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nost.mod.fit.r
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nost.mod.fit.r
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# ggplot(data = filter(ext.s7.df, ext_year<249 & ext == 1), aes(x = ext_year)) + geom_histogram(fill = "white", col = "red") + theme.pop
library("tidyverse")
library("Metrics")
library("caret")
library("MuMIn")
library("lubridate")
library("mgcv")
library("parallel")
library("brms")
library("rlist")
library("e1071")
library("ranger")
library("pdp")
library("tidymv")
library("cowplot")
ris.tot.s7.filter = readRDS("sim_res.RDS")
sim_res = readRDS("sim_res.RDS")
ext.s7.df = data.frame(
sel = sapply(ris.tot.s7.filter, with, selstrength),
ext = sapply(ris.tot.s7.filter, with, extinct),
sdopt = sapply(ris.tot.s7.filter, with, sdopt),
meanopt = sapply(ris.tot.s7.filter, with, meanopt),
mean.pheno = sapply(ris.tot.s7.filter, with, mean(phenomean[(250-10):250])),
ext_year = sapply(ris.tot.s7.filter, with, yearextinct),
age.sex.mat = sapply(ris.tot.s7.filter, with, age.sex.mat),
avg.surv = sapply(ris.tot.s7.filter, with,avg.surv),
sons.mean = sapply(ris.tot.s7.filter, with,sons.mean),
cor.all.est = sapply(ris.tot.s7.filter, with, cor.all.est),
cor.all.p = sapply(ris.tot.s7.filter, with, cor.all.p),
cat.freq = sapply(ris.tot.s7.filter, with, cat.freq),
catastr.mort = sapply(ris.tot.s7.filter, with,catastr.mort)
)
ext.s7.df$sim_rep = 1:nrow(ext.s7.df)
ext.s7.df$year_bc = NA
for (i in 1:nrow(ext.s7.df)) {
if (ext.s7.df$ext[i] == 1) {
cat_happ = which(ris.tot.s7.filter[[i]]$catastr.vett == 1)
cat_happ_bef = cat_happ[which(cat_happ < ext.s7.df$ext_year[i])]
if (length(cat_happ) > 0) {
cat_close = min(ext.s7.df$ext_year[i] - cat_happ_bef)}
if (length(cat_happ) == 0) {
cat_close = NA}
ext.s7.df$year_bc[i] = cat_close
}
}
# with(ext.s7.df, table(sons.mean, age.sex.mat,ext))
# with(ext.s7.df, table(age.sex.mat, ext))
seed_num = 50
set.seed(seed_num)
ext_df = filter(ext.s7.df, ext == 1)
s_w = 10 # length of the sample window (between 5 and 20)
ext_df = filter(ext_df, ext_year > (s_w + 2))
num_ext_out = 2300
set.seed(seed_num)
oft = sample(1:nrow(ext_df), size = num_ext_out, replace = F)
out_for_test = ext_df[oft,]
ext_df = ext_df[-oft,]
ext_df_3 = rbind(ext_df,ext_df,ext_df)
ext_df_3$sampl_bef_ext = NA
set.seed(seed_num)
ext_df_3$sampl_bef_ext = sample(x = 1:s_w ,size = nrow(ext_df_3), replace = T)
ext_df_3$sampl_end = ext_df_3$ext_year - ext_df_3$sampl_bef_ext
ext_df_3$sampl_end = ifelse(ext_df_3$sampl_end>=s_w ,ext_df_3$sampl_end,s_w)
ext_df_3$sampl_beg = ext_df_3$sampl_end - (s_w -1)
ext_df_3$min_pop_win = NA
ext_df_3$max_pop_win = NA
ext_df_3$mean_pop_win = NA
ext_df_3$max_min_pop_win = NA
ext_df_3$cat_win = NA
ext_df_3$max_opt_win = NA
ext_df_3$min_opt_win = NA
ext_df_3$max_min_opt_win = NA
ext_df_3$max_pheno_dist_win = NA
ext_df_3$mean_pheno_dist_win = NA
for (i in 1:nrow(ext_df_3)) {
if (i%%100 == 0){ print(i)}
targ = ext_df_3$sim_rep[i]
if(ext_df_3$ext_year[i] > s_w) {
ext_df_3$min_pop_win[i] = min(sim_res[[targ]]$popsize.post[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]])
ext_df_3$max_pop_win[i] = max(sim_res[[targ]]$popsize.post[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]])
ext_df_3$max_min_pop_win[i] = ext_df_3$max_pop_win[i] - ext_df_3$min_pop_win[i]
ext_df_3$mean_pop_win[i] = mean(sim_res[[targ]]$popsize.post[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]])
