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Copy pathTest 1 imputation of one species at a time.R
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Test 1 imputation of one species at a time.R
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### Installing/Updating packages
required<-c("ape", "raster", "methods","CENFA","pbapply","Rphylopars","RRphylo","dismo","gtools",
"adehabitatMA", "ecospat", "foreach", "doParallel", "PresenceAbsence",
"parallel", "ade4", "sp", "biomod2", "terra", "sf", "adehabitatHS",
"caret","ENMeval","rJava", "devtools")
if(any(!required%in%installed.packages()[,1]))
install.packages(required[which(!required%in%installed.packages()[,1])])
latest_version<-c("ecospat","dismo","biomod2","ENMeval","rJava")
latest_version[which(latest_version%in%rownames(old.packages()))]->to_update
if(length(to_update)>0) install.packages(to_update)
sapply(required,require,character.only=TRUE)
### Loading Data and RRdtn package
setwd("YOUR_DIRECTORY")
getwd()->main.dir
url<-"https://www.dropbox.com/s/wm7qcuwm0nm35w2/ENphylo%20code%26data%202.zip?dl=1"
download.file(url,file.path(main.dir,"ENphylo code&data 2.zip"),mode="wb")
unzip("ENphylo code&data 2.zip")
load("ENphylo code&data/example_data.RData")
source("ENphylo code&data/ecospat.ESM.EnsembleModeling_CUSTOM.R")
source("ENphylo code&data/ecospat.ESM.Projection_CUSTOM.R")
source("ENphylo code&data/ecospat.ESM.Modeling_CUSTOM.R")
MASK_FULL<-raster::raster("ENphylo code&data/variable_bio1.tif")
install_github("pasraia/RRdtn")
require(RRdtn)
###DO NOT RUN, WE HAVE ALREADY UPLOADED "enfa_all_models" OBJECT INTO example_data.RData
###EMBEDDED IN ENphylo code&data.zip TO REDUCE THE COMPUTATIONAL TIME.
# ENphylo_modeling(input_data=DATA_FULL,
# tree=tree,
# input_mask=MASK_FULL,
# obs_col="OBS",
# time_col="TIME_factor",
# min_occ_enfa = 10, ## this argument is not arbitrarily set because no species embedded in Mondanaro et al.2021 dataset has a number of occurrences <=10
# eval_metric_for_imputation ="AUC",
# output_options ="best",
# eval_threshold=0.5)->enfa_all_species
### running analyses ###
npoints=10
dir.create(paste("test_", npoints, "_points", sep=""))
eval_function<-function(test_p, test_a, mm, train_p, model_name){
if(model_name!="ens.biv"){
ev0<- dismo::evaluate(test_p, test_a, mm)
TPR<-ev0@TPR[which.max(ev0@TPR+ev0@TNR)]
TNR<-ev0@TNR[which.max(ev0@TPR+ev0@TNR)]
FPR<-ev0@FPR[which.max(ev0@TPR+ev0@TNR)]
FNR<-ev0@FNR[which.max(ev0@TPR+ev0@TNR)]
TSS<-TPR+TNR-1
AUC<-ev0@auc
SORENSEN<-2*TPR/(FNR + (2*TPR) + FPR)
pred_testing<-rbind(test_p, test_a)
pca<-scale(pred_testing)
pred_train_sites_scaled<-scale(train_p,
center = attr(pca, "scaled:center"),
scale = attr(pca, "scaled:scale"))
repeat{
maha_prob <- suppressWarnings(try(RRdtn:::mahasuhab.custom(as.data.frame(pca),
as.data.frame(pred_train_sites_scaled)),
silent=T))
if(class(maha_prob)!="try-error")break else {
pca<-pca[,-ncol(pca),drop=F]
pred_train_sites_scaled<-pred_train_sites_scaled[,-ncol(pred_train_sites_scaled),drop=F]
warning(paste("Dimensionality was reduced to", ncol(pca), "axes"))
}
}
DDATA<-data.frame(ID=1:nrow(maha_prob),
OBS=c(rep(1, nrow(test_p)), rep(0, nrow(test_a))),
PRED=maha_prob$MD)
th<-optimal.thresholds(DDATA)[3,2]
ev0<-presence.absence.accuracy(DDATA, th)
TPR<-ev0$sensitivity
TNR<-ev0$specificity
FPR<-1-TPR
FNR<-1-TNR
TSS_suit<-TPR+TNR-1
AUC_suit<-ev0$AUC
