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6.1_Maxent_Random_pts_kfolds.R
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6.1_Maxent_Random_pts_kfolds.R
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########################## GOALS AND NOTES ###########################
### Project: Amphibian SDMs
### Author: Jayna Bergman
### Script name: 6.1_Maxent_random_pts_kfolds
### Goal of this Script:
# 1) Run Maxent with random background points for each kfold (training localities)
# 2) Evaluate each model with kfold (testing localities)
### Notes:
#
### Date: September 23, 2022
### Version of R: R version 4.0.3
########################### END SECTION ##############################
############################ LIBRARIES ###############################
library(dismo)
library(sf)
library(terra)
library(rgdal)
library(dplyr)
rm(list=ls())
########################### END SECTION ##############################
###################### FUNCTION DEFINITIONS ##########################
# NA
########################### END SECTION ##############################
########################## DATA AND ANALYSIS #########################
## Set working directory
setwd("C:/Users/jayna/OneDrive/Desktop/School/Lee_Yaw_Lab/Amphibian_SDMs/Data_Analysis")
## Basic maxent arguments used for EVERY model
basicargs <- c("-J","-P","writebackgroundpredictions","maximumiterations=5000")
################################# LTSA ################################
## Set consistent variable names (stays same with all study extents)
LTSA_vars <- gsub(pattern=".tif", replacement="", list.files("./envi_variables/model_subsets/LTSA", pattern='.tif$', all.files=TRUE, full.names=FALSE))
index <- "Type"
## Read in variables cropped to the range of this species & stack
LTSA_rast_list <- list.files("./envi_variables/model_subsets/LTSA", pattern='.tif$', all.files=TRUE, full.names=TRUE)
LTSA_env <- stack(LTSA_rast_list)
plot(LTSA_env[[1]])
###### range study extent ######
## Read in study extent
LTSA_range_extent <- st_read("./study_extents/model_subsets/LTSA/LTSA_range.shp")
## Mask the stack of raster variables
LTSA_range_envi <- mask(LTSA_env, LTSA_range_extent)
plot(LTSA_range_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQPT, regularization - 0.25
LTSA_range_max_args <- c(basicargs, features = c("nohinge", "Threshold"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
LTSA_range_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
LTSA_range_training_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/range/LTSA_trainingdat",i,".csv",sep=""))
LTSA_range_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/range/LTSA_testingdat",i,".csv", sep=""))
## Create new directory
LTSA_range_od <- paste("./Maxent_Random_pts/LTSA/range/LTSA_range_kfold_",i,sep="")
dir.create(LTSA_range_od)
## Maxent command and save
LTSA_range_me <- maxent(x=LTSA_range_envi, p=LTSA_range_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = LTSA_range_max_args, path=LTSA_range_od)
save(LTSA_range_me, file = paste(LTSA_range_od,"/LTSA_range_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(LTSA_range_envi, n = 10000, p = LTSA_range_testing_locs[,c("Long_m", "Lat_m")])
LTSA_range_e <- evaluate(p=LTSA_range_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=LTSA_range_me, x = LTSA_range_envi)
print(LTSA_range_e)
LTSA_range_internal_auc[i] <- LTSA_range_e@auc
dput(LTSA_range_e, file=paste("./Maxent_Random_pts/LTSA/range/LTSA_range_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
LTSA_range_aucdat <- as.data.frame(LTSA_range_internal_auc)
LTSA_range_aucdat$fold <- row.names(LTSA_range_aucdat)
write.csv(LTSA_range_aucdat[,c(2,1)], file = "./Maxent_Random_pts/LTSA/range/LTSA_range_Internal_AUC_random_pts.csv", row.names = FALSE)
###### political study extent ######
## Read in study extent
LTSA_political_extent <- st_read("./study_extents/model_subsets/LTSA/LTSA_political.shp")
## Mask the stack of raster variables
LTSA_political_envi <- mask(LTSA_env, LTSA_political_extent)
plot(LTSA_political_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQP, regularization - 0.25
LTSA_political_max_args <- c(basicargs, features = c("nohinge"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
