--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/brt.R	Wed Sep 11 09:18:25 2024 +0000
@@ -0,0 +1,152 @@
+#16/02/2023
+## Analyse BRT data
+
+### Clean environment 
+rm(list = ls(all.names = TRUE))
+options(warn=1)
+
+### load packages
+
+library(dismo, warn.conflicts = FALSE)
+library(gbm, warn.conflicts = FALSE)
+library(ggplot2, warn.conflicts = FALSE)
+
+
+#load arguments
+args = commandArgs(trailingOnly=TRUE) 
+if (length(args)==0)
+{
+    stop("This tool needs at least one argument")
+}else{
+    enviro <- args[1]
+    species_files <- args[2]
+    abio_para <- args[3]
+    dec_env <- args[8]
+    dec_species <- args[9]
+}
+
+### load data
+
+env = read.table(enviro, dec = dec_env, header = TRUE, sep="\t", na.strings = "-9999")
+pred_vars = strsplit(abio_para, ",")[[1]] 
+data_files = strsplit(species_files,",")
+
+pred.vars <- character(length(pred_vars))
+
+for (i in seq_along(pred_vars)) {
+       pred_var_col <- as.numeric(pred_vars[i])
+       pred.vars[i] <- names(env)[pred_var_col]}
+       
+#environemental parameters
+#Carbo,Grav,Maxbearing,Maxmagnit,Meancurmag,Meansal,Meantheta,Mud,Prof,Rugosity,Sand,Seaice_prod,Sili,Slope,Standcurmag,Standsal,Standtheta
+
+#Load functions
+
+make.brt <- function(spe,data,pred.vars,env,nb_file){
+   cat(paste("  ", spe,":\n  -> optimising BRT model ",sep=""))
+   lr <- 0.05
+   no.trees <- 0
+   while ( no.trees < 1000 & lr > 0.0005 ) {
+    cat(".")
+    try(brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = lr,max.trees = 10000, plot.main = F))
+    # if the gbm does not converge, the return object is null or of size 0
+    if (!is.null(brt_step) ) {
+      if (object.size(brt_step) > 0 ) {
+        no.trees <- brt_step$gbm.call$best.trees
+        print(no.trees)
+      }
+    } else {
+      no.trees <- 0
+      print(no.trees)
+    }
+
+    # decrease the learning rate
+    lr <- lr / 2
+    print(lr)
+  }
+   #plot
+   if (is.null(brt_step)==FALSE){
+     pdf(file = paste("BRT-",spe,".pdf"))
+     gbm.plot(brt_step, write.title = T,show.contrib = T, y.label = "fitted function",plot.layout = c(3,3))
+     dev.off()
+     #total deviance explained as (Leathwick et al., 2006)
+     total_deviance <- brt_step$self.statistics$mean.null
+     cross_validated_residual_deviance <- brt_step$cv.statistics$deviance.mean
+     total_deviance_explained <- (total_deviance - cross_validated_residual_deviance)/total_deviance
+     #Validation file
+     valid = cbind(spe,brt_step$cv.statistics$discrimination.mean,brt_step$gbm.call$tree.complexity,total_deviance_explained)
+     write.table(valid, paste(nb_file,"_brts_validation_ceamarc.tsv",sep=""), quote=FALSE, dec=".",sep="\t" ,row.names=F, col.names=F,append = T)}
+   
+   return(brt_step)
+   }
+
+make.prediction.brt <- function(brt_step){
+  #predictions
+  preds <- predict.gbm(brt_step,env,n.trees=brt_step$gbm.call$best.trees, type="response")
+  preds <- as.data.frame(cbind(env$lat,env$long,preds))
+  colnames(preds) <- c("lat","long","Prediction.index")
+  #carto
+  ggplot()+
+    geom_raster(data = preds , aes(x = long, y = lat, fill = Prediction.index))+
+    geom_raster(data = preds , aes(x = long, y = lat, alpha = Prediction.index))+
+    scale_alpha(range = c(0,1), guide = "none")+
+    scale_fill_viridis_c(
+      alpha = 1,
+      begin = 0,
+      end = 1,
+      direction = -1,
+      option = "D",
+      values = NULL,
+      space = "Lab",
+      na.value = "grey50",
+      guide = "colourbar",
+      aesthetics = "fill")+
+    xlab("Longitude") + ylab("Latitude")+ ggtitle(paste(spe,"Plot of BRT predictions"))+
+    theme(plot.title = element_text(size = 10))+
+    theme(axis.title.y = element_text(size = 10))+
+    theme(axis.title.x = element_text(size = 10))+
+    theme(axis.text.y = element_text(size = 10))+
+    theme(axis.text.x = element_text(size = 10))+
+    theme(legend.text = element_text(size = 10))+
+    theme(legend.title = element_text(size = 10))+ 
+    coord_quickmap()
+  output_directory <- ggsave(paste("BRT-",spe,"_pred_plot.png"))
+  
+  #Write prediction in a file
+  preds <- cbind(preds,spe)
+  write.table(preds, paste(nb_file,"_brts_pred_ceamarc.tsv",sep=""), quote=FALSE, dec=".", row.names=F, col.names=!file.exists(paste(nb_file,"_brts_pred_ceamarc.tsv",sep="")),append = T,sep="\t")
+}
+
+#### RUN BRT ####
+nb_file = 0
+
+# Creating the %!in% operator
+`%!in%` <- Negate(`%in%`)
+
+# Data file browsing
+for (file in data_files[[1]]) {
+  
+  # Reading the file
+  species_data <- read.table(file, dec = dec_species, sep = "\t", header = TRUE, na.strings = "NA", colClasses = "numeric")
+  nb_file = nb_file + 1
+ 
+  # List to store species to predict
+  sp = list()
+  
+  # Selection of columns that are not in 'env' and that are not coordinates or stations
+  for (n in names(species_data)) {
+    if (n %!in% names(env) && n != 'station' && n != 'decimalLatitude' && n != 'decimalLongitude' && n!='lat' && n!='long'){
+       sp = c(sp,n) 
+    }
+  } 
+  # Making predictions for each species 
+  for (spe in sp){
+   try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))
+   }
+}
+
+#Display of abiotic parameters
+cat("Here is the list of your abiotic parameters:\n")
+cat(paste(pred.vars, collapse = ", "), "\n")
+
+