SORENSEN_suit<-2*TPR/(FNR + (2*TPR) + FPR)
CBI<-ecospat.boyce(subset(DDATA, OBS==0)$PRED,
subset(DDATA, OBS==1)$PRED,
PEplot = F)$cor
outcome<-c(AUC=AUC, AUC_suit=AUC_suit, TSS=TSS, TSS_suit=TSS_suit, SORENSEN=SORENSEN,
SORENSEN_suit=SORENSEN_suit, CBI=CBI)
} else {
DDATA<-data.frame(ID=1:nrow(rbind(test_p,test_a)),
OBS=c(rep(1, nrow(test_p)), rep(0, nrow(test_a))),
PRED=rbind(test_p, test_a))
colnames(DDATA)[3]<-"PRED"
th<-optimal.thresholds(DDATA)[3,2]
ev0<-presence.absence.accuracy(DDATA, th)
TPR<-ev0$sensitivity
TNR<-ev0$specificity
FPR<-1-TPR
FNR<-1-TNR
TSS<-TPR+TNR-1
AUC<-ev0$AUC
SORENSEN<-2*TPR/(FNR + (2*TPR) + FPR)
CBI<-ecospat.boyce(subset(DDATA, OBS==0)$PRED,
subset(DDATA, OBS==1)$PRED,
PEplot = F)$cor
outcome<-c(AUC=AUC, TSS=TSS, SORENSEN=SORENSEN, CBI=CBI)
}
return(outcome)
}
jj<-25 ### this sets Rangifer tarandus (the reindeer) only to be modelled.
for(jj in 25){ ### run line 133 instead of lines 131 - 132 to model all species
### for(jj in 1:length(DATA_FULL)){
dir.create(paste("test_", npoints, "_points/", names(enfa_all_species)[jj], sep=""),
recursive=T)
setwd(paste("test_", npoints, "_points/", names(enfa_all_species)[jj], sep=""))
Species_data_start<-DATA_FULL[[jj]]
Species_data1_start<-subset(Species_data_start, OBS==1)
Species_data0_start<-subset(Species_data_start, OBS==0)
print(jj)
list()->extreme_test
zz0<-replicate(1, sample(nrow(Species_data1_start),npoints))
### please notice that in our analyses the
### number of reps is set to 20, so that you have to
### type zz0<-replicate(20,...to reproduce them
save(zz0, file="sampled_points.RData")
for(s in 1){ ### run line 155 instead of line 154 to perform all reps
### loop for(s in c(1:20)){ in order to perform all 20 reps as in our analyses
zz<-zz0[,s]
Species_data1<-Species_data1_start[zz,]
Species_data0<-subset(Species_data0_start,TIME_factor%in%Species_data1$TIME_factor)
Species_data1_for_test<-Species_data1_start[-zz,]
Species_data0_for_test<-subset(Species_data0_start,TIME_factor%in%Species_data1_for_test$TIME_factor)
Species_data<-list(rbind(Species_data1,
Species_data0))
names(Species_data)<-names(enfa_all_species)[[jj]]
enfa_subsampled<-ENphylo_modeling(input_data=Species_data,
tree=tree,
input_mask=MASK_FULL,
obs_col="OBS",
time_col="TIME_factor",
boot_test_perc=20,
boot_reps=10,
min_occ_enfa=npoints-1,
eval_metric_for_imputation ="AUC",
output_options ="best",
eval_threshold=0.5)
test_enfa<-enfa_all_species
test_enfa[[jj]]<-enfa_subsampled[[1]]
gc()
save(enfa_subsampled, file=paste("enfa_subsampled_rep", s, ".RData", sep=""))
enfa_all_species[jj][[1]]$formatted_data<-enfa_subsampled[[1]]$formatted_data
test_imputed<-ENphylo_modeling(external_enfa_models=enfa_all_species,
spec_for_imputation=names(enfa_all_species[jj]),
tree=tree,
nsim=10, #set to 20 in our analyses
si=0.5,
si2=0.5,
eval_metric_for_imputation="AUC",
output_options="full",
eval_threshold=0.7)
closeAllConnections()
gc()
test_imputed[jj]->test_imputed
save(test_imputed, file=paste("test_imputed_rep", s, ".RData", sep=""))
wm<-which(test_imputed[[1]]$calibrated_model$evaluation$AUC>=0.7)
test_imputed_th<-test_imputed
test_imputed_th[[1]]$calibrated_model$co[wm]->test_imputed_th[[1]]$calibrated_model$co
test_imputed_th[[1]]$calibrated_model$evaluation[wm,,drop=F]->test_imputed_th[[1]]$calibrated_model$evaluation
save(test_imputed_th, file=paste("test_imputed_th_rep", s, ".RData", sep=""))