LTSA_political_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
LTSA_political_training_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/political/LTSA_trainingdat",i,".csv",sep=""))
LTSA_political_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/political/LTSA_testingdat",i,".csv", sep=""))
## Create new directory
LTSA_political_od <- paste("./Maxent_Random_pts/LTSA/political/LTSA_political_kfold_",i,sep="")
dir.create(LTSA_political_od)
## Maxent command and save
LTSA_political_me <- maxent(x=LTSA_political_envi, p=LTSA_political_training_locs[,c("Long_m", "Lat_m")], nbg= 5000, removeDuplicates = FALSE, args = LTSA_political_max_args, path=LTSA_political_od)
save(LTSA_political_me, file = paste(LTSA_political_od,"/LTSA_political_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(LTSA_political_envi, n = 5000, p = LTSA_political_testing_locs[,c("Long_m", "Lat_m")])
LTSA_political_e <- evaluate(p=LTSA_political_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=LTSA_political_me, x = LTSA_political_envi)
print(LTSA_political_e)
LTSA_political_internal_auc[i] <- LTSA_political_e@auc
dput(LTSA_political_e, file=paste("./Maxent_Random_pts/LTSA/political/LTSA_political_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
LTSA_political_aucdat <- as.data.frame(LTSA_political_internal_auc)
LTSA_political_aucdat$fold <- row.names(LTSA_political_aucdat)
write.csv(LTSA_political_aucdat[,c(2,1)], file = "./Maxent_Random_pts/LTSA/political/LTSA_political_Internal_AUC_random_pts.csv", row.names = FALSE)
###### ecotone study extent ######
## Read in study extent
LTSA_ecotone_extent <- st_read("./study_extents/model_subsets/LTSA/LTSA_ecotone.shp")
## Mask the stack of raster variables
LTSA_ecotone_envi <- mask(LTSA_env, LTSA_ecotone_extent)
plot(LTSA_ecotone_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQPT, regularization - 0.25
LTSA_ecotone_max_args <- c(basicargs, features = c("Threshold", "nohinge"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
LTSA_ecotone_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
LTSA_ecotone_training_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/ecotone/LTSA_trainingdat",i,".csv",sep=""))
LTSA_ecotone_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/LTSA/ecotone/LTSA_testingdat",i,".csv", sep=""))
## Create new directory
LTSA_ecotone_od <- paste("./Maxent_Random_pts/LTSA/ecotone/LTSA_ecotone_kfold_",i,sep="")
dir.create(LTSA_ecotone_od)
## Maxent command and save
LTSA_ecotone_me <- maxent(x=LTSA_ecotone_envi, p=LTSA_ecotone_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = LTSA_ecotone_max_args, path=LTSA_ecotone_od)
save(LTSA_ecotone_me, file = paste(LTSA_ecotone_od,"/LTSA_ecotone_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(LTSA_ecotone_envi, n = 10000, p = LTSA_ecotone_testing_locs[,c("Long_m", "Lat_m")])
LTSA_ecotone_e <- evaluate(p=LTSA_ecotone_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=LTSA_ecotone_me, x = LTSA_ecotone_envi)
print(LTSA_ecotone_e)
LTSA_ecotone_internal_auc[i] <- LTSA_ecotone_e@auc
dput(LTSA_ecotone_e, file=paste("./Maxent_Random_pts/LTSA/ecotone/LTSA_ecotone_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
LTSA_ecotone_aucdat <- as.data.frame(LTSA_ecotone_internal_auc)
LTSA_ecotone_aucdat$fold <- row.names(LTSA_ecotone_aucdat)
write.csv(LTSA_ecotone_aucdat[,c(2,1)], file = "./Maxent_Random_pts/LTSA/ecotone/LTSA_ecotone_Internal_AUC_random_pts.csv", row.names = FALSE)
########################### END SECTION ##############################
################################# BCFR ################################
## Set consistent variable names (stays same with all study extents)
BCFR_vars <- gsub(pattern=".tif", replacement="", list.files("./envi_variables/model_subsets/BCFR", pattern='.tif$', all.files=TRUE, full.names=FALSE))
index <- "Type"
BCFR_rast_list <- list.files("./envi_variables/model_subsets/BCFR", pattern='.tif$', all.files=TRUE, full.names=TRUE)
BCFR_env <- stack(BCFR_rast_list)
plot(BCFR_env[[1]])
###### range study extent ######
## Read in study extent
BCFR_range_extent <- st_read("./study_extents/model_subsets/BCFR/BCFR_range.shp")
## Mask the stack of raster variables
BCFR_range_envi <- mask(BCFR_env, BCFR_range_extent)
