I worked a lot with MaxEnt in R recently (dismo-package), but only using a crossvalidation to validate my model of bird-habitats (only a single species). Now I want to use a self-created test sample file. I had to pick this points for validation by hand and can't use random test point.
So my R-script looks like this:
library(raster)
library(dismo)
setwd("H:/MaxEnt")
memory.limit(size = 400000)
punkteVG <- read.csv("Validierung_FL_XY_2016.csv", header=T, sep=";", dec=",")
punkteTG <- read.csv("Training_FL_XY_2016.csv", header=T, sep=";", dec=",")
punkteVG$X <- as.numeric(punkteVG$X)
punkteVG$Y <- as.numeric(punkteVG$Y)
punkteTG$X <- as.numeric(punkteTG$X)
punkteTG$Y <- as.numeric(punkteTG$Y)
##### mask NA ######
mask <- raster("final_merge_8class+le_bb_mask.img")
dataframe_VG <- extract(mask, punkteVG)
dataframe_VG[dataframe_VG == 0] <- NA
dataframe_TG <- extract(mask, punkteTG)
dataframe_TG[dataframe_TG == 0] <- NA
punkteVG <- punkteVG*dataframe_VG
punkteTG <- punkteTG*dataframe_TG
#### add the raster dataset ####
habitat_all <- stack("blockstats_stack_8class+le+area_8bit.img")
#### MODEL FITTING #####
library(rJava)
system.file(package = "dismo")
options(java.parameters = "-Xmx1g" )
setwd("H:/MaxEnt/results_8class_LE_AREA")
### backgroundpoints ###
set.seed(0)
backgrVMmax <- randomPoints(habitat_all, 100000, tryf=30)
backgrVM <- randomPoints(habitat_all, 1000, tryf=30)
### Renner (2015) PPM modelfitting Maxent ###
maxentVMmax_Renner<-maxent(habitat_all,punkteTG,backgrVMmax, path=paste('H:/MaxEnt/Ergebnisse_8class_LE_AREA/maxVMmax_Renner',sep=""),
args=c("-P",
"noautofeature",
"nothreshold",
"noproduct",
"maximumbackground=400000",
"noaddsamplestobackground",
"noremoveduplicates",
"replicates=10",
"replicatetype=subsample",
"randomtestpoints=20",
"randomseed=true",
"testsamplesfile=H:/MaxEnt/Validierung_FL_XY_2016_swd_NA"))
After the "maxent()"-command I ran into multiple errors. First I got an error stating that he needs more than 0 (which is the default) "randomtestpoints". So I added "randomtestpoints = 20" (which hopefully doesn't stop the program from using the file). Then I got:
Error: Test samples need to be in SWD format when background data is in SWD format
Error in file(file, "rt") : cannot open the connection
The thing is, when I ran the script with the default crossvalidation like this:
maxentVMmax_Renner<-maxent(habitat_all,punkteTG,backgrVMmax, path=paste('H:/MaxEnt/Ergebnisse_8class_LE_AREA/maxVMmax_Renner',sep=""),
args=c("-P",
"noautofeature",
"nothreshold",
"noproduct",
"maximumbackground=400000",
"noaddsamplestobackground",
"noremoveduplicates",
"replicates=10"))
...all works fine.
Also I tried multiple things to get my csv-validation-data in the correct format. Two rows (labled X and Y), Three rows (labled species, X and Y) and other stuff. I would rather use the "punkteVG"-vector (which is the validation data) I created with read.csv...but it seems MaxEnt wants his file.
I can't imagine my problem is so uncommon. Someone must have used the argument "testsamplesfile" before.
I found out, what the problem was. So here it is, for others to enjoy:
The correct maxent-command for a Subsample-file looks like this:
maxentVMmax_Renner<-maxent(habitat_all, punkteTG, backgrVMmax, path=paste('H:/MaxEnt',sep=""),
args=c("-P",
"noautofeature",
"nothreshold",
"noproduct",
"maximumbackground=400000",
"noaddsamplestobackground",
"noremoveduplicates",
"replicates=1",
"replicatetype=Subsample",
"testsamplesfile=H:/MaxEnt/swd.csv"))
Of course, there can not be multiple replicates, since you got only one subsample. Most importantly the "swd.csv" Subsample-file has to include:
So the last point was the issue here. Basically, if you don't define the species-colum in the Subsample-file, MaxEnt will not know how to assign the data.