wm<-which.max(test_imputed[[1]]$calibrated_model$evaluation$AUC)
test_imputed_best<-test_imputed
test_imputed_best[[1]]$calibrated_model$co[wm]->test_imputed_best[[1]]$calibrated_model$co
test_imputed_best[[1]]$calibrated_model$evaluation[wm,,drop=F]->test_imputed_best[[1]]$calibrated_model$evaluation
save(test_imputed_best, file=paste("test_imputed_best_rep", s, ".RData", sep=""))
## create training dataset ################################################
if(nrow(Species_data0)>10000){
sample(nrow(Species_data0),10000)->k
Species_data0[k,]->Species_data0
}
training_dataset<-rbind(Species_data1,
Species_data0)
training_dataset<-as.data.frame(training_dataset)[,c("OBS","bio4","bio8","bio10","bio13","bio14","bio18")] ## variables selected by VIF
## predict imputed and enfa models on training dataset ####################
ENphylo_prediction(object=test_imputed,
newdata=training_dataset[,-1])[[1]]$imputed_prediction->pred_training_imputed
lapply(pred_training_imputed,function(x){
subset(x,training_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_train_sites_imputed
if(nrow(test_imputed_th[[1]]$calibrated_model$evaluation)>0){
ENphylo_prediction(object=test_imputed_th,
newdata=training_dataset[,-1])[[1]]$imputed_prediction->pred_training_imputed_th
lapply(pred_training_imputed_th,function(x){
subset(x,training_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_train_sites_imputed_th
pred_train_sites_imputed_th<-list(Reduce("+",
Map("*", pred_train_sites_imputed_th,
test_imputed_th[[1]]$calibrated_model$evaluation$AUC))/
length(pred_train_sites_imputed_th))
}
ENphylo_prediction(object=test_imputed_best,
newdata=training_dataset[,-1])[[1]]$imputed_prediction->pred_training_imputed_best
lapply(pred_training_imputed_best,function(x){
subset(x,training_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_train_sites_imputed_best
ENphylo_prediction(object=test_enfa[jj],
newdata=training_dataset[,-1])[[1]]$enfa_prediction->pred_training_enfa
pred_train_sites_enfa<-subset(pred_training_enfa, training_dataset$OBS==1)
pred_train_sites_enfa[,1:test_enfa[[jj]]$calibrated_model$full_model$significant_axes]->pred_train_sites_enfa
gc()
## create testing dataset #################################################
if (nrow(Species_data0_for_test)>10000){
sample(nrow(Species_data0_for_test),10000)->k
Species_data0_for_test[k,]->Species_data0_for_test
}
testing_dataset<-rbind(Species_data1_for_test,
Species_data0_for_test)
testing_dataset<-as.data.frame(testing_dataset)[,c("OBS","bio4","bio8","bio10","bio13","bio14","bio18")]
## predict imputed and enfa models on testing dataset #####################
ENphylo_prediction(object=test_imputed,
newdata=testing_dataset[,-1])[[1]]$imputed_prediction->pred_testing_imputed
lapply(pred_testing_imputed,function(x){
subset(x,testing_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_sites_imputed
lapply(pred_testing_imputed,function(x){
subset(x,testing_dataset$OBS==0)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_background_imputed
if(nrow(test_imputed_th[[1]]$calibrated_model$evaluation)>0){
ENphylo_prediction(object=test_imputed_th,
newdata=testing_dataset[,-1])[[1]]$imputed_prediction->pred_testing_imputed_th
lapply(pred_testing_imputed_th,function(x){
subset(x,testing_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_sites_imputed_th
