Nested cross-validation (CV) for the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.
Install from CRAN
install.packages("nestedcv")
Install from Github
devtools::install_github("myles-lewis/nestedcv")
In this example using iris dataset (multinomial, 3 classes), we fit a glmnet model, tuning both lambda and alpha with 10 x 10-fold nested CV.
library(nestedcv)
data(iris)
y <- iris$Species
x <- as.matrix(iris[, -5])
cores <- parallel::detectCores(logical = FALSE) # detect physical cores
res <- nestcv.glmnet(y, x, family = "multinomial", cv.cores = cores)
summary(res)
Use summary() to see full information from the nested model fitting. coef()
can be used to show the coefficients of the final fitted model.
Filters can be used by setting the filterFUN argument. Options for the filter
function are passed as a list through filter_options.
Output from the nested CV with glmnet can be plotted to show how deviance is affected by alpha and lambda.
plot_alphas(res)
plot_lambdas(res)
The tuning of lambda and alpha for each outer CV fold can be plotted. Here we inspect outer CV fold 1.
plot(res$outer_result[[1]]$cvafit)
ROC curves from left-out folds from both outer and inner CV can be plotted for binary comparisons (see vignette).
Nested CV can also be performed using the caret package framework. Here we use caret for tuning random forest using the ranger package.
res <- nestcv.train(y, x, method = "ranger", cv.cores = cores)
summary(res)