plot(BCFR_range_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQHPT, regularization - 0.25
BCFR_range_max_args <- c(basicargs, features = c("Threshold"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
BCFR_range_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
BCFR_range_training_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/range/BCFR_trainingdat",i,".csv",sep=""))
BCFR_range_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/range/BCFR_testingdat",i,".csv", sep=""))
## Create new directory
BCFR_range_od <- paste("./Maxent_Random_pts/BCFR/range/BCFR_range_kfold_",i,sep="")
dir.create(BCFR_range_od)
## Maxent command and save
BCFR_range_me <- maxent(x=BCFR_range_envi, p=BCFR_range_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = BCFR_range_max_args, path=BCFR_range_od)
save(BCFR_range_me, file = paste(BCFR_range_od,"/BCFR_range_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(BCFR_range_envi, n = 10000, p = BCFR_range_testing_locs[,c("Long_m", "Lat_m")])
BCFR_range_e <- evaluate(p=BCFR_range_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=BCFR_range_me, x = BCFR_range_envi)
print(BCFR_range_e)
BCFR_range_internal_auc[i] <- BCFR_range_e@auc
dput(BCFR_range_e, file=paste("./Maxent_Random_pts/BCFR/range/BCFR_range_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
BCFR_range_aucdat <- as.data.frame(BCFR_range_internal_auc)
BCFR_range_aucdat$fold <- row.names(BCFR_range_aucdat)
write.csv(BCFR_range_aucdat[,c(2,1)], file = "./Maxent_Random_pts/BCFR/range/BCFR_range_Internal_AUC_random_pts.csv", row.names = FALSE)
###### political study extent ######
## Read in study extent
BCFR_political_extent <- st_read("./study_extents/model_subsets/BCFR/BCFR_political.shp")
## Mask the stack of raster variables
BCFR_political_envi <- mask(BCFR_env, BCFR_political_extent)
plot(BCFR_political_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQHP, regularization - 0.25
BCFR_political_max_args <- c(basicargs, features = c(""), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
BCFR_political_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
BCFR_political_training_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/political/BCFR_trainingdat",i,".csv",sep=""))
BCFR_political_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/political/BCFR_testingdat",i,".csv", sep=""))
## Create new directory
BCFR_political_od <- paste("./Maxent_Random_pts/BCFR/political/BCFR_political_kfold_",i,sep="")
dir.create(BCFR_political_od)
## Maxent command and save
BCFR_political_me <- maxent(x=BCFR_political_envi, p=BCFR_political_training_locs[,c("Long_m", "Lat_m")], nbg= 5000, removeDuplicates = FALSE, args = BCFR_political_max_args, path=BCFR_political_od)
save(BCFR_political_me, file = paste(BCFR_political_od,"/BCFR_political_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(BCFR_political_envi, n = 5000, p = BCFR_political_testing_locs[,c("Long_m", "Lat_m")])
BCFR_political_e <- evaluate(p=BCFR_political_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=BCFR_political_me, x = BCFR_political_envi)
print(BCFR_political_e)
BCFR_political_internal_auc[i] <- BCFR_political_e@auc
dput(BCFR_political_e, file=paste("./Maxent_Random_pts/BCFR/political/BCFR_political_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
BCFR_political_aucdat <- as.data.frame(BCFR_political_internal_auc)
BCFR_political_aucdat$fold <- row.names(BCFR_political_aucdat)
write.csv(BCFR_political_aucdat[,c(2,1)], file = "./Maxent_Random_pts/BCFR/political/BCFR_political_Internal_AUC_random_pts.csv", row.names = FALSE)
###### ecotone study extent ######
## Read in study extent
BCFR_ecotone_extent <- st_read("./study_extents/model_subsets/BCFR/BCFR_ecotone.shp")
## Mask the stack of raster variables
BCFR_ecotone_envi <- mask(BCFR_env, BCFR_ecotone_extent)
plot(BCFR_ecotone_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - L, regularization - 0.25
BCFR_ecotone_max_args <- c(basicargs, features = c("nohinge", "noproduct", "noquadratic"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
BCFR_ecotone_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
BCFR_ecotone_training_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/ecotone/BCFR_trainingdat",i,".csv",sep=""))
