasso"
+500	500	0.9	1	0.4	"0.79 ( 0 )"	"6.75 ( 0.73 )"	"2.76 ( 0.07 )"	9.99	0.56	0.79	6.75	2.76	"compLasso"
+1000	500	0.9	0.5	0.73	"0.8 ( 0 )"	"2.48 ( 0.28 )"	"2.79 ( 0.05 )"	5.69	0.37	0.8	2.48	2.79	"compLasso"
+50	1000	0.9	20	0.01	"1 ( 0 )"	"201.98 ( 10.15 )"	"2.34 ( 0.16 )"	205.64	0.98	1	201.98	2.34	"compLasso"
+100	1000	0.9	10	0.03	"0.95 ( 0 )"	"70.81 ( 4.4 )"	"3.05 ( 0.12 )"	73.76	0.96	0.95	70.81	3.05	"compLasso"
+500	1000	0.9	2	0.38	"0.79 ( 0 )"	"7.71 ( 1.09 )"	"2.85 ( 0.06 )"	10.86	0.59	0.79	7.71	2.85	"compLasso"
+1000	1000	0.9	1	0.76	"0.8 ( 0 )"	"2.41 ( 0.17 )"	"2.77 ( 0.07 )"	5.64	0.39	0.8	2.41	2.77	"compLasso"
diff --git a/simulations/results_summary_cts/sim_block_compLasso.txt b/simulations/results_summary_cts/sim_block_compLasso.txt
new file mode 100755
index 0000000..72e2f9b
--- /dev/null
+++ b/simulations/results_summary_cts/sim_block_compLasso.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.03	"0.36 ( 0.01 )"	"2.8 ( 0.36 )"	"5.01 ( 0.09 )"	3.79	"0.62"	0.36	2.8	5.01
+100	50	"0.1"	0.5	0.1	"0.32 ( 0.01 )"	"4.96 ( 0.55 )"	"3.77 ( 0.14 )"	7.19	"0.55"	0.32	4.96	3.77
+500	50	"0.1"	0.1	0.26	"0.28 ( 0 )"	"8.81 ( 0.4 )"	"0.78 ( 0.08 )"	14.03	"0.6"	0.28	8.81	0.78
+1000	50	"0.1"	0.05	0.31	"0.27 ( 0 )"	"8.37 ( 0.37 )"	"0.24 ( 0.06 )"	14.13	"0.56"	0.27	8.37	0.24
+50	100	"0.1"	2	0.04	"0.37 ( 0.02 )"	"2.54 ( 0.37 )"	"5.22 ( 0.08 )"	3.32	"0.63"	0.37	2.54	5.22
+100	100	"0.1"	1	0.07	"0.35 ( 0.01 )"	"3.99 ( 0.57 )"	"4.63 ( 0.1 )"	5.36	"0.56"	0.35	3.99	4.63
+500	100	"0.1"	0.2	0.18	"0.29 ( 0 )"	"13.62 ( 0.62 )"	"1.22 ( 0.11 )"	18.4	"0.69"	0.29	13.6	1.22
+1000	100	"0.1"	0.1	0.23	"0.28 ( 0 )"	"13.35 ( 0.59 )"	"0.47 ( 0.07 )"	18.88	"0.67"	0.28	13.3	0.47
+50	500	"0.1"	10	0.01	"0.36 ( 0.02 )"	"3.53 ( 0.44 )"	"5.62 ( 0.07 )"	3.91	"0.85"	0.36	3.53	5.62
+100	500	"0.1"	5	0.05	"0.36 ( 0.01 )"	"2.58 ( 0.45 )"	"5.29 ( 0.08 )"	3.29	"0.64"	0.36	2.58	5.29
+500	500	"0.1"	1	0.36	"0.32 ( 0.01 )"	"2.42 ( 0.56 )"	"3.74 ( 0.08 )"	4.68	"0.29"	0.32	2.42	3.74
+1000	500	"0.1"	0.5	0.14	"0.3 ( 0 )"	"19.4 ( 1.74 )"	"2.05 ( 0.12 )"	23.35	"0.66"	0.3	19.4	2.05
+50	1000	"0.1"	20	0	"0.37 ( 0.02 )"	"4.53 ( 0.54 )"	"5.71 ( 0.06 )"	4.82	"0.91"	0.37	4.53	5.71
+100	1000	"0.1"	10	0.02	"0.36 ( 0.01 )"	"4.14 ( 0.72 )"	"5.4 ( 0.08 )"	4.74	"0.78"	0.36	4.14	5.4
+500	1000	"0.1"	2	0.52	"0.34 ( 0 )"	"0.93 ( 0.19 )"	"3.83 ( 0.06 )"	3.1	"0.21"	0.34	0.93	3.83
+1000	1000	"0.1"	1	0.21	"0.31 ( 0 )"	"8.59 ( 1.37 )"	"2.96 ( 0.1 )"	11.63	"0.36"	0.31	8.59	2.96
+50	50	"0.3"	1	0.32	"0.8 ( 0.05 )"	"5.34 ( 0.32 )"	"1.21 ( 0.12 )"	10.13	"0.48"	0.8	5.34	1.21
+100	50	"0.3"	0.5	0.5	"0.52 ( 0.02 )"	"4.42 ( 0.52 )"	"0.1 ( 0.03 )"	10.32	"0.33"	0.52	4.42	0.1
+500	50	"0.3"	0.1	0.91	"0.55 ( 0.02 )"	"0.53 ( 0.16 )"	"0 ( 0 )"	6.53	"0.05"	0.55	0.53	0
+1000	50	"0.3"	0.05	0.89	"0.56 ( 0.02 )"	"0.66 ( 0.18 )"	"0 ( 0 )"	6.66	"0.06"	0.56	0.66	0
+50	100	"0.3"	2	0.27	"0.94 ( 0.05 )"	"6 ( 0.34 )"	"1.93 ( 0.14 )"	10.07	"0.54"	0.94	6	1.93
+100	100	"0.3"	1	0.47	"0.53 ( 0.02 )"	"5.24 ( 0.45 )"	"0.24 ( 0.06 )"	11	"0.4"	0.53	5.24	0.24
+500	100	"0.3"	0.2	0.91	"0.54 ( 0.02 )"	"0.54 ( 0.15 )"	"0 ( 0 )"	6.54	"0.06"	0.54	0.54	0
+1000	100	"0.3"	0.1	0.92	"0.55 ( 0.02 )"	"0.51 ( 0.15 )"	"0 ( 0 )"	6.51	"0.05"	0.55	0.51	0
+50	500	"0.3"	10	0.13	"1.31 ( 0.06 )"	"5.44 ( 0.44 )"	"3.96 ( 0.15 )"	7.48	"0.69"	1.31	5.44	3.96
+100	500	"0.3"	5	0.3	"0.73 ( 0.03 )"	"9 ( 0.76 )"	"0.99 ( 0.12 )"	14.01	"0.52"	0.73	6	0.99
+500	500	"0.3"	1	0.95	"0.55 ( 0.01 )"	"0.31 ( 0.08 )"	"0 ( 0 )"	6.31	"0.04"	0.55	0.31	0
+1000	500	"0.3"	0.5	0.92	"0.57 ( 0.02 )"	"0.49 ( 0.14 )"	"0 ( 0 )"	6.49	"0.05"	0.57	0.49	0
+50	1000	"0.3"	20	0.07	"1.55 ( 0.08 )"	"3.96 ( 0.47 )"	"4.74 ( 0.14 )"	5.22	"0.68"	1.55	3.96	4.74
+100	1000	"0.3"	10	0.25	"0.84 ( 0.03 )"	"9.51 ( 0.77 )"	"1.58 ( 0.11 )"	13.93	"0.58"	0.84	9.51	1.58
+500	1000	"0.3"	2	0.92	"0.56 ( 0.01 )"	"0.52 ( 0.14 )"	"0 ( 0 )"	6.52	"0.05"	0.56	0.52	0
+1000	1000	"0.3"	1	0.95	"0.56 ( 0.02 )"	"0.29 ( 0.08 )"	"0 ( 0 )"	6.29	"0.03"	0.56	0.29	0
+50	50	"0.5"	1	0.42	"0.77 ( 0.04 )"	"5.49 ( 0.31 )"	"0.25 ( 0.06 )"	11.24	"0.44"	0.77	5.49	0.25
+100	50	"0.5"	0.5	0.68	"0.6 ( 0.03 )"	"2.34 ( 0.31 )"	"0.01 ( 0.01 )"	8.33	"0.21"	0.6	2.34	0.01
+500	50	"0.5"	0.1	0.93	"0.8 ( 0.03 )"	"0.42 ( 0.12 )"	"0 ( 0 )"	6.42	"0.05"	0.8	0.42	0
+1000	50	"0.5"	0.05	0.88	"0.78 ( 0.03 )"	"0.7 ( 0.21 )"	"0 ( 0 )"	6.7	"0.07"	0.78	0.7	0
+50	100	"0.5"	2	0.36	"1.03 ( 0.07 )"	"5.95 ( 0.33 )"	"0.96 ( 0.11 )"	10.99	"0.49"	1.03	5.95	0.96
+100	100	"0.5"	1	0.65	"0.64 ( 0.03 )"	"2.9 ( 0.32 )"	"0.03 ( 0.02 )"	8.87	"0.27"	0.64	2.9	0.03
+500	100	"0.5"	0.2	0.91	"0.76 ( 0.03 )"	"0.56 ( 0.19 )"	"0 ( 0 )"	6.56	"0.05"	0.76	0.56	0
+1000	100	"0.5"	0.1	0.93	"0.8 ( 0.03 )"	"0.4 ( 0.09 )"	"0 ( 0 )"	6.4	"0.05"	0.8	0.4	0
+50	500	"0.5"	10	0.22	"1.93 ( 0.1 )"	"5.08 ( 0.41 )"	"3.23 ( 0.15 )"	7.85	"0.55"	1.93	5.08	3.23
+100	500	"0.5"	5	0.45	"0.79 ( 0.03 )"	"6.21 ( 0.54 )"	"0.33 ( 0.07 )"	11.88	"0.43"	0.79	6.21	0.33
+500	500	"0.5"	1	0.94	"0.83 ( 0.03 )"	"0.36 ( 0.11 )"	"0 ( 0 )"	6.36	"0.04"	0.83	0.36	0
+1000	500	"0.5"	0.5	0.97	"0.87 ( 0.03 )"	"0.19 ( 0.06 )"	"0 ( 0 )"	6.19	"0.02"	0.87	0.19	0
+50	1000	"0.5"	20	0.15	"2.1 ( 0.11 )"	"5.84 ( 0.49 )"	"3.77 ( 0.14 )"	8.07	"0.66"	2.1	5.84	3.77
+100	1000	"0.5"	10	0.35	"0.91 ( 0.04 )"	"8.88 ( 0.73 )"	"0.52 ( 0.09 )"	14.36	"0.51"	0.91	8.88	0.52
+500	1000	"0.5"	2	0.98	"0.86 ( 0.02 )"	"0.13 ( 0.05 )"	"0 ( 0 )"	6.13	"0.02"	0.86	0.13	0
+1000	1000	"0.5"	1	0.95	"0.85 ( 0.02 )"	"0.3 ( 0.11 )"	"0 ( 0 )"	6.3	"0.03"	0.85	0.3	0
+50	50	"0.7"	1	0.49	"0.8 ( 0.06 )"	"4.27 ( 0.31 )"	"0.24 ( 0.06 )"	10.03	"0.37"	0.8	4.27	0.24
+100	50	"0.7"	0.5	0.75	"0.75 ( 0.03 )"	"1.71 ( 0.28 )"	"0 ( 0 )"	7.71	"0.16"	0.75	1.71	0
+500	50	"0.7"	0.1	0.9	"1 ( 0.04 )"	"0.58 ( 0.19 )"	"0 ( 0 )"	6.58	"0.06"	1	0.58	0
+1000	50	"0.7"	0.05	0.92	"0.98 ( 0.04 )"	"0.43 ( 0.09 )"	"0 ( 0 )"	6.43	"0.05"	0.98	0.43	0
+50	100	"0.7"	2	0.42	"1.14 ( 0.08 )"	"5.46 ( 0.32 )"	"0.55 ( 0.09 )"	10.91	"0.46"	1.14	5.46	0.55
+100	100	"0.7"	1	0.69	"0.82 ( 0.04 )"	"2.46 ( 0.41 )"	"0 ( 0 )"	8.46	"0.21"	0.82	2.46	0
+500	100	"0.7"	0.2	0.89	"1.01 ( 0.04 )"	"0.68 ( 0.23 )"	"0 ( 0 )"	6.68	"0.06"	1.01	0.68	0
+1000	100	"0.7"	0.1	0.94	"1.08 ( 0.04 )"	"0.36 ( 0.09 )"	"0 ( 0 )"	6.36	"0.04"	1.08	0.36	0
+50	500	"0.7"	10	0.26	"2.76 ( 0.17 )"	"5.16 ( 0.4 )"	"2.65 ( 0.15 )"	8.51	"0.51"	2.76	5.16	2.65
+100	500	"0.7"	5	0.52	"0.87 ( 0.03 )"	"5.39 ( 0.52 )"	"0.04 ( 0.02 )"	11.35	"0.39"	0.87	5.39	0.04
+500	500	"0.7"	1	0.94	"1.05 ( 0.04 )"	"0.35 ( 0.1 )"	"0 ( 0 )"	6.35	"0.04"	1.05	0.35	0
+1000	500	"0.7"	0.5	0.98	"1.14 ( 0.04 )"	"0.14 ( 0.04 )"	"0 ( 0 )"	6.14	"0.02"	1.14	0.14	0
+50	1000	"0.7"	20	0.2	"3.02 ( 0.16 )"	"5.41 ( 0.43 )"	"3.39 ( 0.13 )"	8.02	"0.58"	3.02	5.41	3.39
+100	1000	"0.7"	10	0.45	"0.91 ( 0.04 )"	"6.6 ( 0.53 )"	"0.2 ( 0.05 )"	12.4	"0.45"	0.91	6.6	0.2
+500	1000	"0.7"	2	0.95	"1.04 ( 0.04 )"	"0.31 ( 0.1 )"	"0 ( 0 )"	6.31	"0.04"	1.04	0.31	0
+1000	1000	"0.7"	1	0.97	"1.13 ( 0.04 )"	"0.21 ( 0.07 )"	"0 ( 0 )"	6.21	"0.03"	1.13	0.21	0
+50	50	"0.9"	1	0.48	"0.92 ( 0.07 )"	"4.7 ( 0.32 )"	"0.14 ( 0.04 )"	10.56	"0.38"	0.92	4.7	0.14
+100	50	"0.9"	0.5	0.76	"0.94 ( 0.05 )"	"1.59 ( 0.25 )"	"0 ( 0 )"	7.59	"0.15"	0.94	1.59	0
+500	50	"0.9"	0.1	0.92	"1.15 ( 0.06 )"	"0.47 ( 0.11 )"	"0 ( 0 )"	6.47	"0.05"	1.15	0.47	0
+1000	50	"0.9"	0.05	0.89	"1.28 ( 0.05 )"	"0.62 ( 0.19 )"	"0 ( 0 )"	6.62	"0.06"	1.28	0.62	0
+50	100	"0.9"	2	0.42	"1.28 ( 0.08 )"	"5.86 ( 0.29 )"	"0.47 ( 0.09 )"	11.39	"0.48"	1.28	5.86	0.47
+100	100	"0.9"	1	0.81	"0.94 ( 0.04 )"	"1.34 ( 0.23 )"	"0 ( 0 )"	7.34	"0.14"	0.94	1.34	0
+500	100	"0.9"	0.2	0.93	"1.16 ( 0.05 )"	"0.43 ( 0.13 )"	"0 ( 0 )"	6.43	"0.05"	1.16	0.43	0
+1000	100	"0.9"	0.1	0.94	"1.32 ( 0.05 )"	"0.34 ( 0.1 )"	"0 ( 0 )"	6.34	"0.04"	1.32	0.34	0
+50	500	"0.9"	10	0.25	"3.06 ( 0.18 )"	"5.42 ( 0.4 )"	"2.69 ( 0.15 )"	8.73	"0.54"	3.06	5.42	2.69
+100	500	"0.9"	5	0.58	"0.97 ( 0.04 )"	"4.2 ( 0.49 )"	"0.02 ( 0.02 )"	10.18	"0.31"	0.97	4.2	0.02
+500	500	"0.9"	1	0.93	"1.27 ( 0.05 )"	"0.44 ( 0.18 )"	"0 ( 0 )"	6.44	"0.04"	1.27	0.44	0
+1000	500	"0.9"	0.5	0.96	"1.31 ( 0.05 )"	"0.23 ( 0.06 )"	"0 ( 0 )"	6.23	"0.03"	1.31	0.23	0
+50	1000	"0.9"	20	0.23	"3.37 ( 0.19 )"	"5.29 ( 0.41 )"	"3.13 ( 0.15 )"	8.16	"0.57"	3.37	5.29	3.13
+100	1000	"0.9"	10	0.56	"1.14 ( 0.05 )"	"4.56 ( 0.38 )"	"0.07 ( 0.03 )"	10.49	"0.37"	1.14	4.56	0.07
+500	1000	"0.9"	2	0.96	"1.28 ( 0.04 )"	"0.27 ( 0.11 )"	"0 ( 0 )"	6.27	"0.03"	1.28	0.27	0
+1000	1000	"0.9"	1	0.95	"1.35 ( 0.05 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	"0.03"	1.35	0.29	0
diff --git a/simulations/results_summary_cts/sim_block_elnet.txt b/simulations/results_summary_cts/sim_block_elnet.txt
new file mode 100755
index 0000000..92b3236
--- /dev/null
+++ b/simulations/results_summary_cts/sim_block_elnet.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.02	"0.38 ( 0.02 )"	"10.9 ( 0.97 )"	"3.91 ( 0.15 )"	11.99	"0.77"	0.38	10.9	3.91
+100	50	"0.1"	0.5	0.06	"0.34 ( 0.01 )"	"13.39 ( 1.06 )"	"2.45 ( 0.16 )"	15.94	"0.68"	0.34	13.3	2.45
+500	50	"0.1"	0.1	0.13	"0.27 ( 0 )"	"19.21 ( 0.81 )"	"0.14 ( 0.04 )"	24.07	"0.73"	0.27	19.2	0.14
+1000	50	"0.1"	0.05	0.15	"0.26 ( 0 )"	"18.76 ( 0.63 )"	"0.02 ( 0.01 )"	23.74	"0.73"	0.26	18.7	0.02
+50	100	"0.1"	2	0.03	"0.41 ( 0.02 )"	"9.27 ( 0.76 )"	"4.63 ( 0.09 )"	9.64	"0.82"	0.41	9.27	4.63
+100	100	"0.1"	1	0.07	"0.34 ( 0.01 )"	"10.1 ( 1.04 )"	"3.87 ( 0.13 )"	11.23	"0.7"	0.34	10.1	3.87
+500	100	"0.1"	0.2	0.13	"0.29 ( 0 )"	"25.4 ( 0.81 )"	"0.41 ( 0.06 )"	29.99	"0.8"	0.29	25.4	0.41
+1000	100	"0.1"	0.1	0.13	"0.27 ( 0 )"	"28.36 ( 0.94 )"	"0.06 ( 0.03 )"	33.3	"0.81"	0.27	28.3	0.06
+50	500	"0.1"	10	0.01	"0.41 ( 0.02 )"	"30.9 ( 3.51 )"	"4.65 ( 0.13 )"	31.25	"0.94"	0.41	30.9	4.65
+100	500	"0.1"	5	0.03	"0.36 ( 0.01 )"	"20.83 ( 2.77 )"	"4.43 ( 0.1 )"	21.4	"0.87"	0.36	20.8	4.43
+500	500	"0.1"	1	0.08	"0.32 ( 0 )"	"29.71 ( 2.76 )"	"2.55 ( 0.12 )"	32.16	"0.8"	0.32	29.7	2.55
+1000	500	"0.1"	0.5	0.07	"0.28 ( 0 )"	"61.47 ( 1.31 )"	"0.56 ( 0.07 )"	65.91	"0.91"	0.28	61.4	0.56
+50	1000	"0.1"	20	0	"0.41 ( 0.02 )"	"38.1 ( 4.05 )"	"5.01 ( 0.11 )"	38.09	"0.97"	0.41	38.1	5.01
+100	1000	"0.1"	10	0.02	"0.36 ( 0.01 )"	"35.49 ( 4.37 )"	"4.34 ( 0.14 )"	36.15	"0.93"	0.36	35.4	4.34
+500	1000	"0.1"	2	0.1	"0.3 ( 0 )"	"20.28 ( 2.74 )"	"3.25 ( 0.08 )"	22.03	"0.75"	0.3	20.2	3.25
+1000	1000	"0.1"	1	0.05	"0.29 ( 0 )"	"71.65 ( 3.47 )"	"1.5 ( 0.1 )"	75.15	"0.9"	0.29	71.6	1.5
+50	50	"0.3"	1	0.17	"0.56 ( 0.03 )"	"15.98 ( 0.61 )"	"0.26 ( 0.05 )"	20.72	"0.7"	0.56	15.9	0.26
+100	50	"0.3"	0.5	0.2	"0.34 ( 0.01 )"	"15.16 ( 0.6 )"	"0 ( 0 )"	20.16	"0.68"	0.34	15.1	0
+500	50	"0.3"	0.1	0.19	"0.27 ( 0 )"	"15.78 ( 0.58 )"	"0 ( 0 )"	20.78	"0.69"	0.27	15.7	0
+1000	50	"0.3"	0.05	0.22	"0.26 ( 0 )"	"14.34 ( 0.49 )"	"0 ( 0 )"	19.34	"0.67"	0.26	14.3	0
+50	100	"0.3"	2	0.13	"0.81 ( 0.05 )"	"22.23 ( 1.04 )"	"0.7 ( 0.08 )"	26.53	"0.78"	0.81	22.2	0.7
+100	100	"0.3"	1	0.16	"0.42 ( 0.02 )"	"23.17 ( 0.94 )"	"0.02 ( 0.01 )"	28.15	"0.77"	0.42	23.1	0.02
+500	100	"0.3"	0.2	0.18	"0.27 ( 0 )"	"21.25 ( 0.84 )"	"0 ( 0 )"	26.25	"0.74"	0.27	21.2	0
+1000	100	"0.3"	0.1	0.2	"0.26 ( 0 )"	"19.57 ( 0.81 )"	"0 ( 0 )"	24.57	"0.73"	0.26	19.5	0
+50	500	"0.3"	10	0.05	"1.29 ( 0.07 )"	"43.56 ( 3.08 )"	"2.15 ( 0.13 )"	46.41	"0.89"	1.29	43.5	2.15
+100	500	"0.3"	5	0.13	"0.54 ( 0.02 )"	"35.33 ( 1.21 )"	"0.19 ( 0.05 )"	40.14	"0.84"	0.54	35.3	0.19
+500	500	"0.3"	1	0.14	"0.28 ( 0 )"	"35.68 ( 1.62 )"	"0 ( 0 )"	40.68	"0.83"	0.28	35.6	0
+1000	500	"0.3"	0.5	0.14	"0.27 ( 0 )"	"36.15 ( 1.61 )"	"0 ( 0 )"	41.15	"0.83"	0.27	36.1	0
+50	1000	"0.3"	20	0.03	"1.3 ( 0.07 )"	"61.12 ( 4.33 )"	"2.56 ( 0.12 )"	63.56	"0.93"	1.3	61.1	2.56
+100	1000	"0.3"	10	0.1	"0.68 ( 0.03 )"	"41.5 ( 1.64 )"	"0.61 ( 0.08 )"	45.89	"0.87"	0.68	41.5	0.61
+500	1000	"0.3"	2	0.11	"0.29 ( 0 )"	"46.27 ( 2.2 )"	"0 ( 0 )"	51.27	"0.86"	0.29	46.2	0
+1000	1000	"0.3"	1	0.13	"0.27 ( 0 )"	"39.23 ( 2 )"	"0 ( 0 )"	44.23	"0.83"	0.27	39.2	0
+50	50	"0.5"	1	0.19	"0.65 ( 0.05 )"	"15.43 ( 0.64 )"	"0.1 ( 0.04 )"	20.33	"0.68"	0.65	15.4	0.1
+100	50	"0.5"	0.5	0.2	"0.34 ( 0.01 )"	"14.94 ( 0.61 )"	"0 ( 0 )"	19.94	"0.67"	0.34	14.9	0
+500	50	"0.5"	0.1	0.2	"0.27 ( 0 )"	"15.76 ( 0.59 )"	"0 ( 0 )"	20.76	"0.69"	0.27	15.7	0
+1000	50	"0.5"	0.05	0.25	"0.26 ( 0 )"	"12.73 ( 0.5 )"	"0 ( 0 )"	17.73	"0.63"	0.26	12.7	0
+50	100	"0.5"	2	0.16	"0.8 ( 0.05 )"	"21.3 ( 0.83 )"	"0.3 ( 0.06 )"	26	"0.76"	0.8	21.3	0.3
+100	100	"0.5"	1	0.18	"0.37 ( 0.01 )"	"21.74 ( 0.84 )"	"0 ( 0 )"	26.74	"0.75"	0.37	21.7	0
+500	100	"0.5"	0.2	0.19	"0.27 ( 0 )"	"20.71 ( 0.8 )"	"0 ( 0 )"	25.71	"0.74"	0.27	20.7	0
+1000	100	"0.5"	0.1	0.18	"0.26 ( 0 )"	"20.53 ( 0.72 )"	"0 ( 0 )"	25.53	"0.75"	0.26	20.5	0
+50	500	"0.5"	10	0.08	"1.61 ( 0.1 )"	"36.33 ( 2.13 )"	"1.58 ( 0.11 )"	39.75	"0.87"	1.61	36.3	1.58
+100	500	"0.5"	5	0.14	"0.53 ( 0.02 )"	"34.91 ( 1.29 )"	"0.02 ( 0.01 )"	39.89	"0.83"	0.53	34.9	0.02
+500	500	"0.5"	1	0.15	"0.28 ( 0 )"	"32.67 ( 1.7 )"	"0 ( 0 )"	37.67	"0.8"	0.28	32.6	0
+1000	500	"0.5"	0.5	0.14	"0.26 ( 0 )"	"33.71 ( 1.97 )"	"0 ( 0 )"	38.71	"0.8"	0.26	33.7	0
+50	1000	"0.5"	20	0.04	"2 ( 0.1 )"	"52.83 ( 3.84 )"	"2.29 ( 0.1 )"	55.54	"0.91"	2	52.8	2.29
+100	1000	"0.5"	10	0.11	"0.68 ( 0.03 )"	"43.94 ( 1.56 )"	"0.14 ( 0.03 )"	48.8	"0.87"	0.68	43.9	0.14
+500	1000	"0.5"	2	0.14	"0.28 ( 0 )"	"37.63 ( 1.92 )"	"0 ( 0 )"	42.63	"0.83"	0.28	37.6	0
+1000	1000	"0.5"	1	0.14	"0.27 ( 0 )"	"35.42 ( 1.84 )"	"0 ( 0 )"	40.42	"0.82"	0.27	35.4	0
+50	50	"0.7"	1	0.18	"0.63 ( 0.04 )"	"16.71 ( 0.61 )"	"0.04 ( 0.02 )"	21.67	"0.7"	0.63	16.7	0.04
+100	50	"0.7"	0.5	0.22	"0.33 ( 0.01 )"	"14.2 ( 0.55 )"	"0 ( 0 )"	19.2	"0.66"	0.33	14.2	0
+500	50	"0.7"	0.1	0.22	"0.26 ( 0 )"	"13.96 ( 0.52 )"	"0 ( 0 )"	18.96	"0.66"	0.26	13.9	0
+1000	50	"0.7"	0.05	0.25	"0.26 ( 0 )"	"12.77 ( 0.52 )"	"0 ( 0 )"	17.77	"0.63"	0.26	12.7	0
+50	100	"0.7"	2	0.18	"0.86 ( 0.07 )"	"19.83 ( 0.86 )"	"0.11 ( 0.03 )"	24.72	"0.74"	0.86	19.8	0.11
+100	100	"0.7"	1	0.17	"0.38 ( 0.01 )"	"22.59 ( 1.11 )"	"0 ( 0 )"	27.59	"0.75"	0.38	22.5	0
+500	100	"0.7"	0.2	0.21	"0.28 ( 0 )"	"17.78 ( 0.8 )"	"0 ( 0 )"	22.78	"0.7"	0.28	17.7	0
+1000	100	"0.7"	0.1	0.21	"0.26 ( 0 )"	"18.55 ( 0.87 )"	"0 ( 0 )"	23.55	"0.71"	0.26	18.5	0
+50	500	"0.7"	10	0.1	"2.19 ( 0.14 )"	"33.13 ( 1.76 )"	"1.25 ( 0.11 )"	36.88	"0.86"	2.19	33.1	1.25
+100	500	"0.7"	5	0.13	"0.51 ( 0.02 )"	"37.39 ( 1.42 )"	"0 ( 0 )"	42.39	"0.84"	0.51	37.3	0
+500	500	"0.7"	1	0.16	"0.28 ( 0 )"	"30.18 ( 1.66 )"	"0 ( 0 )"	35.18	"0.79"	0.28	30.1	0
+1000	500	"0.7"	0.5	0.15	"0.26 ( 0 )"	"33.3 ( 1.59 )"	"0 ( 0 )"	38.3	"0.81"	0.26	33.3	0
+50	1000	"0.7"	20	0.06	"2.43 ( 0.14 )"	"41.11 ( 2.8 )"	"1.98 ( 0.11 )"	44.13	"0.89"	2.43	41.1	1.98
+100	1000	"0.7"	10	0.12	"0.68 ( 0.03 )"	"42.68 ( 1.53 )"	"0.02 ( 0.01 )"	47.66	"0.86"	0.68	42.6	0.02
+500	1000	"0.7"	2	0.12	"0.29 ( 0 )"	"42.75 ( 2.32 )"	"0 ( 0 )"	47.75	"0.84"	0.29	42.7	0
+1000	1000	"0.7"	1	0.14	"0.27 ( 0 )"	"36.88 ( 2.33 )"	"0 ( 0 )"	41.88	"0.81"	0.27	36.8	0
+50	50	"0.9"	1	0.21	"0.56 ( 0.03 )"	"14.5 ( 0.57 )"	"0.03 ( 0.02 )"	19.47	"0.67"	0.56	14.5	0.03
+100	50	"0.9"	0.5	0.23	"0.33 ( 0.01 )"	"13.9 ( 0.6 )"	"0 ( 0 )"	18.9	"0.65"	0.33	13.9	0
+500	50	"0.9"	0.1	0.25	"0.26 ( 0 )"	"12.75 ( 0.49 )"	"0 ( 0 )"	17.75	"0.63"	0.26	12.7	0
+1000	50	"0.9"	0.05	0.25	"0.26 ( 0 )"	"12.64 ( 0.49 )"	"0 ( 0 )"	17.64	"0.63"	0.26	12.6	0
+50	100	"0.9"	2	0.18	"0.75 ( 0.04 )"	"20.94 ( 0.7 )"	"0.07 ( 0.03 )"	25.87	"0.75"	0.75	20.9	0.07
+100	100	"0.9"	1	0.18	"0.38 ( 0.01 )"	"20.8 ( 0.95 )"	"0 ( 0 )"	25.8	"0.73"	0.38	20.8	0
+500	100	"0.9"	0.2	0.2	"0.26 ( 0 )"	"18.74 ( 0.84 )"	"0 ( 0 )"	23.74	"0.72"	0.26	18.7	0
+1000	100	"0.9"	0.1	0.19	"0.26 ( 0 )"	"19.91 ( 0.91 )"	"0 ( 0 )"	24.91	"0.72"	0.26	19.9	0
+50	500	"0.9"	10	0.1	"2.21 ( 0.15 )"	"35.26 ( 2.1 )"	"0.98 ( 0.09 )"	39.28	"0.85"	2.21	35.2	0.98
+100	500	"0.9"	5	0.14	"0.5 ( 0.02 )"	"35.7 ( 1.48 )"	"0 ( 0 )"	40.7	"0.83"	0.5	35.7	0
+500	500	"0.9"	1	0.15	"0.28 ( 0 )"	"32.6 ( 1.76 )"	"0 ( 0 )"	37.6	"0.81"	0.28	32.6	0
+1000	500	"0.9"	0.5	0.16	"0.26 ( 0 )"	"30.4 ( 1.66 )"	"0 ( 0 )"	35.4	"0.79"	0.26	30.4	0
+50	1000	"0.9"	20	0.07	"3.06 ( 0.18 )"	"43.09 ( 2.82 )"	"1.57 ( 0.1 )"	46.52	"0.88"	3.06	43	1.57
+100	1000	"0.9"	10	0.12	"0.59 ( 0.03 )"	"43.91 ( 1.31 )"	"0 ( 0 )"	48.91	"0.87"	0.59	43.9	0
+500	1000	"0.9"	2	0.12	"0.29 ( 0 )"	"41.57 ( 2.28 )"	"0 ( 0 )"	46.57	"0.84"	0.29	41.5	0
+1000	1000	"0.9"	1	0.13	"0.27 ( 0 )"	"38.76 ( 2.16 )"	"0 ( 0 )"	43.76	"0.82"	0.27	38.7	0
diff --git a/simulations/results_summary_cts/sim_block_lasso.txt b/simulations/results_summary_cts/sim_block_lasso.txt
new file mode 100755
index 0000000..0c68943
--- /dev/null
+++ b/simulations/results_summary_cts/sim_block_lasso.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.05	"0.36 ( 0.01 )"	"3.52 ( 0.22 )"	"4.92 ( 0.08 )"	3.6	"0.65"	0.36	3.52	4.92
+100	50	"0.1"	0.5	0.18	"0.31 ( 0.01 )"	"2.71 ( 0.25 )"	"4.4 ( 0.1 )"	3.31	"0.42"	0.31	2.71	4.4
+500	50	"0.1"	0.1	0.35	"0.29 ( 0 )"	"5.42 ( 0.25 )"	"1.97 ( 0.11 )"	8.45	"0.48"	0.29	5.42	1.97
+1000	50	"0.1"	0.05	0.44	"0.28 ( 0 )"	"4.91 ( 0.2 )"	"1.57 ( 0.09 )"	8.34	"0.44"	0.28	4.91	1.57
+50	100	"0.1"	2	0.06	"0.37 ( 0.02 )"	"4.73 ( 0.22 )"	"4.79 ( 0.08 )"	4.94	"0.72"	0.37	4.73	4.79