ext_df_3$cat_win[i] = ifelse(sum(which(sim_res[[targ]]$catastr.vett == 1) %in% (ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]) > 0),"y","n")
ext_df_3$max_opt_win[i] = max(sim_res[[targ]]$optimum[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]])
ext_df_3$min_opt_win[i] = min(sim_res[[targ]]$optimum[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]])
ext_df_3$max_min_opt_win[i] = ext_df_3$max_opt_win[i] - ext_df_3$min_opt_win[i]
ext_df_3$max_pheno_dist_win[i] = max(abs(sim_res[[targ]]$phenomean[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]] - sim_res[[targ]]$optimum[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]]))
ext_df_3$mean_pheno_dist_win[i] = mean(abs(sim_res[[targ]]$phenomean[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]] - sim_res[[targ]]$optimum[ext_df_3$sampl_beg[i]:ext_df_3$sampl_end[i]]))
}
}
### For out for test ####
out_for_test$sampl_bef_ext = NA
set.seed(seed_num)
out_for_test$sampl_bef_ext = sample(x = 1:s_w ,size = nrow(out_for_test), replace = T)
out_for_test$sampl_end = out_for_test$ext_year - out_for_test$sampl_bef_ext
out_for_test$sampl_end = ifelse(out_for_test$sampl_end>=s_w ,out_for_test$sampl_end,s_w)
out_for_test$sampl_beg = out_for_test$sampl_end - (s_w -1)
out_for_test$min_pop_win = NA
out_for_test$max_pop_win = NA
out_for_test$mean_pop_win = NA
out_for_test$max_min_pop_win = NA
out_for_test$cat_win = NA
out_for_test$max_opt_win = NA
out_for_test$min_opt_win = NA
out_for_test$max_min_opt_win = NA
out_for_test$max_pheno_dist_win = NA
out_for_test$mean_pheno_dist_win = NA
for (i in 1:nrow(out_for_test)) {
if (i%%100 == 0){ print(i)}
targ = out_for_test$sim_rep[i]
if(out_for_test$ext_year[i] > s_w) {
out_for_test$min_pop_win[i] = min(sim_res[[targ]]$popsize.post[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]])
out_for_test$max_pop_win[i] = max(sim_res[[targ]]$popsize.post[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]])
out_for_test$max_min_pop_win[i] = out_for_test$max_pop_win[i] - out_for_test$min_pop_win[i]
out_for_test$mean_pop_win[i] = mean(sim_res[[targ]]$popsize.post[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]])
out_for_test$cat_win[i] = ifelse(sum(which(sim_res[[targ]]$catastr.vett == 1) %in% (out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]) > 0),"y","n")
out_for_test$max_opt_win[i] = max(sim_res[[targ]]$optimum[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]])
out_for_test$min_opt_win[i] = min(sim_res[[targ]]$optimum[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]])
out_for_test$max_min_opt_win[i] = out_for_test$max_opt_win[i] - out_for_test$min_opt_win[i]
out_for_test$max_pheno_dist_win[i] = max(abs(sim_res[[targ]]$phenomean[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]] - sim_res[[targ]]$optimum[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]]))
out_for_test$mean_pheno_dist_win[i] = mean(abs(sim_res[[targ]]$phenomean[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]] - sim_res[[targ]]$optimum[out_for_test$sampl_beg[i]:out_for_test$sampl_end[i]]))
}
}
surv_df = filter(ext.s7.df, ext == 0)
surv_df$sampl_bef_ext = NA
surv_df$sampl_bef_ext = NA
set.seed(seed_num)
surv_df$sampl_end = sample(x = 20:240,size = nrow(surv_df), replace = T)
surv_df$sampl_beg = surv_df$sampl_end - (s_w - 1)
surv_df$min_pop_win = NA
surv_df$max_pop_win = NA
surv_df$mean_pop_win = NA
surv_df$max_min_pop_win = NA
surv_df$cat_win = NA
surv_df$max_opt_win = NA
surv_df$min_opt_win = NA
surv_df$max_min_opt_win = NA
for (i in 1:nrow(surv_df)) {
