diff brt.R @ 1:b38b954b92b9 draft

planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/Ecoregionalization_workflow commit 459ba1277acd7d8d4a02f90dbd7ff444bf8eac92

--- a/brt.R	Wed Oct 18 09:58:34 2023 +0000
+++ b/brt.R	Wed Jan 24 15:53:20 2024 +0000
@@ -1,109 +1,121 @@
-#16/02/2023
-## Analyse BRT data Ceamarc
-
-### 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]
-}
-
-### load data
-
-env = read.table(enviro, header = TRUE, dec = ".", na.strings = "-9999")
-pred.vars = strsplit(abio_para, ",")[[1]] 
-data_files = strsplit(species_files,",")
-
-#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){
-   brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = 0.0001,max.trees = 10000,plot.main = F)
-   #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.txt",sep=""), quote=FALSE, dec=".", row.names=F, col.names=T,append = T)
-}
-
-#### RUN BRT ####
-nb_file = 0
-
-for (file in data_files[[1]]) {
-  species_data <- read.table(file, dec = ",", sep = ";", header = TRUE, na.strings = "na", colClasses = "numeric")
-  nb_file = nb_file + 1
-  `%!in%` <- Negate(`%in%`)
-  sp = list()
-  for (n in names(species_data)) {
-    if (n %!in% names(env) && n != 'station'){
-       sp = cbind(sp,n)
-    }
-  }
-  
-  for (spe in sp){
-   try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))
-   }
-}
+#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){
+   brt_step <- gbm.step(data= data, gbm.x = pred.vars, gbm.y = spe, family = "bernoulli", tree.complexity = 2, learning.rate = 0.0001,max.trees = 10000,plot.main = F)
+   #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=T,append = T,sep="\t")
+}
+
+#### RUN BRT ####
+nb_file = 0
+
+for (file in data_files[[1]]) {
+  species_data <- read.table(file, dec = dec_species, sep = "\t", header = TRUE, na.strings = "NA", colClasses = "numeric")
+  nb_file = nb_file + 1
+  `%!in%` <- Negate(`%in%`)
+  sp = list()
+  for (n in names(species_data)) {
+    if (n %!in% names(env) && n != 'station'){
+       sp = cbind(sp,n)
+    }
+  }     
+  for (spe in sp){
+   try(make.prediction.brt(make.brt(spe,species_data,pred.vars,env,nb_file)))
+   }
+}
+
+cat("Here is the list of your abiotic parameters:\n")
+cat(paste(pred.vars, collapse = ", "), "\n")
+
+