+100	100	"0.1"	1	0.13	"0.34 ( 0.01 )"	"3 ( 0.15 )"	"4.75 ( 0.09 )"	3.25	"0.57"	0.34	3	4.75
+500	100	"0.1"	0.2	0.24	"0.3 ( 0 )"	"8.56 ( 0.5 )"	"2.13 ( 0.12 )"	11.43	"0.57"	0.3	8.56	2.13
+1000	100	"0.1"	0.1	0.33	"0.29 ( 0 )"	"7.77 ( 0.33 )"	"1.44 ( 0.08 )"	11.33	"0.56"	0.29	7.77	1.44
+50	500	"0.1"	10	0.01	"0.37 ( 0.02 )"	"10.58 ( 0.31 )"	"5.25 ( 0.07 )"	10.33	"0.92"	0.37	10.5	5.25
+100	500	"0.1"	5	0.05	"0.35 ( 0.01 )"	"6.23 ( 0.26 )"	"4.94 ( 0.08 )"	6.29	"0.82"	0.35	6.23	4.94
+500	500	"0.1"	1	0.33	"0.32 ( 0.01 )"	"3.77 ( 0.75 )"	"3.69 ( 0.08 )"	5.08	"0.27"	0.32	3.77	3.69
+1000	500	"0.1"	0.5	0.14	"0.29 ( 0 )"	"20.61 ( 1.39 )"	"2.11 ( 0.1 )"	23.5	"0.69"	0.29	20.6	2.11
+50	1000	"0.1"	20	0.01	"0.39 ( 0.02 )"	"13.99 ( 0.32 )"	"5.31 ( 0.07 )"	13.68	"0.95"	0.39	13.9	5.31
+100	1000	"0.1"	10	0.03	"0.35 ( 0.01 )"	"9.56 ( 0.33 )"	"5.01 ( 0.08 )"	9.55	"0.89"	0.35	9.56	5.01
+500	1000	"0.1"	2	0.48	"0.33 ( 0 )"	"1.83 ( 0.19 )"	"3.97 ( 0.07 )"	2.86	"0.2"	0.33	1.83	3.97
+1000	1000	"0.1"	1	0.17	"0.3 ( 0 )"	"13.42 ( 1.74 )"	"2.77 ( 0.09 )"	15.65	"0.47"	0.3	13.4	2.77
+50	50	"0.3"	1	0.27	"0.72 ( 0.04 )"	"8.7 ( 0.42 )"	"0.79 ( 0.09 )"	12.91	"0.56"	0.72	8.7	0.79
+100	50	"0.3"	0.5	0.39	"0.4 ( 0.01 )"	"7.91 ( 0.47 )"	"0.01 ( 0.01 )"	12.9	"0.48"	0.4	7.91	0.01
+500	50	"0.3"	0.1	0.63	"0.28 ( 0 )"	"3.86 ( 0.25 )"	"0 ( 0 )"	8.86	"0.28"	0.28	3.86	0
+1000	50	"0.3"	0.05	0.78	"0.27 ( 0 )"	"2.45 ( 0.14 )"	"0 ( 0 )"	7.45	"0.17"	0.27	2.45	0
+50	100	"0.3"	2	0.21	"0.82 ( 0.05 )"	"12.31 ( 0.43 )"	"1.14 ( 0.1 )"	16.17	"0.68"	0.82	12.3	1.14
+100	100	"0.3"	1	0.34	"0.44 ( 0.01 )"	"10.58 ( 0.62 )"	"0.09 ( 0.03 )"	15.49	"0.56"	0.44	10.5	0.09
+500	100	"0.3"	0.2	0.61	"0.27 ( 0 )"	"4.51 ( 0.29 )"	"0 ( 0 )"	9.51	"0.32"	0.27	4.51	0
+1000	100	"0.3"	0.1	0.69	"0.27 ( 0 )"	"3.46 ( 0.29 )"	"0 ( 0 )"	8.46	"0.24"	0.27	3.46	0
+50	500	"0.3"	10	0.09	"1.25 ( 0.07 )"	"20.71 ( 0.36 )"	"2.63 ( 0.11 )"	23.08	"0.85"	1.25	20.7	2.63
+100	500	"0.3"	5	0.23	"0.59 ( 0.02 )"	"18.01 ( 0.69 )"	"0.35 ( 0.06 )"	22.66	"0.73"	0.59	18	0.35
+500	500	"0.3"	1	0.47	"0.29 ( 0 )"	"7.7 ( 0.54 )"	"0 ( 0 )"	12.7	"0.45"	0.29	7.7	0
+1000	500	"0.3"	0.5	0.53	"0.27 ( 0 )"	"6.14 ( 0.57 )"	"0 ( 0 )"	11.14	"0.36"	0.27	6.14	0
+50	1000	"0.3"	20	0.06	"1.51 ( 0.08 )"	"24.06 ( 0.36 )"	"3.21 ( 0.11 )"	25.85	"0.89"	1.51	24	3.21
+100	1000	"0.3"	10	0.17	"0.67 ( 0.03 )"	"22.69 ( 0.61 )"	"0.73 ( 0.08 )"	26.96	"0.8"	0.67	22.6	0.73
+500	1000	"0.3"	2	0.35	"0.29 ( 0 )"	"11.72 ( 0.9 )"	"0 ( 0 )"	16.72	"0.55"	0.29	11.7	0
+1000	1000	"0.3"	1	0.51	"0.27 ( 0 )"	"6.69 ( 0.52 )"	"0 ( 0 )"	11.69	"0.41"	0.27	6.69	0
+50	50	"0.5"	1	0.33	"0.64 ( 0.03 )"	"9.36 ( 0.45 )"	"0.12 ( 0.04 )"	14.24	"0.55"	0.64	9.36	0.12
+100	50	"0.5"	0.5	0.41	"0.36 ( 0.01 )"	"7.39 ( 0.39 )"	"0 ( 0 )"	12.39	"0.47"	0.36	7.39	0
+500	50	"0.5"	0.1	0.7	"0.28 ( 0 )"	"3.18 ( 0.23 )"	"0 ( 0 )"	8.18	"0.22"	0.28	3.18	0
+1000	50	"0.5"	0.05	0.82	"0.27 ( 0 )"	"2.11 ( 0.13 )"	"0 ( 0 )"	7.11	"0.13"	0.27	2.11	0
+50	100	"0.5"	2	0.28	"0.78 ( 0.05 )"	"12.05 ( 0.47 )"	"0.38 ( 0.07 )"	16.67	"0.64"	0.78	12	0.38
+100	100	"0.5"	1	0.37	"0.42 ( 0.02 )"	"9.57 ( 0.58 )"	"0 ( 0 )"	14.57	"0.54"	0.42	9.57	0
+500	100	"0.5"	0.2	0.59	"0.27 ( 0 )"	"4.7 ( 0.3 )"	"0 ( 0 )"	9.7	"0.33"	0.27	4.7	0
+1000	100	"0.5"	0.1	0.78	"0.27 ( 0 )"	"2.58 ( 0.16 )"	"0 ( 0 )"	7.58	"0.18"	0.27	2.58	0
+50	500	"0.5"	10	0.12	"1.64 ( 0.1 )"	"21.8 ( 0.36 )"	"1.82 ( 0.11 )"	24.98	"0.83"	1.64	21.8	1.82
+100	500	"0.5"	5	0.24	"0.55 ( 0.02 )"	"18.37 ( 0.77 )"	"0.04 ( 0.02 )"	23.33	"0.72"	0.55	18.3	0.04
+500	500	"0.5"	1	0.46	"0.3 ( 0 )"	"7.82 ( 0.64 )"	"0 ( 0 )"	12.82	"0.44"	0.3	7.82	0
+1000	500	"0.5"	0.5	0.65	"0.28 ( 0 )"	"4.16 ( 0.33 )"	"0 ( 0 )"	9.16	"0.28"	0.28	4.16	0
+50	1000	"0.5"	20	0.09	"1.84 ( 0.1 )"	"24.67 ( 0.36 )"	"2.56 ( 0.11 )"	27.11	"0.87"	1.84	24.6	2.56
+100	1000	"0.5"	10	0.21	"0.64 ( 0.03 )"	"22.01 ( 0.65 )"	"0.12 ( 0.03 )"	26.89	"0.77"	0.64	22	0.12
+500	1000	"0.5"	2	0.45	"0.29 ( 0 )"	"8.22 ( 0.73 )"	"0 ( 0 )"	13.22	"0.44"	0.29	8.22	0
+1000	1000	"0.5"	1	0.53	"0.27 ( 0 )"	"6.36 ( 0.51 )"	"0 ( 0 )"	11.36	"0.39"	0.27	6.36	0
+50	50	"0.7"	1	0.36	"0.57 ( 0.04 )"	"8.63 ( 0.41 )"	"0.06 ( 0.02 )"	13.57	"0.52"	0.57	8.63	0.06
+100	50	"0.7"	0.5	0.45	"0.37 ( 0.01 )"	"6.73 ( 0.39 )"	"0 ( 0 )"	11.73	"0.44"	0.37	6.73	0
+500	50	"0.7"	0.1	0.71	"0.28 ( 0 )"	"3.01 ( 0.2 )"	"0 ( 0 )"	8.01	"0.22"	0.28	3.01	0
+1000	50	"0.7"	0.05	0.85	"0.27 ( 0 )"	"1.89 ( 0.12 )"	"0 ( 0 )"	6.89	"0.11"	0.27	1.89	0
+50	100	"0.7"	2	0.3	"0.82 ( 0.05 )"	"11.87 ( 0.44 )"	"0.17 ( 0.05 )"	16.7	"0.63"	0.82	11.8	0.17
+100	100	"0.7"	1	0.37	"0.41 ( 0.02 )"	"9.7 ( 0.63 )"	"0 ( 0 )"	14.7	"0.53"	0.41	9.7	0
+500	100	"0.7"	0.2	0.66	"0.28 ( 0 )"	"3.85 ( 0.3 )"	"0 ( 0 )"	8.85	"0.26"	0.28	3.85	0
+1000	100	"0.7"	0.1	0.76	"0.27 ( 0 )"	"2.71 ( 0.23 )"	"0 ( 0 )"	7.71	"0.17"	0.27	2.71	0
+50	500	"0.7"	10	0.15	"2.03 ( 0.14 )"	"21.53 ( 0.36 )"	"1.18 ( 0.11 )"	25.35	"0.81"	2.03	21.5	1.18
+100	500	"0.7"	5	0.28	"0.54 ( 0.02 )"	"16.11 ( 0.63 )"	"0 ( 0 )"	21.11	"0.69"	0.54	16.1	0
+500	500	"0.7"	1	0.51	"0.29 ( 0 )"	"6.69 ( 0.45 )"	"0 ( 0 )"	11.69	"0.42"	0.29	6.69	0
+1000	500	"0.7"	0.5	0.62	"0.28 ( 0 )"	"4.65 ( 0.37 )"	"0 ( 0 )"	9.65	"0.3"	0.28	4.65	0
+50	1000	"0.7"	20	0.1	"2.51 ( 0.13 )"	"25.24 ( 0.32 )"	"2.11 ( 0.12 )"	28.13	"0.86"	2.51	25.2	2.11
+100	1000	"0.7"	10	0.22	"0.59 ( 0.02 )"	"21.52 ( 0.68 )"	"0.01 ( 0.01 )"	26.51	"0.76"	0.59	21.5	0.01
+500	1000	"0.7"	2	0.47	"0.3 ( 0 )"	"7.81 ( 0.61 )"	"0 ( 0 )"	12.81	"0.45"	0.3	7.81	0
+1000	1000	"0.7"	1	0.65	"0.28 ( 0 )"	"4.14 ( 0.35 )"	"0 ( 0 )"	9.14	"0.27"	0.28	4.14	0
+50	50	"0.9"	1	0.32	"0.61 ( 0.05 )"	"9.61 ( 0.49 )"	"0.07 ( 0.03 )"	14.54	"0.55"	0.61	9.61	0.07
+100	50	"0.9"	0.5	0.44	"0.37 ( 0.01 )"	"6.82 ( 0.39 )"	"0 ( 0 )"	11.82	"0.44"	0.37	6.82	0
+500	50	"0.9"	0.1	0.75	"0.27 ( 0 )"	"2.68 ( 0.17 )"	"0 ( 0 )"	7.68	"0.19"	0.27	2.68	0
+1000	50	"0.9"	0.05	0.87	"0.27 ( 0 )"	"1.76 ( 0.11 )"	"0 ( 0 )"	6.76	"0.09"	0.27	1.76	0
+50	100	"0.9"	2	0.29	"0.87 ( 0.05 )"	"12.12 ( 0.37 )"	"0.17 ( 0.05 )"	16.95	"0.64"	0.87	12.1	0.17
+100	100	"0.9"	1	0.4	"0.38 ( 0.01 )"	"8.72 ( 0.53 )"	"0 ( 0 )"	13.72	"0.51"	0.38	8.72	0
+500	100	"0.9"	0.2	0.67	"0.27 ( 0 )"	"3.68 ( 0.28 )"	"0 ( 0 )"	8.68	"0.25"	0.27	3.68	0
+1000	100	"0.9"	0.1	0.76	"0.27 ( 0 )"	"2.72 ( 0.23 )"	"0 ( 0 )"	7.72	"0.17"	0.27	2.72	0
+50	500	"0.9"	10	0.14	"2.29 ( 0.15 )"	"23.7 ( 0.43 )"	"1.18 ( 0.11 )"	27.52	"0.82"	2.29	23.7	1.18
+100	500	"0.9"	5	0.28	"0.51 ( 0.02 )"	"16.13 ( 0.62 )"	"0 ( 0 )"	21.13	"0.69"	0.51	16.1	0
+500	500	"0.9"	1	0.5	"0.29 ( 0 )"	"6.87 ( 0.54 )"	"0 ( 0 )"	11.87	"0.41"	0.29	6.87	0
+1000	500	"0.9"	0.5	0.71	"0.27 ( 0 )"	"3.37 ( 0.27 )"	"0 ( 0 )"	8.37	"0.23"	0.27	3.37	0
+50	1000	"0.9"	20	0.11	"2.46 ( 0.16 )"	"25.37 ( 0.35 )"	"1.73 ( 0.12 )"	28.64	"0.85"	2.46	25.3	1.73
+100	1000	"0.9"	10	0.22	"0.62 ( 0.03 )"	"21.43 ( 0.62 )"	"0 ( 0 )"	26.43	"0.76"	0.62	21.4	0
+500	1000	"0.9"	2	0.46	"0.3 ( 0 )"	"7.99 ( 0.79 )"	"0 ( 0 )"	12.99	"0.42"	0.3	7.99	0
+1000	1000	"0.9"	1	0.6	"0.28 ( 0 )"	"4.93 ( 0.43 )"	"0 ( 0 )"	9.93	"0.31"	0.28	4.93	0
diff --git a/simulations/results_summary_cts/sim_block_rf.txt b/simulations/results_summary_cts/sim_block_rf.txt
new file mode 100755
index 0000000..badb16b
--- /dev/null
+++ b/simulations/results_summary_cts/sim_block_rf.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"OOB"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"	"OOB_mean"
+50	50	"0.1"	1	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	"0.59 ( 0.01 )"	0	"NaN"	0.08	1	6	0.59
+100	50	"0.1"	0.5	0.03	"0.07 ( 0 )"	"2.26 ( 0.17 )"	"5.06 ( 0.12 )"	"0.58 ( 0 )"	3.04	"0.7"	0.07	2.26	5.06	0.58
+500	50	"0.1"	0.1	0.23	"0.05 ( 0 )"	"2.34 ( 0.16 )"	"3.77 ( 0.14 )"	"0.56 ( 0 )"	4.56	"0.49"	0.05	2.34	3.77	0.56
+1000	50	"0.1"	0.05	0.51	"0.05 ( 0 )"	"2.37 ( 0.16 )"	"2.54 ( 0.11 )"	"0.55 ( 0 )"	5.83	"0.38"	0.05	2.37	2.54	0.55
+50	100	"0.1"	2	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	"0.6 ( 0.01 )"	0	"NaN"	0.08	1	6	0.6
+100	100	"0.1"	1	0.01	"0.07 ( 0 )"	"4.76 ( 0.27 )"	"5.51 ( 0.09 )"	"0.58 ( 0 )"	5.24	"0.91"	0.07	4.76	5.51	0.58
+500	100	"0.1"	0.2	0.22	"0.05 ( 0 )"	"4.86 ( 0.25 )"	"3.36 ( 0.15 )"	"0.57 ( 0 )"	7.5	"0.63"	0.05	4.86	3.36	0.57
+1000	100	"0.1"	0.1	0.52	"0.05 ( 0 )"	"4.65 ( 0.2 )"	"1.99 ( 0.13 )"	"0.56 ( 0 )"	8.66	"0.52"	0.05	4.65	1.99	0.56
+50	500	"0.1"	10	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	"0.59 ( 0.01 )"	0	"NaN"	0.08	1	6	0.59
+100	500	"0.1"	5	0	"0.07 ( 0 )"	"23.53 ( 0.53 )"	"5.27 ( 0.11 )"	"0.59 ( 0 )"	24.26	"0.97"	0.07	23.5	5.27	0.59
+500	500	"0.1"	1	0.07	"0.06 ( 0 )"	"23.98 ( 0.51 )"	"3.3 ( 0.16 )"	"0.58 ( 0 )"	26.68	"0.9"	0.06	23.9	3.3	0.58
+1000	500	"0.1"	0.5	0.17	"0.05 ( 0 )"	"23.6 ( 0.51 )"	"1.93 ( 0.15 )"	"0.57 ( 0 )"	27.67	"0.85"	0.05	23.6	1.93	0.57
+50	1000	"0.1"	20	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	"0.59 ( 0.01 )"	0	"NaN"	0.08	1	6	0.59
+100	1000	"0.1"	10	0	"0.06 ( 0 )"	"48.27 ( 0.79 )"	"5.46 ( 0.08 )"	"0.59 ( 0 )"	48.81	"0.99"	0.06	48.2	5.46	0.59
+500	1000	"0.1"	2	0.02	"0.06 ( 0 )"	"48.48 ( 0.7 )"	"3.65 ( 0.16 )"	"0.58 ( 0 )"	50.83	"0.95"	0.06	48.4	3.65	0.58
+1000	1000	"0.1"	1	0.07	"0.05 ( 0 )"	"47.72 ( 0.68 )"	"2.25 ( 0.14 )"	"0.58 ( 0 )"	51.47	"0.93"	0.05	47.7	2.25	0.58
+50	50	"0.3"	1	0	"0.31 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1.14 ( 0.01 )"	0	"NaN"	0.31	1	6	1.14
+100	50	"0.3"	0.5	0.05	"0.25 ( 0.01 )"	"1.97 ( 0.13 )"	"4.84 ( 0.08 )"	"1.05 ( 0.01 )"	3.11	"0.59"	0.25	1.97	4.84	1.05
+500	50	"0.3"	0.1	0.65	"0.16 ( 0 )"	"0.74 ( 0.08 )"	"1.78 ( 0.08 )"	"0.84 ( 0 )"	4.96	"0.14"	0.16	0.74	1.78	0.84
+1000	50	"0.3"	0.05	0.84	"0.13 ( 0 )"	"0.3 ( 0.05 )"	"0.69 ( 0.07 )"	"0.77 ( 0 )"	5.61	"0.05"	0.13	0.3	0.69	0.77
+50	100	"0.3"	2	0	"0.32 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1.2 ( 0.01 )"	0	"NaN"	0.32	1	6	1.2
+100	100	"0.3"	1	0.05	"0.25 ( 0.01 )"	"4.14 ( 0.18 )"	"4.63 ( 0.1 )"	"1.14 ( 0.01 )"	5.51	"0.74"	0.25	4.14	4.63	1.14
+500	100	"0.3"	0.2	0.47	"0.19 ( 0 )"	"2.7 ( 0.14 )"	"1.77 ( 0.09 )"	"0.93 ( 0 )"	6.93	"0.37"	0.19	2.7	1.77	0.93
+1000	100	"0.3"	0.1	0.66	"0.16 ( 0 )"	"1.78 ( 0.13 )"	"0.64 ( 0.07 )"	"0.86 ( 0 )"	7.14	"0.23"	0.16	1.78	0.64	0.86
+50	500	"0.3"	10	0	"0.36 ( 0.02 )"	"1 ( 0 )"	"6 ( 0 )"	"1.23 ( 0.01 )"	0	"NaN"	0.36	1	6	1.23
+100	500	"0.3"	5	0.01	"0.25 ( 0.01 )"	"25.01 ( 0.53 )"	"4.87 ( 0.09 )"	"1.22 ( 0.01 )"	26.14	"0.96"	0.25	25	4.87	1.22
+500	500	"0.3"	1	0.09	"0.23 ( 0 )"	"22.67 ( 0.44 )"	"2.42 ( 0.09 )"	"1.14 ( 0 )"	26.25	"0.86"	0.23	22.6	2.42	1.14
+1000	500	"0.3"	0.5	0.16	"0.22 ( 0 )"	"19.38 ( 0.47 )"	"1.14 ( 0.07 )"	"1.11 ( 0 )"	24.24	"0.79"	0.22	19.3	1.14	1.11
+50	1000	"0.3"	20	0	"0.34 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1.26 ( 0.01 )"	0	"NaN"	0.34	1	6	1.26
+100	1000	"0.3"	10	0	"0.26 ( 0.01 )"	"48.76 ( 0.63 )"	"5.22 ( 0.09 )"	"1.21 ( 0.01 )"	49.54	"0.98"	0.26	48.7	5.22	1.21
+500	1000	"0.3"	2	0.04	"0.23 ( 0 )"	"46.28 ( 0.69 )"	"2.72 ( 0.1 )"	"1.19 ( 0 )"	49.56	"0.93"	0.23	46.2	2.72	1.19
+1000	1000	"0.3"	1	0.07	"0.23 ( 0 )"	"44.67 ( 0.68 )"	"1.33 ( 0.08 )"	"1.17 ( 0 )"	49.34	"0.9"	0.23	44.6	1.33	1.17
+50	50	"0.5"	1	0	"0.6 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	"1.46 ( 0.01 )"	0	"NaN"	0.6	1	6	1.46
+100	50	"0.5"	0.5	0.09	"0.42 ( 0.01 )"	"1.75 ( 0.14 )"	"4.6 ( 0.09 )"	"1.33 ( 0.01 )"	3.15	"0.5"	0.42	1.75	4.6	1.33
+500	50	"0.5"	0.1	0.71	"0.26 ( 0 )"	"0.56 ( 0.07 )"	"1.57 ( 0.08 )"	"1.02 ( 0 )"	4.99	"0.1"	0.26	0.56	1.57	1.02
+1000	50	"0.5"	0.05	0.85	"0.2 ( 0 )"	"0.24 ( 0.05 )"	"0.73 ( 0.07 )"	"0.93 ( 0 )"	5.51	"0.04"	0.2	0.24	0.73	0.93
+50	100	"0.5"	2	0	"0.6 ( 0.02 )"	"1 ( 0 )"	"6 ( 0 )"	"1.56 ( 0.01 )"	0	"NaN"	0.6	1	6	1.56
+100	100	"0.5"	1	0.06	"0.47 ( 0.02 )"	"4.15 ( 0.17 )"	"4.61 ( 0.09 )"	"1.45 ( 0.01 )"	5.54	"0.74"	0.47	4.15	4.61	1.45
+500	100	"0.5"	0.2	0.52	"0.32 ( 0 )"	"2.32 ( 0.14 )"	"1.5 ( 0.09 )"	"1.16 ( 0 )"	6.82	"0.32"	0.32	2.32	1.5	1.16
+1000	100	"0.5"	0.1	0.73	"0.26 ( 0 )"	"1.21 ( 0.1 )"	"0.6 ( 0.07 )"	"1.06 ( 0 )"	6.61	"0.17"	0.26	1.21	0.6	1.06
+50	500	"0.5"	10	0	"0.58 ( 0.02 )"	"1 ( 0 )"	"6 ( 0 )"	"1.64 ( 0.02 )"	0	"NaN"	0.58	1	6	1.64
+100	500	"0.5"	5	0.01	"0.46 ( 0.01 )"	"24.25 ( 0.46 )"	"4.96 ( 0.09 )"	"1.6 ( 0.01 )"	25.29	"0.96"	0.46	24.2	4.96	1.6
+500	500	"0.5"	1	0.11	"0.41 ( 0 )"	"21.23 ( 0.42 )"	"2.01 ( 0.1 )"	"1.49 ( 0 )"	25.22	"0.84"	0.41	21.2	2.01	1.49
+1000	500	"0.5"	0.5	0.17	"0.37 ( 0 )"	"18.91 ( 0.41 )"	"0.96 ( 0.07 )"	"1.44 ( 0 )"	23.95	"0.78"	0.37	18.9	0.96	1.44
+50	1000	"0.5"	20	0	"0.57 ( 0.02 )"	"1 ( 0 )"	"6 ( 0 )"	"1.64 ( 0.02 )"	0	"NaN"	0.57	1	6	1.64
+100	1000	"0.5"	10	0	"0.47 ( 0.01 )"	"49.88 ( 0.68 )"	"5.13 ( 0.08 )"	"1.62 ( 0.01 )"	50.75	"0.98"	0.47	49.8	5.13	1.62
+500	1000	"0.5"	2	0.05	"0.42 ( 0 )"	"45.98 ( 0.68 )"	"2.22 ( 0.09 )"	"1.57 ( 0 )"	49.76	"0.92"	0.42	45.9	2.22	1.57
+1000	1000	"0.5"	1	0.08	"0.4 ( 0 )"	"44.42 ( 0.63 )"	"1.13 ( 0.07 )"	"1.53 ( 0 )"	49.29	"0.9"	0.4	44.4	1.13	1.53
+50	50	"0.7"	1	0	"0.8 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"1.72 ( 0.02 )"	0	"NaN"	0.8	1	6	1.72
+100	50	"0.7"	0.5	0.11	"0.59 ( 0.02 )"	"1.85 ( 0.14 )"	"4.52 ( 0.08 )"	"1.57 ( 0.01 )"	3.32	"0.51"	0.59	1.85	4.52	1.57
+500	50	"0.7"	0.1	0.71	"0.35 ( 0 )"	"0.51 ( 0.07 )"	"1.72 ( 0.09 )"	"1.17 ( 0 )"	4.79	"0.1"	0.35	0.51	1.72	1.17
+1000	50	"0.7"	0.05	0.87	"0.27 ( 0 )"	"0.18 ( 0.05 )"	"0.72 ( 0.07 )"	"1.06 ( 0 )"	5.46	"0.03"	0.27	0.18	0.72	1.06
+50	100	"0.7"	2	0	"0.92 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"1.85 ( 0.02 )"	0	"NaN"	0.92	1	6	1.85
+100	100	"0.7"	1	0.05	"0.63 ( 0.02 )"	"4.69 ( 0.18 )"	"4.65 ( 0.09 )"	"1.7 ( 0.01 )"	6.04	"0.77"	0.63	4.69	4.65	1.7
+500	100	"0.7"	0.2	0.53	"0.43 ( 0 )"	"2.27 ( 0.15 )"	"1.46 ( 0.09 )"	"1.36 ( 0 )"	6.81	"0.31"	0.43	2.27	1.46	1.36
+1000	100	"0.7"	0.1	0.71	"0.35 ( 0 )"	"1.47 ( 0.13 )"	"0.63 ( 0.06 )"	"1.22 ( 0 )"	6.84	"0.19"	0.35	1.47	0.63	1.22
+50	500	"0.7"	10	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"2 ( 0.02 )"	0	"NaN"	0.91	1	6	2
+100	500	"0.7"	5	0.01	"0.69 ( 0.02 )"	"24.52 ( 0.45 )"	"4.6 ( 0.09 )"	"1.93 ( 0.01 )"	25.92	"0.95"	0.69	24.5	4.6	1.93
+500	500	"0.7"	1	0.12	"0.57 ( 0.01 )"	"20.96 ( 0.41 )"	"1.86 ( 0.09 )"	"1.77 ( 0.01 )"	25.1	"0.83"	0.57	20.9	1.86	1.77
+1000	500	"0.7"	0.5	0.17	"0.52 ( 0 )"	"18.88 ( 0.46 )"	"1.01 ( 0.08 )"	"1.7 ( 0 )"	23.87	"0.78"	0.52	18.8	1.01	1.7
+50	1000	"0.7"	20	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"1.98 ( 0.02 )"	0	"NaN"	0.91	1	6	1.98
+100	1000	"0.7"	10	0.01	"0.66 ( 0.02 )"	"49.65 ( 0.71 )"	"4.89 ( 0.09 )"	"1.93 ( 0.01 )"	50.76	"0.98"	0.66	49.6	4.89	1.93
+500	1000	"0.7"	2	0.05	"0.6 ( 0.01 )"	"47.23 ( 0.73 )"	"2.18 ( 0.1 )"	"1.88 ( 0.01 )"	51.05	"0.92"	0.6	47.2	2.18	1.88
+1000	1000	"0.7"	1	0.08	"0.57 ( 0 )"	"43.91 ( 0.67 )"	"1.1 ( 0.08 )"	"1.82 ( 0 )"	48.81	"0.9"	0.57	43.9	1.1	1.82
+50	50	"0.9"	1	0	"1.1 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"1.99 ( 0.02 )"	0	"NaN"	1.1	1	6	1.99
+100	50	"0.9"	0.5	0.16	"0.78 ( 0.02 )"	"1.56 ( 0.11 )"	"4.38 ( 0.09 )"	"1.78 ( 0.01 )"	3.16	"0.46"	0.78	1.56	4.38	1.78
+500	50	"0.9"	0.1	0.74	"0.44 ( 0 )"	"0.45 ( 0.06 )"	"1.49 ( 0.08 )"	"1.31 ( 0 )"	4.96	"0.08"	0.44	0.45	1.49	1.31
+1000	50	"0.9"	0.05	0.86	"0.34 ( 0 )"	"0.14 ( 0.04 )"	"0.86 ( 0.08 )"	"1.17 ( 0 )"	5.28	"0.02"	0.34	0.14	0.86	1.17
+50	100	"0.9"	2	0	"1.11 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.06 ( 0.02 )"	0	"NaN"	1.11	1	6	2.06
+100	100	"0.9"	1	0.09	"0.84 ( 0.02 )"	"3.94 ( 0.18 )"	"4.32 ( 0.09 )"	"1.95 ( 0.01 )"	5.62	"0.68"	0.84	3.94	4.32	1.95
+500	100	"0.9"	0.2	0.55	"0.54 ( 0.01 )"	"2.11 ( 0.15 )"	"1.47 ( 0.08 )"	"1.51 ( 0 )"	6.64	"0.29"	0.54	2.11	1.47	1.51
+1000	100	"0.9"	0.1	0.76	"0.44 ( 0 )"	"1.05 ( 0.11 )"	"0.55 ( 0.06 )"	"1.36 ( 0 )"	6.5	"0.14"	0.44	1.05	0.55	1.36
+50	500	"0.9"	10	0	"1.18 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.2 ( 0.02 )"	0	"NaN"	1.18	1	6	2.2
+100	500	"0.9"	5	0.01	"0.86 ( 0.02 )"	"24.13 ( 0.47 )"	"4.7 ( 0.09 )"	"2.18 ( 0.02 )"	25.43	"0.95"	0.86	24.1	4.7	2.18
+500	500	"0.9"	1	0.12	"0.73 ( 0.01 )"	"20.83 ( 0.45 )"	"2.04 ( 0.09 )"	"2 ( 0.01 )"	24.79	"0.84"	0.73	20.8	2.04	2
+1000	500	"0.9"	0.5	0.18	"0.68 ( 0.01 )"	"18.47 ( 0.43 )"	"1 ( 0.07 )"	"1.92 ( 0 )"	23.47	"0.78"	0.68	18.4	0	1.92
+50	1000	"0.9"	20	0	"1.22 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.3 ( 0.03 )"	0	"NaN"	1.22	1	6	2.3
+100	1000	"0.9"	10	0.01	"0.93 ( 0.02 )"	"49.18 ( 0.69 )"	"4.67 ( 0.09 )"	"2.22 ( 0.02 )"	50.51	"0.97"	0.93	49.1	4.67	2.22
+500	1000	"0.9"	2	0.06	"0.78 ( 0.01 )"	"46.01 ( 0.7 )"	"2.14 ( 0.09 )"	"2.12 ( 0.01 )"	49.87	"0.92"	0.78	46	2.14	2.12
+1000	1000	"0.9"	1	0.08	"0.74 ( 0.01 )"	"43.05 ( 0.72 )"	"1.17 ( 0.08 )"	"2.07 ( 0 )"	47.88	"0.9"	0.74	43	1.17	2.07
diff --git a/simulations/results_summary_cts/sim_ind_compLasso.txt b/simulations/results_summary_cts/sim_ind_compLasso.txt
new file mode 100755
index 0000000..52ebf9a
--- /dev/null
+++ b/simulations/results_summary_cts/sim_ind_compLasso.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	1	0.61	"0.8 ( 0.05 )"	"2.99 ( 0.27 )"	"0.05 ( 0.02 )"	8.94	"0.28"	0.8	2.99	0.05
+100	50	0.5	0.87	"0.98 ( 0.06 )"	"0.79 ( 0.15 )"	"0 ( 0 )"	6.79	"0.09"	0.98	0.79	0
+500	50	0.1	0.91	"1.08 ( 0.05 )"	"0.51 ( 0.11 )"	"0 ( 0 )"	6.51	"0.06"	1.08	0.51	0