BCFR_ecotone_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/BCFR/ecotone/BCFR_testingdat",i,".csv", sep=""))
## Create new directory
BCFR_ecotone_od <- paste("./Maxent_Random_pts/BCFR/ecotone/BCFR_ecotone_kfold_",i,sep="")
dir.create(BCFR_ecotone_od)
## Maxent command and save
BCFR_ecotone_me <- maxent(x=BCFR_ecotone_envi, p=BCFR_ecotone_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = BCFR_ecotone_max_args, path=BCFR_ecotone_od)
save(BCFR_ecotone_me, file = paste(BCFR_ecotone_od,"/BCFR_ecotone_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(BCFR_ecotone_envi, n = 10000, p = BCFR_ecotone_testing_locs[,c("Long_m", "Lat_m")])
BCFR_ecotone_e <- evaluate(p=BCFR_ecotone_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=BCFR_ecotone_me, x = BCFR_ecotone_envi)
print(BCFR_ecotone_e)
BCFR_ecotone_internal_auc[i] <- BCFR_ecotone_e@auc
dput(BCFR_ecotone_e, file=paste("./Maxent_Random_pts/BCFR/ecotone/BCFR_ecotone_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
BCFR_ecotone_aucdat <- as.data.frame(BCFR_ecotone_internal_auc)
BCFR_ecotone_aucdat$fold <- row.names(BCFR_ecotone_aucdat)
write.csv(BCFR_ecotone_aucdat[,c(2,1)], file = "./Maxent_Random_pts/BCFR/ecotone/BCFR_ecotone_Internal_AUC_random_pts.csv", row.names = FALSE)
########################### END SECTION ##############################
################################# CATO ################################
## Set consistent variable names (stays same with all study extents)
CATO_vars <- gsub(pattern=".tif", replacement="", list.files("./envi_variables/model_subsets/CATO", pattern='.tif$', all.files=TRUE, full.names=FALSE))
index <- "Type"
CATO_rast_list <- list.files("./envi_variables/model_subsets/CATO", pattern='.tif$', all.files=TRUE, full.names=TRUE)
CATO_env <- stack(CATO_rast_list)
plot(CATO_env[[1]])
###### range study extent ######
## Read in study extent
CATO_range_extent <- st_read("./study_extents/model_subsets/CATO/CATO_range.shp")
## Mask the stack of raster variables
CATO_range_envi <- mask(CATO_env, CATO_range_extent)
plot(CATO_range_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQH, regularization - 0.25
CATO_range_max_args <- c(basicargs, features = c("noproduct"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CATO_range_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CATO_range_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/range/CATO_trainingdat",i,".csv",sep=""))
CATO_range_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/range/CATO_testingdat",i,".csv", sep=""))
## Create new directory
CATO_range_od <- paste("./Maxent_Random_pts/CATO/range/CATO_range_kfold_",i,sep="")
dir.create(CATO_range_od)
## Maxent command and save
CATO_range_me <- maxent(x=CATO_range_envi, p=CATO_range_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = CATO_range_max_args, path=CATO_range_od)
save(CATO_range_me, file = paste(CATO_range_od,"/CATO_range_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CATO_range_envi, n = 10000, p = CATO_range_testing_locs[,c("Long_m", "Lat_m")])
CATO_range_e <- evaluate(p=CATO_range_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CATO_range_me, x = CATO_range_envi)
print(CATO_range_e)
CATO_range_internal_auc[i] <- CATO_range_e@auc
dput(CATO_range_e, file=paste("./Maxent_Random_pts/CATO/range/CATO_range_kfold_",i,"/internal_eval_",i,sep=""))
}
CATO_range_aucdat <- as.data.frame(CATO_range_internal_auc)
CATO_range_aucdat$fold <- row.names(CATO_range_aucdat)
write.csv(CATO_range_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CATO/range/CATO_range_Internal_AUC_random_pts.csv", row.names = FALSE)
###### political study extent ######
## Read in study extent
CATO_political_extent <- st_read("./study_extents/model_subsets/CATO/CATO_political.shp")
## Mask the stack of raster variables
CATO_political_envi <- mask(CATO_env, CATO_political_extent)
plot(CATO_political_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQ, regularization - 2
CATO_political_max_args <- c(basicargs, features = c("noproduct","nohinge"), "betamultiplier=2")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CATO_political_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CATO_political_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/political/CATO_trainingdat",i,".csv",sep=""))