pred_test_sites_imputed_th<-list(Reduce("+",
Map("*", pred_test_sites_imputed_th,
test_imputed_th[[1]]$calibrated_model$evaluation$AUC))/
length(pred_test_sites_imputed_th))
lapply(pred_testing_imputed_th,function(x){
subset(x,testing_dataset$OBS==0)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_background_imputed_th
pred_test_background_imputed_th<-list(Reduce("+",
Map("*", pred_test_background_imputed_th,
test_imputed_th[[1]]$calibrated_model$evaluation$AUC))/
length(pred_test_background_imputed_th))
}
ENphylo_prediction(object=test_imputed_best,
newdata=testing_dataset[,-1])[[1]]$imputed_prediction->pred_testing_imputed_best
lapply(pred_testing_imputed_best,function(x){
subset(x,testing_dataset$OBS==1)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_sites_imputed_best
lapply(pred_testing_imputed_best,function(x){
subset(x,testing_dataset$OBS==0)->xx
xx[,grep("swap",colnames(xx),invert=T)]->xx
})->pred_test_background_imputed_best
ENphylo_prediction(object=test_enfa[jj],
newdata=testing_dataset[,-1])[[1]]$enfa_prediction->pred_testing_enfa
pred_testing_enfa[,1:test_enfa[[jj]]$calibrated_model$full_model$significant_axes]->pred_testing_enfa
pred_test_sites_enfa<-subset(pred_testing_enfa, testing_dataset$OBS==1)
pred_test_background_enfa<-subset(pred_testing_enfa, testing_dataset$OBS==0)
gc()
## calculate Mahalanobis distance for training ones only ##################
lapply(1:length(pred_train_sites_imputed),function(x){
repeat{
mm_imputed <- suppressWarnings(try(mahal(pred_train_sites_imputed[[x]]),
silent=T))
if(class(mm_imputed)!="try-error")break else {
pred_train_sites_imputed[[x]]<-pred_train_sites_imputed[[x]][,-ncol(pred_train_sites_imputed[[x]]),drop=F]
}
}
mm_imputed
})->mm_imputed
min(sapply(mm_imputed,function(x)ncol(x@presence)))->min_ax
lapply(pred_train_sites_imputed,function(x)x[,1:min_ax])->pred_train_sites_imputed
lapply(pred_test_sites_imputed,function(x)x[,1:min_ax])->pred_test_sites_imputed
lapply(pred_test_background_imputed,function(x)x[,1:min_ax])->pred_test_background_imputed
if(nrow(test_imputed_th[[1]]$calibrated_model$evaluation)>0){
lapply(1:length(pred_train_sites_imputed_th),function(x){
repeat{
mm_imputed_th <- suppressWarnings(try(mahal(pred_train_sites_imputed_th[[x]]),
silent=T))
if(class(mm_imputed_th)!="try-error")break else {
pred_train_sites_imputed_th[[x]]<-pred_train_sites_imputed_th[[x]][,-ncol(pred_train_sites_imputed_th[[x]]),drop=F]
}
}
mm_imputed_th
})->mm_imputed_th
min(sapply(mm_imputed_th,function(x)ncol(x@presence)))->min_ax
lapply(pred_train_sites_imputed_th,function(x)x[,1:min_ax])->pred_train_sites_imputed_th
lapply(pred_test_sites_imputed_th,function(x)x[,1:min_ax])->pred_test_sites_imputed_th
lapply(pred_test_background_imputed_th,function(x)x[,1:min_ax])->pred_test_background_imputed_th
}
lapply(1:length(pred_train_sites_imputed_best),function(x){
repeat{
mm_imputed_best <- suppressWarnings(try(mahal(pred_train_sites_imputed_best[[x]]),
silent=T))
if(class(mm_imputed_best)!="try-error")break else {
pred_train_sites_imputed_best[[x]]<-pred_train_sites_imputed_best[[x]][,-ncol(pred_train_sites_imputed_best[[x]]),drop=F]
}
}
mm_imputed_best
})->mm_imputed_best
min(sapply(mm_imputed_best,function(x)ncol(x@presence)))->min_ax
lapply(pred_train_sites_imputed_best,function(x)x[,1:min_ax])->pred_train_sites_imputed_best