+1000	50	0.05	0.93	"1.18 ( 0.04 )"	"0.39 ( 0.15 )"	"0 ( 0 )"	6.39	"0.04"	1.18	0.39	0
+50	100	2	0.52	"1.05 ( 0.07 )"	"4.3 ( 0.31 )"	"0.24 ( 0.05 )"	10.06	"0.37"	1.05	4.3	0.24
+100	100	1	0.85	"0.94 ( 0.04 )"	"1.02 ( 0.19 )"	"0 ( 0 )"	7.02	"0.11"	0.94	1.02	0
+500	100	0.2	0.96	"1.16 ( 0.04 )"	"0.25 ( 0.09 )"	"0 ( 0 )"	6.25	"0.03"	1.16	0.25	0
+1000	100	0.1	0.89	"1.04 ( 0.05 )"	"0.71 ( 0.15 )"	"0 ( 0 )"	6.71	"0.08"	1.04	0.71	0
+50	500	10	0.38	"2.18 ( 0.13 )"	"5.53 ( 0.36 )"	"1.27 ( 0.11 )"	10.26	"0.48"	2.18	5.53	1.27
+100	500	5	0.75	"1 ( 0.05 )"	"1.9 ( 0.29 )"	"0.03 ( 0.02 )"	7.87	"0.18"	1	1.9	0.03
+500	500	1	0.96	"1.18 ( 0.04 )"	"0.27 ( 0.13 )"	"0 ( 0 )"	6.27	"0.03"	1.18	0.27	0
+1000	500	0.5	0.96	"1.24 ( 0.04 )"	"0.25 ( 0.1 )"	"0 ( 0 )"	6.25	"0.03"	1.24	0.25	0
+50	1000	20	0.32	"3.09 ( 0.23 )"	"5.56 ( 0.36 )"	"1.95 ( 0.14 )"	9.61	"0.51"	3.09	5.56	1.95
+100	1000	10	0.67	"1.1 ( 0.07 )"	"2.8 ( 0.37 )"	"0.04 ( 0.02 )"	8.76	"0.24"	1.1	2.8	0.04
+500	1000	2	0.96	"1.12 ( 0.04 )"	"0.27 ( 0.08 )"	"0 ( 0 )"	6.27	"0.03"	1.12	0.27	0
+1000	1000	1	0.96	"1.16 ( 0.04 )"	"0.24 ( 0.07 )"	"0 ( 0 )"	6.24	"0.03"	1.16	0.24	0
diff --git a/simulations/results_summary_cts/sim_ind_elnet.txt b/simulations/results_summary_cts/sim_ind_elnet.txt
new file mode 100755
index 0000000..0273d31
--- /dev/null
+++ b/simulations/results_summary_cts/sim_ind_elnet.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	1	0.2	"0.53 ( 0.03 )"	"14.99 ( 0.59 )"	"0 ( 0 )"	19.99	"0.67"	0.53	14.9	0
+100	50	0.5	0.23	"0.34 ( 0.01 )"	"13.46 ( 0.55 )"	"0 ( 0 )"	18.46	"0.65"	0.34	13.4	0
+500	50	0.1	0.23	"0.26 ( 0 )"	"13.65 ( 0.56 )"	"0 ( 0 )"	18.65	"0.64"	0.26	13.6	0
+1000	50	0.05	0.27	"0.25 ( 0 )"	"11.76 ( 0.53 )"	"0 ( 0 )"	16.76	"0.6"	0.25	11.7	0
+50	100	2	0.19	"0.74 ( 0.06 )"	"19.45 ( 0.66 )"	"0.04 ( 0.02 )"	24.41	"0.74"	0.74	19.4	0.04
+100	100	1	0.2	"0.38 ( 0.01 )"	"19.2 ( 1.02 )"	"0 ( 0 )"	24.2	"0.71"	0.38	19.2	0
+500	100	0.2	0.23	"0.27 ( 0 )"	"16.28 ( 0.78 )"	"0 ( 0 )"	21.28	"0.67"	0.27	16.2	0
+1000	100	0.1	0.23	"0.26 ( 0 )"	"17.05 ( 0.76 )"	"0 ( 0 )"	22.05	"0.69"	0.26	17	0
+50	500	10	0.13	"1.65 ( 0.13 )"	"30.38 ( 1.24 )"	"0.58 ( 0.08 )"	34.8	"0.83"	1.65	30.3	0.58
+100	500	5	0.15	"0.5 ( 0.02 )"	"32.18 ( 1.44 )"	"0 ( 0 )"	37.18	"0.81"	0.5	32.1	0
+500	500	1	0.16	"0.28 ( 0 )"	"29.79 ( 1.73 )"	"0 ( 0 )"	34.79	"0.78"	0.28	29.7	0
+1000	500	0.5	0.18	"0.26 ( 0 )"	"27.21 ( 1.58 )"	"0 ( 0 )"	32.21	"0.77"	0.26	27.2	0
+50	1000	20	0.1	"2.5 ( 0.22 )"	"36.6 ( 2.03 )"	"1.12 ( 0.09 )"	40.48	"0.86"	2.5	36.6	1.12
+100	1000	10	0.13	"0.56 ( 0.02 )"	"40.61 ( 1.71 )"	"0 ( 0 )"	45.61	"0.85"	0.56	40.6	0
+500	1000	2	0.14	"0.27 ( 0 )"	"35.98 ( 2.2 )"	"0 ( 0 )"	40.98	"0.8"	0.27	35.9	0
+1000	1000	1	0.15	"0.27 ( 0 )"	"34.65 ( 2.02 )"	"0 ( 0 )"	39.65	"0.79"	0.27	34.6	0
diff --git a/simulations/results_summary_cts/sim_ind_lasso.txt b/simulations/results_summary_cts/sim_ind_lasso.txt
new file mode 100755
index 0000000..5321933
--- /dev/null
+++ b/simulations/results_summary_cts/sim_ind_lasso.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	1	0.37	"0.54 ( 0.03 )"	"8.36 ( 0.45 )"	"0.02 ( 0.01 )"	13.34	"0.51"	0.54	8.36	0.02
+100	50	0.5	0.51	"0.36 ( 0.01 )"	"5.5 ( 0.42 )"	"0 ( 0 )"	10.5	"0.37"	0.36	5.5	0
+500	50	0.1	0.79	"0.28 ( 0 )"	"2.33 ( 0.14 )"	"0 ( 0 )"	7.33	"0.15"	0.28	2.33	0
+1000	50	0.05	0.86	"0.26 ( 0 )"	"1.82 ( 0.13 )"	"0 ( 0 )"	6.82	"0.1"	0.26	1.82	0
+50	100	2	0.32	"0.66 ( 0.04 )"	"11.1 ( 0.38 )"	"0.08 ( 0.03 )"	16.02	"0.61"	0.66	11.1	0.08
+100	100	1	0.46	"0.41 ( 0.01 )"	"7.23 ( 0.4 )"	"0 ( 0 )"	12.23	"0.46"	0.41	7.23	0
+500	100	0.2	0.71	"0.29 ( 0 )"	"3.26 ( 0.25 )"	"0 ( 0 )"	8.26	"0.22"	0.29	3.26	0
+1000	100	0.1	0.82	"0.27 ( 0 )"	"2.25 ( 0.15 )"	"0 ( 0 )"	7.25	"0.15"	0.27	2.25	0
+50	500	10	0.19	"1.41 ( 0.11 )"	"19.89 ( 0.38 )"	"0.64 ( 0.08 )"	24.25	"0.77"	1.41	19.8	0.64
+100	500	5	0.3	"0.48 ( 0.02 )"	"14.71 ( 0.7 )"	"0 ( 0 )"	19.71	"0.66"	0.48	14.7	0
+500	500	1	0.61	"0.28 ( 0 )"	"4.82 ( 0.34 )"	"0 ( 0 )"	9.82	"0.32"	0.28	4.82	0
+1000	500	0.5	0.72	"0.27 ( 0 )"	"3.35 ( 0.32 )"	"0 ( 0 )"	8.35	"0.21"	0.27	3.35	0
+50	1000	20	0.14	"2.24 ( 0.19 )"	"23.95 ( 0.37 )"	"1.19 ( 0.09 )"	27.76	"0.82"	2.24	23.9	1.19
+100	1000	10	0.25	"0.53 ( 0.02 )"	"18.53 ( 0.59 )"	"0 ( 0 )"	23.53	"0.73"	0.53	18.5	0
+500	1000	2	0.52	"0.29 ( 0 )"	"6.51 ( 0.45 )"	"0 ( 0 )"	11.51	"0.4"	0.29	6.51	0
+1000	1000	1	0.64	"0.27 ( 0 )"	"4.39 ( 0.56 )"	"0 ( 0 )"	9.39	"0.26"	0.27	4.39	0
diff --git a/simulations/results_summary_cts/sim_ind_rf.txt b/simulations/results_summary_cts/sim_ind_rf.txt
new file mode 100755
index 0000000..a82ed1d
--- /dev/null
+++ b/simulations/results_summary_cts/sim_ind_rf.txt
@@ -0,0 +1,17 @@
+"N"	"P"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"OOB"	"num_select"	"FDR"	"Index"	"MSE_mean"	"FP_mean"	"FN_mean"	"OOB_mean"
+50	50	1	0	"1.31 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	"2.09 ( 0.02 )"	0	"NaN"	1	1.31	1	6	2.09
+100	50	0.5	0.18	"0.85 ( 0.02 )"	"1.85 ( 0.11 )"	"4.11 ( 0.1 )"	"1.79 ( 0.01 )"	3.74	"0.48"	2	0.85	1.85	4.11	1.79
+500	50	0.1	0.8	"0.47 ( 0.01 )"	"0.3 ( 0.05 )"	"1.61 ( 0.08 )"	"1.31 ( 0 )"	4.69	"0.05"	3	0.47	0.3	1.61	1.31
+1000	50	0.05	0.88	"0.35 ( 0 )"	"0.07 ( 0.03 )"	"0.78 ( 0.07 )"	"1.17 ( 0 )"	5.29	"0.01"	4	0.35	0.07	0.78	1.17
+50	100	2	0	"1.32 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.23 ( 0.02 )"	0	"NaN"	5	1.32	1	6	2.23
+100	100	1	0.08	"0.91 ( 0.03 )"	"4.33 ( 0.22 )"	"4.45 ( 0.09 )"	"2.02 ( 0.01 )"	5.88	"0.72"	6	0.91	4.33	4.45	2.02
+500	100	0.2	0.6	"0.59 ( 0.01 )"	"1.91 ( 0.15 )"	"1.41 ( 0.07 )"	"1.54 ( 0 )"	6.5	"0.27"	7	0.59	1.91	1.41	1.54
+1000	100	0.1	0.81	"0.47 ( 0 )"	"0.71 ( 0.09 )"	"0.53 ( 0.06 )"	"1.39 ( 0 )"	6.18	"0.1"	8	0.47	0.71	0.53	1.39
+50	500	10	0	"1.33 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.48 ( 0.03 )"	0	"NaN"	9	1.33	1	6	2.48
+100	500	5	0.02	"1.07 ( 0.03 )"	"23.8 ( 0.52 )"	"4.26 ( 0.1 )"	"2.43 ( 0.02 )"	25.54	"0.93"	10	1.07	23.8	4.26	2.43
+500	500	1	0.14	"0.84 ( 0.01 )"	"19.92 ( 0.41 )"	"1.71 ( 0.08 )"	"2.14 ( 0.01 )"	24.21	"0.82"	11	0.84	19.9	1.71	2.14
+1000	500	0.5	0.19	"0.78 ( 0.01 )"	"18.73 ( 0.42 )"	"0.84 ( 0.06 )"	"2.06 ( 0 )"	23.89	"0.78"	12	0.78	18.7	0.84	2.06
+50	1000	20	0	"1.31 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.48 ( 0.03 )"	0	"NaN"	13	1.31	1	6	2.48
+100	1000	10	0.01	"1.06 ( 0.03 )"	"48.48 ( 0.63 )"	"4.33 ( 0.1 )"	"2.43 ( 0.02 )"	50.15	"0.97"	14	1.06	48.4	4.33	2.43
+500	1000	2	0.06	"0.89 ( 0.01 )"	"44.77 ( 0.71 )"	"1.81 ( 0.1 )"	"2.3 ( 0.01 )"	48.96	"0.91"	15	0.89	44.7	1.81	2.3
+1000	1000	1	0.09	"0.85 ( 0.01 )"	"41.77 ( 0.68 )"	"0.99 ( 0.07 )"	"2.25 ( 0.01 )"	46.78	"0.89"	16	0.85	41.7	0.99	2.25
diff --git a/simulations/results_summary_cts/sim_toe_compLasso.txt b/simulations/results_summary_cts/sim_toe_compLasso.txt
new file mode 100755
index 0000000..8394de1
--- /dev/null
+++ b/simulations/results_summary_cts/sim_toe_compLasso.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.56	"0.88 ( 0.05 )"	"3.68 ( 0.29 )"	"0.06 ( 0.03 )"	9.62	"0.33"	0.88	3.68	0.06
+100	50	"0.1"	0.5	0.77	"0.87 ( 0.05 )"	"1.51 ( 0.35 )"	"0 ( 0 )"	7.51	"0.12"	0.87	1.51	0
+500	50	"0.1"	0.1	0.88	"0.99 ( 0.05 )"	"0.73 ( 0.19 )"	"0 ( 0 )"	6.73	"0.07"	0.99	0.73	0
+1000	50	"0.1"	0.05	0.9	"1.09 ( 0.04 )"	"0.55 ( 0.17 )"	"0 ( 0 )"	6.55	"0.05"	1.09	0.55	0
+50	100	"0.1"	2	0.53	"1.12 ( 0.1 )"	"4.24 ( 0.3 )"	"0.23 ( 0.06 )"	10.01	"0.37"	1.12	4.24	0.23
+100	100	"0.1"	1	0.79	"0.85 ( 0.04 )"	"1.51 ( 0.34 )"	"0 ( 0 )"	7.51	"0.13"	0.85	1.51	0
+500	100	"0.1"	0.2	0.95	"1.13 ( 0.04 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	"0.03"	1.13	0.29	0
+1000	100	"0.1"	0.1	0.93	"1.03 ( 0.04 )"	"0.42 ( 0.12 )"	"0 ( 0 )"	6.42	"0.05"	1.03	0.42	0
+50	500	"0.1"	10	0.36	"2.54 ( 0.18 )"	"5.13 ( 0.35 )"	"1.78 ( 0.12 )"	9.35	"0.48"	2.54	5.13	1.78
+100	500	"0.1"	5	0.71	"0.98 ( 0.04 )"	"2.4 ( 0.35 )"	"0 ( 0 )"	8.4	"0.21"	0.98	2.4	0
+500	500	"0.1"	1	0.96	"1.1 ( 0.04 )"	"0.25 ( 0.08 )"	"0 ( 0 )"	6.25	"0.03"	1.1	0.25	0
+1000	500	"0.1"	0.5	0.96	"1.18 ( 0.04 )"	"0.24 ( 0.08 )"	"0 ( 0 )"	6.24	"0.03"	1.18	0.24	0
+50	1000	"0.1"	20	0.29	"3.37 ( 0.2 )"	"4.59 ( 0.36 )"	"2.63 ( 0.15 )"	7.96	"0.5"	3.37	4.59	2.63
+100	1000	"0.1"	10	0.61	"1.03 ( 0.04 )"	"3.82 ( 0.46 )"	"0.01 ( 0.01 )"	9.81	"0.29"	1.03	3.82	0.01
+500	1000	"0.1"	2	0.96	"1.1 ( 0.03 )"	"0.24 ( 0.1 )"	"0 ( 0 )"	6.24	"0.03"	1.1	0.24	0
+1000	1000	"0.1"	1	0.97	"1.16 ( 0.03 )"	"0.16 ( 0.06 )"	"0 ( 0 )"	6.16	"0.02"	1.16	0.16	0
+50	50	"0.3"	1	0.5	"0.76 ( 0.05 )"	"4.6 ( 0.27 )"	"0.07 ( 0.03 )"	10.53	"0.4"	0.76	4.6	0.07
+100	50	"0.3"	0.5	0.75	"0.7 ( 0.04 )"	"1.65 ( 0.28 )"	"0 ( 0 )"	7.65	"0.16"	0.7	1.65	0
+500	50	"0.3"	0.1	0.93	"0.92 ( 0.04 )"	"0.39 ( 0.12 )"	"0 ( 0 )"	6.39	"0.04"	0.92	0.39	0
+1000	50	"0.3"	0.05	0.93	"0.92 ( 0.03 )"	"0.38 ( 0.1 )"	"0 ( 0 )"	6.38	"0.04"	0.92	0.38	0
+50	100	"0.3"	2	0.43	"1.12 ( 0.07 )"	"5.19 ( 0.3 )"	"0.63 ( 0.09 )"	10.56	"0.44"	1.12	5.19	0.63
+100	100	"0.3"	1	0.7	"0.73 ( 0.04 )"	"2.38 ( 0.32 )"	"0 ( 0 )"	8.38	"0.22"	0.73	2.38	0
+500	100	"0.3"	0.2	0.87	"0.82 ( 0.04 )"	"0.84 ( 0.2 )"	"0 ( 0 )"	6.84	"0.08"	0.82	0.84	0
+1000	100	"0.3"	0.1	0.93	"0.87 ( 0.04 )"	"0.41 ( 0.08 )"	"0 ( 0 )"	6.41	"0.05"	0.87	0.41	0
+50	500	"0.3"	10	0.29	"2.5 ( 0.14 )"	"5.62 ( 0.34 )"	"2.39 ( 0.12 )"	9.23	"0.55"	2.5	5.62	2.39
+100	500	"0.3"	5	0.52	"0.84 ( 0.04 )"	"5.23 ( 0.57 )"	"0.06 ( 0.02 )"	11.17	"0.37"	0.84	5.23	0.06
+500	500	"0.3"	1	0.96	"0.93 ( 0.03 )"	"0.25 ( 0.08 )"	"0 ( 0 )"	6.25	"0.03"	0.93	0.25	0
+1000	500	"0.3"	0.5	0.94	"0.9 ( 0.03 )"	"0.38 ( 0.1 )"	"0 ( 0 )"	6.38	"0.04"	0.9	0.38	0
+50	1000	"0.3"	20	0.28	"2.99 ( 0.17 )"	"4.75 ( 0.36 )"	"3 ( 0.13 )"	7.75	"0.53"	2.99	4.75	0
+100	1000	"0.3"	10	0.46	"0.93 ( 0.04 )"	"6.63 ( 0.6 )"	"0.1 ( 0.03 )"	12.53	"0.43"	0.93	6.63	0.1
+500	1000	"0.3"	2	0.96	"0.89 ( 0.03 )"	"0.28 ( 0.09 )"	"0 ( 0 )"	6.28	"0.03"	0.89	0.28	0
+1000	1000	"0.3"	1	0.95	"0.93 ( 0.03 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	"0.03"	0.93	0.29	0
+50	50	"0.5"	1	0.44	"0.72 ( 0.05 )"	"5.25 ( 0.31 )"	"0.24 ( 0.06 )"	11.01	"0.43"	0.72	5.25	0.24
+100	50	"0.5"	0.5	0.62	"0.55 ( 0.02 )"	"2.98 ( 0.3 )"	"0 ( 0 )"	8.98	"0.27"	0.55	2.98	0
+500	50	"0.5"	0.1	0.9	"0.62 ( 0.02 )"	"0.57 ( 0.13 )"	"0 ( 0 )"	6.57	"0.06"	0.62	0.57	0
+1000	50	"0.5"	0.05	0.93	"0.67 ( 0.02 )"	"0.39 ( 0.12 )"	"0 ( 0 )"	6.39	"0.04"	0.67	0.39	0
+50	100	"0.5"	2	0.37	"1.15 ( 0.07 )"	"5.35 ( 0.36 )"	"1.25 ( 0.11 )"	10.1	"0.45"	1.15	5.35	1.25
+100	100	"0.5"	1	0.59	"0.68 ( 0.03 )"	"3.57 ( 0.36 )"	"0.06 ( 0.02 )"	9.51	"0.31"	0.68	3.57	0.06
+500	100	"0.5"	0.2	0.94	"0.68 ( 0.02 )"	"0.34 ( 0.1 )"	"0 ( 0 )"	6.34	"0.04"	0.68	0.34	0
+1000	100	"0.5"	0.1	0.95	"0.71 ( 0.02 )"	"0.27 ( 0.09 )"	"0 ( 0 )"	6.27	"0.03"	0.71	0.27	0
+50	500	"0.5"	10	0.29	"2.28 ( 0.14 )"	"5.5 ( 0.38 )"	"2.81 ( 0.1 )"	8.69	"0.55"	2.28	5.5	2.81
+100	500	"0.5"	5	0.38	"0.82 ( 0.04 )"	"8.32 ( 0.64 )"	"0.38 ( 0.07 )"	13.94	"0.51"	0.82	8.32	0.38
+500	500	"0.5"	1	0.93	"0.67 ( 0.02 )"	"0.45 ( 0.12 )"	"0 ( 0 )"	6.45	"0.05"	0.67	0.45	0
+1000	500	"0.5"	0.5	0.94	"0.69 ( 0.02 )"	"0.35 ( 0.1 )"	"0 ( 0 )"	6.35	"0.04"	0.69	0.35	0
+50	1000	"0.5"	20	0.23	"2.44 ( 0.13 )"	"5.82 ( 0.36 )"	"3.37 ( 0.1 )"	8.45	"0.63"	2.44	5.82	3.37
+100	1000	"0.5"	10	0.3	"0.96 ( 0.05 )"	"10.94 ( 0.72 )"	"0.61 ( 0.1 )"	16.33	"0.59"	0.96	10.9	0.61
+500	1000	"0.5"	2	0.96	"0.7 ( 0.02 )"	"0.22 ( 0.06 )"	"0 ( 0 )"	6.22	"0.03"	0.7	0.22	0
+1000	1000	"0.5"	1	0.95	"0.71 ( 0.02 )"	"0.33 ( 0.13 )"	"0 ( 0 )"	6.33	"0.03"	0.71	0.33	0
+50	50	"0.7"	1	0.36	"0.76 ( 0.05 )"	"5.83 ( 0.31 )"	"0.81 ( 0.1 )"	11.02	"0.48"	0.76	5.83	0.81
+100	50	"0.7"	0.5	0.48	"0.43 ( 0.02 )"	"4.89 ( 0.38 )"	"0.08 ( 0.03 )"	10.81	"0.4"	0.43	4.89	0.08
+500	50	"0.7"	0.1	0.81	"0.44 ( 0.01 )"	"1.23 ( 0.22 )"	"0 ( 0 )"	7.23	"0.12"	0.44	1.23	0
+1000	50	"0.7"	0.05	0.88	"0.45 ( 0.01 )"	"0.74 ( 0.13 )"	"0 ( 0 )"	6.74	"0.09"	0.45	0.74	0
+50	100	"0.7"	2	0.31	"0.99 ( 0.06 )"	"5.89 ( 0.36 )"	"1.88 ( 0.11 )"	10.01	"0.53"	0.99	5.89	1.88
+100	100	"0.7"	1	0.41	"0.48 ( 0.02 )"	"6.91 ( 0.55 )"	"0.22 ( 0.05 )"	12.69	"0.47"	0.48	6.91	0.22
+500	100	"0.7"	0.2	0.82	"0.45 ( 0.01 )"	"1.24 ( 0.19 )"	"0 ( 0 )"	7.24	"0.13"	0.45	1.24	0
+1000	100	"0.7"	0.1	0.92	"0.48 ( 0.01 )"	"0.5 ( 0.14 )"	"0 ( 0 )"	6.5	"0.05"	0.48	0.5	0
+50	500	"0.7"	10	0.27	"1.81 ( 0.09 )"	"5.27 ( 0.39 )"	"3.43 ( 0.09 )"	7.84	"0.56"	1.81	5.27	3.43
+100	500	"0.7"	5	0.27	"0.85 ( 0.05 )"	"9.56 ( 0.73 )"	"1.42 ( 0.12 )"	14.14	"0.59"	0.85	9.56	1.42
+500	500	"0.7"	1	0.74	"0.46 ( 0.01 )"	"2.11 ( 0.26 )"	"0 ( 0 )"	8.11	"0.2"	0.46	2.11	0
+1000	500	"0.7"	0.5	0.87	"0.46 ( 0.01 )"	"0.93 ( 0.16 )"	"0 ( 0 )"	6.93	"0.1"	0.46	0.93	0
+50	1000	"0.7"	20	0.21	"1.81 ( 0.08 )"	"6.05 ( 0.39 )"	"3.58 ( 0.1 )"	8.47	"0.64"	1.81	6.05	3.58
+100	1000	"0.7"	10	0.27	"1.1 ( 0.06 )"	"8.36 ( 0.68 )"	"2.1 ( 0.09 )"	12.26	"0.58"	1.1	8.36	2.1
+500	1000	"0.7"	2	0.69	"0.47 ( 0.01 )"	"2.72 ( 0.33 )"	"0 ( 0 )"	8.72	"0.24"	0.47	2.72	0
+1000	1000	"0.7"	1	0.91	"0.5 ( 0.01 )"	"0.57 ( 0.16 )"	"0 ( 0 )"	6.57	"0.06"	0.5	0.57	0
+50	50	"0.9"	1	0.32	"0.54 ( 0.03 )"	"4.91 ( 0.32 )"	"1.96 ( 0.11 )"	8.95	"0.49"	0.54	4.91	1.96
+100	50	"0.9"	0.5	0.35	"0.41 ( 0.02 )"	"5.64 ( 0.41 )"	"0.96 ( 0.11 )"	10.68	"0.46"	0.41	5.64	0.96
+500	50	"0.9"	0.1	0.67	"0.31 ( 0.01 )"	"2.66 ( 0.22 )"	"0.01 ( 0.01 )"	8.65	"0.27"	0.31	2.66	0.01
+1000	50	"0.9"	0.05	0.71	"0.3 ( 0 )"	"2.18 ( 0.19 )"	"0 ( 0 )"	8.18	"0.23"	0.3	2.18	0
+50	100	"0.9"	2	0.31	"0.66 ( 0.04 )"	"4.9 ( 0.33 )"	"2.83 ( 0.09 )"	8.07	"0.54"	0.66	4.9	2.83
+100	100	"0.9"	1	0.32	"0.47 ( 0.02 )"	"6.56 ( 0.48 )"	"1.45 ( 0.11 )"	11.11	"0.52"	0.47	6.56	1.45
+500	100	"0.9"	0.2	0.59	"0.32 ( 0.01 )"	"4.04 ( 0.34 )"	"0.01 ( 0.01 )"	10.03	"0.35"	0.32	4.04	0.01
+1000	100	"0.9"	0.1	0.67	"0.3 ( 0 )"	"2.85 ( 0.24 )"	"0 ( 0 )"	8.85	"0.28"	0.3	2.85	0
+50	500	"0.9"	10	0.23	"0.89 ( 0.05 )"	"5.88 ( 0.44 )"	"3.74 ( 0.07 )"	8.14	"0.62"	0.89	5.88	3.74
+100	500	"0.9"	5	0.24	"0.6 ( 0.02 )"	"8.59 ( 0.65 )"	"2.63 ( 0.08 )"	11.96	"0.64"	0.6	8.59	2.63
+500	500	"0.9"	1	0.41	"0.34 ( 0.01 )"	"8.17 ( 0.48 )"	"0.17 ( 0.04 )"	14	"0.54"	0.34	8.17	0.17
+1000	500	"0.9"	0.5	0.58	"0.32 ( 0 )"	"4.24 ( 0.3 )"	"0.01 ( 0.01 )"	10.23	"0.37"	0.32	4.24	0.01
+50	1000	"0.9"	20	0.19	"1.04 ( 0.06 )"	"5.24 ( 0.46 )"	"4.23 ( 0.08 )"	7.01	"0.6"	1.04	5.24	4.23
+100	1000	"0.9"	10	0.22	"0.67 ( 0.02 )"	"9.03 ( 0.73 )"	"3.02 ( 0.07 )"	12.01	"0.66"	0.67	9.03	3.02
+500	1000	"0.9"	2	0.34	"0.35 ( 0.01 )"	"10.52 ( 0.68 )"	"0.32 ( 0.05 )"	16.2	"0.59"	0.35	10.5	0.32
+1000	1000	"0.9"	1	0.57	"0.33 ( 0 )"	"4.48 ( 0.3 )"	"0.01 ( 0.01 )"	10.47	"0.39"	0.33	4.48	0.01
diff --git a/simulations/results_summary_cts/sim_toe_elnet.txt b/simulations/results_summary_cts/sim_toe_elnet.txt
new file mode 100755
index 0000000..0fa8cef
--- /dev/null
+++ b/simulations/results_summary_cts/sim_toe_elnet.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.21	"0.6 ( 0.03 )"	"14.72 ( 0.57 )"	"0.01 ( 0.01 )"	19.71	"0.67"	0.6	14.7	0.01
+100	50	"0.1"	0.5	0.23	"0.34 ( 0.01 )"	"13.7 ( 0.66 )"	"0 ( 0 )"	18.7	"0.64"	0.34	13.7	0
+500	50	"0.1"	0.1	0.23	"0.26 ( 0 )"	"13.59 ( 0.55 )"	"0 ( 0 )"	18.59	"0.65"	0.26	13.5	0
+1000	50	"0.1"	0.05	0.25	"0.26 ( 0 )"	"12.64 ( 0.54 )"	"0 ( 0 )"	17.64	"0.63"	0.26	12.6	0
+50	100	"0.1"	2	0.2	"0.69 ( 0.05 )"	"19.33 ( 0.67 )"	"0 ( 0 )"	24.33	"0.73"	0.69	19.3	0
+100	100	"0.1"	1	0.22	"0.38 ( 0.01 )"	"17.77 ( 0.8 )"	"0 ( 0 )"	22.77	"0.7"	0.38	17.7	0
+500	100	"0.1"	0.2	0.21	"0.26 ( 0 )"	"18.57 ( 0.83 )"	"0 ( 0 )"	23.57	"0.71"	0.26	18.5	0
+1000	100	"0.1"	0.1	0.22	"0.26 ( 0 )"	"17.23 ( 0.72 )"	"0 ( 0 )"	22.23	"0.7"	0.26	17.2	0
+50	500	"0.1"	10	0.13	"1.74 ( 0.11 )"	"29.67 ( 1.23 )"	"0.63 ( 0.07 )"	34.04	"0.83"	1.74	29.6	0.63
+100	500	"0.1"	5	0.16	"0.51 ( 0.02 )"	"30.41 ( 1.35 )"	"0 ( 0 )"	35.41	"0.81"	0.51	30.4	0
+500	500	"0.1"	1	0.16	"0.27 ( 0 )"	"29.38 ( 1.53 )"	"0 ( 0 )"	34.38	"0.79"	0.27	29.3	0
+1000	500	"0.1"	0.5	0.18	"0.27 ( 0 )"	"26.73 ( 1.56 )"	"0 ( 0 )"	31.73	"0.77"	0.27	26.7	0
+50	1000	"0.1"	20	0.09	"2.5 ( 0.16 )"	"39.01 ( 2.7 )"	"1.34 ( 0.1 )"	42.67	"0.87"	2.5	39	1.34
+100	1000	"0.1"	10	0.13	"0.57 ( 0.03 )"	"39.81 ( 1.68 )"	"0 ( 0 )"	44.81	"0.85"	0.57	39.8	0
+500	1000	"0.1"	2	0.14	"0.27 ( 0 )"	"37.88 ( 2.19 )"	"0 ( 0 )"	42.88	"0.82"	0.27	37.8	0
+1000	1000	"0.1"	1	0.16	"0.26 ( 0 )"	"31.4 ( 1.69 )"	"0 ( 0 )"	36.4	"0.8"	0.26	31.4	0
+50	50	"0.3"	1	0.21	"0.53 ( 0.03 )"	"14.65 ( 0.58 )"	"0.03 ( 0.02 )"	19.62	"0.67"	0.53	14.6	0.03
+100	50	"0.3"	0.5	0.2	"0.34 ( 0.01 )"	"14.91 ( 0.58 )"	"0 ( 0 )"	19.91	"0.67"	0.34	14.9	0
+500	50	"0.3"	0.1	0.23	"0.27 ( 0 )"	"13.94 ( 0.57 )"	"0 ( 0 )"	18.94	"0.65"	0.27	13.9	0
+1000	50	"0.3"	0.05	0.27	"0.26 ( 0 )"	"11.94 ( 0.43 )"	"0 ( 0 )"	16.94	"0.62"	0.26	11.9	0
+50	100	"0.3"	2	0.19	"0.72 ( 0.04 )"	"19.67 ( 0.66 )"	"0.08 ( 0.03 )"	24.59	"0.74"	0.72	19.6	0.08
+100	100	"0.3"	1	0.19	"0.38 ( 0.01 )"	"20.48 ( 0.85 )"	"0 ( 0 )"	25.48	"0.74"	0.38	20.4	0
+500	100	"0.3"	0.2	0.2	"0.26 ( 0 )"	"19.3 ( 0.93 )"	"0 ( 0 )"	24.3	"0.72"	0.26	19.3	0
+1000	100	"0.3"	0.1	0.2	"0.26 ( 0 )"	"19 ( 0.86 )"	"0 ( 0 )"	24	"0.71"	0.26	19	0
+50	500	"0.3"	10	0.1	"1.94 ( 0.12 )"	"32.52 ( 1.94 )"	"1.33 ( 0.1 )"	36.19	"0.85"	1.94	32.5	1.33
+100	500	"0.3"	5	0.14	"0.51 ( 0.02 )"	"34.04 ( 1.37 )"	"0.01 ( 0.01 )"	39.03	"0.83"	0.51	34	0.01