CATO_political_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/political/CATO_testingdat",i,".csv", sep=""))
## Create new directory
CATO_political_od <- paste("./Maxent_Random_pts/CATO/political/CATO_political_kfold_",i,sep="")
dir.create(CATO_political_od)
## Maxent command and save
CATO_political_me <- maxent(x=CATO_political_envi, p=CATO_political_training_locs[,c("Long_m", "Lat_m")], nbg= 5000, removeDuplicates = FALSE, args = CATO_political_max_args, path=CATO_political_od)
save(CATO_political_me, file = paste(CATO_political_od,"/CATO_political_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CATO_political_envi, n = 5000, p = CATO_political_testing_locs[,c("Long_m", "Lat_m")])
CATO_political_e <- evaluate(p=CATO_political_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CATO_political_me, x = CATO_political_envi)
print(CATO_political_e)
CATO_political_internal_auc[i] <- CATO_political_e@auc
dput(CATO_political_e, file=paste("./Maxent_Random_pts/CATO/political/CATO_political_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
CATO_political_aucdat <- as.data.frame(CATO_political_internal_auc)
CATO_political_aucdat$fold <- row.names(CATO_political_aucdat)
write.csv(CATO_political_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CATO/political/CATO_political_Internal_AUC_random_pts.csv", row.names = FALSE)
###### ecotone study extent ######
## Read in study extent
CATO_ecotone_extent <- st_read("./study_extents/model_subsets/CATO/CATO_ecotone.shp")
## Mask the stack of raster variables
CATO_ecotone_envi <- mask(CATO_env, CATO_ecotone_extent)
plot(CATO_ecotone_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQT, regularization - 1
CATO_ecotone_max_args <- c(basicargs, features = c("Threshold", "noproduct", "nohinge"), "betamultiplier=1")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CATO_ecotone_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CATO_ecotone_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/ecotone/CATO_trainingdat",i,".csv",sep=""))
CATO_ecotone_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CATO/ecotone/CATO_testingdat",i,".csv", sep=""))
## Create new directory
CATO_ecotone_od <- paste("./Maxent_Random_pts/CATO/ecotone/CATO_ecotone_kfold_",i,sep="")
dir.create(CATO_ecotone_od)
## Maxent command and save
CATO_ecotone_me <- maxent(x=CATO_ecotone_envi, p=CATO_ecotone_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = CATO_ecotone_max_args, path=CATO_ecotone_od)
save(CATO_ecotone_me, file = paste(CATO_ecotone_od,"/CATO_ecotone_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CATO_ecotone_envi, n = 10000, p = CATO_ecotone_testing_locs[,c("Long_m", "Lat_m")])
CATO_ecotone_e <- evaluate(p=CATO_ecotone_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CATO_ecotone_me, x = CATO_ecotone_envi)
print(CATO_ecotone_e)
CATO_ecotone_internal_auc[i] <- CATO_ecotone_e@auc
dput(CATO_ecotone_e, file=paste("./Maxent_Random_pts/CATO/ecotone/CATO_ecotone_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
CATO_ecotone_aucdat <- as.data.frame(CATO_ecotone_internal_auc)
CATO_ecotone_aucdat$fold <- row.names(CATO_ecotone_aucdat)
write.csv(CATO_ecotone_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CATO/ecotone/CATO_ecotone_Internal_AUC_random_pts.csv", row.names = FALSE)
########################### END SECTION ##############################
################################# CSFR ################################
## Set consistent variable names (stays same with all study extents)
CSFR_vars <- gsub(pattern=".tif", replacement="", list.files("./envi_variables/model_subsets/CSFR", pattern='.tif$', all.files=TRUE, full.names=FALSE))
index <- "Type"
CSFR_rast_list <- list.files("./envi_variables/model_subsets/CSFR", pattern='.tif$', all.files=TRUE, full.names=TRUE)
CSFR_env <- stack(CSFR_rast_list)
plot(CSFR_env[[1]])
###### range study extent ######
## Read in study extent
CSFR_range_extent <- st_read("./study_extents/model_subsets/CSFR/CSFR_range.shp")
## Mask the stack of raster variables
CSFR_range_envi <- mask(CSFR_env, CSFR_range_extent)
plot(CSFR_range_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQ, regularization - 0.25