lapply(pred_test_sites_imputed_best,function(x)x[,1:min_ax])->pred_test_sites_imputed_best
lapply(pred_test_background_imputed_best,function(x)x[,1:min_ax])->pred_test_background_imputed_best
mm_enfa<-mahal(pred_train_sites_enfa)
pblapply(1:length(pred_train_sites_imputed),function(j){
params_eval<-list(imputed=list(test_p=pred_test_sites_imputed[[j]],
test_a=pred_test_background_imputed[[j]],
mm=mm_imputed[[j]],
model_name="imputed",
train_p=pred_train_sites_imputed[[j]]))
lapply(params_eval, function(x){
eval_function(test_p=x$test_p,
test_a=x$test_a,
mm=x$mm,
model_name=x$model_name,
train_p=x$train_p)
})
})->eval_imputed
do.call(rbind,unlist(eval_imputed, recursive=FALSE))->eval_imputed
if(nrow(test_imputed_th[[1]]$calibrated_model$evaluation)>0){
pblapply(1:length(pred_train_sites_imputed_th),function(j){
params_eval<-list(imputed_th=list(test_p=pred_test_sites_imputed_th[[j]],
test_a=pred_test_background_imputed_th[[j]],
mm=mm_imputed_th[[j]],
model_name="imputed",
train_p=pred_train_sites_imputed_th[[j]]))
lapply(params_eval, function(x){
eval_function(test_p=x$test_p,
test_a=x$test_a,
mm=x$mm,
model_name=x$model_name,
train_p=x$train_p)
})
})->eval_imputed_th
do.call(rbind,unlist(eval_imputed_th, recursive=FALSE))->eval_imputed_th
} else eval_imputed_th<-c(AUC=0.5,
AUC_suit=0.5,
TSS=0,
TSS_suit=0,
SORENSEN=0,
SORENSEN_suit=0,
CBI=0)
pblapply(1:length(pred_train_sites_imputed_best),function(j){
params_eval<-list(imputed_best=list(test_p=pred_test_sites_imputed_best[[j]],
test_a=pred_test_background_imputed_best[[j]],
mm=mm_imputed_best[[j]],
model_name="imputed",
train_p=pred_train_sites_imputed_best[[j]]))
lapply(params_eval, function(x){
eval_function(test_p=x$test_p,
test_a=x$test_a,
mm=x$mm,
model_name=x$model_name,
train_p=x$train_p)
})
})->eval_imputed_best
do.call(rbind,unlist(eval_imputed_best, recursive=FALSE))->eval_imputed_best
## ESM CALIBRATION ########################################################
# Some recommendations before running the next lines:
# 1) please ensure R and Java have matching architectures (i.e. 32 or 64 bit).
# 2) please ensure "maxent.jar" file is in the your LibPath dismo/java folder.
pp_maxent_full<-rbind(Species_data1,Species_data0)
covars<-grep("bio", colnames(pp_maxent_full), value=T)
covars<-combn(covars, 2, simplify = F)
cl<-makeCluster(detectCores()*0.5)
clusterExport(cl, c("Species_data1",
"Species_data0"),
envir = environment())
clusterEvalQ(cl, {
library(sf)
library(ecospat)
library(ENMeval)
library(biomod2)
library(caret)
})
ESM_settings<-pblapply(covars, function(var){
pp_maxent<-rbind(Species_data1[,c("OBS", "TIME_factor", var)],
Species_data0[,c("OBS", "TIME_factor", var)]) ### training dataset
occs<-subset(cbind(st_coordinates(pp_maxent), pp_maxent), OBS==1)
bg<-subset(cbind(st_coordinates(pp_maxent), pp_maxent), OBS==0)
as.data.frame(pp_maxent)->pp_maxent
enmeval_results<-ENMevaluate(occs=as.data.frame(occs)[,c("X", "Y", var)],
bg=as.data.frame(bg)[,c("X", "Y", var)],
tune.args=list(fc = c("L", "LQ", "H", "LQH", "LQHP", "LQHPT"),
rm = seq(0.5, 4, 0.5)),
partitions="none",
algorithm = "maxent.jar",
other.settings = list(abs.auc.diff = FALSE,
pred.type = "logistic",
validation.bg = "full"),
parallel=F,
quiet=TRUE)
best_model_aic<-subset(enmeval_results@results, !is.na(AICc))
MAXENT<-enmeval_results@tune.settings[as.numeric(rownames(best_model_aic)[which.min(best_model_aic$AICc)]),]