+500	500	"0.3"	1	0.16	"0.28 ( 0 )"	"30.92 ( 1.44 )"	"0 ( 0 )"	35.92	"0.8"	0.28	30.9	0
+1000	500	"0.3"	0.5	0.16	"0.26 ( 0 )"	"30.61 ( 1.55 )"	"0 ( 0 )"	35.61	"0.79"	0.26	30.6	0
+50	1000	"0.3"	20	0.08	"2.46 ( 0.17 )"	"35.99 ( 1.99 )"	"1.83 ( 0.1 )"	39.16	"0.88"	2.46	35.9	1.83
+100	1000	"0.3"	10	0.12	"0.6 ( 0.03 )"	"43.92 ( 1.49 )"	"0.01 ( 0.01 )"	48.91	"0.86"	0.6	43.9	0.01
+500	1000	"0.3"	2	0.13	"0.28 ( 0 )"	"38.46 ( 2.16 )"	"0 ( 0 )"	43.46	"0.84"	0.28	38.4	0
+1000	1000	"0.3"	1	0.13	"0.27 ( 0 )"	"38.29 ( 2.09 )"	"0 ( 0 )"	43.29	"0.83"	0.27	38.2	0
+50	50	"0.5"	1	0.2	"0.57 ( 0.04 )"	"14.64 ( 0.66 )"	"0.09 ( 0.03 )"	19.55	"0.66"	0.57	14.6	0.09
+100	50	"0.5"	0.5	0.21	"0.33 ( 0.01 )"	"14.76 ( 0.55 )"	"0 ( 0 )"	19.76	"0.67"	0.33	14.7	0
+500	50	"0.5"	0.1	0.23	"0.26 ( 0 )"	"13.76 ( 0.51 )"	"0 ( 0 )"	18.76	"0.66"	0.26	13.7	0
+1000	50	"0.5"	0.05	0.23	"0.26 ( 0 )"	"13.4 ( 0.49 )"	"0 ( 0 )"	18.4	"0.65"	0.26	13.4	0
+50	100	"0.5"	2	0.17	"0.8 ( 0.04 )"	"20.05 ( 0.68 )"	"0.34 ( 0.06 )"	24.71	"0.75"	0.8	20	0.34
+100	100	"0.5"	1	0.17	"0.41 ( 0.02 )"	"21.92 ( 0.83 )"	"0 ( 0 )"	26.92	"0.76"	0.41	21.9	0
+500	100	"0.5"	0.2	0.2	"0.27 ( 0 )"	"18.97 ( 0.78 )"	"0 ( 0 )"	23.97	"0.72"	0.27	18.9	0
+1000	100	"0.5"	0.1	0.2	"0.26 ( 0 )"	"19.53 ( 0.66 )"	"0 ( 0 )"	24.53	"0.74"	0.26	19.5	0
+50	500	"0.5"	10	0.09	"1.86 ( 0.11 )"	"29.92 ( 1.25 )"	"1.88 ( 0.1 )"	33.04	"0.86"	1.86	29.9	1.88
+100	500	"0.5"	5	0.13	"0.59 ( 0.02 )"	"37.33 ( 1.25 )"	"0.07 ( 0.03 )"	42.26	"0.85"	0.59	37.3	0.07
+500	500	"0.5"	1	0.13	"0.28 ( 0 )"	"37.1 ( 1.54 )"	"0 ( 0 )"	42.1	"0.83"	0.28	37.1	0
+1000	500	"0.5"	0.5	0.13	"0.27 ( 0 )"	"36.71 ( 1.5 )"	"0 ( 0 )"	41.71	"0.83"	0.27	36.7	0
+50	1000	"0.5"	20	0.07	"2.33 ( 0.14 )"	"35.29 ( 1.69 )"	"2.53 ( 0.1 )"	37.76	"0.9"	2.33	35.2	2.53
+100	1000	"0.5"	10	0.11	"0.73 ( 0.03 )"	"44.83 ( 1.58 )"	"0.18 ( 0.05 )"	49.65	"0.87"	0.73	44.8	0.18
+500	1000	"0.5"	2	0.12	"0.29 ( 0 )"	"42.86 ( 2.28 )"	"0 ( 0 )"	47.86	"0.85"	0.29	42.8	0
+1000	1000	"0.5"	1	0.13	"0.27 ( 0 )"	"41.01 ( 1.99 )"	"0 ( 0 )"	46.01	"0.84"	0.27	41	0
+50	50	"0.7"	1	0.18	"0.6 ( 0.04 )"	"15.25 ( 0.54 )"	"0.25 ( 0.05 )"	20	"0.69"	0.6	15.2	0.25
+100	50	"0.7"	0.5	0.2	"0.35 ( 0.01 )"	"15.66 ( 0.62 )"	"0 ( 0 )"	20.66	"0.69"	0.35	15.6	0
+500	50	"0.7"	0.1	0.24	"0.26 ( 0 )"	"13.41 ( 0.46 )"	"0 ( 0 )"	18.41	"0.65"	0.26	13.4	0
+1000	50	"0.7"	0.05	0.26	"0.26 ( 0 )"	"12.18 ( 0.6 )"	"0 ( 0 )"	17.18	"0.61"	0.26	12.1	0
+50	100	"0.7"	2	0.15	"0.81 ( 0.04 )"	"19.25 ( 0.81 )"	"0.79 ( 0.09 )"	23.46	"0.75"	0.81	19.2	0.79
+100	100	"0.7"	1	0.18	"0.36 ( 0.01 )"	"21.79 ( 0.78 )"	"0.01 ( 0.01 )"	26.78	"0.76"	0.36	21.7	0.01
+500	100	"0.7"	0.2	0.18	"0.27 ( 0 )"	"21.43 ( 0.75 )"	"0 ( 0 )"	26.43	"0.75"	0.27	21.4	0
+1000	100	"0.7"	0.1	0.21	"0.26 ( 0 )"	"18.91 ( 0.6 )"	"0 ( 0 )"	23.91	"0.73"	0.26	18.9	0
+50	500	"0.7"	10	0.09	"1.48 ( 0.09 )"	"27.19 ( 0.95 )"	"2.71 ( 0.08 )"	29.48	"0.88"	1.48	27.1	2.71
+100	500	"0.7"	5	0.11	"0.6 ( 0.02 )"	"37.9 ( 1.54 )"	"0.51 ( 0.07 )"	42.39	"0.86"	0.6	37.9	0.51
+500	500	"0.7"	1	0.13	"0.29 ( 0 )"	"38.11 ( 1.41 )"	"0 ( 0 )"	43.11	"0.84"	0.29	38.1	0
+1000	500	"0.7"	0.5	0.13	"0.27 ( 0 )"	"38.16 ( 1.26 )"	"0 ( 0 )"	43.16	"0.85"	0.27	38.1	0
+50	1000	"0.7"	20	0.06	"1.66 ( 0.09 )"	"35.91 ( 1.86 )"	"3.03 ( 0.09 )"	37.88	"0.91"	1.66	35.9	3.03
+100	1000	"0.7"	10	0.09	"0.78 ( 0.04 )"	"39.69 ( 1.62 )"	"1.11 ( 0.1 )"	43.58	"0.88"	0.78	39.6	1.11
+500	1000	"0.7"	2	0.11	"0.3 ( 0 )"	"49.45 ( 1.98 )"	"0 ( 0 )"	54.45	"0.87"	0.3	49.4	0
+1000	1000	"0.7"	1	0.11	"0.27 ( 0 )"	"48.15 ( 1.76 )"	"0 ( 0 )"	53.15	"0.87"	0.27	48.1	0
+50	50	"0.9"	1	0.16	"0.5 ( 0.02 )"	"13.26 ( 0.77 )"	"1.03 ( 0.11 )"	17.23	"0.66"	0.5	13.2	1.03
+100	50	"0.9"	0.5	0.21	"0.35 ( 0.01 )"	"14 ( 0.69 )"	"0.27 ( 0.06 )"	18.73	"0.66"	0.35	14	0.27
+500	50	"0.9"	0.1	0.29	"0.27 ( 0 )"	"11.63 ( 0.41 )"	"0 ( 0 )"	16.63	"0.62"	0.27	11.6	0
+1000	50	"0.9"	0.05	0.29	"0.26 ( 0 )"	"12.06 ( 0.52 )"	"0 ( 0 )"	17.06	"0.62"	0.26	12	0
+50	100	"0.9"	2	0.15	"0.56 ( 0.03 )"	"15.35 ( 0.8 )"	"1.83 ( 0.12 )"	18.52	"0.75"	0.56	15.3	1.83
+100	100	"0.9"	1	0.18	"0.4 ( 0.02 )"	"18.69 ( 0.92 )"	"0.41 ( 0.07 )"	23.28	"0.73"	0.4	18.6	0.41
+500	100	"0.9"	0.2	0.23	"0.27 ( 0 )"	"17.36 ( 0.62 )"	"0 ( 0 )"	22.36	"0.71"	0.27	17.3	0
+1000	100	"0.9"	0.1	0.26	"0.26 ( 0 )"	"16.05 ( 0.62 )"	"0 ( 0 )"	21.05	"0.69"	0.26	16	0
+50	500	"0.9"	10	0.08	"0.88 ( 0.04 )"	"25.11 ( 1.93 )"	"3.23 ( 0.08 )"	26.88	"0.87"	0.88	25.1	3.23
+100	500	"0.9"	5	0.1	"0.53 ( 0.02 )"	"27.99 ( 1.26 )"	"2.15 ( 0.1 )"	30.84	"0.86"	0.53	27.9	2.15
+500	500	"0.9"	1	0.12	"0.29 ( 0 )"	"41.14 ( 1.02 )"	"0 ( 0 )"	46.14	"0.86"	0.29	41.1	0
+1000	500	"0.9"	0.5	0.13	"0.27 ( 0 )"	"39.21 ( 0.84 )"	"0 ( 0 )"	44.21	"0.86"	0.27	39.2	0
+50	1000	"0.9"	20	0.04	"0.94 ( 0.05 )"	"36.59 ( 3.6 )"	"3.72 ( 0.08 )"	37.87	"0.92"	0.94	36.5	3.72
+100	1000	"0.9"	10	0.09	"0.59 ( 0.02 )"	"32.05 ( 1.85 )"	"2.59 ( 0.06 )"	34.46	"0.88"	0.59	32	2.59
+500	1000	"0.9"	2	0.09	"0.3 ( 0 )"	"57.69 ( 1.57 )"	"0 ( 0 )"	62.69	"0.9"	0.3	57.6	0
+1000	1000	"0.9"	1	0.1	"0.27 ( 0 )"	"52.35 ( 1.6 )"	"0 ( 0 )"	57.35	"0.89"	0.27	52.3	0
diff --git a/simulations/results_summary_cts/sim_toe_lasso.txt b/simulations/results_summary_cts/sim_toe_lasso.txt
new file mode 100755
index 0000000..58c3971
--- /dev/null
+++ b/simulations/results_summary_cts/sim_toe_lasso.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"
+50	50	"0.1"	1	0.36	"0.62 ( 0.04 )"	"8.59 ( 0.46 )"	"0.02 ( 0.01 )"	13.57	"0.51"	0.62	8.59	0.02
+100	50	"0.1"	0.5	0.47	"0.37 ( 0.01 )"	"6.3 ( 0.43 )"	"0 ( 0 )"	11.3	"0.4"	0.37	6.3	0
+500	50	"0.1"	0.1	0.73	"0.28 ( 0 )"	"2.88 ( 0.21 )"	"0 ( 0 )"	7.88	"0.2"	0.28	2.88	0
+1000	50	"0.1"	0.05	0.89	"0.27 ( 0 )"	"1.66 ( 0.11 )"	"0 ( 0 )"	6.66	"0.08"	0.27	1.66	0
+50	100	"0.1"	2	0.32	"0.67 ( 0.05 )"	"11.84 ( 0.4 )"	"0 ( 0 )"	16.84	"0.62"	0.67	11.8	0
+100	100	"0.1"	1	0.44	"0.4 ( 0.01 )"	"7.71 ( 0.56 )"	"0 ( 0 )"	12.71	"0.46"	0.4	7.71	0
+500	100	"0.1"	0.2	0.7	"0.27 ( 0 )"	"3.35 ( 0.22 )"	"0 ( 0 )"	8.35	"0.23"	0.27	3.35	0
+1000	100	"0.1"	0.1	0.81	"0.27 ( 0 )"	"2.29 ( 0.23 )"	"0 ( 0 )"	7.29	"0.13"	0.27	2.29	0
+50	500	"0.1"	10	0.18	"1.61 ( 0.1 )"	"20.85 ( 0.38 )"	"0.69 ( 0.08 )"	25.16	"0.78"	1.61	20.8	0.69
+100	500	"0.1"	5	0.3	"0.53 ( 0.02 )"	"14.69 ( 0.7 )"	"0 ( 0 )"	19.69	"0.66"	0.53	14.6	0
+500	500	"0.1"	1	0.55	"0.28 ( 0 )"	"5.86 ( 0.51 )"	"0 ( 0 )"	10.86	"0.36"	0.28	5.86	0
+1000	500	"0.1"	0.5	0.78	"0.28 ( 0 )"	"2.69 ( 0.21 )"	"0 ( 0 )"	7.69	"0.17"	0.28	2.69	0
+50	1000	"0.1"	20	0.13	"2.23 ( 0.14 )"	"24.37 ( 0.39 )"	"1.36 ( 0.1 )"	28.01	"0.83"	2.23	24.3	1.36
+100	1000	"0.1"	10	0.25	"0.57 ( 0.03 )"	"18.79 ( 0.7 )"	"0 ( 0 )"	23.79	"0.73"	0.57	18.7	0
+500	1000	"0.1"	2	0.49	"0.28 ( 0 )"	"7.08 ( 0.53 )"	"0 ( 0 )"	12.08	"0.41"	0.28	7.08	0
+1000	1000	"0.1"	1	0.7	"0.27 ( 0 )"	"3.59 ( 0.33 )"	"0 ( 0 )"	8.59	"0.23"	0.27	3.59	0
+50	50	"0.3"	1	0.35	"0.57 ( 0.03 )"	"8.86 ( 0.42 )"	"0.05 ( 0.02 )"	13.81	"0.53"	0.57	8.86	0.05
+100	50	"0.3"	0.5	0.47	"0.36 ( 0.01 )"	"6.18 ( 0.36 )"	"0 ( 0 )"	11.18	"0.41"	0.36	6.18	0
+500	50	"0.3"	0.1	0.7	"0.28 ( 0 )"	"3.14 ( 0.21 )"	"0 ( 0 )"	8.14	"0.22"	0.28	3.14	0
+1000	50	"0.3"	0.05	0.86	"0.27 ( 0 )"	"1.86 ( 0.11 )"	"0 ( 0 )"	6.86	"0.11"	0.27	1.86	0
+50	100	"0.3"	2	0.31	"0.76 ( 0.04 )"	"11.12 ( 0.37 )"	"0.17 ( 0.04 )"	15.95	"0.62"	0.76	11.1	0.17
+100	100	"0.3"	1	0.38	"0.41 ( 0.01 )"	"9.43 ( 0.53 )"	"0 ( 0 )"	14.43	"0.54"	0.41	9.43	0
+500	100	"0.3"	0.2	0.63	"0.27 ( 0 )"	"4.16 ( 0.27 )"	"0 ( 0 )"	9.16	"0.29"	0.27	4.16	0
+1000	100	"0.3"	0.1	0.8	"0.27 ( 0 )"	"2.36 ( 0.17 )"	"0 ( 0 )"	7.36	"0.15"	0.27	2.36	0
+50	500	"0.3"	10	0.14	"1.78 ( 0.11 )"	"21.4 ( 0.41 )"	"1.39 ( 0.1 )"	25.01	"0.81"	1.78	21.4	1.39
+100	500	"0.3"	5	0.26	"0.53 ( 0.02 )"	"16.81 ( 0.77 )"	"0.02 ( 0.01 )"	21.79	"0.7"	0.53	16.8	0.02
+500	500	"0.3"	1	0.55	"0.29 ( 0 )"	"5.89 ( 0.51 )"	"0 ( 0 )"	10.89	"0.36"	0.29	5.89	0
+1000	500	"0.3"	0.5	0.69	"0.27 ( 0 )"	"3.68 ( 0.32 )"	"0 ( 0 )"	8.68	"0.25"	0.27	3.68	0
+50	1000	"0.3"	20	0.11	"2.21 ( 0.15 )"	"25.14 ( 0.35 )"	"1.87 ( 0.1 )"	28.27	"0.85"	2.21	25.1	1.87
+100	1000	"0.3"	10	0.22	"0.61 ( 0.03 )"	"21.11 ( 0.75 )"	"0.02 ( 0.01 )"	26.09	"0.75"	0.61	21.1	0.02
+500	1000	"0.3"	2	0.47	"0.28 ( 0 )"	"7.69 ( 0.63 )"	"0 ( 0 )"	12.69	"0.45"	0.28	7.69	0
+1000	1000	"0.3"	1	0.58	"0.27 ( 0 )"	"5.27 ( 0.46 )"	"0 ( 0 )"	10.27	"0.33"	0.27	5.27	0
+50	50	"0.5"	1	0.35	"0.63 ( 0.04 )"	"8.51 ( 0.41 )"	"0.17 ( 0.04 )"	13.34	"0.53"	0.63	8.51	0.17
+100	50	"0.5"	0.5	0.42	"0.37 ( 0.01 )"	"7.21 ( 0.33 )"	"0.01 ( 0.01 )"	12.2	"0.47"	0.37	7.21	0.01
+500	50	"0.5"	0.1	0.66	"0.27 ( 0 )"	"3.64 ( 0.21 )"	"0 ( 0 )"	8.64	"0.27"	0.27	3.64	0
+1000	50	"0.5"	0.05	0.78	"0.27 ( 0 )"	"2.46 ( 0.15 )"	"0 ( 0 )"	7.46	"0.17"	0.27	2.46	0
+50	100	"0.5"	2	0.24	"0.8 ( 0.05 )"	"13.21 ( 0.46 )"	"0.6 ( 0.07 )"	17.61	"0.68"	0.8	13.2	0.6
+100	100	"0.5"	1	0.36	"0.44 ( 0.02 )"	"10 ( 0.48 )"	"0.02 ( 0.01 )"	14.98	"0.56"	0.44	10	0.02
+500	100	"0.5"	0.2	0.62	"0.29 ( 0 )"	"4.39 ( 0.28 )"	"0 ( 0 )"	9.39	"0.31"	0.29	4.39	0
+1000	100	"0.5"	0.1	0.71	"0.27 ( 0 )"	"3.24 ( 0.22 )"	"0 ( 0 )"	8.24	"0.23"	0.27	3.24	0
+50	500	"0.5"	10	0.12	"1.73 ( 0.1 )"	"21.69 ( 0.32 )"	"1.93 ( 0.09 )"	24.76	"0.83"	1.73	21.6	1.93
+100	500	"0.5"	5	0.23	"0.62 ( 0.03 )"	"19.6 ( 0.76 )"	"0.16 ( 0.04 )"	24.44	"0.74"	0.62	19.6	0.16
+500	500	"0.5"	1	0.4	"0.29 ( 0 )"	"9.79 ( 0.55 )"	"0 ( 0 )"	14.79	"0.54"	0.29	9.79	0
+1000	500	"0.5"	0.5	0.51	"0.28 ( 0 )"	"6.66 ( 0.64 )"	"0 ( 0 )"	11.66	"0.38"	0.28	6.66	0
+50	1000	"0.5"	20	0.09	"2.17 ( 0.13 )"	"25.13 ( 0.34 )"	"2.69 ( 0.1 )"	27.44	"0.88"	2.17	25.1	2.69
+100	1000	"0.5"	10	0.19	"0.75 ( 0.03 )"	"23.22 ( 0.79 )"	"0.26 ( 0.05 )"	27.96	"0.78"	0.75	23.2	0.26
+500	1000	"0.5"	2	0.39	"0.3 ( 0 )"	"10.46 ( 0.63 )"	"0 ( 0 )"	15.46	"0.54"	0.3	10.4	0
+1000	1000	"0.5"	1	0.48	"0.28 ( 0 )"	"7.42 ( 0.6 )"	"0 ( 0 )"	12.42	"0.42"	0.28	7.42	0
+50	50	"0.7"	1	0.3	"0.66 ( 0.04 )"	"9.27 ( 0.39 )"	"0.47 ( 0.06 )"	13.8	"0.57"	0.66	9.27	0.47
+100	50	"0.7"	0.5	0.39	"0.37 ( 0.01 )"	"7.9 ( 0.35 )"	"0.05 ( 0.03 )"	12.85	"0.5"	0.37	7.9	0.05
+500	50	"0.7"	0.1	0.6	"0.27 ( 0 )"	"4.38 ( 0.21 )"	"0 ( 0 )"	9.38	"0.33"	0.27	4.38	0
+1000	50	"0.7"	0.05	0.74	"0.27 ( 0 )"	"2.84 ( 0.14 )"	"0 ( 0 )"	7.84	"0.21"	0.27	2.84	0
+50	100	"0.7"	2	0.23	"0.8 ( 0.04 )"	"11.57 ( 0.53 )"	"1.26 ( 0.1 )"	15.31	"0.66"	0.8	11.5	1.26
+100	100	"0.7"	1	0.31	"0.39 ( 0.01 )"	"11.68 ( 0.51 )"	"0.1 ( 0.04 )"	16.58	"0.61"	0.39	11.6	0.1
+500	100	"0.7"	0.2	0.49	"0.28 ( 0 )"	"6.56 ( 0.32 )"	"0 ( 0 )"	11.56	"0.44"	0.28	6.56	0
+1000	100	"0.7"	0.1	0.63	"0.27 ( 0 )"	"4.24 ( 0.26 )"	"0 ( 0 )"	9.24	"0.3"	0.27	4.24	0
+50	500	"0.7"	10	0.12	"1.37 ( 0.08 )"	"19.8 ( 0.33 )"	"2.78 ( 0.08 )"	22.02	"0.85"	1.37	19.8	2.78
+100	500	"0.7"	5	0.19	"0.62 ( 0.03 )"	"18.58 ( 0.85 )"	"0.88 ( 0.08 )"	22.7	"0.75"	0.62	18.5	0.88
+500	500	"0.7"	1	0.36	"0.3 ( 0 )"	"11.31 ( 0.6 )"	"0 ( 0 )"	16.31	"0.58"	0.3	11.3	0
+1000	500	"0.7"	0.5	0.46	"0.27 ( 0 )"	"7.93 ( 0.49 )"	"0 ( 0 )"	12.93	"0.47"	0.27	7.93	0
+50	1000	"0.7"	20	0.09	"1.5 ( 0.08 )"	"22.99 ( 0.35 )"	"3.2 ( 0.09 )"	24.79	"0.88"	1.5	22.9	3.2
+100	1000	"0.7"	10	0.14	"0.77 ( 0.04 )"	"22.43 ( 0.68 )"	"1.45 ( 0.1 )"	25.98	"0.82"	0.77	22.4	1.45
+500	1000	"0.7"	2	0.3	"0.31 ( 0 )"	"14.7 ( 0.84 )"	"0 ( 0 )"	19.7	"0.65"	0.31	14.7	0
+1000	1000	"0.7"	1	0.41	"0.28 ( 0 )"	"9.63 ( 0.54 )"	"0 ( 0 )"	14.63	"0.53"	0.28	9.63	0
+50	50	"0.9"	1	0.31	"0.55 ( 0.03 )"	"5.65 ( 0.29 )"	"2.17 ( 0.13 )"	8.48	"0.51"	0.55	5.65	2.17
+100	50	"0.9"	0.5	0.38	"0.38 ( 0.02 )"	"5.85 ( 0.3 )"	"1.03 ( 0.11 )"	9.82	"0.45"	0.38	5.85	1.03
+500	50	"0.9"	0.1	0.61	"0.28 ( 0 )"	"4.34 ( 0.19 )"	"0.01 ( 0.01 )"	9.33	"0.33"	0.28	4.34	0.01
+1000	50	"0.9"	0.05	0.76	"0.27 ( 0 )"	"2.71 ( 0.12 )"	"0 ( 0 )"	7.71	"0.2"	0.27	2.71	0
+50	100	"0.9"	2	0.26	"0.6 ( 0.03 )"	"7.26 ( 0.35 )"	"2.69 ( 0.1 )"	9.57	"0.62"	0.6	7.26	2.69
+100	100	"0.9"	1	0.31	"0.44 ( 0.01 )"	"7.79 ( 0.39 )"	"1.51 ( 0.11 )"	11.28	"0.57"	0.44	7.79	1.51
+500	100	"0.9"	0.2	0.51	"0.29 ( 0 )"	"6.45 ( 0.27 )"	"0 ( 0 )"	11.45	"0.45"	0.29	6.45	0
+1000	100	"0.9"	0.1	0.61	"0.27 ( 0 )"	"4.72 ( 0.21 )"	"0 ( 0 )"	9.72	"0.35"	0.27	4.72	0
+50	500	"0.9"	10	0.13	"0.81 ( 0.04 )"	"13.78 ( 0.36 )"	"3.54 ( 0.08 )"	15.24	"0.83"	0.81	13.7	3.54
+100	500	"0.9"	5	0.18	"0.54 ( 0.02 )"	"13.78 ( 0.72 )"	"2.58 ( 0.08 )"	16.2	"0.76"	0.54	13.7	2.58
+500	500	"0.9"	1	0.25	"0.3 ( 0 )"	"18.18 ( 0.69 )"	"0.03 ( 0.02 )"	23.15	"0.72"	0.3	18.1	0.03
+1000	500	"0.9"	0.5	0.33	"0.28 ( 0 )"	"12.68 ( 0.57 )"	"0 ( 0 )"	17.68	"0.63"	0.28	12.6	0
+50	1000	"0.9"	20	0.08	"0.9 ( 0.05 )"	"18.15 ( 0.34 )"	"3.92 ( 0.08 )"	19.23	"0.89"	0.9	18.1	3.92
+100	1000	"0.9"	10	0.16	"0.59 ( 0.02 )"	"15.99 ( 0.66 )"	"2.88 ( 0.06 )"	18.11	"0.81"	0.59	15.9	2.88
+500	1000	"0.9"	2	0.2	"0.31 ( 0 )"	"24.62 ( 1.01 )"	"0.05 ( 0.02 )"	29.57	"0.77"	0.31	24.6	0.05
+1000	1000	"0.9"	1	0.25	"0.28 ( 0 )"	"18.51 ( 0.86 )"	"0 ( 0 )"	23.51	"0.71"	0.28	18.5	0
diff --git a/simulations/results_summary_cts/sim_toe_rf.txt b/simulations/results_summary_cts/sim_toe_rf.txt
new file mode 100755
index 0000000..65fd568
--- /dev/null
+++ b/simulations/results_summary_cts/sim_toe_rf.txt
@@ -0,0 +1,81 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"OOB"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"	"OOB_mean"
+50	50	"0.1"	1	0	"1.4 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	"1.98 ( 0.02 )"	0	"NaN"	1.4	1	6	1.98
+100	50	"0.1"	0.5	0.19	"0.86 ( 0.03 )"	"1.87 ( 0.14 )"	"4.25 ( 0.09 )"	"1.76 ( 0.01 )"	3.62	"0.48"	0.86	1.87	4.25	1.76
+500	50	"0.1"	0.1	0.77	"0.44 ( 0.01 )"	"0.32 ( 0.05 )"	"1.81 ( 0.07 )"	"1.29 ( 0 )"	4.51	"0.06"	0.44	0.32	1.81	1.29
+1000	50	"0.1"	0.05	0.86	"0.34 ( 0 )"	"0.15 ( 0.04 )"	"0.93 ( 0.07 )"	"1.15 ( 0 )"	5.22	"0.02"	0.34	0.15	0.93	1.15
+50	100	"0.1"	2	0	"1.36 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	"2.19 ( 0.02 )"	0	"NaN"	1.36	1	6	2.19
+100	100	"0.1"	1	0.12	"0.93 ( 0.03 )"	"4.11 ( 0.18 )"	"4.17 ( 0.1 )"	"1.99 ( 0.01 )"	5.93	"0.68"	0.93	4.11	4.17	1.99
+500	100	"0.1"	0.2	0.57	"0.56 ( 0.01 )"	"2.08 ( 0.14 )"	"1.52 ( 0.08 )"	"1.51 ( 0 )"	6.56	"0.29"	0.56	2.08	1.52	1.51
+1000	100	"0.1"	0.1	0.74	"0.45 ( 0 )"	"1.11 ( 0.1 )"	"0.74 ( 0.07 )"	"1.37 ( 0 )"	6.37	"0.16"	0.45	1.11	0.74	1.37
+50	500	"0.1"	10	0	"1.29 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.39 ( 0.02 )"	0	"NaN"	1.29	1	6	2.39
+100	500	"0.1"	5	0.02	"0.95 ( 0.02 )"	"24.82 ( 0.56 )"	"4.55 ( 0.09 )"	"2.35 ( 0.02 )"	26.27	"0.94"	0.95	24.8	4.55	2.35
+500	500	"0.1"	1	0.13	"0.84 ( 0.01 )"	"20.8 ( 0.48 )"	"1.79 ( 0.1 )"	"2.12 ( 0.01 )"	25.01	"0.83"	0.84	20.8	1.79	2.12
+1000	500	"0.1"	0.5	0.18	"0.76 ( 0.01 )"	"18.72 ( 0.37 )"	"0.91 ( 0.07 )"	"2.02 ( 0 )"	23.81	"0.78"	0.76	18.7	0.91	2.02
+50	1000	"0.1"	20	0	"1.35 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	"2.42 ( 0.02 )"	0	"NaN"	1.35	1	6	2.42
+100	1000	"0.1"	10	0.01	"1 ( 0.03 )"	"49.42 ( 0.7 )"	"4.71 ( 0.09 )"	"2.39 ( 0.02 )"	50.71	"0.97"	1	49.4	4.71	2.39
+500	1000	"0.1"	2	0.06	"0.87 ( 0.01 )"	"46.19 ( 0.64 )"	"1.97 ( 0.09 )"	"2.25 ( 0.01 )"	50.22	"0.92"	0.87	46.1	1.97	2.25
+1000	1000	"0.1"	1	0.09	"0.85 ( 0.01 )"	"40.92 ( 0.66 )"	"0.98 ( 0.07 )"	"2.21 ( 0 )"	45.94	"0.89"	0.85	40.9	0.98	2.21
+50	50	"0.3"	1	0	"1.22 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"1.87 ( 0.02 )"	0	"NaN"	1.22	1	6	1.87
+100	50	"0.3"	0.5	0.23	"0.77 ( 0.02 )"	"1.72 ( 0.11 )"	"4.4 ( 0.08 )"	"1.65 ( 0.01 )"	3.32	"0.48"	0.77	1.72	4.4	1.65
+500	50	"0.3"	0.1	0.7	"0.39 ( 0 )"	"0.54 ( 0.06 )"	"1.98 ( 0.08 )"	"1.23 ( 0 )"	4.56	"0.11"	0.39	0.54	1.98	1.23
+1000	50	"0.3"	0.05	0.82	"0.31 ( 0 )"	"0.3 ( 0.05 )"	"1.19 ( 0.08 )"	"1.1 ( 0 )"	5.11	"0.05"	0.31	0.3	1.19	1.1
+50	100	"0.3"	2	0	"1.15 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.03 ( 0.02 )"	0	"NaN"	1.15	1	6	2.03
+100	100	"0.3"	1	0.15	"0.81 ( 0.02 )"	"3.77 ( 0.16 )"	"4.28 ( 0.07 )"	"1.87 ( 0.01 )"	5.49	"0.67"	0.81	3.77	4.28	1.87
+500	100	"0.3"	0.2	0.52	"0.51 ( 0.01 )"	"2.21 ( 0.14 )"	"1.94 ( 0.09 )"	"1.45 ( 0 )"	6.27	"0.33"	0.51	2.21	1.94	1.45
+1000	100	"0.3"	0.1	0.75	"0.41 ( 0 )"	"1.4 ( 0.1 )"	"0.76 ( 0.07 )"	"1.31 ( 0 )"	6.64	"0.2"	0.41	1.4	0.76	1.31
+50	500	"0.3"	10	0	"1.14 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.24 ( 0.02 )"	0	"NaN"	1.14	1	6	2.24
+100	500	"0.3"	5	0.02	"0.89 ( 0.03 )"	"24.48 ( 0.46 )"	"4.69 ( 0.08 )"	"2.21 ( 0.01 )"	25.79	"0.95"	0.89	24.4	4.69	2.21
+500	500	"0.3"	1	0.12	"0.73 ( 0.01 )"	"20.86 ( 0.41 )"	"2.1 ( 0.09 )"	"1.99 ( 0.01 )"	24.76	"0.84"	0.73	20.8	2.1	1.99
+1000	500	"0.3"	0.5	0.19	"0.68 ( 0.01 )"	"18.71 ( 0.45 )"	"1.13 ( 0.08 )"	"1.91 ( 0 )"	23.58	"0.79"	0.68	18.7	1.13	1.91
+50	1000	"0.3"	20	0	"1.2 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"2.29 ( 0.02 )"	0	"NaN"	1.2	1	6	2.29
+100	1000	"0.3"	10	0.01	"0.92 ( 0.03 )"	"48.8 ( 0.82 )"	"4.79 ( 0.08 )"	"2.23 ( 0.02 )"	50.01	"0.98"	0.92	48.8	4.79	2.23
+500	1000	"0.3"	2	0.05	"0.78 ( 0.01 )"	"44.58 ( 0.68 )"	"2.48 ( 0.09 )"	"2.13 ( 0.01 )"	48.1	"0.93"	0.78	44.5	2.48	2.13
+1000	1000	"0.3"	1	0.08	"0.74 ( 0.01 )"	"42.87 ( 0.73 )"	"1.44 ( 0.09 )"	"2.07 ( 0 )"	47.43	"0.9"	0.74	42.8	1.44	2.07
+50	50	"0.5"	1	0	"0.99 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	"1.68 ( 0.02 )"	0	"NaN"	0.99	1	6	1.68