CSFR_range_max_args <- c(basicargs, features = c("nohinge", "noproduct"), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CSFR_range_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CSFR_range_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/range/CSFR_trainingdat",i,".csv",sep=""))
CSFR_range_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/range/CSFR_testingdat",i,".csv", sep=""))
## Create new directory
CSFR_range_od <- paste("./Maxent_Random_pts/CSFR/range/CSFR_range_kfold_",i,sep="")
dir.create(CSFR_range_od)
## Maxent command and save
CSFR_range_me <- maxent(x=CSFR_range_envi, p=CSFR_range_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = CSFR_range_max_args, path=CSFR_range_od)
save(CSFR_range_me, file = paste(CSFR_range_od,"/CSFR_range_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CSFR_range_envi, n = 10000, p = CSFR_range_testing_locs[,c("Long_m", "Lat_m")])
CSFR_range_e <- evaluate(p=CSFR_range_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CSFR_range_me, x = CSFR_range_envi)
print(CSFR_range_e)
CSFR_range_internal_auc[i] <- CSFR_range_e@auc
dput(CSFR_range_e, file=paste("./Maxent_Random_pts/CSFR/range/CSFR_range_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
CSFR_range_aucdat <- as.data.frame(CSFR_range_internal_auc)
CSFR_range_aucdat$fold <- row.names(CSFR_range_aucdat)
write.csv(CSFR_range_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CSFR/range/CSFR_range_Internal_AUC_random_pts.csv", row.names = FALSE)
###### political study extent ######
## Read in study extent
CSFR_political_extent <- st_read("./study_extents/model_subsets/CSFR/CSFR_political.shp")
## Mask the stack of raster variables
CSFR_political_envi <- mask(CSFR_env, CSFR_political_extent)
plot(CSFR_political_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQH, regularization - 0.5
CSFR_political_max_args <- c(basicargs, features = c("noproduct"), "betamultiplier=0.5")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CSFR_political_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CSFR_political_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/political/CSFR_trainingdat",i,".csv",sep=""))
CSFR_political_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/political/CSFR_testingdat",i,".csv", sep=""))
## Create new directory
CSFR_political_od <- paste("./Maxent_Random_pts/CSFR/political/CSFR_political_kfold_",i,sep="")
dir.create(CSFR_political_od)
## Maxent command and save
CSFR_political_me <- maxent(x=CSFR_political_envi, p=CSFR_political_training_locs[,c("Long_m", "Lat_m")], nbg= 5000, removeDuplicates = FALSE, args = CSFR_political_max_args, path=CSFR_political_od)
save(CSFR_political_me, file = paste(CSFR_political_od,"/CSFR_political_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CSFR_political_envi, n = 5000, p = CSFR_political_testing_locs[,c("Long_m", "Lat_m")])
CSFR_political_e <- evaluate(p=CSFR_political_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CSFR_political_me, x = CSFR_political_envi)
print(CSFR_political_e)
CSFR_political_internal_auc[i] <- CSFR_political_e@auc
dput(CSFR_political_e, file=paste("./Maxent_Random_pts/CSFR/political/CSFR_political_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
CSFR_political_aucdat <- as.data.frame(CSFR_political_internal_auc)
CSFR_political_aucdat$fold <- row.names(CSFR_political_aucdat)
write.csv(CSFR_political_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CSFR/political/CSFR_political_Internal_AUC_random_pts.csv", row.names = FALSE)
###### ecotone study extent ######
## Read in study extent
CSFR_ecotone_extent <- st_read("./study_extents/model_subsets/CSFR/CSFR_ecotone.shp")
## Mask the stack of raster variables
CSFR_ecotone_envi <- mask(CSFR_env, CSFR_ecotone_extent)
plot(CSFR_ecotone_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQT, regularization - 2
CSFR_ecotone_max_args <- c(basicargs, features = c("Threshold", "nohinge", "noproduct"), "betamultiplier=2")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
CSFR_ecotone_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
CSFR_ecotone_training_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/ecotone/CSFR_trainingdat",i,".csv",sep=""))