if (i%%100 == 0){ print(i)}
targ = surv_df$sim_rep[i]
surv_df$min_pop_win[i] = min(sim_res[[targ]]$popsize.post[surv_df$sampl_beg[i]:surv_df$sampl_end[i]])
surv_df$max_pop_win[i] = max(sim_res[[targ]]$popsize.post[surv_df$sampl_beg[i]:surv_df$sampl_end[i]])
surv_df$max_min_pop_win[i] = surv_df$max_pop_win[i] - surv_df$min_pop_win[i]
surv_df$mean_pop_win[i] = mean(sim_res[[targ]]$popsize.post[surv_df$sampl_beg[i]:surv_df$sampl_end[i]])
surv_df$cat_win[i] = ifelse(sum(which(sim_res[[targ]]$catastr.vett == 1) %in% (surv_df$sampl_beg[i]:surv_df$sampl_end[i]) > 0),"y","n")
surv_df$max_opt_win[i] = max(sim_res[[targ]]$optimum[surv_df$sampl_beg[i]:surv_df$sampl_end[i]])
surv_df$min_opt_win[i] = min(sim_res[[targ]]$optimum[surv_df$sampl_beg[i]:surv_df$sampl_end[i]])
surv_df$max_min_opt_win[i] = surv_df$max_opt_win[i] - surv_df$min_opt_win[i]
surv_df$max_pheno_dist_win[i] = max(abs(sim_res[[targ]]$phenomean[surv_df$sampl_beg[i]:surv_df$sampl_end[i]] - sim_res[[targ]]$optimum[surv_df$sampl_beg[i]:surv_df$sampl_end[i]]))
surv_df$mean_pheno_dist_win[i] = mean(abs(sim_res[[targ]]$phenomean[surv_df$sampl_beg[i]:surv_df$sampl_end[i]] - sim_res[[targ]]$optimum[surv_df$sampl_beg[i]:surv_df$sampl_end[i]]))
}
pred_df = rbind(ext_df_3,surv_df)
require(rms)
require(sampling)
#require(usdm)
######## start preparation of dataset #############
pred_df$test = 0
pos_0 = which(pred_df$ext == 0)
set.seed(seed_num) #make the analysis repeatable
pos_oft = sample(pos_0, size = num_ext_out, replace = F)
pred_df$test[pos_oft] = 1
out_for_test$test = 1
test_pred_df = rbind(pred_df, out_for_test)
test_pred_df$cont_cons = 1:nrow(test_pred_df)
# saveRDS(test_pred_df,"test_pred_df.RDS")
#
#
# ext.stand.df = scale(test_pred_df[,c("min_pop_win","max_pop_win", "max_min_pop_win", "mean_pop_win", "max_opt_win", "max_min_opt_win","sons.mean","age.sex.mat", "max_pheno_dist_win","mean_pheno_dist_win")], center = TRUE, scale = TRUE) #standardize predictors
#
# ext.stand.df = as.data.frame(ext.stand.df) #transform in data frame
#
# ext.stand.df = cbind(ext.stand.df,test_pred_df$ext, test_pred_df$cat_win,test_pred_df$sel) #add a column with extinction (1/0) coming from test_pred_df
#
# colnames(ext.stand.df)[ncol(ext.stand.df)-2] = "ext"
# colnames(ext.stand.df)[ncol(ext.stand.df)-1] = "cat_win"#rename the column
# colnames(ext.stand.df)[ncol(ext.stand.df)] = "sel"
# ext.stand.df$sel = as.factor(ext.stand.df$sel)
#
# # ext.stand.df %>% left_join(., select(test_pred_df, cont_cons, test,sim_rep,year_bc, e))
#
#
# ext.stand.df$test = test_pred_df$test
#
# ext.stand.df$sim_rep = test_pred_df$sim_rep
# ext.stand.df$year_bc = test_pred_df$year_bc
# ext.stand.df$sampl_bef_ext = test_pred_df$sampl_bef_ext
# ext.stand.df$sampl_end = test_pred_df$sampl_end
# ext.stand.df$sampl_beg = test_pred_df$sampl_beg
# ext.stand.df$cont_cons = test_pred_df$cont_cons
# ext.stand.df$ext_year = test_pred_df$ext_year
#
#
# ######## dataset without standardization #####
#
# # ext.nostand.df = test_pred_df[,c("sel","cat.freq","pop.ext10.rand","addvar.mean.10.rand")]
# #
# # ext.nostand.df = as.data.frame(ext.nostand.df) #transform in data frame
# #
# # ext.nostand.df = cbind(ext.nostand.df,test_pred_df$ext) #add a column with extinction (1/0) coming from test_pred_df
# #
# # colnames(ext.nostand.df)[ncol(ext.nostand.df)] = "ext" #rename the column
#
# ########
set.seed(seed_num) #make the analysis repeatable