+100	50	"0.5"	0.5	0.24	"0.62 ( 0.02 )"	"1.74 ( 0.13 )"	"4.39 ( 0.08 )"	"1.5 ( 0.01 )"	3.34	"0.47"	0.62	1.74	4.39	1.5
+500	50	"0.5"	0.1	0.7	"0.32 ( 0 )"	"0.98 ( 0.09 )"	"2.53 ( 0.08 )"	"1.14 ( 0 )"	4.45	"0.2"	0.32	0.98	2.53	1.14
+1000	50	"0.5"	0.05	0.78	"0.26 ( 0 )"	"0.91 ( 0.07 )"	"1.71 ( 0.07 )"	"1.03 ( 0 )"	5.2	"0.16"	0.26	0.91	1.71	1.03
+50	100	"0.5"	2	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"1.84 ( 0.02 )"	0	"NaN"	0.91	1	6	1.84
+100	100	"0.5"	1	0.15	"0.68 ( 0.02 )"	"3.72 ( 0.19 )"	"4.42 ( 0.08 )"	"1.69 ( 0.01 )"	5.3	"0.66"	0.68	3.72	4.42	1.69
+500	100	"0.5"	0.2	0.56	"0.4 ( 0 )"	"2.59 ( 0.13 )"	"2.1 ( 0.08 )"	"1.32 ( 0 )"	6.49	"0.38"	0.4	2.59	2.1	1.32
+1000	100	"0.5"	0.1	0.76	"0.34 ( 0 )"	"2.09 ( 0.1 )"	"1.08 ( 0.07 )"	"1.2 ( 0 )"	7.01	"0.29"	0.34	2.09	1.08	1.2
+50	500	"0.5"	10	0	"0.95 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"2.04 ( 0.02 )"	0	"NaN"	0.95	1	6	2.04
+100	500	"0.5"	5	0.03	"0.75 ( 0.02 )"	"24.36 ( 0.56 )"	"4.57 ( 0.07 )"	"1.97 ( 0.01 )"	25.79	"0.94"	0.75	24.3	4.57	1.97
+500	500	"0.5"	1	0.13	"0.58 ( 0.01 )"	"20.66 ( 0.49 )"	"2.51 ( 0.07 )"	"1.79 ( 0.01 )"	24.15	"0.85"	0.58	20.6	2.51	1.79
+1000	500	"0.5"	0.5	0.2	"0.55 ( 0 )"	"18.4 ( 0.44 )"	"1.77 ( 0.07 )"	"1.72 ( 0 )"	22.63	"0.81"	0.55	18.4	1.77	1.72
+50	1000	"0.5"	20	0	"0.94 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	"2.04 ( 0.02 )"	0	"NaN"	0.94	1	6	2.04
+100	1000	"0.5"	10	0.01	"0.72 ( 0.02 )"	"48.5 ( 0.74 )"	"4.75 ( 0.08 )"	"2.03 ( 0.02 )"	49.75	"0.97"	0.72	48.5	4.75	2.03
+500	1000	"0.5"	2	0.05	"0.65 ( 0.01 )"	"46.07 ( 0.71 )"	"2.83 ( 0.09 )"	"1.93 ( 0.01 )"	49.24	"0.93"	0.65	46	2.83	1.93
+1000	1000	"0.5"	1	0.09	"0.61 ( 0.01 )"	"41.21 ( 0.68 )"	"1.84 ( 0.08 )"	"1.87 ( 0 )"	45.37	"0.91"	0.61	41.2	1.84	1.87
+50	50	"0.7"	1	0	"0.66 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	"1.44 ( 0.01 )"	0	"NaN"	0.66	1	6	1.44
+100	50	"0.7"	0.5	0.28	"0.44 ( 0.01 )"	"1.84 ( 0.14 )"	"4.44 ( 0.08 )"	"1.26 ( 0.01 )"	3.4	"0.48"	0.44	1.84	4.44	1.26
+500	50	"0.7"	0.1	0.74	"0.23 ( 0 )"	"1.29 ( 0.07 )"	"2.76 ( 0.07 )"	"0.98 ( 0 )"	4.53	"0.27"	0.23	1.29	2.76	0.98
+1000	50	"0.7"	0.05	0.85	"0.19 ( 0 )"	"1.57 ( 0.07 )"	"2.29 ( 0.06 )"	"0.9 ( 0 )"	5.28	"0.29"	0.19	1.57	2.29	0.9
+50	100	"0.7"	2	0	"0.67 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	"1.5 ( 0.02 )"	0	"NaN"	0.67	1	6	1.5
+100	100	"0.7"	1	0.14	"0.47 ( 0.01 )"	"4.64 ( 0.25 )"	"4.32 ( 0.09 )"	"1.4 ( 0.01 )"	6.32	"0.7"	0.47	4.64	4.32	1.4
+500	100	"0.7"	0.2	0.63	"0.28 ( 0 )"	"2.64 ( 0.14 )"	"2.52 ( 0.07 )"	"1.14 ( 0 )"	6.12	"0.41"	0.28	2.64	2.52	1.14
+1000	100	"0.7"	0.1	0.8	"0.25 ( 0 )"	"2.42 ( 0.1 )"	"1.76 ( 0.05 )"	"1.05 ( 0 )"	6.66	"0.35"	0.25	2.42	1.76	1.05
+50	500	"0.7"	10	0	"0.7 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	"1.68 ( 0.02 )"	0	"NaN"	0.7	1	6	1.68
+100	500	"0.7"	5	0.03	"0.49 ( 0.02 )"	"24.87 ( 0.57 )"	"4.59 ( 0.09 )"	"1.64 ( 0.01 )"	26.28	"0.94"	0.49	24.8	4.59	1.64
+500	500	"0.7"	1	0.13	"0.4 ( 0 )"	"21.21 ( 0.55 )"	"2.97 ( 0.08 )"	"1.49 ( 0 )"	24.24	"0.87"	0.4	21.2	2.97	1.49
+1000	500	"0.7"	0.5	0.21	"0.38 ( 0 )"	"17.4 ( 0.46 )"	"1.98 ( 0.07 )"	"1.43 ( 0 )"	21.42	"0.8"	0.38	17.4	1.98	1.43
+50	1000	"0.7"	20	0	"0.74 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	"1.69 ( 0.02 )"	0	"NaN"	0.74	1	6	1.69
+100	1000	"0.7"	10	0.01	"0.5 ( 0.01 )"	"49.02 ( 0.67 )"	"4.84 ( 0.08 )"	"1.69 ( 0.01 )"	50.18	"0.98"	0.5	49	4.84	1.69
+500	1000	"0.7"	2	0.07	"0.44 ( 0 )"	"45.41 ( 0.68 )"	"3.02 ( 0.07 )"	"1.59 ( 0.01 )"	48.39	"0.94"	0.44	45.4	3.02	1.59
+1000	1000	"0.7"	1	0.09	"0.42 ( 0 )"	"42.15 ( 0.71 )"	"2.37 ( 0.07 )"	"1.56 ( 0 )"	45.78	"0.92"	0.42	42.1	2.37	1.56
+50	50	"0.9"	1	0	"0.27 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"0.96 ( 0.01 )"	0	"NaN"	0.27	1	6	0.96
+100	50	"0.9"	0.5	0.19	"0.19 ( 0.01 )"	"2.14 ( 0.17 )"	"4.84 ( 0.08 )"	"0.89 ( 0.01 )"	3.28	"0.58"	0.19	2.14	4.84	0.89
+500	50	"0.9"	0.1	0.67	"0.12 ( 0 )"	"1.43 ( 0.09 )"	"3.13 ( 0.08 )"	"0.74 ( 0 )"	4.3	"0.32"	0.12	1.43	3.13	0.74
+1000	50	"0.9"	0.05	0.85	"0.1 ( 0 )"	"1.5 ( 0.07 )"	"2.46 ( 0.06 )"	"0.7 ( 0 )"	5.04	"0.29"	0.1	1.5	2.46	0.7
+50	100	"0.9"	2	0	"0.3 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1 ( 0.01 )"	0	"NaN"	0.3	1	6	1
+100	100	"0.9"	1	0.16	"0.19 ( 0.01 )"	"4.23 ( 0.22 )"	"4.48 ( 0.09 )"	"0.98 ( 0.01 )"	5.74	"0.72"	0.19	4.23	4.48	0.98
+500	100	"0.9"	0.2	0.57	"0.14 ( 0 )"	"2.86 ( 0.16 )"	"2.89 ( 0.07 )"	"0.82 ( 0 )"	5.97	"0.45"	0.14	2.86	2.89	0.82
+1000	100	"0.9"	0.1	0.8	"0.12 ( 0 )"	"2.42 ( 0.11 )"	"1.89 ( 0.05 )"	"0.78 ( 0 )"	6.53	"0.36"	0.12	2.42	1.89	0.78
+50	500	"0.9"	10	0	"0.29 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1.1 ( 0.01 )"	0	"NaN"	0.29	1	6	1.1
+100	500	"0.9"	5	0.02	"0.21 ( 0.01 )"	"24.34 ( 0.71 )"	"4.72 ( 0.09 )"	"1.08 ( 0.01 )"	25.62	"0.95"	0.21	24.3	4.72	1.08
+500	500	"0.9"	1	0.14	"0.17 ( 0 )"	"21.38 ( 0.6 )"	"3 ( 0.07 )"	"0.99 ( 0 )"	24.38	"0.87"	0.17	21.3	0	0.99
+1000	500	"0.9"	0.5	0.2	"0.16 ( 0 )"	"18.68 ( 0.53 )"	"2.35 ( 0.06 )"	"0.96 ( 0 )"	22.33	"0.83"	0.16	18.6	2.35	0.96
+50	1000	"0.9"	20	0	"0.29 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	"1.11 ( 0.01 )"	0	"NaN"	0.29	1	6	1.11
+100	1000	"0.9"	10	0.01	"0.2 ( 0.01 )"	"49.91 ( 0.83 )"	"4.99 ( 0.09 )"	"1.09 ( 0.01 )"	50.92	"0.98"	0.2	49.9	4.99	1.09
+500	1000	"0.9"	2	0.06	"0.19 ( 0 )"	"46.34 ( 0.86 )"	"3.13 ( 0.08 )"	"1.04 ( 0 )"	49.21	"0.94"	0.19	46.3	3.13	1.04
+1000	1000	"0.9"	1	0.09	"0.18 ( 0 )"	"43.52 ( 0.89 )"	"2.7 ( 0.07 )"	"1.02 ( 0 )"	46.82	"0.93"	0.18	43.5	2.7	1.02
diff --git a/simulations/results_summary_cts/table_block_all.txt b/simulations/results_summary_cts/table_block_all.txt
new file mode 100755
index 0000000..9819ec1
--- /dev/null
+++ b/simulations/results_summary_cts/table_block_all.txt
@@ -0,0 +1,321 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	0.1	1	0.05	"0.36 ( 0.01 )"	"3.52 ( 0.22 )"	"4.92 ( 0.08 )"	3.6	0.65	0.36	3.52	4.92	"lasso"
+100	50	0.1	0.5	0.18	"0.31 ( 0.01 )"	"2.71 ( 0.25 )"	"4.4 ( 0.1 )"	3.31	0.42	0.31	2.71	4.4	"lasso"
+500	50	0.1	0.1	0.35	"0.29 ( 0 )"	"5.42 ( 0.25 )"	"1.97 ( 0.11 )"	8.45	0.48	0.29	5.42	1.97	"lasso"
+1000	50	0.1	0.05	0.44	"0.28 ( 0 )"	"4.91 ( 0.2 )"	"1.57 ( 0.09 )"	8.34	0.44	0.28	4.91	1.57	"lasso"
+50	100	0.1	2	0.06	"0.37 ( 0.02 )"	"4.73 ( 0.22 )"	"4.79 ( 0.08 )"	4.94	0.72	0.37	4.73	4.79	"lasso"
+100	100	0.1	1	0.13	"0.34 ( 0.01 )"	"3 ( 0.15 )"	"4.75 ( 0.09 )"	3.25	0.57	0.34	3	4.75	"lasso"
+500	100	0.1	0.2	0.24	"0.3 ( 0 )"	"8.56 ( 0.5 )"	"2.13 ( 0.12 )"	11.43	0.57	0.3	8.56	2.13	"lasso"
+1000	100	0.1	0.1	0.33	"0.29 ( 0 )"	"7.77 ( 0.33 )"	"1.44 ( 0.08 )"	11.33	0.56	0.29	7.77	1.44	"lasso"
+50	500	0.1	10	0.01	"0.37 ( 0.02 )"	"10.58 ( 0.31 )"	"5.25 ( 0.07 )"	10.33	0.92	0.37	10.5	5.25	"lasso"
+100	500	0.1	5	0.05	"0.35 ( 0.01 )"	"6.23 ( 0.26 )"	"4.94 ( 0.08 )"	6.29	0.82	0.35	6.23	4.94	"lasso"
+500	500	0.1	1	0.33	"0.32 ( 0.01 )"	"3.77 ( 0.75 )"	"3.69 ( 0.08 )"	5.08	0.27	0.32	3.77	3.69	"lasso"
+1000	500	0.1	0.5	0.14	"0.29 ( 0 )"	"20.61 ( 1.39 )"	"2.11 ( 0.1 )"	23.5	0.69	0.29	20.6	2.11	"lasso"
+50	1000	0.1	20	0.01	"0.39 ( 0.02 )"	"13.99 ( 0.32 )"	"5.31 ( 0.07 )"	13.68	0.95	0.39	13.9	5.31	"lasso"
+100	1000	0.1	10	0.03	"0.35 ( 0.01 )"	"9.56 ( 0.33 )"	"5.01 ( 0.08 )"	9.55	0.89	0.35	9.56	5.01	"lasso"
+500	1000	0.1	2	0.48	"0.33 ( 0 )"	"1.83 ( 0.19 )"	"3.97 ( 0.07 )"	2.86	0.2	0.33	1.83	3.97	"lasso"
+1000	1000	0.1	1	0.17	"0.3 ( 0 )"	"13.42 ( 1.74 )"	"2.77 ( 0.09 )"	15.65	0.47	0.3	13.4	2.77	"lasso"
+50	50	0.3	1	0.27	"0.72 ( 0.04 )"	"8.7 ( 0.42 )"	"0.79 ( 0.09 )"	12.91	0.56	0.72	8.7	0.79	"lasso"
+100	50	0.3	0.5	0.39	"0.4 ( 0.01 )"	"7.91 ( 0.47 )"	"0.01 ( 0.01 )"	12.9	0.48	0.4	7.91	0.01	"lasso"
+500	50	0.3	0.1	0.63	"0.28 ( 0 )"	"3.86 ( 0.25 )"	"0 ( 0 )"	8.86	0.28	0.28	3.86	0	"lasso"
+1000	50	0.3	0.05	0.78	"0.27 ( 0 )"	"2.45 ( 0.14 )"	"0 ( 0 )"	7.45	0.17	0.27	2.45	0	"lasso"
+50	100	0.3	2	0.21	"0.82 ( 0.05 )"	"12.31 ( 0.43 )"	"1.14 ( 0.1 )"	16.17	0.68	0.82	12.3	1.14	"lasso"
+100	100	0.3	1	0.34	"0.44 ( 0.01 )"	"10.58 ( 0.62 )"	"0.09 ( 0.03 )"	15.49	0.56	0.44	10.5	0.09	"lasso"
+500	100	0.3	0.2	0.61	"0.27 ( 0 )"	"4.51 ( 0.29 )"	"0 ( 0 )"	9.51	0.32	0.27	4.51	0	"lasso"
+1000	100	0.3	0.1	0.69	"0.27 ( 0 )"	"3.46 ( 0.29 )"	"0 ( 0 )"	8.46	0.24	0.27	3.46	0	"lasso"
+50	500	0.3	10	0.09	"1.25 ( 0.07 )"	"20.71 ( 0.36 )"	"2.63 ( 0.11 )"	23.08	0.85	1.25	20.7	2.63	"lasso"
+100	500	0.3	5	0.23	"0.59 ( 0.02 )"	"18.01 ( 0.69 )"	"0.35 ( 0.06 )"	22.66	0.73	0.59	18	0.35	"lasso"
+500	500	0.3	1	0.47	"0.29 ( 0 )"	"7.7 ( 0.54 )"	"0 ( 0 )"	12.7	0.45	0.29	7.7	0	"lasso"
+1000	500	0.3	0.5	0.53	"0.27 ( 0 )"	"6.14 ( 0.57 )"	"0 ( 0 )"	11.14	0.36	0.27	6.14	0	"lasso"
+50	1000	0.3	20	0.06	"1.51 ( 0.08 )"	"24.06 ( 0.36 )"	"3.21 ( 0.11 )"	25.85	0.89	1.51	24	3.21	"lasso"
+100	1000	0.3	10	0.17	"0.67 ( 0.03 )"	"22.69 ( 0.61 )"	"0.73 ( 0.08 )"	26.96	0.8	0.67	22.6	0.73	"lasso"
+500	1000	0.3	2	0.35	"0.29 ( 0 )"	"11.72 ( 0.9 )"	"0 ( 0 )"	16.72	0.55	0.29	11.7	0	"lasso"
+1000	1000	0.3	1	0.51	"0.27 ( 0 )"	"6.69 ( 0.52 )"	"0 ( 0 )"	11.69	0.41	0.27	6.69	0	"lasso"
+50	50	0.5	1	0.33	"0.64 ( 0.03 )"	"9.36 ( 0.45 )"	"0.12 ( 0.04 )"	14.24	0.55	0.64	9.36	0.12	"lasso"
+100	50	0.5	0.5	0.41	"0.36 ( 0.01 )"	"7.39 ( 0.39 )"	"0 ( 0 )"	12.39	0.47	0.36	7.39	0	"lasso"
+500	50	0.5	0.1	0.7	"0.28 ( 0 )"	"3.18 ( 0.23 )"	"0 ( 0 )"	8.18	0.22	0.28	3.18	0	"lasso"
+1000	50	0.5	0.05	0.82	"0.27 ( 0 )"	"2.11 ( 0.13 )"	"0 ( 0 )"	7.11	0.13	0.27	2.11	0	"lasso"
+50	100	0.5	2	0.28	"0.78 ( 0.05 )"	"12.05 ( 0.47 )"	"0.38 ( 0.07 )"	16.67	0.64	0.78	12	0.38	"lasso"
+100	100	0.5	1	0.37	"0.42 ( 0.02 )"	"9.57 ( 0.58 )"	"0 ( 0 )"	14.57	0.54	0.42	9.57	0	"lasso"
+500	100	0.5	0.2	0.59	"0.27 ( 0 )"	"4.7 ( 0.3 )"	"0 ( 0 )"	9.7	0.33	0.27	4.7	0	"lasso"
+1000	100	0.5	0.1	0.78	"0.27 ( 0 )"	"2.58 ( 0.16 )"	"0 ( 0 )"	7.58	0.18	0.27	2.58	0	"lasso"
+50	500	0.5	10	0.12	"1.64 ( 0.1 )"	"21.8 ( 0.36 )"	"1.82 ( 0.11 )"	24.98	0.83	1.64	21.8	1.82	"lasso"
+100	500	0.5	5	0.24	"0.55 ( 0.02 )"	"18.37 ( 0.77 )"	"0.04 ( 0.02 )"	23.33	0.72	0.55	18.3	0.04	"lasso"
+500	500	0.5	1	0.46	"0.3 ( 0 )"	"7.82 ( 0.64 )"	"0 ( 0 )"	12.82	0.44	0.3	7.82	0	"lasso"
+1000	500	0.5	0.5	0.65	"0.28 ( 0 )"	"4.16 ( 0.33 )"	"0 ( 0 )"	9.16	0.28	0.28	4.16	0	"lasso"
+50	1000	0.5	20	0.09	"1.84 ( 0.1 )"	"24.67 ( 0.36 )"	"2.56 ( 0.11 )"	27.11	0.87	1.84	24.6	2.56	"lasso"
+100	1000	0.5	10	0.21	"0.64 ( 0.03 )"	"22.01 ( 0.65 )"	"0.12 ( 0.03 )"	26.89	0.77	0.64	22	0.12	"lasso"
+500	1000	0.5	2	0.45	"0.29 ( 0 )"	"8.22 ( 0.73 )"	"0 ( 0 )"	13.22	0.44	0.29	8.22	0	"lasso"
+1000	1000	0.5	1	0.53	"0.27 ( 0 )"	"6.36 ( 0.51 )"	"0 ( 0 )"	11.36	0.39	0.27	6.36	0	"lasso"
+50	50	0.7	1	0.36	"0.57 ( 0.04 )"	"8.63 ( 0.41 )"	"0.06 ( 0.02 )"	13.57	0.52	0.57	8.63	0.06	"lasso"
+100	50	0.7	0.5	0.45	"0.37 ( 0.01 )"	"6.73 ( 0.39 )"	"0 ( 0 )"	11.73	0.44	0.37	6.73	0	"lasso"
+500	50	0.7	0.1	0.71	"0.28 ( 0 )"	"3.01 ( 0.2 )"	"0 ( 0 )"	8.01	0.22	0.28	3.01	0	"lasso"
+1000	50	0.7	0.05	0.85	"0.27 ( 0 )"	"1.89 ( 0.12 )"	"0 ( 0 )"	6.89	0.11	0.27	1.89	0	"lasso"
+50	100	0.7	2	0.3	"0.82 ( 0.05 )"	"11.87 ( 0.44 )"	"0.17 ( 0.05 )"	16.7	0.63	0.82	11.8	0.17	"lasso"
+100	100	0.7	1	0.37	"0.41 ( 0.02 )"	"9.7 ( 0.63 )"	"0 ( 0 )"	14.7	0.53	0.41	9.7	0	"lasso"
+500	100	0.7	0.2	0.66	"0.28 ( 0 )"	"3.85 ( 0.3 )"	"0 ( 0 )"	8.85	0.26	0.28	3.85	0	"lasso"
+1000	100	0.7	0.1	0.76	"0.27 ( 0 )"	"2.71 ( 0.23 )"	"0 ( 0 )"	7.71	0.17	0.27	2.71	0	"lasso"
+50	500	0.7	10	0.15	"2.03 ( 0.14 )"	"21.53 ( 0.36 )"	"1.18 ( 0.11 )"	25.35	0.81	2.03	21.5	1.18	"lasso"
+100	500	0.7	5	0.28	"0.54 ( 0.02 )"	"16.11 ( 0.63 )"	"0 ( 0 )"	21.11	0.69	0.54	16.1	0	"lasso"
+500	500	0.7	1	0.51	"0.29 ( 0 )"	"6.69 ( 0.45 )"	"0 ( 0 )"	11.69	0.42	0.29	6.69	0	"lasso"
+1000	500	0.7	0.5	0.62	"0.28 ( 0 )"	"4.65 ( 0.37 )"	"0 ( 0 )"	9.65	0.3	0.28	4.65	0	"lasso"
+50	1000	0.7	20	0.1	"2.51 ( 0.13 )"	"25.24 ( 0.32 )"	"2.11 ( 0.12 )"	28.13	0.86	2.51	25.2	2.11	"lasso"
+100	1000	0.7	10	0.22	"0.59 ( 0.02 )"	"21.52 ( 0.68 )"	"0.01 ( 0.01 )"	26.51	0.76	0.59	21.5	0.01	"lasso"
+500	1000	0.7	2	0.47	"0.3 ( 0 )"	"7.81 ( 0.61 )"	"0 ( 0 )"	12.81	0.45	0.3	7.81	0	"lasso"
+1000	1000	0.7	1	0.65	"0.28 ( 0 )"	"4.14 ( 0.35 )"	"0 ( 0 )"	9.14	0.27	0.28	4.14	0	"lasso"
+50	50	0.9	1	0.32	"0.61 ( 0.05 )"	"9.61 ( 0.49 )"	"0.07 ( 0.03 )"	14.54	0.55	0.61	9.61	0.07	"lasso"
+100	50	0.9	0.5	0.44	"0.37 ( 0.01 )"	"6.82 ( 0.39 )"	"0 ( 0 )"	11.82	0.44	0.37	6.82	0	"lasso"
+500	50	0.9	0.1	0.75	"0.27 ( 0 )"	"2.68 ( 0.17 )"	"0 ( 0 )"	7.68	0.19	0.27	2.68	0	"lasso"
+1000	50	0.9	0.05	0.87	"0.27 ( 0 )"	"1.76 ( 0.11 )"	"0 ( 0 )"	6.76	0.09	0.27	1.76	0	"lasso"
+50	100	0.9	2	0.29	"0.87 ( 0.05 )"	"12.12 ( 0.37 )"	"0.17 ( 0.05 )"	16.95	0.64	0.87	12.1	0.17	"lasso"
+100	100	0.9	1	0.4	"0.38 ( 0.01 )"	"8.72 ( 0.53 )"	"0 ( 0 )"	13.72	0.51	0.38	8.72	0	"lasso"
+500	100	0.9	0.2	0.67	"0.27 ( 0 )"	"3.68 ( 0.28 )"	"0 ( 0 )"	8.68	0.25	0.27	3.68	0	"lasso"
+1000	100	0.9	0.1	0.76	"0.27 ( 0 )"	"2.72 ( 0.23 )"	"0 ( 0 )"	7.72	0.17	0.27	2.72	0	"lasso"
+50	500	0.9	10	0.14	"2.29 ( 0.15 )"	"23.7 ( 0.43 )"	"1.18 ( 0.11 )"	27.52	0.82	2.29	23.7	1.18	"lasso"
+100	500	0.9	5	0.28	"0.51 ( 0.02 )"	"16.13 ( 0.62 )"	"0 ( 0 )"	21.13	0.69	0.51	16.1	0	"lasso"
+500	500	0.9	1	0.5	"0.29 ( 0 )"	"6.87 ( 0.54 )"	"0 ( 0 )"	11.87	0.41	0.29	6.87	0	"lasso"
+1000	500	0.9	0.5	0.71	"0.27 ( 0 )"	"3.37 ( 0.27 )"	"0 ( 0 )"	8.37	0.23	0.27	3.37	0	"lasso"
+50	1000	0.9	20	0.11	"2.46 ( 0.16 )"	"25.37 ( 0.35 )"	"1.73 ( 0.12 )"	28.64	0.85	2.46	25.3	1.73	"lasso"
+100	1000	0.9	10	0.22	"0.62 ( 0.03 )"	"21.43 ( 0.62 )"	"0 ( 0 )"	26.43	0.76	0.62	21.4	0	"lasso"
+500	1000	0.9	2	0.46	"0.3 ( 0 )"	"7.99 ( 0.79 )"	"0 ( 0 )"	12.99	0.42	0.3	7.99	0	"lasso"
+1000	1000	0.9	1	0.6	"0.28 ( 0 )"	"4.93 ( 0.43 )"	"0 ( 0 )"	9.93	0.31	0.28	4.93	0	"lasso"
+50	50	0.1	1	0.02	"0.38 ( 0.02 )"	"10.9 ( 0.97 )"	"3.91 ( 0.15 )"	11.99	0.77	0.38	10.9	3.91	"elnet"
+100	50	0.1	0.5	0.06	"0.34 ( 0.01 )"	"13.39 ( 1.06 )"	"2.45 ( 0.16 )"	15.94	0.68	0.34	13.3	2.45	"elnet"
+500	50	0.1	0.1	0.13	"0.27 ( 0 )"	"19.21 ( 0.81 )"	"0.14 ( 0.04 )"	24.07	0.73	0.27	19.2	0.14	"elnet"
+1000	50	0.1	0.05	0.15	"0.26 ( 0 )"	"18.76 ( 0.63 )"	"0.02 ( 0.01 )"	23.74	0.73	0.26	18.7	0.02	"elnet"
+50	100	0.1	2	0.03	"0.41 ( 0.02 )"	"9.27 ( 0.76 )"	"4.63 ( 0.09 )"	9.64	0.82	0.41	9.27	4.63	"elnet"
+100	100	0.1	1	0.07	"0.34 ( 0.01 )"	"10.1 ( 1.04 )"	"3.87 ( 0.13 )"	11.23	0.7	0.34	10.1	3.87	"elnet"
+500	100	0.1	0.2	0.13	"0.29 ( 0 )"	"25.4 ( 0.81 )"	"0.41 ( 0.06 )"	29.99	0.8	0.29	25.4	0.41	"elnet"
+1000	100	0.1	0.1	0.13	"0.27 ( 0 )"	"28.36 ( 0.94 )"	"0.06 ( 0.03 )"	33.3	0.81	0.27	28.3	0.06	"elnet"
+50	500	0.1	10	0.01	"0.41 ( 0.02 )"	"30.9 ( 3.51 )"	"4.65 ( 0.13 )"	31.25	0.94	0.41	30.9	4.65	"elnet"
+100	500	0.1	5	0.03	"0.36 ( 0.01 )"	"20.83 ( 2.77 )"	"4.43 ( 0.1 )"	21.4	0.87	0.36	20.8	4.43	"elnet"
+500	500	0.1	1	0.08	"0.32 ( 0 )"	"29.71 ( 2.76 )"	"2.55 ( 0.12 )"	32.16	0.8	0.32	29.7	2.55	"elnet"
+1000	500	0.1	0.5	0.07	"0.28 ( 0 )"	"61.47 ( 1.31 )"	"0.56 ( 0.07 )"	65.91	0.91	0.28	61.4	0.56	"elnet"
+50	1000	0.1	20	0	"0.41 ( 0.02 )"	"38.1 ( 4.05 )"	"5.01 ( 0.11 )"	38.09	0.97	0.41	38.1	5.01	"elnet"
+100	1000	0.1	10	0.02	"0.36 ( 0.01 )"	"35.49 ( 4.37 )"	"4.34 ( 0.14 )"	36.15	0.93	0.36	35.4	4.34	"elnet"
+500	1000	0.1	2	0.1	"0.3 ( 0 )"	"20.28 ( 2.74 )"	"3.25 ( 0.08 )"	22.03	0.75	0.3	20.2	3.25	"elnet"
+1000	1000	0.1	1	0.05	"0.29 ( 0 )"	"71.65 ( 3.47 )"	"1.5 ( 0.1 )"	75.15	0.9	0.29	71.6	1.5	"elnet"
+50	50	0.3	1	0.17	"0.56 ( 0.03 )"	"15.98 ( 0.61 )"	"0.26 ( 0.05 )"	20.72	0.7	0.56	15.9	0.26	"elnet"
+100	50	0.3	0.5	0.2	"0.34 ( 0.01 )"	"15.16 ( 0.6 )"	"0 ( 0 )"	20.16	0.68	0.34	15.1	0	"elnet"
+500	50	0.3	0.1	0.19	"0.27 ( 0 )"	"15.78 ( 0.58 )"	"0 ( 0 )"	20.78	0.69	0.27	15.7	0	"elnet"
+1000	50	0.3	0.05	0.22	"0.26 ( 0 )"	"14.34 ( 0.49 )"	"0 ( 0 )"	19.34	0.67	0.26	14.3	0	"elnet"
+50	100	0.3	2	0.13	"0.81 ( 0.05 )"	"22.23 ( 1.04 )"	"0.7 ( 0.08 )"	26.53	0.78	0.81	22.2	0.7	"elnet"
+100	100	0.3	1	0.16	"0.42 ( 0.02 )"	"23.17 ( 0.94 )"	"0.02 ( 0.01 )"	28.15	0.77	0.42	23.1	0.02	"elnet"
+500	100	0.3	0.2	0.18	"0.27 ( 0 )"	"21.25 ( 0.84 )"	"0 ( 0 )"	26.25	0.74	0.27	21.2	0	"elnet"
+1000	100	0.3	0.1	0.2	"0.26 ( 0 )"	"19.57 ( 0.81 )"	"0 ( 0 )"	24.57	0.73	0.26	19.5	0	"elnet"
+50	500	0.3	10	0.05	"1.29 ( 0.07 )"	"43.56 ( 3.08 )"	"2.15 ( 0.13 )"	46.41	0.89	1.29	43.5	2.15	"elnet"
+100	500	0.3	5	0.13	"0.54 ( 0.02 )"	"35.33 ( 1.21 )"	"0.19 ( 0.05 )"	40.14	0.84	0.54	35.3	0.19	"elnet"
+500	500	0.3	1	0.14	"0.28 ( 0 )"	"35.68 ( 1.62 )"	"0 ( 0 )"	40.68	0.83	0.28	35.6	0	"elnet"
+1000	500	0.3	0.5	0.14	"0.27 ( 0 )"	"36.15 ( 1.61 )"	"0 ( 0 )"	41.15	0.83	0.27	36.1	0	"elnet"
+50	1000	0.3	20	0.03	"1.3 ( 0.07 )"	"61.12 ( 4.33 )"	"2.56 ( 0.12 )"	63.56	0.93	1.3	61.1	2.56	"elnet"
+100	1000	0.3	10	0.1	"0.68 ( 0.03 )"	"41.5 ( 1.64 )"	"0.61 ( 0.08 )"	45.89	0.87	0.68	41.5	0.61	"elnet"
+500	1000	0.3	2	0.11	"0.29 ( 0 )"	"46.27 ( 2.2 )"	"0 ( 0 )"	51.27	0.86	0.29	46.2	0	"elnet"
+1000	1000	0.3	1	0.13	"0.27 ( 0 )"	"39.23 ( 2 )"	"0 ( 0 )"	44.23	0.83	0.27	39.2	0	"elnet"
+50	50	0.5	1	0.19	"0.65 ( 0.05 )"	"15.43 ( 0.64 )"	"0.1 ( 0.04 )"	20.33	0.68	0.65	15.4	0.1	"elnet"
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+100	500	0.5	5	0.14	"0.53 ( 0.02 )"	"34.91 ( 1.29 )"	"0.02 ( 0.01 )"	39.89	0.83	0.53	34.9	0.02	"elnet"