CSFR_ecotone_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/CSFR/ecotone/CSFR_testingdat",i,".csv", sep=""))
## Create new directory
CSFR_ecotone_od <- paste("./Maxent_Random_pts/CSFR/ecotone/CSFR_ecotone_kfold_",i,sep="")
dir.create(CSFR_ecotone_od)
## Maxent command and save
CSFR_ecotone_me <- maxent(x=CSFR_ecotone_envi, p=CSFR_ecotone_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = CSFR_ecotone_max_args, path=CSFR_ecotone_od)
save(CSFR_ecotone_me, file = paste(CSFR_ecotone_od,"/CSFR_ecotone_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(CSFR_ecotone_envi, n = 10000, p = CSFR_ecotone_testing_locs[,c("Long_m", "Lat_m")])
CSFR_ecotone_e <- evaluate(p=CSFR_ecotone_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=CSFR_ecotone_me, x = CSFR_ecotone_envi)
print(CSFR_ecotone_e)
CSFR_ecotone_internal_auc[i] <- CSFR_ecotone_e@auc
dput(CSFR_ecotone_e, file=paste("./Maxent_Random_pts/CSFR/ecotone/CSFR_ecotone_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
CSFR_ecotone_aucdat <- as.data.frame(CSFR_ecotone_internal_auc)
CSFR_ecotone_aucdat$fold <- row.names(CSFR_ecotone_aucdat)
write.csv(CSFR_ecotone_aucdat[,c(2,1)], file = "./Maxent_Random_pts/CSFR/ecotone/CSFR_ecotone_Internal_AUC_random_pts.csv", row.names = FALSE)
########################### END SECTION ##############################
################################# TISA ################################
## Set consistent variable names (stays same with all study extents)
TISA_vars <- gsub(pattern=".tif", replacement="", list.files("./envi_variables/model_subsets/TISA", pattern='.tif$', all.files=TRUE, full.names=FALSE))
index <- "Type"
TISA_rast_list <- list.files("./envi_variables/model_subsets/TISA", pattern='.tif$', all.files=TRUE, full.names=TRUE)
TISA_env <- stack(TISA_rast_list)
#plot(TISA_env[[1]])
###### range study extent ######
## Read in study extent
TISA_range_extent <- st_read("./study_extents/model_subsets/TISA/TISA_range.shp")
## Mask the stack of raster variables
TISA_range_envi <- mask(TISA_env, TISA_range_extent)
plot(TISA_range_envi[[1]])
## Setting maxent arguments (basicargs set at the beginning of the script)
## parameters (features and regularization) from 4.1_tuning_features_regularization
# features - LQHP, regularization - 0.25
TISA_range_max_args <- c(basicargs, features = c(""), "betamultiplier=0.25")
### For loop to loop through kfolds ###
x <- c(1,2,3,4,5)
TISA_range_internal_auc <- rep(NA, length(x))
for (i in 1:length(x)){
## Read in Training localities and testing localities
TISA_range_training_locs <- read.csv(file = paste("./kfolds/Random_bg/TISA/range/TISA_trainingdat",i,".csv",sep=""))
TISA_range_testing_locs <- read.csv(file = paste("./kfolds/Random_bg/TISA/range/TISA_testingdat",i,".csv", sep=""))
## Create new directory
TISA_range_od <- paste("./Maxent_Random_pts/TISA/range/TISA_range_kfold_",i,sep="")
dir.create(TISA_range_od)
## Maxent command and save
TISA_range_me <- maxent(x=TISA_range_envi, p=TISA_range_training_locs[,c("Long_m", "Lat_m")], nbg= 10000, removeDuplicates = FALSE, args = TISA_range_max_args, path=TISA_range_od)
save(TISA_range_me, file = paste(TISA_range_od,"/TISA_range_modelObject_",i,sep=""))
## Evaluate by creating random points
eval_background_pts <- randomPoints(TISA_range_envi, n = 10000, p = TISA_range_testing_locs[,c("Long_m", "Lat_m")])
TISA_range_e <- evaluate(p=TISA_range_testing_locs[,c("Long_m", "Lat_m")], a=eval_background_pts, model=TISA_range_me, x = TISA_range_envi)
print(TISA_range_e)
TISA_range_internal_auc[i] <- TISA_range_e@auc
dput(TISA_range_e, file=paste("./Maxent_Random_pts/TISA/range/TISA_range_kfold_",i,"/internal_eval_",i,sep=""))
}
## Saving auc scores as a csv
TISA_range_aucdat <- as.data.frame(TISA_range_internal_auc)
TISA_range_aucdat$fold <- row.names(TISA_range_aucdat)
write.csv(TISA_range_aucdat[,c(2,1)], file = "./Maxent_Random_pts/TISA/range/TISA_range_Internal_AUC_random_pts.csv", row.names = FALSE)
###### political study extent ######
## Read in study extent
TISA_political_extent <- st_read("./study_extents/model_subsets/TISA/TISA_political.shp")
## Mask the stack of raster variables
TISA_political_envi <- mask(TISA_env, TISA_political_extent)
plot(TISA_political_envi[[1]])