# prop.0 = nrow(ext.stand.df[ext.stand.df$ext == 0,])/nrow(ext.stand.df) #find the proportion of replicates persisting
# prop.1 = 1- prop.0 #find the proportion of replicates going extinct
#
# tot.to.exclude = 0.2 * nrow(ext.stand.df) ##exclude 20% for the validation dataset
#
#
# s = strata(ext.stand.df,"ext",size=c(tot.to.exclude*prop.1,tot.to.exclude*prop.0 ), method="srswor") #created stratification
# ext.stand.df.val = ext.stand.df[s$ID_unit,] #validation dataset
# ext.stand.df.cal = ext.stand.df[-s$ID_unit,] #calibration dataset
ext.stand.df.val = filter(test_pred_df,test == 1) #validation dataset
ext.stand.df.cal = filter(test_pred_df,test == 0) #calibration dataset
saveRDS(ext.stand.df.val,"nost.ext.stand.df.val.RDS")
saveRDS(ext.stand.df.cal,"nost.ext.stand.df.cal.RDS")
############## end of preparation of dataset ###############
ext.lrm.full = with(ext.stand.df.cal, lrm(ext ~ sel + min_pop_win + mean_pop_win + max_opt_win + sons.mean + age.sex.mat + cat_win + max_pheno_dist_win + mean_pheno_dist_win)) #full glm (using Harrell's rms package) model
saveRDS(ext.lrm.full,"nost.ext.lrm.full.RDS")
dd <- datadist(ext.stand.df.cal)
options(datadist="dd")
ext.lrm.base = with(ext.stand.df.cal, lrm(ext ~ sel + max_opt_win + sons.mean + age.sex.mat + cat_win + max_pheno_dist_win + mean_pheno_dist_win, x = TRUE, y = TRUE)) #full glm (using Harrell's rms package) model
#
saveRDS(ext.lrm.base,"nost.ext.lrm.base.RDS")
ext.gam.full = gam(as.factor(ext) ~ as.factor(sel) + s(min_pop_win) + s(mean_pop_win) + s(max_opt_win) + sons.mean + age.sex.mat + as.factor(cat_win) +
s(max_pheno_dist_win) + s(mean_pheno_dist_win), family = "binomial",data = ext.stand.df.cal,na.action = "na.fail", select = TRUE)
saveRDS(ext.gam.full,"nost.ext.gam.full.RDS")
ext.gam.base = gam(as.factor(ext) ~ as.factor(sel) + s(max_opt_win) + sons.mean + age.sex.mat + as.factor(cat_win) +
s(max_pheno_dist_win) + s(mean_pheno_dist_win), family = "binomial",data = ext.stand.df.cal,na.action = "na.fail", select = TRUE)
saveRDS(ext.gam.base,"nost.ext.gam.base.RDS")
set.seed(seed_num)
library(parallel)
# Calculate the number of cores
no_cores <- detectCores() - 1
fitControl <- trainControl(
## Repeated 5–fold CV
method = "repeatedcv",
number = 2,
## repeated 10 times
repeats = 2,
allowParallel = TRUE,
#verboseIter = TRUE,
returnResamp = "all",search = "random")
library(doParallel)
cl <- makePSOCKcluster(5)
registerDoParallel(cl)
ext.rf.full <- train(as.factor(ext) ~ sel + min_pop_win + mean_pop_win + max_opt_win + sons.mean + age.sex.mat + cat_win + max_pheno_dist_win + mean_pheno_dist_win,
data = ext.stand.df.cal,
method = 'ranger',
# should be set high at least p/3
tuneLength = 10,
trControl = fitControl,
## parameters passed onto the ranger function
# the bigger the better.
num.trees = 1000,
importance = "permutation")
saveRDS(ext.rf.full,"nost.ext.rf.full.RDS")
stopCluster(cl)
registerDoSEQ()
set.seed(seed_num)
library(parallel)
# Calculate the number of cores
no_cores <- detectCores() - 1
library(doParallel)
# create the cluster for caret to use
cl <- makePSOCKcluster(no_cores)
registerDoParallel(cl)
ext.rf.red <- train(as.factor(ext) ~ sel + max_opt_win + sons.mean + age.sex.mat + cat_win + max_pheno_dist_win + mean_pheno_dist_win,
data = ext.stand.df.cal,
method = 'ranger',
# should be set high at least p/3
tuneLength = 10,
trControl = fitControl,
## parameters passed onto the ranger function
# the bigger the better.
num.trees = 1000,
importance = "permutation")
saveRDS(ext.rf.red,"nost.ext.rf.red.RDS")
stopCluster(cl)
registerDoSEQ()