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+500	1000	0.5	2	0.14	"0.28 ( 0 )"	"37.63 ( 1.92 )"	"0 ( 0 )"	42.63	0.83	0.28	37.6	0	"elnet"
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+50	500	0.7	10	0.1	"2.19 ( 0.14 )"	"33.13 ( 1.76 )"	"1.25 ( 0.11 )"	36.88	0.86	2.19	33.1	1.25	"elnet"
+100	500	0.7	5	0.13	"0.51 ( 0.02 )"	"37.39 ( 1.42 )"	"0 ( 0 )"	42.39	0.84	0.51	37.3	0	"elnet"
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+1000	500	0.7	0.5	0.15	"0.26 ( 0 )"	"33.3 ( 1.59 )"	"0 ( 0 )"	38.3	0.81	0.26	33.3	0	"elnet"
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+100	1000	0.7	10	0.12	"0.68 ( 0.03 )"	"42.68 ( 1.53 )"	"0.02 ( 0.01 )"	47.66	0.86	0.68	42.6	0.02	"elnet"
+500	1000	0.7	2	0.12	"0.29 ( 0 )"	"42.75 ( 2.32 )"	"0 ( 0 )"	47.75	0.84	0.29	42.7	0	"elnet"
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+500	50	0.1	0.1	0.23	"0.05 ( 0 )"	"2.34 ( 0.16 )"	"3.77 ( 0.14 )"	4.56	0.49	0.05	2.34	3.77	"rf"
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+50	500	0.1	10	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.08	0	6	"rf"
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+50	1000	0.1	20	0	"0.08 ( 0 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.08	0	6	"rf"
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+500	1000	0.1	2	0.02	"0.06 ( 0 )"	"48.48 ( 0.7 )"	"3.65 ( 0.16 )"	50.83	0.95	0.06	48.4	3.65	"rf"
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+500	50	0.5	0.1	0.71	"0.26 ( 0 )"	"0.56 ( 0.07 )"	"1.57 ( 0.08 )"	4.99	0.1	0.26	0.56	1.57	"rf"
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+50	50	0.7	1	0	"0.8 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.8	0	6	"rf"
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+500	50	0.7	0.1	0.71	"0.35 ( 0 )"	"0.51 ( 0.07 )"	"1.72 ( 0.09 )"	4.79	0.1	0.35	0.51	1.72	"rf"
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+50	100	0.7	2	0	"0.92 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.92	0	6	"rf"
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+500	100	0.7	0.2	0.53	"0.43 ( 0 )"	"2.27 ( 0.15 )"	"1.46 ( 0.09 )"	6.81	0.31	0.43	2.27	1.46	"rf"
+1000	100	0.7	0.1	0.71	"0.35 ( 0 )"	"1.47 ( 0.13 )"	"0.63 ( 0.06 )"	6.84	0.19	0.35	1.47	0.63	"rf"
+50	500	0.7	10	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.91	0	6	"rf"
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+50	1000	0.7	20	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.91	0	6	"rf"
+100	1000	0.7	10	0.01	"0.66 ( 0.02 )"	"49.65 ( 0.71 )"	"4.89 ( 0.09 )"	50.76	0.98	0.66	49.6	4.89	"rf"
+500	1000	0.7	2	0.05	"0.6 ( 0.01 )"	"47.23 ( 0.73 )"	"2.18 ( 0.1 )"	51.05	0.92	0.6	47.2	2.18	"rf"
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+50	50	0.9	1	0	"1.1 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.1	0	6	"rf"
+100	50	0.9	0.5	0.16	"0.78 ( 0.02 )"	"1.56 ( 0.11 )"	"4.38 ( 0.09 )"	3.16	0.46	0.78	1.56	4.38	"rf"
+500	50	0.9	0.1	0.74	"0.44 ( 0 )"	"0.45 ( 0.06 )"	"1.49 ( 0.08 )"	4.96	0.08	0.44	0.45	1.49	"rf"
+1000	50	0.9	0.05	0.86	"0.34 ( 0 )"	"0.14 ( 0.04 )"	"0.86 ( 0.08 )"	5.28	0.02	0.34	0.14	0.86	"rf"
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+500	100	0.9	0.2	0.55	"0.54 ( 0.01 )"	"2.11 ( 0.15 )"	"1.47 ( 0.08 )"	6.64	0.29	0.54	2.11	1.47	"rf"
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+50	500	0.9	10	0	"1.18 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.18	0	6	"rf"
+100	500	0.9	5	0.01	"0.86 ( 0.02 )"	"24.13 ( 0.47 )"	"4.7 ( 0.09 )"	25.43	0.95	0.86	24.1	4.7	"rf"
+500	500	0.9	1	0.12	"0.73 ( 0.01 )"	"20.83 ( 0.45 )"	"2.04 ( 0.09 )"	24.79	0.84	0.73	20.8	2.04	"rf"
+1000	500	0.9	0.5	0.18	"0.68 ( 0.01 )"	"18.47 ( 0.43 )"	"1 ( 0.07 )"	23.47	0.78	0.68	18.4	0	"rf"
+50	1000	0.9	20	0	"1.22 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.22	0	6	"rf"
+100	1000	0.9	10	0.01	"0.93 ( 0.02 )"	"49.18 ( 0.69 )"	"4.67 ( 0.09 )"	50.51	0.97	0.93	49.1	4.67	"rf"
+500	1000	0.9	2	0.06	"0.78 ( 0.01 )"	"46.01 ( 0.7 )"	"2.14 ( 0.09 )"	49.87	0.92	0.78	46	2.14	"rf"
+1000	1000	0.9	1	0.08	"0.74 ( 0.01 )"	"43.05 ( 0.72 )"	"1.17 ( 0.08 )"	47.88	0.9	0.74	43	1.17	"rf"
+50	50	0.1	1	0.03	"0.36 ( 0.01 )"	"2.8 ( 0.36 )"	"5.01 ( 0.09 )"	3.79	0.62	0.36	2.8	5.01	"compLasso"
+100	50	0.1	0.5	0.1	"0.32 ( 0.01 )"	"4.96 ( 0.55 )"	"3.77 ( 0.14 )"	7.19	0.55	0.32	4.96	3.77	"compLasso"
+500	50	0.1	0.1	0.26	"0.28 ( 0 )"	"8.81 ( 0.4 )"	"0.78 ( 0.08 )"	14.03	0.6	0.28	8.81	0.78	"compLasso"
+1000	50	0.1	0.05	0.31	"0.27 ( 0 )"	"8.37 ( 0.37 )"	"0.24 ( 0.06 )"	14.13	0.56	0.27	8.37	0.24	"compLasso"
+50	100	0.1	2	0.04	"0.37 ( 0.02 )"	"2.54 ( 0.37 )"	"5.22 ( 0.08 )"	3.32	0.63	0.37	2.54	5.22	"compLasso"
+100	100	0.1	1	0.07	"0.35 ( 0.01 )"	"3.99 ( 0.57 )"	"4.63 ( 0.1 )"	5.36	0.56	0.35	3.99	4.63	"compLasso"
+500	100	0.1	0.2	0.18	"0.29 ( 0 )"	"13.62 ( 0.62 )"	"1.22 ( 0.11 )"	18.4	0.69	0.29	13.6	1.22	"compLasso"
+1000	100	0.1	0.1	0.23	"0.28 ( 0 )"	"13.35 ( 0.59 )"	"0.47 ( 0.07 )"	18.88	0.67	0.28	13.3	0.47	"compLasso"
+50	500	0.1	10	0.01	"0.36 ( 0.02 )"	"3.53 ( 0.44 )"	"5.62 ( 0.07 )"	3.91	0.85	0.36	3.53	5.62	"compLasso"
+100	500	0.1	5	0.05	"0.36 ( 0.01 )"	"2.58 ( 0.45 )"	"5.29 ( 0.08 )"	3.29	0.64	0.36	2.58	5.29	"compLasso"
+500	500	0.1	1	0.36	"0.32 ( 0.01 )"	"2.42 ( 0.56 )"	"3.74 ( 0.08 )"	4.68	0.29	0.32	2.42	3.74	"compLasso"
+1000	500	0.1	0.5	0.14	"0.3 ( 0 )"	"19.4 ( 1.74 )"	"2.05 ( 0.12 )"	23.35	0.66	0.3	19.4	2.05	"compLasso"
+50	1000	0.1	20	0	"0.37 ( 0.02 )"	"4.53 ( 0.54 )"	"5.71 ( 0.06 )"	4.82	0.91	0.37	4.53	5.71	"compLasso"
+100	1000	0.1	10	0.02	"0.36 ( 0.01 )"	"4.14 ( 0.72 )"	"5.4 ( 0.08 )"	4.74	0.78	0.36	4.14	5.4	"compLasso"
+500	1000	0.1	2	0.52	"0.34 ( 0 )"	"0.93 ( 0.19 )"	"3.83 ( 0.06 )"	3.1	0.21	0.34	0.93	3.83	"compLasso"
+1000	1000	0.1	1	0.21	"0.31 ( 0 )"	"8.59 ( 1.37 )"	"2.96 ( 0.1 )"	11.63	0.36	0.31	8.59	2.96	"compLasso"
+50	50	0.3	1	0.32	"0.8 ( 0.05 )"	"5.34 ( 0.32 )"	"1.21 ( 0.12 )"	10.13	0.48	0.8	5.34	1.21	"compLasso"
+100	50	0.3	0.5	0.5	"0.52 ( 0.02 )"	"4.42 ( 0.52 )"	"0.1 ( 0.03 )"	10.32	0.33	0.52	4.42	0.1	"compLasso"
+500	50	0.3	0.1	0.91	"0.55 ( 0.02 )"	"0.53 ( 0.16 )"	"0 ( 0 )"	6.53	0.05	0.55	0.53	0	"compLasso"
+1000	50	0.3	0.05	0.89	"0.56 ( 0.02 )"	"0.66 ( 0.18 )"	"0 ( 0 )"	6.66	0.06	0.56	0.66	0	"compLasso"
+50	100	0.3	2	0.27	"0.94 ( 0.05 )"	"6 ( 0.34 )"	"1.93 ( 0.14 )"	10.07	0.54	0.94	6	1.93	"compLasso"
+100	100	0.3	1	0.47	"0.53 ( 0.02 )"	"5.24 ( 0.45 )"	"0.24 ( 0.06 )"	11	0.4	0.53	5.24	0.24	"compLasso"
+500	100	0.3	0.2	0.91	"0.54 ( 0.02 )"	"0.54 ( 0.15 )"	"0 ( 0 )"	6.54	0.06	0.54	0.54	0	"compLasso"
+1000	100	0.3	0.1	0.92	"0.55 ( 0.02 )"	"0.51 ( 0.15 )"	"0 ( 0 )"	6.51	0.05	0.55	0.51	0	"compLasso"
+50	500	0.3	10	0.13	"1.31 ( 0.06 )"	"5.44 ( 0.44 )"	"3.96 ( 0.15 )"	7.48	0.69	1.31	5.44	3.96	"compLasso"
+100	500	0.3	5	0.3	"0.73 ( 0.03 )"	"9 ( 0.76 )"	"0.99 ( 0.12 )"	14.01	0.52	0.73	6	0.99	"compLasso"
+500	500	0.3	1	0.95	"0.55 ( 0.01 )"	"0.31 ( 0.08 )"	"0 ( 0 )"	6.31	0.04	0.55	0.31	0	"compLasso"
+1000	500	0.3	0.5	0.92	"0.57 ( 0.02 )"	"0.49 ( 0.14 )"	"0 ( 0 )"	6.49	0.05	0.57	0.49	0	"compLasso"
+50	1000	0.3	20	0.07	"1.55 ( 0.08 )"	"3.96 ( 0.47 )"	"4.74 ( 0.14 )"	5.22	0.68	1.55	3.96	4.74	"compLasso"
+100	1000	0.3	10	0.25	"0.84 ( 0.03 )"	"9.51 ( 0.77 )"	"1.58 ( 0.11 )"	13.93	0.58	0.84	9.51	1.58	"compLasso"
+500	1000	0.3	2	0.92	"0.56 ( 0.01 )"	"0.52 ( 0.14 )"	"0 ( 0 )"	6.52	0.05	0.56	0.52	0	"compLasso"
+1000	1000	0.3	1	0.95	"0.56 ( 0.02 )"	"0.29 ( 0.08 )"	"0 ( 0 )"	6.29	0.03	0.56	0.29	0	"compLasso"
+50	50	0.5	1	0.42	"0.77 ( 0.04 )"	"5.49 ( 0.31 )"	"0.25 ( 0.06 )"	11.24	0.44	0.77	5.49	0.25	"compLasso"
+100	50	0.5	0.5	0.68	"0.6 ( 0.03 )"	"2.34 ( 0.31 )"	"0.01 ( 0.01 )"	8.33	0.21	0.6	2.34	0.01	"compLasso"
+500	50	0.5	0.1	0.93	"0.8 ( 0.03 )"	"0.42 ( 0.12 )"	"0 ( 0 )"	6.42	0.05	0.8	0.42	0	"compLasso"
+1000	50	0.5	0.05	0.88	"0.78 ( 0.03 )"	"0.7 ( 0.21 )"	"0 ( 0 )"	6.7	0.07	0.78	0.7	0	"compLasso"
+50	100	0.5	2	0.36	"1.03 ( 0.07 )"	"5.95 ( 0.33 )"	"0.96 ( 0.11 )"	10.99	0.49	1.03	5.95	0.96	"compLasso"
+100	100	0.5	1	0.65	"0.64 ( 0.03 )"	"2.9 ( 0.32 )"	"0.03 ( 0.02 )"	8.87	0.27	0.64	2.9	0.03	"compLasso"
+500	100	0.5	0.2	0.91	"0.76 ( 0.03 )"	"0.56 ( 0.19 )"	"0 ( 0 )"	6.56	0.05	0.76	0.56	0	"compLasso"
+1000	100	0.5	0.1	0.93	"0.8 ( 0.03 )"	"0.4 ( 0.09 )"	"0 ( 0 )"	6.4	0.05	0.8	0.4	0	"compLasso"
+50	500	0.5	10	0.22	"1.93 ( 0.1 )"	"5.08 ( 0.41 )"	"3.23 ( 0.15 )"	7.85	0.55	1.93	5.08	3.23	"compLasso"
+100	500	0.5	5	0.45	"0.79 ( 0.03 )"	"6.21 ( 0.54 )"	"0.33 ( 0.07 )"	11.88	0.43	0.79	6.21	0.33	"compLasso"
+500	500	0.5	1	0.94	"0.83 ( 0.03 )"	"0.36 ( 0.11 )"	"0 ( 0 )"	6.36	0.04	0.83	0.36	0	"compLasso"
+1000	500	0.5	0.5	0.97	"0.87 ( 0.03 )"	"0.19 ( 0.06 )"	"0 ( 0 )"	6.19	0.02	0.87	0.19	0	"compLasso"
+50	1000	0.5	20	0.15	"2.1 ( 0.11 )"	"5.84 ( 0.49 )"	"3.77 ( 0.14 )"	8.07	0.66	2.1	5.84	3.77	"compLasso"
+100	1000	0.5	10	0.35	"0.91 ( 0.04 )"	"8.88 ( 0.73 )"	"0.52 ( 0.09 )"	14.36	0.51	0.91	8.88	0.52	"compLasso"
+500	1000	0.5	2	0.98	"0.86 ( 0.02 )"	"0.13 ( 0.05 )"	"0 ( 0 )"	6.13	0.02	0.86	0.13	0	"compLasso"
+1000	1000	0.5	1	0.95	"0.85 ( 0.02 )"	"0.3 ( 0.11 )"	"0 ( 0 )"	6.3	0.03	0.85	0.3	0	"compLasso"
+50	50	0.7	1	0.49	"0.8 ( 0.06 )"	"4.27 ( 0.31 )"	"0.24 ( 0.06 )"	10.03	0.37	0.8	4.27	0.24	"compLasso"
+100	50	0.7	0.5	0.75	"0.75 ( 0.03 )"	"1.71 ( 0.28 )"	"0 ( 0 )"	7.71	0.16	0.75	1.71	0	"compLasso"
+500	50	0.7	0.1	0.9	"1 ( 0.04 )"	"0.58 ( 0.19 )"	"0 ( 0 )"	6.58	0.06	1	0.58	0	"compLasso"
+1000	50	0.7	0.05	0.92	"0.98 ( 0.04 )"	"0.43 ( 0.09 )"	"0 ( 0 )"	6.43	0.05	0.98	0.43	0	"compLasso"
+50	100	0.7	2	0.42	"1.14 ( 0.08 )"	"5.46 ( 0.32 )"	"0.55 ( 0.09 )"	10.91	0.46	1.14	5.46	0.55	"compLasso"
+100	100	0.7	1	0.69	"0.82 ( 0.04 )"	"2.46 ( 0.41 )"	"0 ( 0 )"	8.46	0.21	0.82	2.46	0	"compLasso"
+500	100	0.7	0.2	0.89	"1.01 ( 0.04 )"	"0.68 ( 0.23 )"	"0 ( 0 )"	6.68	0.06	1.01	0.68	0	"compLasso"
+1000	100	0.7	0.1	0.94	"1.08 ( 0.04 )"	"0.36 ( 0.09 )"	"0 ( 0 )"	6.36	0.04	1.08	0.36	0	"compLasso"
+50	500	0.7	10	0.26	"2.76 ( 0.17 )"	"5.16 ( 0.4 )"	"2.65 ( 0.15 )"	8.51	0.51	2.76	5.16	2.65	"compLasso"
+100	500	0.7	5	0.52	"0.87 ( 0.03 )"	"5.39 ( 0.52 )"	"0.04 ( 0.02 )"	11.35	0.39	0.87	5.39	0.04	"compLasso"
+500	500	0.7	1	0.94	"1.05 ( 0.04 )"	"0.35 ( 0.1 )"	"0 ( 0 )"	6.35	0.04	1.05	0.35	0	"compLasso"
+1000	500	0.7	0.5	0.98	"1.14 ( 0.04 )"	"0.14 ( 0.04 )"	"0 ( 0 )"	6.14	0.02	1.14	0.14	0	"compLasso"
+50	1000	0.7	20	0.2	"3.02 ( 0.16 )"	"5.41 ( 0.43 )"	"3.39 ( 0.13 )"	8.02	0.58	3.02	5.41	3.39	"compLasso"
+100	1000	0.7	10	0.45	"0.91 ( 0.04 )"	"6.6 ( 0.53 )"	"0.2 ( 0.05 )"	12.4	0.45	0.91	6.6	0.2	"compLasso"
+500	1000	0.7	2	0.95	"1.04 ( 0.04 )"	"0.31 ( 0.1 )"	"0 ( 0 )"	6.31	0.04	1.04	0.31	0	"compLasso"
+1000	1000	0.7	1	0.97	"1.13 ( 0.04 )"	"0.21 ( 0.07 )"	"0 ( 0 )"	6.21	0.03	1.13	0.21	0	"compLasso"
+50	50	0.9	1	0.48	"0.92 ( 0.07 )"	"4.7 ( 0.32 )"	"0.14 ( 0.04 )"	10.56	0.38	0.92	4.7	0.14	"compLasso"
+100	50	0.9	0.5	0.76	"0.94 ( 0.05 )"	"1.59 ( 0.25 )"	"0 ( 0 )"	7.59	0.15	0.94	1.59	0	"compLasso"
+500	50	0.9	0.1	0.92	"1.15 ( 0.06 )"	"0.47 ( 0.11 )"	"0 ( 0 )"	6.47	0.05	1.15	0.47	0	"compLasso"
+1000	50	0.9	0.05	0.89	"1.28 ( 0.05 )"	"0.62 ( 0.19 )"	"0 ( 0 )"	6.62	0.06	1.28	0.62	0	"compLasso"
+50	100	0.9	2	0.42	"1.28 ( 0.08 )"	"5.86 ( 0.29 )"	"0.47 ( 0.09 )"	11.39	0.48	1.28	5.86	0.47	"compLasso"
+100	100	0.9	1	0.81	"0.94 ( 0.04 )"	"1.34 ( 0.23 )"	"0 ( 0 )"	7.34	0.14	0.94	1.34	0	"compLasso"
+500	100	0.9	0.2	0.93	"1.16 ( 0.05 )"	"0.43 ( 0.13 )"	"0 ( 0 )"	6.43	0.05	1.16	0.43	0	"compLasso"
+1000	100	0.9	0.1	0.94	"1.32 ( 0.05 )"	"0.34 ( 0.1 )"	"0 ( 0 )"	6.34	0.04	1.32	0.34	0	"compLasso"
+50	500	0.9	10	0.25	"3.06 ( 0.18 )"	"5.42 ( 0.4 )"	"2.69 ( 0.15 )"	8.73	0.54	3.06	5.42	2.69	"compLasso"
+100	500	0.9	5	0.58	"0.97 ( 0.04 )"	"4.2 ( 0.49 )"	"0.02 ( 0.02 )"	10.18	0.31	0.97	4.2	0.02	"compLasso"
+500	500	0.9	1	0.93	"1.27 ( 0.05 )"	"0.44 ( 0.18 )"	"0 ( 0 )"	6.44	0.04	1.27	0.44	0	"compLasso"
+1000	500	0.9	0.5	0.96	"1.31 ( 0.05 )"	"0.23 ( 0.06 )"	"0 ( 0 )"	6.23	0.03	1.31	0.23	0	"compLasso"
+50	1000	0.9	20	0.23	"3.37 ( 0.19 )"	"5.29 ( 0.41 )"	"3.13 ( 0.15 )"	8.16	0.57	3.37	5.29	3.13	"compLasso"
+100	1000	0.9	10	0.56	"1.14 ( 0.05 )"	"4.56 ( 0.38 )"	"0.07 ( 0.03 )"	10.49	0.37	1.14	4.56	0.07	"compLasso"
+500	1000	0.9	2	0.96	"1.28 ( 0.04 )"	"0.27 ( 0.11 )"	"0 ( 0 )"	6.27	0.03	1.28	0.27	0	"compLasso"
+1000	1000	0.9	1	0.95	"1.35 ( 0.05 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	0.03	1.35	0.29	0	"compLasso"
diff --git a/simulations/results_summary_cts/table_ind_all.txt b/simulations/results_summary_cts/table_ind_all.txt
new file mode 100755
index 0000000..3952135
--- /dev/null
+++ b/simulations/results_summary_cts/table_ind_all.txt
@@ -0,0 +1,65 @@
+"N"	"P"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	1	0.37	"0.54 ( 0.03 )"	"8.36 ( 0.45 )"	"0.02 ( 0.01 )"	13.34	0.51	0.54	8.36	0.02	"lasso"
+100	50	0.5	0.51	"0.36 ( 0.01 )"	"5.5 ( 0.42 )"	"0 ( 0 )"	10.5	0.37	0.36	5.5	0	"lasso"
+500	50	0.1	0.79	"0.28 ( 0 )"	"2.33 ( 0.14 )"	"0 ( 0 )"	7.33	0.15	0.28	2.33	0	"lasso"
+1000	50	0.05	0.86	"0.26 ( 0 )"	"1.82 ( 0.13 )"	"0 ( 0 )"	6.82	0.1	0.26	1.82	0	"lasso"
+50	100	2	0.32	"0.66 ( 0.04 )"	"11.1 ( 0.38 )"	"0.08 ( 0.03 )"	16.02	0.61	0.66	11.1	0.08	"lasso"
+100	100	1	0.46	"0.41 ( 0.01 )"	"7.23 ( 0.4 )"	"0 ( 0 )"	12.23	0.46	0.41	7.23	0	"lasso"
+500	100	0.2	0.71	"0.29 ( 0 )"	"3.26 ( 0.25 )"	"0 ( 0 )"	8.26	0.22	0.29	3.26	0	"lasso"
+1000	100	0.1	0.82	"0.27 ( 0 )"	"2.25 ( 0.15 )"	"0 ( 0 )"	7.25	0.15	0.27	2.25	0	"lasso"
+50	500	10	0.19	"1.41 ( 0.11 )"	"19.89 ( 0.38 )"	"0.64 ( 0.08 )"	24.25	0.77	1.41	19.8	0.64	"lasso"
+100	500	5	0.3	"0.48 ( 0.02 )"	"14.71 ( 0.7 )"	"0 ( 0 )"	19.71	0.66	0.48	14.7	0	"lasso"
+500	500	1	0.61	"0.28 ( 0 )"	"4.82 ( 0.34 )"	"0 ( 0 )"	9.82	0.32	0.28	4.82	0	"lasso"
+1000	500	0.5	0.72	"0.27 ( 0 )"	"3.35 ( 0.32 )"	"0 ( 0 )"	8.35	0.21	0.27	3.35	0	"lasso"
+50	1000	20	0.14	"2.24 ( 0.19 )"	"23.95 ( 0.37 )"	"1.19 ( 0.09 )"	27.76	0.82	2.24	23.9	1.19	"lasso"
+100	1000	10	0.25	"0.53 ( 0.02 )"	"18.53 ( 0.59 )"	"0 ( 0 )"	23.53	0.73	0.53	18.5	0	"lasso"
+500	1000	2	0.52	"0.29 ( 0 )"	"6.51 ( 0.45 )"	"0 ( 0 )"	11.51	0.4	0.29	6.51	0	"lasso"
+1000	1000	1	0.64	"0.27 ( 0 )"	"4.39 ( 0.56 )"	"0 ( 0 )"	9.39	0.26	0.27	4.39	0	"lasso"
+50	50	1	0.2	"0.53 ( 0.03 )"	"14.99 ( 0.59 )"	"0 ( 0 )"	19.99	0.67	0.53	14.9	0	"elnet"
+100	50	0.5	0.23	"0.34 ( 0.01 )"	"13.46 ( 0.55 )"	"0 ( 0 )"	18.46	0.65	0.34	13.4	0	"elnet"
+500	50	0.1	0.23	"0.26 ( 0 )"	"13.65 ( 0.56 )"	"0 ( 0 )"	18.65	0.64	0.26	13.6	0	"elnet"
+1000	50	0.05	0.27	"0.25 ( 0 )"	"11.76 ( 0.53 )"	"0 ( 0 )"	16.76	0.6	0.25	11.7	0	"elnet"
+50	100	2	0.19	"0.74 ( 0.06 )"	"19.45 ( 0.66 )"	"0.04 ( 0.02 )"	24.41	0.74	0.74	19.4	0.04	"elnet"
+100	100	1	0.2	"0.38 ( 0.01 )"	"19.2 ( 1.02 )"	"0 ( 0 )"	24.2	0.71	0.38	19.2	0	"elnet"
+500	100	0.2	0.23	"0.27 ( 0 )"	"16.28 ( 0.78 )"	"0 ( 0 )"	21.28	0.67	0.27	16.2	0	"elnet"
+1000	100	0.1	0.23	"0.26 ( 0 )"	"17.05 ( 0.76 )"	"0 ( 0 )"	22.05	0.69	0.26	17	0	"elnet"
+50	500	10	0.13	"1.65 ( 0.13 )"	"30.38 ( 1.24 )"	"0.58 ( 0.08 )"	34.8	0.83	1.65	30.3	0.58	"elnet"
+100	500	5	0.15	"0.5 ( 0.02 )"	"32.18 ( 1.44 )"	"0 ( 0 )"	37.18	0.81	0.5	32.1	0	"elnet"
+500	500	1	0.16	"0.28 ( 0 )"	"29.79 ( 1.73 )"	"0 ( 0 )"	34.79	0.78	0.28	29.7	0	"elnet"
+1000	500	0.5	0.18	"0.26 ( 0 )"	"27.21 ( 1.58 )"	"0 ( 0 )"	32.21	0.77	0.26	27.2	0	"elnet"
+50	1000	20	0.1	"2.5 ( 0.22 )"	"36.6 ( 2.03 )"	"1.12 ( 0.09 )"	40.48	0.86	2.5	36.6	1.12	"elnet"
+100	1000	10	0.13	"0.56 ( 0.02 )"	"40.61 ( 1.71 )"	"0 ( 0 )"	45.61	0.85	0.56	40.6	0	"elnet"
+500	1000	2	0.14	"0.27 ( 0 )"	"35.98 ( 2.2 )"	"0 ( 0 )"	40.98	0.8	0.27	35.9	0	"elnet"
+1000	1000	1	0.15	"0.27 ( 0 )"	"34.65 ( 2.02 )"	"0 ( 0 )"	39.65	0.79	0.27	34.6	0	"elnet"
+50	50	1	0	"1.31 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.31	0	6	"rf"
+100	50	0.5	0.18	"0.85 ( 0.02 )"	"1.85 ( 0.11 )"	"4.11 ( 0.1 )"	3.74	0.48	0.85	1.85	4.11	"rf"
+500	50	0.1	0.8	"0.47 ( 0.01 )"	"0.3 ( 0.05 )"	"1.61 ( 0.08 )"	4.69	0.05	0.47	0.3	1.61	"rf"
+1000	50	0.05	0.88	"0.35 ( 0 )"	"0.07 ( 0.03 )"	"0.78 ( 0.07 )"	5.29	0.01	0.35	0.07	0.78	"rf"
+50	100	2	0	"1.32 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.32	0	6	"rf"
+100	100	1	0.08	"0.91 ( 0.03 )"	"4.33 ( 0.22 )"	"4.45 ( 0.09 )"	5.88	0.72	0.91	4.33	4.45	"rf"
+500	100	0.2	0.6	"0.59 ( 0.01 )"	"1.91 ( 0.15 )"	"1.41 ( 0.07 )"	6.5	0.27	0.59	1.91	1.41	"rf"
+1000	100	0.1	0.81	"0.47 ( 0 )"	"0.71 ( 0.09 )"	"0.53 ( 0.06 )"	6.18	0.1	0.47	0.71	0.53	"rf"
+50	500	10	0	"1.33 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.33	0	6	"rf"
+100	500	5	0.02	"1.07 ( 0.03 )"	"23.8 ( 0.52 )"	"4.26 ( 0.1 )"	25.54	0.93	1.07	23.8	4.26	"rf"
+500	500	1	0.14	"0.84 ( 0.01 )"	"19.92 ( 0.41 )"	"1.71 ( 0.08 )"	24.21	0.82	0.84	19.9	1.71	"rf"
+1000	500	0.5	0.19	"0.78 ( 0.01 )"	"18.73 ( 0.42 )"	"0.84 ( 0.06 )"	23.89	0.78	0.78	18.7	0.84	"rf"
+50	1000	20	0	"1.31 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.31	0	6	"rf"
+100	1000	10	0.01	"1.06 ( 0.03 )"	"48.48 ( 0.63 )"	"4.33 ( 0.1 )"	50.15	0.97	1.06	48.4	4.33	"rf"
+500	1000	2	0.06	"0.89 ( 0.01 )"	"44.77 ( 0.71 )"	"1.81 ( 0.1 )"	48.96	0.91	0.89	44.7	1.81	"rf"
+1000	1000	1	0.09	"0.85 ( 0.01 )"	"41.77 ( 0.68 )"	"0.99 ( 0.07 )"	46.78	0.89	0.85	41.7	0.99	"rf"
+50	50	1	0.61	"0.8 ( 0.05 )"	"2.99 ( 0.27 )"	"0.05 ( 0.02 )"	8.94	0.28	0.8	2.99	0.05	"compLasso"