mydata<-BIOMOD_FormatingData(resp.var=ifelse(pp_maxent$OBS==0, NA, 1),
expl.var=pp_maxent[,var],
resp.name = paste(paste(var,collapse="."),sep="."),
resp.xy = as.data.frame(rbind(occs, bg))[,c("X","Y")])
trControl<-trainControl(method="boot",
p=0.80,
number=10,
summaryFunction = twoClassSummary,
classProbs = T,
returnData = F)
models.options = BIOMOD_ModelingOptions()
fm <- list()
GLM.results <- NULL
i <- 0
resp <- ifelse(is.na(mydata@data.species), "Absence", "Presence")
for (type in c("simple", "quadratic", "polynomial")) {
for (IA in c(0,1)) {
i <- i + 1
try(tune.GLM <- caret::train(makeFormula("resp",
mydata@data.env.var,
type = type, interaction.level = IA),
data = cbind(mydata@data.env.var, resp = resp),
method = "glmStepAIC", trControl = trControl,
weights = NULL))
try(GLM.results <- rbind(GLM.results, cbind(tune.GLM$results,
il = IA, type = type)))
try(fm[[i]] <- formula(tune.GLM$finalModel))
}
}
glm.best <- which.max(GLM.results$ROC)
models.options@GLM$interaction.level <- GLM.results[glm.best,
"il"]
models.options@GLM$type <- as.character(GLM.results[glm.best,
"type"])
models.options@MAXENT.Phillips$betamultiplier<-as.numeric(MAXENT$rm)
if(MAXENT$fc=="L"){
models.options@MAXENT.Phillips$linear<-T
models.options@MAXENT.Phillips$quadratic<-F
models.options@MAXENT.Phillips$product<-F
models.options@MAXENT.Phillips$threshold<-F
models.options@MAXENT.Phillips$hinge<-F
}
if(MAXENT$fc=="LQ"){
models.options@MAXENT.Phillips$linear<-T
models.options@MAXENT.Phillips$quadratic<-T
models.options@MAXENT.Phillips$product<-F
models.options@MAXENT.Phillips$threshold<-F
models.options@MAXENT.Phillips$hinge<-F
}
if(MAXENT$fc=="H"){
models.options@MAXENT.Phillips$linear<-F
models.options@MAXENT.Phillips$quadratic<-F
models.options@MAXENT.Phillips$product<-F
models.options@MAXENT.Phillips$threshold<-F
models.options@MAXENT.Phillips$hinge<-T
}
if(MAXENT$fc=="LQH"){
models.options@MAXENT.Phillips$linear<-T
models.options@MAXENT.Phillips$quadratic<-T
models.options@MAXENT.Phillips$product<-F
models.options@MAXENT.Phillips$threshold<-F
models.options@MAXENT.Phillips$hinge<-T
}
if(MAXENT$fc=="LQHP"){
models.options@MAXENT.Phillips$linear<-T
models.options@MAXENT.Phillips$quadratic<-T
models.options@MAXENT.Phillips$product<-T
models.options@MAXENT.Phillips$threshold<-F
models.options@MAXENT.Phillips$hinge<-T
}
if(MAXENT$fc=="LQHPT"){
models.options@MAXENT.Phillips$linear<-T
models.options@MAXENT.Phillips$quadratic<-T
models.options@MAXENT.Phillips$product<-T
models.options@MAXENT.Phillips$threshold<-T
models.options@MAXENT.Phillips$hinge<-T
}
return(models.options)
}, cl=cl)
stopCluster(cl)
closeAllConnections()
gc()
ESM_settings<-lapply(ESM_settings, function(x){
list(MAXENT=data.frame(linear=x@MAXENT.Phillips$linear,
quadratic=x@MAXENT.Phillips$quadratic,
product=x@MAXENT.Phillips$product,
threshold=x@MAXENT.Phillips$threshold,
hinge=x@MAXENT.Phillips$hinge,
betamultiplier=x@MAXENT.Phillips$betamultiplier),
GLM=data.frame(type=x@GLM$type,
int=x@GLM$interaction.level))
})
MAXENT_settings<-do.call(rbind, lapply(ESM_settings, "[[", 1))
GLM_settings<-do.call(rbind, lapply(ESM_settings, "[[", 2))
GLM_settings<-apply(GLM_settings, 2, function(x){
ll<-as.data.frame(table(x))
ll[which.max(ll$Freq),1]
})
MAXENT_settings<-apply(MAXENT_settings, 2, function(x){
ll<-as.data.frame(table(x))
ll[which.max(ll$Freq),1]
