Regional_SDM/4_predictModelToStudyArea_Loop.R at master · akconley/Regional_SDM · GitHub

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# File: 4_predictModelToStudyArea.r
# Purpose: create the distribution model prediction raster

## start with a fresh workspace with no objects loaded
library(raster)
library(rgdal)
library(randomForest)

####
## two lines need your attention. The one directly below (loc_scripts)
## and about line 26 where you choose which Rdata file to use,

loc_scripts <- "C:\\Users\\Public\\Git_Repos\\Regional_SDM"

# get paths, other settings
source(paste(loc_scripts,"0_pathsAndSettings.R", sep="/"))
# get the customized version of the predict function
source(paste(loc_scripts, "RasterPredictMod.R", sep = "/"))

# load data ----
# get the rdata file
setwd(loc_RDataOut)
fileList <- dir(pattern = ".Rdata$",full.names=FALSE)
fileList
fileList<-fileList[c(3,5:19)]
len_fileList<-length(fileList)
len_fileList
# choose one to run, load it #### requires editing ####
for (i in 1:len_fileList){
n <- i
print (n)

print (fileList[[n]])
load(fileList[[n]])

##Make the raster stack
stackOrder <- names(df.full)[indVarCols]
loc_scripts <- "C:\\Users\\Public\\Git_Repos\\Regional_SDM"
source(paste(loc_scripts,"0_pathsAndSettings.R", sep="/"))
source(paste(loc_scripts, "RasterPredictMod.R", sep = "/"))
setwd(loc_envVars)
rasL <- paste(stackOrder,".tif", sep="")
fullL <- as.list(paste(loc_envVars, rasL, sep="/"))
names(fullL) <- stackOrder
envStack <- stack(fullL)

# run prediction ----
setwd(loc_RDataOut)
fileNm <- paste(loc_outRas, "\\", ElementNames$Code, "_",Sys.Date(),".tif", sep = "")
outRas <- predictRF(envStack, rf.full, progress="text", index=2, na.rm=TRUE, type="prob", filename=fileNm, format = "GTiff", overwrite=TRUE)

#writeRaster(outRas, filename=paste(fileNm, "_2",sep=""), format = "GTiff", overwrite = TRUE)
setwd(loc_RDataOut)
rm(list=ls()[!ls() %in%
                c("p_fileList","predictRF","fileList","loc_scripts","loc_outRas","loc_RDataOut","loc_bkgPts","loc_spPts","loc_envVars","nm_db_file","loc_otherSpatial","len_fileList")])

}
## clean up ----
# remove all objects before moving on to the next script
rm(list=ls())