+100	50	0.5	0.87	"0.98 ( 0.06 )"	"0.79 ( 0.15 )"	"0 ( 0 )"	6.79	0.09	0.98	0.79	0	"compLasso"
+500	50	0.1	0.91	"1.08 ( 0.05 )"	"0.51 ( 0.11 )"	"0 ( 0 )"	6.51	0.06	1.08	0.51	0	"compLasso"
+1000	50	0.05	0.93	"1.18 ( 0.04 )"	"0.39 ( 0.15 )"	"0 ( 0 )"	6.39	0.04	1.18	0.39	0	"compLasso"
+50	100	2	0.52	"1.05 ( 0.07 )"	"4.3 ( 0.31 )"	"0.24 ( 0.05 )"	10.06	0.37	1.05	4.3	0.24	"compLasso"
+100	100	1	0.85	"0.94 ( 0.04 )"	"1.02 ( 0.19 )"	"0 ( 0 )"	7.02	0.11	0.94	1.02	0	"compLasso"
+500	100	0.2	0.96	"1.16 ( 0.04 )"	"0.25 ( 0.09 )"	"0 ( 0 )"	6.25	0.03	1.16	0.25	0	"compLasso"
+1000	100	0.1	0.89	"1.04 ( 0.05 )"	"0.71 ( 0.15 )"	"0 ( 0 )"	6.71	0.08	1.04	0.71	0	"compLasso"
+50	500	10	0.38	"2.18 ( 0.13 )"	"5.53 ( 0.36 )"	"1.27 ( 0.11 )"	10.26	0.48	2.18	5.53	1.27	"compLasso"
+100	500	5	0.75	"1 ( 0.05 )"	"1.9 ( 0.29 )"	"0.03 ( 0.02 )"	7.87	0.18	1	1.9	0.03	"compLasso"
+500	500	1	0.96	"1.18 ( 0.04 )"	"0.27 ( 0.13 )"	"0 ( 0 )"	6.27	0.03	1.18	0.27	0	"compLasso"
+1000	500	0.5	0.96	"1.24 ( 0.04 )"	"0.25 ( 0.1 )"	"0 ( 0 )"	6.25	0.03	1.24	0.25	0	"compLasso"
+50	1000	20	0.32	"3.09 ( 0.23 )"	"5.56 ( 0.36 )"	"1.95 ( 0.14 )"	9.61	0.51	3.09	5.56	1.95	"compLasso"
+100	1000	10	0.67	"1.1 ( 0.07 )"	"2.8 ( 0.37 )"	"0.04 ( 0.02 )"	8.76	0.24	1.1	2.8	0.04	"compLasso"
+500	1000	2	0.96	"1.12 ( 0.04 )"	"0.27 ( 0.08 )"	"0 ( 0 )"	6.27	0.03	1.12	0.27	0	"compLasso"
+1000	1000	1	0.96	"1.16 ( 0.04 )"	"0.24 ( 0.07 )"	"0 ( 0 )"	6.24	0.03	1.16	0.24	0	"compLasso"
diff --git a/simulations/results_summary_cts/table_toe_all.txt b/simulations/results_summary_cts/table_toe_all.txt
new file mode 100755
index 0000000..4bfa30c
--- /dev/null
+++ b/simulations/results_summary_cts/table_toe_all.txt
@@ -0,0 +1,321 @@
+"N"	"P"	"Corr"	"Ratio"	"Stab"	"MSE"	"FP"	"FN"	"num_select"	"FDR"	"MSE_mean"	"FP_mean"	"FN_mean"	"method"
+50	50	0.1	1	0.36	"0.62 ( 0.04 )"	"8.59 ( 0.46 )"	"0.02 ( 0.01 )"	13.57	0.51	0.62	8.59	0.02	"lasso"
+100	50	0.1	0.5	0.47	"0.37 ( 0.01 )"	"6.3 ( 0.43 )"	"0 ( 0 )"	11.3	0.4	0.37	6.3	0	"lasso"
+500	50	0.1	0.1	0.73	"0.28 ( 0 )"	"2.88 ( 0.21 )"	"0 ( 0 )"	7.88	0.2	0.28	2.88	0	"lasso"
+1000	50	0.1	0.05	0.89	"0.27 ( 0 )"	"1.66 ( 0.11 )"	"0 ( 0 )"	6.66	0.08	0.27	1.66	0	"lasso"
+50	100	0.1	2	0.32	"0.67 ( 0.05 )"	"11.84 ( 0.4 )"	"0 ( 0 )"	16.84	0.62	0.67	11.8	0	"lasso"
+100	100	0.1	1	0.44	"0.4 ( 0.01 )"	"7.71 ( 0.56 )"	"0 ( 0 )"	12.71	0.46	0.4	7.71	0	"lasso"
+500	100	0.1	0.2	0.7	"0.27 ( 0 )"	"3.35 ( 0.22 )"	"0 ( 0 )"	8.35	0.23	0.27	3.35	0	"lasso"
+1000	100	0.1	0.1	0.81	"0.27 ( 0 )"	"2.29 ( 0.23 )"	"0 ( 0 )"	7.29	0.13	0.27	2.29	0	"lasso"
+50	500	0.1	10	0.18	"1.61 ( 0.1 )"	"20.85 ( 0.38 )"	"0.69 ( 0.08 )"	25.16	0.78	1.61	20.8	0.69	"lasso"
+100	500	0.1	5	0.3	"0.53 ( 0.02 )"	"14.69 ( 0.7 )"	"0 ( 0 )"	19.69	0.66	0.53	14.6	0	"lasso"
+500	500	0.1	1	0.55	"0.28 ( 0 )"	"5.86 ( 0.51 )"	"0 ( 0 )"	10.86	0.36	0.28	5.86	0	"lasso"
+1000	500	0.1	0.5	0.78	"0.28 ( 0 )"	"2.69 ( 0.21 )"	"0 ( 0 )"	7.69	0.17	0.28	2.69	0	"lasso"
+50	1000	0.1	20	0.13	"2.23 ( 0.14 )"	"24.37 ( 0.39 )"	"1.36 ( 0.1 )"	28.01	0.83	2.23	24.3	1.36	"lasso"
+100	1000	0.1	10	0.25	"0.57 ( 0.03 )"	"18.79 ( 0.7 )"	"0 ( 0 )"	23.79	0.73	0.57	18.7	0	"lasso"
+500	1000	0.1	2	0.49	"0.28 ( 0 )"	"7.08 ( 0.53 )"	"0 ( 0 )"	12.08	0.41	0.28	7.08	0	"lasso"
+1000	1000	0.1	1	0.7	"0.27 ( 0 )"	"3.59 ( 0.33 )"	"0 ( 0 )"	8.59	0.23	0.27	3.59	0	"lasso"
+50	50	0.3	1	0.35	"0.57 ( 0.03 )"	"8.86 ( 0.42 )"	"0.05 ( 0.02 )"	13.81	0.53	0.57	8.86	0.05	"lasso"
+100	50	0.3	0.5	0.47	"0.36 ( 0.01 )"	"6.18 ( 0.36 )"	"0 ( 0 )"	11.18	0.41	0.36	6.18	0	"lasso"
+500	50	0.3	0.1	0.7	"0.28 ( 0 )"	"3.14 ( 0.21 )"	"0 ( 0 )"	8.14	0.22	0.28	3.14	0	"lasso"
+1000	50	0.3	0.05	0.86	"0.27 ( 0 )"	"1.86 ( 0.11 )"	"0 ( 0 )"	6.86	0.11	0.27	1.86	0	"lasso"
+50	100	0.3	2	0.31	"0.76 ( 0.04 )"	"11.12 ( 0.37 )"	"0.17 ( 0.04 )"	15.95	0.62	0.76	11.1	0.17	"lasso"
+100	100	0.3	1	0.38	"0.41 ( 0.01 )"	"9.43 ( 0.53 )"	"0 ( 0 )"	14.43	0.54	0.41	9.43	0	"lasso"
+500	100	0.3	0.2	0.63	"0.27 ( 0 )"	"4.16 ( 0.27 )"	"0 ( 0 )"	9.16	0.29	0.27	4.16	0	"lasso"
+1000	100	0.3	0.1	0.8	"0.27 ( 0 )"	"2.36 ( 0.17 )"	"0 ( 0 )"	7.36	0.15	0.27	2.36	0	"lasso"
+50	500	0.3	10	0.14	"1.78 ( 0.11 )"	"21.4 ( 0.41 )"	"1.39 ( 0.1 )"	25.01	0.81	1.78	21.4	1.39	"lasso"
+100	500	0.3	5	0.26	"0.53 ( 0.02 )"	"16.81 ( 0.77 )"	"0.02 ( 0.01 )"	21.79	0.7	0.53	16.8	0.02	"lasso"
+500	500	0.3	1	0.55	"0.29 ( 0 )"	"5.89 ( 0.51 )"	"0 ( 0 )"	10.89	0.36	0.29	5.89	0	"lasso"
+1000	500	0.3	0.5	0.69	"0.27 ( 0 )"	"3.68 ( 0.32 )"	"0 ( 0 )"	8.68	0.25	0.27	3.68	0	"lasso"
+50	1000	0.3	20	0.11	"2.21 ( 0.15 )"	"25.14 ( 0.35 )"	"1.87 ( 0.1 )"	28.27	0.85	2.21	25.1	1.87	"lasso"
+100	1000	0.3	10	0.22	"0.61 ( 0.03 )"	"21.11 ( 0.75 )"	"0.02 ( 0.01 )"	26.09	0.75	0.61	21.1	0.02	"lasso"
+500	1000	0.3	2	0.47	"0.28 ( 0 )"	"7.69 ( 0.63 )"	"0 ( 0 )"	12.69	0.45	0.28	7.69	0	"lasso"
+1000	1000	0.3	1	0.58	"0.27 ( 0 )"	"5.27 ( 0.46 )"	"0 ( 0 )"	10.27	0.33	0.27	5.27	0	"lasso"
+50	50	0.5	1	0.35	"0.63 ( 0.04 )"	"8.51 ( 0.41 )"	"0.17 ( 0.04 )"	13.34	0.53	0.63	8.51	0.17	"lasso"
+100	50	0.5	0.5	0.42	"0.37 ( 0.01 )"	"7.21 ( 0.33 )"	"0.01 ( 0.01 )"	12.2	0.47	0.37	7.21	0.01	"lasso"
+500	50	0.5	0.1	0.66	"0.27 ( 0 )"	"3.64 ( 0.21 )"	"0 ( 0 )"	8.64	0.27	0.27	3.64	0	"lasso"
+1000	50	0.5	0.05	0.78	"0.27 ( 0 )"	"2.46 ( 0.15 )"	"0 ( 0 )"	7.46	0.17	0.27	2.46	0	"lasso"
+50	100	0.5	2	0.24	"0.8 ( 0.05 )"	"13.21 ( 0.46 )"	"0.6 ( 0.07 )"	17.61	0.68	0.8	13.2	0.6	"lasso"
+100	100	0.5	1	0.36	"0.44 ( 0.02 )"	"10 ( 0.48 )"	"0.02 ( 0.01 )"	14.98	0.56	0.44	10	0.02	"lasso"
+500	100	0.5	0.2	0.62	"0.29 ( 0 )"	"4.39 ( 0.28 )"	"0 ( 0 )"	9.39	0.31	0.29	4.39	0	"lasso"
+1000	100	0.5	0.1	0.71	"0.27 ( 0 )"	"3.24 ( 0.22 )"	"0 ( 0 )"	8.24	0.23	0.27	3.24	0	"lasso"
+50	500	0.5	10	0.12	"1.73 ( 0.1 )"	"21.69 ( 0.32 )"	"1.93 ( 0.09 )"	24.76	0.83	1.73	21.6	1.93	"lasso"
+100	500	0.5	5	0.23	"0.62 ( 0.03 )"	"19.6 ( 0.76 )"	"0.16 ( 0.04 )"	24.44	0.74	0.62	19.6	0.16	"lasso"
+500	500	0.5	1	0.4	"0.29 ( 0 )"	"9.79 ( 0.55 )"	"0 ( 0 )"	14.79	0.54	0.29	9.79	0	"lasso"
+1000	500	0.5	0.5	0.51	"0.28 ( 0 )"	"6.66 ( 0.64 )"	"0 ( 0 )"	11.66	0.38	0.28	6.66	0	"lasso"
+50	1000	0.5	20	0.09	"2.17 ( 0.13 )"	"25.13 ( 0.34 )"	"2.69 ( 0.1 )"	27.44	0.88	2.17	25.1	2.69	"lasso"
+100	1000	0.5	10	0.19	"0.75 ( 0.03 )"	"23.22 ( 0.79 )"	"0.26 ( 0.05 )"	27.96	0.78	0.75	23.2	0.26	"lasso"
+500	1000	0.5	2	0.39	"0.3 ( 0 )"	"10.46 ( 0.63 )"	"0 ( 0 )"	15.46	0.54	0.3	10.4	0	"lasso"
+1000	1000	0.5	1	0.48	"0.28 ( 0 )"	"7.42 ( 0.6 )"	"0 ( 0 )"	12.42	0.42	0.28	7.42	0	"lasso"
+50	50	0.7	1	0.3	"0.66 ( 0.04 )"	"9.27 ( 0.39 )"	"0.47 ( 0.06 )"	13.8	0.57	0.66	9.27	0.47	"lasso"
+100	50	0.7	0.5	0.39	"0.37 ( 0.01 )"	"7.9 ( 0.35 )"	"0.05 ( 0.03 )"	12.85	0.5	0.37	7.9	0.05	"lasso"
+500	50	0.7	0.1	0.6	"0.27 ( 0 )"	"4.38 ( 0.21 )"	"0 ( 0 )"	9.38	0.33	0.27	4.38	0	"lasso"
+1000	50	0.7	0.05	0.74	"0.27 ( 0 )"	"2.84 ( 0.14 )"	"0 ( 0 )"	7.84	0.21	0.27	2.84	0	"lasso"
+50	100	0.7	2	0.23	"0.8 ( 0.04 )"	"11.57 ( 0.53 )"	"1.26 ( 0.1 )"	15.31	0.66	0.8	11.5	1.26	"lasso"
+100	100	0.7	1	0.31	"0.39 ( 0.01 )"	"11.68 ( 0.51 )"	"0.1 ( 0.04 )"	16.58	0.61	0.39	11.6	0.1	"lasso"
+500	100	0.7	0.2	0.49	"0.28 ( 0 )"	"6.56 ( 0.32 )"	"0 ( 0 )"	11.56	0.44	0.28	6.56	0	"lasso"
+1000	100	0.7	0.1	0.63	"0.27 ( 0 )"	"4.24 ( 0.26 )"	"0 ( 0 )"	9.24	0.3	0.27	4.24	0	"lasso"
+50	500	0.7	10	0.12	"1.37 ( 0.08 )"	"19.8 ( 0.33 )"	"2.78 ( 0.08 )"	22.02	0.85	1.37	19.8	2.78	"lasso"
+100	500	0.7	5	0.19	"0.62 ( 0.03 )"	"18.58 ( 0.85 )"	"0.88 ( 0.08 )"	22.7	0.75	0.62	18.5	0.88	"lasso"
+500	500	0.7	1	0.36	"0.3 ( 0 )"	"11.31 ( 0.6 )"	"0 ( 0 )"	16.31	0.58	0.3	11.3	0	"lasso"
+1000	500	0.7	0.5	0.46	"0.27 ( 0 )"	"7.93 ( 0.49 )"	"0 ( 0 )"	12.93	0.47	0.27	7.93	0	"lasso"
+50	1000	0.7	20	0.09	"1.5 ( 0.08 )"	"22.99 ( 0.35 )"	"3.2 ( 0.09 )"	24.79	0.88	1.5	22.9	3.2	"lasso"
+100	1000	0.7	10	0.14	"0.77 ( 0.04 )"	"22.43 ( 0.68 )"	"1.45 ( 0.1 )"	25.98	0.82	0.77	22.4	1.45	"lasso"
+500	1000	0.7	2	0.3	"0.31 ( 0 )"	"14.7 ( 0.84 )"	"0 ( 0 )"	19.7	0.65	0.31	14.7	0	"lasso"
+1000	1000	0.7	1	0.41	"0.28 ( 0 )"	"9.63 ( 0.54 )"	"0 ( 0 )"	14.63	0.53	0.28	9.63	0	"lasso"
+50	50	0.9	1	0.31	"0.55 ( 0.03 )"	"5.65 ( 0.29 )"	"2.17 ( 0.13 )"	8.48	0.51	0.55	5.65	2.17	"lasso"
+100	50	0.9	0.5	0.38	"0.38 ( 0.02 )"	"5.85 ( 0.3 )"	"1.03 ( 0.11 )"	9.82	0.45	0.38	5.85	1.03	"lasso"
+500	50	0.9	0.1	0.61	"0.28 ( 0 )"	"4.34 ( 0.19 )"	"0.01 ( 0.01 )"	9.33	0.33	0.28	4.34	0.01	"lasso"
+1000	50	0.9	0.05	0.76	"0.27 ( 0 )"	"2.71 ( 0.12 )"	"0 ( 0 )"	7.71	0.2	0.27	2.71	0	"lasso"
+50	100	0.9	2	0.26	"0.6 ( 0.03 )"	"7.26 ( 0.35 )"	"2.69 ( 0.1 )"	9.57	0.62	0.6	7.26	2.69	"lasso"
+100	100	0.9	1	0.31	"0.44 ( 0.01 )"	"7.79 ( 0.39 )"	"1.51 ( 0.11 )"	11.28	0.57	0.44	7.79	1.51	"lasso"
+500	100	0.9	0.2	0.51	"0.29 ( 0 )"	"6.45 ( 0.27 )"	"0 ( 0 )"	11.45	0.45	0.29	6.45	0	"lasso"
+1000	100	0.9	0.1	0.61	"0.27 ( 0 )"	"4.72 ( 0.21 )"	"0 ( 0 )"	9.72	0.35	0.27	4.72	0	"lasso"
+50	500	0.9	10	0.13	"0.81 ( 0.04 )"	"13.78 ( 0.36 )"	"3.54 ( 0.08 )"	15.24	0.83	0.81	13.7	3.54	"lasso"
+100	500	0.9	5	0.18	"0.54 ( 0.02 )"	"13.78 ( 0.72 )"	"2.58 ( 0.08 )"	16.2	0.76	0.54	13.7	2.58	"lasso"
+500	500	0.9	1	0.25	"0.3 ( 0 )"	"18.18 ( 0.69 )"	"0.03 ( 0.02 )"	23.15	0.72	0.3	18.1	0.03	"lasso"
+1000	500	0.9	0.5	0.33	"0.28 ( 0 )"	"12.68 ( 0.57 )"	"0 ( 0 )"	17.68	0.63	0.28	12.6	0	"lasso"
+50	1000	0.9	20	0.08	"0.9 ( 0.05 )"	"18.15 ( 0.34 )"	"3.92 ( 0.08 )"	19.23	0.89	0.9	18.1	3.92	"lasso"
+100	1000	0.9	10	0.16	"0.59 ( 0.02 )"	"15.99 ( 0.66 )"	"2.88 ( 0.06 )"	18.11	0.81	0.59	15.9	2.88	"lasso"
+500	1000	0.9	2	0.2	"0.31 ( 0 )"	"24.62 ( 1.01 )"	"0.05 ( 0.02 )"	29.57	0.77	0.31	24.6	0.05	"lasso"
+1000	1000	0.9	1	0.25	"0.28 ( 0 )"	"18.51 ( 0.86 )"	"0 ( 0 )"	23.51	0.71	0.28	18.5	0	"lasso"
+50	50	0.1	1	0.21	"0.6 ( 0.03 )"	"14.72 ( 0.57 )"	"0.01 ( 0.01 )"	19.71	0.67	0.6	14.7	0.01	"elnet"
+100	50	0.1	0.5	0.23	"0.34 ( 0.01 )"	"13.7 ( 0.66 )"	"0 ( 0 )"	18.7	0.64	0.34	13.7	0	"elnet"
+500	50	0.1	0.1	0.23	"0.26 ( 0 )"	"13.59 ( 0.55 )"	"0 ( 0 )"	18.59	0.65	0.26	13.5	0	"elnet"
+1000	50	0.1	0.05	0.25	"0.26 ( 0 )"	"12.64 ( 0.54 )"	"0 ( 0 )"	17.64	0.63	0.26	12.6	0	"elnet"
+50	100	0.1	2	0.2	"0.69 ( 0.05 )"	"19.33 ( 0.67 )"	"0 ( 0 )"	24.33	0.73	0.69	19.3	0	"elnet"
+100	100	0.1	1	0.22	"0.38 ( 0.01 )"	"17.77 ( 0.8 )"	"0 ( 0 )"	22.77	0.7	0.38	17.7	0	"elnet"
+500	100	0.1	0.2	0.21	"0.26 ( 0 )"	"18.57 ( 0.83 )"	"0 ( 0 )"	23.57	0.71	0.26	18.5	0	"elnet"
+1000	100	0.1	0.1	0.22	"0.26 ( 0 )"	"17.23 ( 0.72 )"	"0 ( 0 )"	22.23	0.7	0.26	17.2	0	"elnet"
+50	500	0.1	10	0.13	"1.74 ( 0.11 )"	"29.67 ( 1.23 )"	"0.63 ( 0.07 )"	34.04	0.83	1.74	29.6	0.63	"elnet"
+100	500	0.1	5	0.16	"0.51 ( 0.02 )"	"30.41 ( 1.35 )"	"0 ( 0 )"	35.41	0.81	0.51	30.4	0	"elnet"
+500	500	0.1	1	0.16	"0.27 ( 0 )"	"29.38 ( 1.53 )"	"0 ( 0 )"	34.38	0.79	0.27	29.3	0	"elnet"
+1000	500	0.1	0.5	0.18	"0.27 ( 0 )"	"26.73 ( 1.56 )"	"0 ( 0 )"	31.73	0.77	0.27	26.7	0	"elnet"
+50	1000	0.1	20	0.09	"2.5 ( 0.16 )"	"39.01 ( 2.7 )"	"1.34 ( 0.1 )"	42.67	0.87	2.5	39	1.34	"elnet"
+100	1000	0.1	10	0.13	"0.57 ( 0.03 )"	"39.81 ( 1.68 )"	"0 ( 0 )"	44.81	0.85	0.57	39.8	0	"elnet"
+500	1000	0.1	2	0.14	"0.27 ( 0 )"	"37.88 ( 2.19 )"	"0 ( 0 )"	42.88	0.82	0.27	37.8	0	"elnet"
+1000	1000	0.1	1	0.16	"0.26 ( 0 )"	"31.4 ( 1.69 )"	"0 ( 0 )"	36.4	0.8	0.26	31.4	0	"elnet"
+50	50	0.3	1	0.21	"0.53 ( 0.03 )"	"14.65 ( 0.58 )"	"0.03 ( 0.02 )"	19.62	0.67	0.53	14.6	0.03	"elnet"
+100	50	0.3	0.5	0.2	"0.34 ( 0.01 )"	"14.91 ( 0.58 )"	"0 ( 0 )"	19.91	0.67	0.34	14.9	0	"elnet"
+500	50	0.3	0.1	0.23	"0.27 ( 0 )"	"13.94 ( 0.57 )"	"0 ( 0 )"	18.94	0.65	0.27	13.9	0	"elnet"
+1000	50	0.3	0.05	0.27	"0.26 ( 0 )"	"11.94 ( 0.43 )"	"0 ( 0 )"	16.94	0.62	0.26	11.9	0	"elnet"
+50	100	0.3	2	0.19	"0.72 ( 0.04 )"	"19.67 ( 0.66 )"	"0.08 ( 0.03 )"	24.59	0.74	0.72	19.6	0.08	"elnet"
+100	100	0.3	1	0.19	"0.38 ( 0.01 )"	"20.48 ( 0.85 )"	"0 ( 0 )"	25.48	0.74	0.38	20.4	0	"elnet"
+500	100	0.3	0.2	0.2	"0.26 ( 0 )"	"19.3 ( 0.93 )"	"0 ( 0 )"	24.3	0.72	0.26	19.3	0	"elnet"
+1000	100	0.3	0.1	0.2	"0.26 ( 0 )"	"19 ( 0.86 )"	"0 ( 0 )"	24	0.71	0.26	19	0	"elnet"
+50	500	0.3	10	0.1	"1.94 ( 0.12 )"	"32.52 ( 1.94 )"	"1.33 ( 0.1 )"	36.19	0.85	1.94	32.5	1.33	"elnet"
+100	500	0.3	5	0.14	"0.51 ( 0.02 )"	"34.04 ( 1.37 )"	"0.01 ( 0.01 )"	39.03	0.83	0.51	34	0.01	"elnet"
+500	500	0.3	1	0.16	"0.28 ( 0 )"	"30.92 ( 1.44 )"	"0 ( 0 )"	35.92	0.8	0.28	30.9	0	"elnet"
+1000	500	0.3	0.5	0.16	"0.26 ( 0 )"	"30.61 ( 1.55 )"	"0 ( 0 )"	35.61	0.79	0.26	30.6	0	"elnet"
+50	1000	0.3	20	0.08	"2.46 ( 0.17 )"	"35.99 ( 1.99 )"	"1.83 ( 0.1 )"	39.16	0.88	2.46	35.9	1.83	"elnet"
+100	1000	0.3	10	0.12	"0.6 ( 0.03 )"	"43.92 ( 1.49 )"	"0.01 ( 0.01 )"	48.91	0.86	0.6	43.9	0.01	"elnet"
+500	1000	0.3	2	0.13	"0.28 ( 0 )"	"38.46 ( 2.16 )"	"0 ( 0 )"	43.46	0.84	0.28	38.4	0	"elnet"
+1000	1000	0.3	1	0.13	"0.27 ( 0 )"	"38.29 ( 2.09 )"	"0 ( 0 )"	43.29	0.83	0.27	38.2	0	"elnet"
+50	50	0.5	1	0.2	"0.57 ( 0.04 )"	"14.64 ( 0.66 )"	"0.09 ( 0.03 )"	19.55	0.66	0.57	14.6	0.09	"elnet"
+100	50	0.5	0.5	0.21	"0.33 ( 0.01 )"	"14.76 ( 0.55 )"	"0 ( 0 )"	19.76	0.67	0.33	14.7	0	"elnet"
+500	50	0.5	0.1	0.23	"0.26 ( 0 )"	"13.76 ( 0.51 )"	"0 ( 0 )"	18.76	0.66	0.26	13.7	0	"elnet"
+1000	50	0.5	0.05	0.23	"0.26 ( 0 )"	"13.4 ( 0.49 )"	"0 ( 0 )"	18.4	0.65	0.26	13.4	0	"elnet"
+50	100	0.5	2	0.17	"0.8 ( 0.04 )"	"20.05 ( 0.68 )"	"0.34 ( 0.06 )"	24.71	0.75	0.8	20	0.34	"elnet"
+100	100	0.5	1	0.17	"0.41 ( 0.02 )"	"21.92 ( 0.83 )"	"0 ( 0 )"	26.92	0.76	0.41	21.9	0	"elnet"
+500	100	0.5	0.2	0.2	"0.27 ( 0 )"	"18.97 ( 0.78 )"	"0 ( 0 )"	23.97	0.72	0.27	18.9	0	"elnet"
+1000	100	0.5	0.1	0.2	"0.26 ( 0 )"	"19.53 ( 0.66 )"	"0 ( 0 )"	24.53	0.74	0.26	19.5	0	"elnet"
+50	500	0.5	10	0.09	"1.86 ( 0.11 )"	"29.92 ( 1.25 )"	"1.88 ( 0.1 )"	33.04	0.86	1.86	29.9	1.88	"elnet"
+100	500	0.5	5	0.13	"0.59 ( 0.02 )"	"37.33 ( 1.25 )"	"0.07 ( 0.03 )"	42.26	0.85	0.59	37.3	0.07	"elnet"
+500	500	0.5	1	0.13	"0.28 ( 0 )"	"37.1 ( 1.54 )"	"0 ( 0 )"	42.1	0.83	0.28	37.1	0	"elnet"
+1000	500	0.5	0.5	0.13	"0.27 ( 0 )"	"36.71 ( 1.5 )"	"0 ( 0 )"	41.71	0.83	0.27	36.7	0	"elnet"
+50	1000	0.5	20	0.07	"2.33 ( 0.14 )"	"35.29 ( 1.69 )"	"2.53 ( 0.1 )"	37.76	0.9	2.33	35.2	2.53	"elnet"
+100	1000	0.5	10	0.11	"0.73 ( 0.03 )"	"44.83 ( 1.58 )"	"0.18 ( 0.05 )"	49.65	0.87	0.73	44.8	0.18	"elnet"
+500	1000	0.5	2	0.12	"0.29 ( 0 )"	"42.86 ( 2.28 )"	"0 ( 0 )"	47.86	0.85	0.29	42.8	0	"elnet"
+1000	1000	0.5	1	0.13	"0.27 ( 0 )"	"41.01 ( 1.99 )"	"0 ( 0 )"	46.01	0.84	0.27	41	0	"elnet"
+50	50	0.7	1	0.18	"0.6 ( 0.04 )"	"15.25 ( 0.54 )"	"0.25 ( 0.05 )"	20	0.69	0.6	15.2	0.25	"elnet"
+100	50	0.7	0.5	0.2	"0.35 ( 0.01 )"	"15.66 ( 0.62 )"	"0 ( 0 )"	20.66	0.69	0.35	15.6	0	"elnet"
+500	50	0.7	0.1	0.24	"0.26 ( 0 )"	"13.41 ( 0.46 )"	"0 ( 0 )"	18.41	0.65	0.26	13.4	0	"elnet"
+1000	50	0.7	0.05	0.26	"0.26 ( 0 )"	"12.18 ( 0.6 )"	"0 ( 0 )"	17.18	0.61	0.26	12.1	0	"elnet"
+50	100	0.7	2	0.15	"0.81 ( 0.04 )"	"19.25 ( 0.81 )"	"0.79 ( 0.09 )"	23.46	0.75	0.81	19.2	0.79	"elnet"
+100	100	0.7	1	0.18	"0.36 ( 0.01 )"	"21.79 ( 0.78 )"	"0.01 ( 0.01 )"	26.78	0.76	0.36	21.7	0.01	"elnet"
+500	100	0.7	0.2	0.18	"0.27 ( 0 )"	"21.43 ( 0.75 )"	"0 ( 0 )"	26.43	0.75	0.27	21.4	0	"elnet"
+1000	100	0.7	0.1	0.21	"0.26 ( 0 )"	"18.91 ( 0.6 )"	"0 ( 0 )"	23.91	0.73	0.26	18.9	0	"elnet"
+50	500	0.7	10	0.09	"1.48 ( 0.09 )"	"27.19 ( 0.95 )"	"2.71 ( 0.08 )"	29.48	0.88	1.48	27.1	2.71	"elnet"
+100	500	0.7	5	0.11	"0.6 ( 0.02 )"	"37.9 ( 1.54 )"	"0.51 ( 0.07 )"	42.39	0.86	0.6	37.9	0.51	"elnet"
+500	500	0.7	1	0.13	"0.29 ( 0 )"	"38.11 ( 1.41 )"	"0 ( 0 )"	43.11	0.84	0.29	38.1	0	"elnet"
+1000	500	0.7	0.5	0.13	"0.27 ( 0 )"	"38.16 ( 1.26 )"	"0 ( 0 )"	43.16	0.85	0.27	38.1	0	"elnet"
+50	1000	0.7	20	0.06	"1.66 ( 0.09 )"	"35.91 ( 1.86 )"	"3.03 ( 0.09 )"	37.88	0.91	1.66	35.9	3.03	"elnet"
+100	1000	0.7	10	0.09	"0.78 ( 0.04 )"	"39.69 ( 1.62 )"	"1.11 ( 0.1 )"	43.58	0.88	0.78	39.6	1.11	"elnet"
+500	1000	0.7	2	0.11	"0.3 ( 0 )"	"49.45 ( 1.98 )"	"0 ( 0 )"	54.45	0.87	0.3	49.4	0	"elnet"
+1000	1000	0.7	1	0.11	"0.27 ( 0 )"	"48.15 ( 1.76 )"	"0 ( 0 )"	53.15	0.87	0.27	48.1	0	"elnet"
+50	50	0.9	1	0.16	"0.5 ( 0.02 )"	"13.26 ( 0.77 )"	"1.03 ( 0.11 )"	17.23	0.66	0.5	13.2	1.03	"elnet"
+100	50	0.9	0.5	0.21	"0.35 ( 0.01 )"	"14 ( 0.69 )"	"0.27 ( 0.06 )"	18.73	0.66	0.35	14	0.27	"elnet"
+500	50	0.9	0.1	0.29	"0.27 ( 0 )"	"11.63 ( 0.41 )"	"0 ( 0 )"	16.63	0.62	0.27	11.6	0	"elnet"
+1000	50	0.9	0.05	0.29	"0.26 ( 0 )"	"12.06 ( 0.52 )"	"0 ( 0 )"	17.06	0.62	0.26	12	0	"elnet"
+50	100	0.9	2	0.15	"0.56 ( 0.03 )"	"15.35 ( 0.8 )"	"1.83 ( 0.12 )"	18.52	0.75	0.56	15.3	1.83	"elnet"
+100	100	0.9	1	0.18	"0.4 ( 0.02 )"	"18.69 ( 0.92 )"	"0.41 ( 0.07 )"	23.28	0.73	0.4	18.6	0.41	"elnet"
+500	100	0.9	0.2	0.23	"0.27 ( 0 )"	"17.36 ( 0.62 )"	"0 ( 0 )"	22.36	0.71	0.27	17.3	0	"elnet"
+1000	100	0.9	0.1	0.26	"0.26 ( 0 )"	"16.05 ( 0.62 )"	"0 ( 0 )"	21.05	0.69	0.26	16	0	"elnet"
+50	500	0.9	10	0.08	"0.88 ( 0.04 )"	"25.11 ( 1.93 )"	"3.23 ( 0.08 )"	26.88	0.87	0.88	25.1	3.23	"elnet"
+100	500	0.9	5	0.1	"0.53 ( 0.02 )"	"27.99 ( 1.26 )"	"2.15 ( 0.1 )"	30.84	0.86	0.53	27.9	2.15	"elnet"
+500	500	0.9	1	0.12	"0.29 ( 0 )"	"41.14 ( 1.02 )"	"0 ( 0 )"	46.14	0.86	0.29	41.1	0	"elnet"
+1000	500	0.9	0.5	0.13	"0.27 ( 0 )"	"39.21 ( 0.84 )"	"0 ( 0 )"	44.21	0.86	0.27	39.2	0	"elnet"
+50	1000	0.9	20	0.04	"0.94 ( 0.05 )"	"36.59 ( 3.6 )"	"3.72 ( 0.08 )"	37.87	0.92	0.94	36.5	3.72	"elnet"