})
MAXENT_settings<-as.data.frame(t(as.data.frame(MAXENT_settings)))
MAXENT_settings[1,1:5]<-MAXENT_settings[1,1:5]==1
myBiomodOption<-Print_Default_ModelingOptions()
myBiomodOption@GLM$test<-"none"
myBiomodOption@GLM$type<-as.character(GLM_settings[1])
myBiomodOption@GLM$interaction.level<-as.numeric(as.character(GLM_settings[2]))
myBiomodOption@MAXENT.Phillips$path_to_maxent.jar=paste(installed.packages()["dismo",2],"dismo","java",sep="/") # please ensure this path contains "maxent.jar"
myBiomodOption@MAXENT.Phillips$linear=as.logical(MAXENT_settings$linear)
myBiomodOption@MAXENT.Phillips$quadratic=as.logical(MAXENT_settings$quadratic)
myBiomodOption@MAXENT.Phillips$product=as.logical(MAXENT_settings$product)
myBiomodOption@MAXENT.Phillips$threshold=as.logical(MAXENT_settings$threshold)
myBiomodOption@MAXENT.Phillips$hinge=as.logical(MAXENT_settings$hinge)
myBiomodOption@MAXENT.Phillips$betamultiplier=as.numeric(MAXENT_settings$betamultiplier)
occs<-subset(cbind(st_coordinates(pp_maxent_full), pp_maxent_full), OBS==1)
bg<-subset(cbind(st_coordinates(pp_maxent_full), pp_maxent_full), OBS==0)
mydata<-BIOMOD_FormatingData(resp.var=ifelse(pp_maxent_full$OBS==0, NA, 1),
expl.var=as.data.frame(pp_maxent_full)[,grep("bio",
colnames(pp_maxent_full))],
resp.name = paste("rep", s, sep="."),
resp.xy = as.data.frame(rbind(occs, bg))[,c("X","Y")])
mymodel<-ecospat.ESM.Modeling_CUSTOM(data=mydata,
DataSplit = 80,
NbRunEval = 10,
DataSplitTable=NULL,
models=c("GLM","RF","MAXENT.Phillips"),
weighting.score = "AUC",
models.options = myBiomodOption,
cleanup = T,
parallel = T,
ncores = detectCores()*0.5)
gc()
myEM<-ecospat.ESM.EnsembleModeling_CUSTOM(ESM.modeling.output=mymodel,
weighting.score = "AUC",
threshold = 0.7,
models=c("GLM", "RF", "MAXENT.Phillips"))
if(any(myEM$weights.EF[,2]!=0)){
## preparation testing dataset for Ensemble bivariate
myproj<-ecospat.ESM.Projection_CUSTOM(mymodel,
proj.name=paste("rep", npoints, sep="."),
new.env = testing_dataset,
parallel=T,
ncores=10)
gc()
source(paste(main.dir,"ecospat.ESM.EnsembleProjection_CUSTOM.R",sep="/"))
r<-myEF$EF/1000
pred_test_background_ENS<-data.frame(EF=subset(r, testing_dataset$OBS==0))
pred_test_sites_ENS<-data.frame(EF=subset(r, testing_dataset$OBS==1))
gc()
# #### EVALUATION ############################################################
params_eval<-list(enfa=list(test_p=pred_test_sites_enfa,
test_a=pred_test_background_enfa,
mm=mm_enfa,
model_name="enfa",
train_p=pred_train_sites_enfa),
ens.biv=list(test_p=pred_test_sites_ENS,
test_a=pred_test_background_ENS,
train_p=NULL,
mm=NULL,
model_name="ens.biv"))
lapply(params_eval, function(x){
eval_function(test_p=x$test_p,
test_a=x$test_a,
mm=x$mm,
model_name=x$model_name,
train_p=x$train_p)
})->eval_others
} else {
params_eval<-list(enfa=list(test_p=pred_test_sites_enfa,
test_a=pred_test_background_enfa,
mm=mm_enfa,
model_name="enfa",
train_p=pred_train_sites_enfa))
lapply(params_eval, function(x){
eval_function(test_p=x$test_p,
test_a=x$test_a,
mm=x$mm,
model_name=x$model_name,
train_p=x$train_p)
})->eval_others
eval_others$ens.biv<-c(AUC=0.5,
TSS=0,
SORENSEN=0,
CBI=0)
}
extreme_test[[s]]<-list(enfa=eval_others$enfa,
ens.biv=eval_others$ens.biv,
imputed.full=eval_imputed,
imputed.th=eval_imputed_th,
imputed.best=eval_imputed_best)
}
save(extreme_test, file="external_evaluation.RData")
setwd(main.dir)
}