+100	1000	0.9	10	0.09	"0.59 ( 0.02 )"	"32.05 ( 1.85 )"	"2.59 ( 0.06 )"	34.46	0.88	0.59	32	2.59	"elnet"
+500	1000	0.9	2	0.09	"0.3 ( 0 )"	"57.69 ( 1.57 )"	"0 ( 0 )"	62.69	0.9	0.3	57.6	0	"elnet"
+1000	1000	0.9	1	0.1	"0.27 ( 0 )"	"52.35 ( 1.6 )"	"0 ( 0 )"	57.35	0.89	0.27	52.3	0	"elnet"
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+100	50	0.1	0.5	0.19	"0.86 ( 0.03 )"	"1.87 ( 0.14 )"	"4.25 ( 0.09 )"	3.62	0.48	0.86	1.87	4.25	"rf"
+500	50	0.1	0.1	0.77	"0.44 ( 0.01 )"	"0.32 ( 0.05 )"	"1.81 ( 0.07 )"	4.51	0.06	0.44	0.32	1.81	"rf"
+1000	50	0.1	0.05	0.86	"0.34 ( 0 )"	"0.15 ( 0.04 )"	"0.93 ( 0.07 )"	5.22	0.02	0.34	0.15	0.93	"rf"
+50	100	0.1	2	0	"1.36 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.36	0	6	"rf"
+100	100	0.1	1	0.12	"0.93 ( 0.03 )"	"4.11 ( 0.18 )"	"4.17 ( 0.1 )"	5.93	0.68	0.93	4.11	4.17	"rf"
+500	100	0.1	0.2	0.57	"0.56 ( 0.01 )"	"2.08 ( 0.14 )"	"1.52 ( 0.08 )"	6.56	0.29	0.56	2.08	1.52	"rf"
+1000	100	0.1	0.1	0.74	"0.45 ( 0 )"	"1.11 ( 0.1 )"	"0.74 ( 0.07 )"	6.37	0.16	0.45	1.11	0.74	"rf"
+50	500	0.1	10	0	"1.29 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.29	0	6	"rf"
+100	500	0.1	5	0.02	"0.95 ( 0.02 )"	"24.82 ( 0.56 )"	"4.55 ( 0.09 )"	26.27	0.94	0.95	24.8	4.55	"rf"
+500	500	0.1	1	0.13	"0.84 ( 0.01 )"	"20.8 ( 0.48 )"	"1.79 ( 0.1 )"	25.01	0.83	0.84	20.8	1.79	"rf"
+1000	500	0.1	0.5	0.18	"0.76 ( 0.01 )"	"18.72 ( 0.37 )"	"0.91 ( 0.07 )"	23.81	0.78	0.76	18.7	0.91	"rf"
+50	1000	0.1	20	0	"1.35 ( 0.06 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.35	0	6	"rf"
+100	1000	0.1	10	0.01	"1 ( 0.03 )"	"49.42 ( 0.7 )"	"4.71 ( 0.09 )"	50.71	0.97	1	49.4	4.71	"rf"
+500	1000	0.1	2	0.06	"0.87 ( 0.01 )"	"46.19 ( 0.64 )"	"1.97 ( 0.09 )"	50.22	0.92	0.87	46.1	1.97	"rf"
+1000	1000	0.1	1	0.09	"0.85 ( 0.01 )"	"40.92 ( 0.66 )"	"0.98 ( 0.07 )"	45.94	0.89	0.85	40.9	0.98	"rf"
+50	50	0.3	1	0	"1.22 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.22	0	6	"rf"
+100	50	0.3	0.5	0.23	"0.77 ( 0.02 )"	"1.72 ( 0.11 )"	"4.4 ( 0.08 )"	3.32	0.48	0.77	1.72	4.4	"rf"
+500	50	0.3	0.1	0.7	"0.39 ( 0 )"	"0.54 ( 0.06 )"	"1.98 ( 0.08 )"	4.56	0.11	0.39	0.54	1.98	"rf"
+1000	50	0.3	0.05	0.82	"0.31 ( 0 )"	"0.3 ( 0.05 )"	"1.19 ( 0.08 )"	5.11	0.05	0.31	0.3	1.19	"rf"
+50	100	0.3	2	0	"1.15 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.15	0	6	"rf"
+100	100	0.3	1	0.15	"0.81 ( 0.02 )"	"3.77 ( 0.16 )"	"4.28 ( 0.07 )"	5.49	0.67	0.81	3.77	4.28	"rf"
+500	100	0.3	0.2	0.52	"0.51 ( 0.01 )"	"2.21 ( 0.14 )"	"1.94 ( 0.09 )"	6.27	0.33	0.51	2.21	1.94	"rf"
+1000	100	0.3	0.1	0.75	"0.41 ( 0 )"	"1.4 ( 0.1 )"	"0.76 ( 0.07 )"	6.64	0.2	0.41	1.4	0.76	"rf"
+50	500	0.3	10	0	"1.14 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.14	0	6	"rf"
+100	500	0.3	5	0.02	"0.89 ( 0.03 )"	"24.48 ( 0.46 )"	"4.69 ( 0.08 )"	25.79	0.95	0.89	24.4	4.69	"rf"
+500	500	0.3	1	0.12	"0.73 ( 0.01 )"	"20.86 ( 0.41 )"	"2.1 ( 0.09 )"	24.76	0.84	0.73	20.8	2.1	"rf"
+1000	500	0.3	0.5	0.19	"0.68 ( 0.01 )"	"18.71 ( 0.45 )"	"1.13 ( 0.08 )"	23.58	0.79	0.68	18.7	1.13	"rf"
+50	1000	0.3	20	0	"1.2 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	1.2	0	6	"rf"
+100	1000	0.3	10	0.01	"0.92 ( 0.03 )"	"48.8 ( 0.82 )"	"4.79 ( 0.08 )"	50.01	0.98	0.92	48.8	4.79	"rf"
+500	1000	0.3	2	0.05	"0.78 ( 0.01 )"	"44.58 ( 0.68 )"	"2.48 ( 0.09 )"	48.1	0.93	0.78	44.5	2.48	"rf"
+1000	1000	0.3	1	0.08	"0.74 ( 0.01 )"	"42.87 ( 0.73 )"	"1.44 ( 0.09 )"	47.43	0.9	0.74	42.8	1.44	"rf"
+50	50	0.5	1	0	"0.99 ( 0.05 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.99	0	6	"rf"
+100	50	0.5	0.5	0.24	"0.62 ( 0.02 )"	"1.74 ( 0.13 )"	"4.39 ( 0.08 )"	3.34	0.47	0.62	1.74	4.39	"rf"
+500	50	0.5	0.1	0.7	"0.32 ( 0 )"	"0.98 ( 0.09 )"	"2.53 ( 0.08 )"	4.45	0.2	0.32	0.98	2.53	"rf"
+1000	50	0.5	0.05	0.78	"0.26 ( 0 )"	"0.91 ( 0.07 )"	"1.71 ( 0.07 )"	5.2	0.16	0.26	0.91	1.71	"rf"
+50	100	0.5	2	0	"0.91 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.91	0	6	"rf"
+100	100	0.5	1	0.15	"0.68 ( 0.02 )"	"3.72 ( 0.19 )"	"4.42 ( 0.08 )"	5.3	0.66	0.68	3.72	4.42	"rf"
+500	100	0.5	0.2	0.56	"0.4 ( 0 )"	"2.59 ( 0.13 )"	"2.1 ( 0.08 )"	6.49	0.38	0.4	2.59	2.1	"rf"
+1000	100	0.5	0.1	0.76	"0.34 ( 0 )"	"2.09 ( 0.1 )"	"1.08 ( 0.07 )"	7.01	0.29	0.34	2.09	1.08	"rf"
+50	500	0.5	10	0	"0.95 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.95	0	6	"rf"
+100	500	0.5	5	0.03	"0.75 ( 0.02 )"	"24.36 ( 0.56 )"	"4.57 ( 0.07 )"	25.79	0.94	0.75	24.3	4.57	"rf"
+500	500	0.5	1	0.13	"0.58 ( 0.01 )"	"20.66 ( 0.49 )"	"2.51 ( 0.07 )"	24.15	0.85	0.58	20.6	2.51	"rf"
+1000	500	0.5	0.5	0.2	"0.55 ( 0 )"	"18.4 ( 0.44 )"	"1.77 ( 0.07 )"	22.63	0.81	0.55	18.4	1.77	"rf"
+50	1000	0.5	20	0	"0.94 ( 0.04 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.94	0	6	"rf"
+100	1000	0.5	10	0.01	"0.72 ( 0.02 )"	"48.5 ( 0.74 )"	"4.75 ( 0.08 )"	49.75	0.97	0.72	48.5	4.75	"rf"
+500	1000	0.5	2	0.05	"0.65 ( 0.01 )"	"46.07 ( 0.71 )"	"2.83 ( 0.09 )"	49.24	0.93	0.65	46	2.83	"rf"
+1000	1000	0.5	1	0.09	"0.61 ( 0.01 )"	"41.21 ( 0.68 )"	"1.84 ( 0.08 )"	45.37	0.91	0.61	41.2	1.84	"rf"
+50	50	0.7	1	0	"0.66 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.66	0	6	"rf"
+100	50	0.7	0.5	0.28	"0.44 ( 0.01 )"	"1.84 ( 0.14 )"	"4.44 ( 0.08 )"	3.4	0.48	0.44	1.84	4.44	"rf"
+500	50	0.7	0.1	0.74	"0.23 ( 0 )"	"1.29 ( 0.07 )"	"2.76 ( 0.07 )"	4.53	0.27	0.23	1.29	2.76	"rf"
+1000	50	0.7	0.05	0.85	"0.19 ( 0 )"	"1.57 ( 0.07 )"	"2.29 ( 0.06 )"	5.28	0.29	0.19	1.57	2.29	"rf"
+50	100	0.7	2	0	"0.67 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.67	0	6	"rf"
+100	100	0.7	1	0.14	"0.47 ( 0.01 )"	"4.64 ( 0.25 )"	"4.32 ( 0.09 )"	6.32	0.7	0.47	4.64	4.32	"rf"
+500	100	0.7	0.2	0.63	"0.28 ( 0 )"	"2.64 ( 0.14 )"	"2.52 ( 0.07 )"	6.12	0.41	0.28	2.64	2.52	"rf"
+1000	100	0.7	0.1	0.8	"0.25 ( 0 )"	"2.42 ( 0.1 )"	"1.76 ( 0.05 )"	6.66	0.35	0.25	2.42	1.76	"rf"
+50	500	0.7	10	0	"0.7 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.7	0	6	"rf"
+100	500	0.7	5	0.03	"0.49 ( 0.02 )"	"24.87 ( 0.57 )"	"4.59 ( 0.09 )"	26.28	0.94	0.49	24.8	4.59	"rf"
+500	500	0.7	1	0.13	"0.4 ( 0 )"	"21.21 ( 0.55 )"	"2.97 ( 0.08 )"	24.24	0.87	0.4	21.2	2.97	"rf"
+1000	500	0.7	0.5	0.21	"0.38 ( 0 )"	"17.4 ( 0.46 )"	"1.98 ( 0.07 )"	21.42	0.8	0.38	17.4	1.98	"rf"
+50	1000	0.7	20	0	"0.74 ( 0.03 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.74	0	6	"rf"
+100	1000	0.7	10	0.01	"0.5 ( 0.01 )"	"49.02 ( 0.67 )"	"4.84 ( 0.08 )"	50.18	0.98	0.5	49	4.84	"rf"
+500	1000	0.7	2	0.07	"0.44 ( 0 )"	"45.41 ( 0.68 )"	"3.02 ( 0.07 )"	48.39	0.94	0.44	45.4	3.02	"rf"
+1000	1000	0.7	1	0.09	"0.42 ( 0 )"	"42.15 ( 0.71 )"	"2.37 ( 0.07 )"	45.78	0.92	0.42	42.1	2.37	"rf"
+50	50	0.9	1	0	"0.27 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.27	0	6	"rf"
+100	50	0.9	0.5	0.19	"0.19 ( 0.01 )"	"2.14 ( 0.17 )"	"4.84 ( 0.08 )"	3.28	0.58	0.19	2.14	4.84	"rf"
+500	50	0.9	0.1	0.67	"0.12 ( 0 )"	"1.43 ( 0.09 )"	"3.13 ( 0.08 )"	4.3	0.32	0.12	1.43	3.13	"rf"
+1000	50	0.9	0.05	0.85	"0.1 ( 0 )"	"1.5 ( 0.07 )"	"2.46 ( 0.06 )"	5.04	0.29	0.1	1.5	2.46	"rf"
+50	100	0.9	2	0	"0.3 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.3	0	6	"rf"
+100	100	0.9	1	0.16	"0.19 ( 0.01 )"	"4.23 ( 0.22 )"	"4.48 ( 0.09 )"	5.74	0.72	0.19	4.23	4.48	"rf"
+500	100	0.9	0.2	0.57	"0.14 ( 0 )"	"2.86 ( 0.16 )"	"2.89 ( 0.07 )"	5.97	0.45	0.14	2.86	2.89	"rf"
+1000	100	0.9	0.1	0.8	"0.12 ( 0 )"	"2.42 ( 0.11 )"	"1.89 ( 0.05 )"	6.53	0.36	0.12	2.42	1.89	"rf"
+50	500	0.9	10	0	"0.29 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.29	0	6	"rf"
+100	500	0.9	5	0.02	"0.21 ( 0.01 )"	"24.34 ( 0.71 )"	"4.72 ( 0.09 )"	25.62	0.95	0.21	24.3	4.72	"rf"
+500	500	0.9	1	0.14	"0.17 ( 0 )"	"21.38 ( 0.6 )"	"3 ( 0.07 )"	24.38	0.87	0.17	21.3	0	"rf"
+1000	500	0.9	0.5	0.2	"0.16 ( 0 )"	"18.68 ( 0.53 )"	"2.35 ( 0.06 )"	22.33	0.83	0.16	18.6	2.35	"rf"
+50	1000	0.9	20	0	"0.29 ( 0.01 )"	"1 ( 0 )"	"6 ( 0 )"	0	NA	0.29	0	6	"rf"
+100	1000	0.9	10	0.01	"0.2 ( 0.01 )"	"49.91 ( 0.83 )"	"4.99 ( 0.09 )"	50.92	0.98	0.2	49.9	4.99	"rf"
+500	1000	0.9	2	0.06	"0.19 ( 0 )"	"46.34 ( 0.86 )"	"3.13 ( 0.08 )"	49.21	0.94	0.19	46.3	3.13	"rf"
+1000	1000	0.9	1	0.09	"0.18 ( 0 )"	"43.52 ( 0.89 )"	"2.7 ( 0.07 )"	46.82	0.93	0.18	43.5	2.7	"rf"
+50	50	0.1	1	0.56	"0.88 ( 0.05 )"	"3.68 ( 0.29 )"	"0.06 ( 0.03 )"	9.62	0.33	0.88	3.68	0.06	"compLasso"
+100	50	0.1	0.5	0.77	"0.87 ( 0.05 )"	"1.51 ( 0.35 )"	"0 ( 0 )"	7.51	0.12	0.87	1.51	0	"compLasso"
+500	50	0.1	0.1	0.88	"0.99 ( 0.05 )"	"0.73 ( 0.19 )"	"0 ( 0 )"	6.73	0.07	0.99	0.73	0	"compLasso"
+1000	50	0.1	0.05	0.9	"1.09 ( 0.04 )"	"0.55 ( 0.17 )"	"0 ( 0 )"	6.55	0.05	1.09	0.55	0	"compLasso"
+50	100	0.1	2	0.53	"1.12 ( 0.1 )"	"4.24 ( 0.3 )"	"0.23 ( 0.06 )"	10.01	0.37	1.12	4.24	0.23	"compLasso"
+100	100	0.1	1	0.79	"0.85 ( 0.04 )"	"1.51 ( 0.34 )"	"0 ( 0 )"	7.51	0.13	0.85	1.51	0	"compLasso"
+500	100	0.1	0.2	0.95	"1.13 ( 0.04 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	0.03	1.13	0.29	0	"compLasso"
+1000	100	0.1	0.1	0.93	"1.03 ( 0.04 )"	"0.42 ( 0.12 )"	"0 ( 0 )"	6.42	0.05	1.03	0.42	0	"compLasso"
+50	500	0.1	10	0.36	"2.54 ( 0.18 )"	"5.13 ( 0.35 )"	"1.78 ( 0.12 )"	9.35	0.48	2.54	5.13	1.78	"compLasso"
+100	500	0.1	5	0.71	"0.98 ( 0.04 )"	"2.4 ( 0.35 )"	"0 ( 0 )"	8.4	0.21	0.98	2.4	0	"compLasso"
+500	500	0.1	1	0.96	"1.1 ( 0.04 )"	"0.25 ( 0.08 )"	"0 ( 0 )"	6.25	0.03	1.1	0.25	0	"compLasso"
+1000	500	0.1	0.5	0.96	"1.18 ( 0.04 )"	"0.24 ( 0.08 )"	"0 ( 0 )"	6.24	0.03	1.18	0.24	0	"compLasso"
+50	1000	0.1	20	0.29	"3.37 ( 0.2 )"	"4.59 ( 0.36 )"	"2.63 ( 0.15 )"	7.96	0.5	3.37	4.59	2.63	"compLasso"
+100	1000	0.1	10	0.61	"1.03 ( 0.04 )"	"3.82 ( 0.46 )"	"0.01 ( 0.01 )"	9.81	0.29	1.03	3.82	0.01	"compLasso"
+500	1000	0.1	2	0.96	"1.1 ( 0.03 )"	"0.24 ( 0.1 )"	"0 ( 0 )"	6.24	0.03	1.1	0.24	0	"compLasso"
+1000	1000	0.1	1	0.97	"1.16 ( 0.03 )"	"0.16 ( 0.06 )"	"0 ( 0 )"	6.16	0.02	1.16	0.16	0	"compLasso"
+50	50	0.3	1	0.5	"0.76 ( 0.05 )"	"4.6 ( 0.27 )"	"0.07 ( 0.03 )"	10.53	0.4	0.76	4.6	0.07	"compLasso"
+100	50	0.3	0.5	0.75	"0.7 ( 0.04 )"	"1.65 ( 0.28 )"	"0 ( 0 )"	7.65	0.16	0.7	1.65	0	"compLasso"
+500	50	0.3	0.1	0.93	"0.92 ( 0.04 )"	"0.39 ( 0.12 )"	"0 ( 0 )"	6.39	0.04	0.92	0.39	0	"compLasso"
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+50	100	0.3	2	0.43	"1.12 ( 0.07 )"	"5.19 ( 0.3 )"	"0.63 ( 0.09 )"	10.56	0.44	1.12	5.19	0.63	"compLasso"
+100	100	0.3	1	0.7	"0.73 ( 0.04 )"	"2.38 ( 0.32 )"	"0 ( 0 )"	8.38	0.22	0.73	2.38	0	"compLasso"
+500	100	0.3	0.2	0.87	"0.82 ( 0.04 )"	"0.84 ( 0.2 )"	"0 ( 0 )"	6.84	0.08	0.82	0.84	0	"compLasso"
+1000	100	0.3	0.1	0.93	"0.87 ( 0.04 )"	"0.41 ( 0.08 )"	"0 ( 0 )"	6.41	0.05	0.87	0.41	0	"compLasso"
+50	500	0.3	10	0.29	"2.5 ( 0.14 )"	"5.62 ( 0.34 )"	"2.39 ( 0.12 )"	9.23	0.55	2.5	5.62	2.39	"compLasso"
+100	500	0.3	5	0.52	"0.84 ( 0.04 )"	"5.23 ( 0.57 )"	"0.06 ( 0.02 )"	11.17	0.37	0.84	5.23	0.06	"compLasso"
+500	500	0.3	1	0.96	"0.93 ( 0.03 )"	"0.25 ( 0.08 )"	"0 ( 0 )"	6.25	0.03	0.93	0.25	0	"compLasso"
+1000	500	0.3	0.5	0.94	"0.9 ( 0.03 )"	"0.38 ( 0.1 )"	"0 ( 0 )"	6.38	0.04	0.9	0.38	0	"compLasso"
+50	1000	0.3	20	0.28	"2.99 ( 0.17 )"	"4.75 ( 0.36 )"	"3 ( 0.13 )"	7.75	0.53	2.99	4.75	0	"compLasso"
+100	1000	0.3	10	0.46	"0.93 ( 0.04 )"	"6.63 ( 0.6 )"	"0.1 ( 0.03 )"	12.53	0.43	0.93	6.63	0.1	"compLasso"
+500	1000	0.3	2	0.96	"0.89 ( 0.03 )"	"0.28 ( 0.09 )"	"0 ( 0 )"	6.28	0.03	0.89	0.28	0	"compLasso"
+1000	1000	0.3	1	0.95	"0.93 ( 0.03 )"	"0.29 ( 0.09 )"	"0 ( 0 )"	6.29	0.03	0.93	0.29	0	"compLasso"
+50	50	0.5	1	0.44	"0.72 ( 0.05 )"	"5.25 ( 0.31 )"	"0.24 ( 0.06 )"	11.01	0.43	0.72	5.25	0.24	"compLasso"
+100	50	0.5	0.5	0.62	"0.55 ( 0.02 )"	"2.98 ( 0.3 )"	"0 ( 0 )"	8.98	0.27	0.55	2.98	0	"compLasso"
+500	50	0.5	0.1	0.9	"0.62 ( 0.02 )"	"0.57 ( 0.13 )"	"0 ( 0 )"	6.57	0.06	0.62	0.57	0	"compLasso"
+1000	50	0.5	0.05	0.93	"0.67 ( 0.02 )"	"0.39 ( 0.12 )"	"0 ( 0 )"	6.39	0.04	0.67	0.39	0	"compLasso"
+50	100	0.5	2	0.37	"1.15 ( 0.07 )"	"5.35 ( 0.36 )"	"1.25 ( 0.11 )"	10.1	0.45	1.15	5.35	1.25	"compLasso"
+100	100	0.5	1	0.59	"0.68 ( 0.03 )"	"3.57 ( 0.36 )"	"0.06 ( 0.02 )"	9.51	0.31	0.68	3.57	0.06	"compLasso"
+500	100	0.5	0.2	0.94	"0.68 ( 0.02 )"	"0.34 ( 0.1 )"	"0 ( 0 )"	6.34	0.04	0.68	0.34	0	"compLasso"
+1000	100	0.5	0.1	0.95	"0.71 ( 0.02 )"	"0.27 ( 0.09 )"	"0 ( 0 )"	6.27	0.03	0.71	0.27	0	"compLasso"
+50	500	0.5	10	0.29	"2.28 ( 0.14 )"	"5.5 ( 0.38 )"	"2.81 ( 0.1 )"	8.69	0.55	2.28	5.5	2.81	"compLasso"
+100	500	0.5	5	0.38	"0.82 ( 0.04 )"	"8.32 ( 0.64 )"	"0.38 ( 0.07 )"	13.94	0.51	0.82	8.32	0.38	"compLasso"
+500	500	0.5	1	0.93	"0.67 ( 0.02 )"	"0.45 ( 0.12 )"	"0 ( 0 )"	6.45	0.05	0.67	0.45	0	"compLasso"
+1000	500	0.5	0.5	0.94	"0.69 ( 0.02 )"	"0.35 ( 0.1 )"	"0 ( 0 )"	6.35	0.04	0.69	0.35	0	"compLasso"
+50	1000	0.5	20	0.23	"2.44 ( 0.13 )"	"5.82 ( 0.36 )"	"3.37 ( 0.1 )"	8.45	0.63	2.44	5.82	3.37	"compLasso"
+100	1000	0.5	10	0.3	"0.96 ( 0.05 )"	"10.94 ( 0.72 )"	"0.61 ( 0.1 )"	16.33	0.59	0.96	10.9	0.61	"compLasso"
+500	1000	0.5	2	0.96	"0.7 ( 0.02 )"	"0.22 ( 0.06 )"	"0 ( 0 )"	6.22	0.03	0.7	0.22	0	"compLasso"
+1000	1000	0.5	1	0.95	"0.71 ( 0.02 )"	"0.33 ( 0.13 )"	"0 ( 0 )"	6.33	0.03	0.71	0.33	0	"compLasso"
+50	50	0.7	1	0.36	"0.76 ( 0.05 )"	"5.83 ( 0.31 )"	"0.81 ( 0.1 )"	11.02	0.48	0.76	5.83	0.81	"compLasso"
+100	50	0.7	0.5	0.48	"0.43 ( 0.02 )"	"4.89 ( 0.38 )"	"0.08 ( 0.03 )"	10.81	0.4	0.43	4.89	0.08	"compLasso"
+500	50	0.7	0.1	0.81	"0.44 ( 0.01 )"	"1.23 ( 0.22 )"	"0 ( 0 )"	7.23	0.12	0.44	1.23	0	"compLasso"
+1000	50	0.7	0.05	0.88	"0.45 ( 0.01 )"	"0.74 ( 0.13 )"	"0 ( 0 )"	6.74	0.09	0.45	0.74	0	"compLasso"
+50	100	0.7	2	0.31	"0.99 ( 0.06 )"	"5.89 ( 0.36 )"	"1.88 ( 0.11 )"	10.01	0.53	0.99	5.89	1.88	"compLasso"
+100	100	0.7	1	0.41	"0.48 ( 0.02 )"	"6.91 ( 0.55 )"	"0.22 ( 0.05 )"	12.69	0.47	0.48	6.91	0.22	"compLasso"
+500	100	0.7	0.2	0.82	"0.45 ( 0.01 )"	"1.24 ( 0.19 )"	"0 ( 0 )"	7.24	0.13	0.45	1.24	0	"compLasso"
+1000	100	0.7	0.1	0.92	"0.48 ( 0.01 )"	"0.5 ( 0.14 )"	"0 ( 0 )"	6.5	0.05	0.48	0.5	0	"compLasso"
+50	500	0.7	10	0.27	"1.81 ( 0.09 )"	"5.27 ( 0.39 )"	"3.43 ( 0.09 )"	7.84	0.56	1.81	5.27	3.43	"compLasso"
+100	500	0.7	5	0.27	"0.85 ( 0.05 )"	"9.56 ( 0.73 )"	"1.42 ( 0.12 )"	14.14	0.59	0.85	9.56	1.42	"compLasso"
+500	500	0.7	1	0.74	"0.46 ( 0.01 )"	"2.11 ( 0.26 )"	"0 ( 0 )"	8.11	0.2	0.46	2.11	0	"compLasso"
+1000	500	0.7	0.5	0.87	"0.46 ( 0.01 )"	"0.93 ( 0.16 )"	"0 ( 0 )"	6.93	0.1	0.46	0.93	0	"compLasso"
+50	1000	0.7	20	0.21	"1.81 ( 0.08 )"	"6.05 ( 0.39 )"	"3.58 ( 0.1 )"	8.47	0.64	1.81	6.05	3.58	"compLasso"
+100	1000	0.7	10	0.27	"1.1 ( 0.06 )"	"8.36 ( 0.68 )"	"2.1 ( 0.09 )"	12.26	0.58	1.1	8.36	2.1	"compLasso"
+500	1000	0.7	2	0.69	"0.47 ( 0.01 )"	"2.72 ( 0.33 )"	"0 ( 0 )"	8.72	0.24	0.47	2.72	0	"compLasso"
+1000	1000	0.7	1	0.91	"0.5 ( 0.01 )"	"0.57 ( 0.16 )"	"0 ( 0 )"	6.57	0.06	0.5	0.57	0	"compLasso"
+50	50	0.9	1	0.32	"0.54 ( 0.03 )"	"4.91 ( 0.32 )"	"1.96 ( 0.11 )"	8.95	0.49	0.54	4.91	1.96	"compLasso"
+100	50	0.9	0.5	0.35	"0.41 ( 0.02 )"	"5.64 ( 0.41 )"	"0.96 ( 0.11 )"	10.68	0.46	0.41	5.64	0.96	"compLasso"
+500	50	0.9	0.1	0.67	"0.31 ( 0.01 )"	"2.66 ( 0.22 )"	"0.01 ( 0.01 )"	8.65	0.27	0.31	2.66	0.01	"compLasso"
+1000	50	0.9	0.05	0.71	"0.3 ( 0 )"	"2.18 ( 0.19 )"	"0 ( 0 )"	8.18	0.23	0.3	2.18	0	"compLasso"
+50	100	0.9	2	0.31	"0.66 ( 0.04 )"	"4.9 ( 0.33 )"	"2.83 ( 0.09 )"	8.07	0.54	0.66	4.9	2.83	"compLasso"
+100	100	0.9	1	0.32	"0.47 ( 0.02 )"	"6.56 ( 0.48 )"	"1.45 ( 0.11 )"	11.11	0.52	0.47	6.56	1.45	"compLasso"
+500	100	0.9	0.2	0.59	"0.32 ( 0.01 )"	"4.04 ( 0.34 )"	"0.01 ( 0.01 )"	10.03	0.35	0.32	4.04	0.01	"compLasso"
+1000	100	0.9	0.1	0.67	"0.3 ( 0 )"	"2.85 ( 0.24 )"	"0 ( 0 )"	8.85	0.28	0.3	2.85	0	"compLasso"
+50	500	0.9	10	0.23	"0.89 ( 0.05 )"	"5.88 ( 0.44 )"	"3.74 ( 0.07 )"	8.14	0.62	0.89	5.88	3.74	"compLasso"
+100	500	0.9	5	0.24	"0.6 ( 0.02 )"	"8.59 ( 0.65 )"	"2.63 ( 0.08 )"	11.96	0.64	0.6	8.59	2.63	"compLasso"
+500	500	0.9	1	0.41	"0.34 ( 0.01 )"	"8.17 ( 0.48 )"	"0.17 ( 0.04 )"	14	0.54	0.34	8.17	0.17	"compLasso"
+1000	500	0.9	0.5	0.58	"0.32 ( 0 )"	"4.24 ( 0.3 )"	"0.01 ( 0.01 )"	10.23	0.37	0.32	4.24	0.01	"compLasso"
+50	1000	0.9	20	0.19	"1.04 ( 0.06 )"	"5.24 ( 0.46 )"	"4.23 ( 0.08 )"	7.01	0.6	1.04	5.24	4.23	"compLasso"
+100	1000	0.9	10	0.22	"0.67 ( 0.02 )"	"9.03 ( 0.73 )"	"3.02 ( 0.07 )"	12.01	0.66	0.67	9.03	3.02	"compLasso"
+500	1000	0.9	2	0.34	"0.35 ( 0.01 )"	"10.52 ( 0.68 )"	"0.32 ( 0.05 )"	16.2	0.59	0.35	10.5	0.32	"compLasso"
+1000	1000	0.9	1	0.57	"0.33 ( 0 )"	"4.48 ( 0.3 )"	"0.01 ( 0.01 )"	10.47	0.39	0.33	4.48	0.01	"compLasso"
diff --git a/simulations/sim_data_generation/run_sim_data.sh b/simulations/sim_data_generation/run_sim_data.sh
new file mode 100755
index 0000000..6297c12
--- /dev/null
+++ b/simulations/sim_data_generation/run_sim_data.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+#PBS -N sim_ind_toe
+#PBS -l walltime=500:00:00
+#PBS -l nodes=1:ppn=4
+#PBS -l mem=50gb
+#PBS -V
+#PBS -j oe
+#PBS -d .
+
+set -e
+cpus=$PBS_NUM_PPN
+
+export TMPDIR=/panfs/panfs1.ucsd.edu/panscratch/$USER/Stability_2020
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+export TMPDIR=$TMPDIR/sim_data
+[ ! -d $TMPDIR ] && mkdir $TMPDIR
+#tmp=$(mktemp -d --tmpdir)
+#export TMPDIR=$tmp
+#trap "rm -r $tmp; unset TMPDIR" EXIT
+
+# do something
+source activate r-env
+Rscript sim_dat_ind_toeplitz.R $TMPDIR
+Rscript sim_dat_block.R $TMPDIR
+source deactivate r-env
+
+#mv $tmp/outdir ./outdir