I'm following a tutorial on GitHub by John Baums to create a presence only species distribution model using the MAXENT method.
Every line of code is working until I need to run the object me, which is the function that calls Maxent's Java program, maxent.jar, and I get that error message below.
I tried to install rJava to run this line of code again:
#Install the package rJava
install.packages('rJava')
#Open the library rJava
library (rJava)
Once I've installed rJava and tried to run the code again, my session in RStudio is aborted:
R session Aborted where R encounters a fatal error
You can download all the data needed with the code below to reproduce this problem.
Does anyone know how to fix this issue?
library(viridis)
library (devtools)
library(dismo)
library(raster)
#Install the pacakge rasterVis
devtools::install_github('oscarperpinan/rasterVis')
#Open the library rasterVis
library(rasterVis)
#Install the package rmaxent
install.packages("remotes")
remotes::install_github("johnbaums/rmaxent")
#Open the library rmaxent
library(rmaxent)
#Let's import the B. variegatus occurrence and predictor data from the appropriate paths:
occ_file <- system.file('ex/bradypus.csv', package='dismo')
occ <- read.table(occ_file, header=TRUE, sep=',')[,-1]
library(raster)
pred_files <- list.files(system.file('ex', package='dismo'), '\\.grd$', full.names=TRUE )
predictors <- stack(pred_files)
#The object predictors is a RasterStack comprising nine raster layers, one for each #predictor used in the model. We can now fit the model using the maxent function from the #dismo package. Note that this function calls Maxent's Java program, maxent.jar
me <- maxent(predictors, occ, factors='biome', args=c('hinge=false', 'threshold=false'))
Error message:
Loading required namespace: rJava
JVMJ9VM015W Initialization error for library j9gc29(2): Failed to instantiate compressed references metadata. 200M requested
Error in .jinit(parameters = parameters) :
Cannot create Java virtual machine (-4)
You need to have Java installed for this code to work. You can download Java from here. The following was tested on a Windows PC using Java for Windows downloaded here. Just use the default install location.
The macOS version that worked for the OP, JAVA 23, can be found here.
After installing Java, you can check that your system is configured correctly by running system("java -version") in an R console. It should look similar to this:
system("java -version")
# java version "1.8.0_421"
# Java(TM) SE Runtime Environment (build 1.8.0_421-b09)
# Java HotSpot(TM) 64-Bit Server VM (build 25.421-b09, mixed mode)
# [1] 0
If R can't see Java or the code still does not work, search online for how to configure R correctly. This will differ depending on your operating system.
Also, the plot code did not work for me, so alternative plot code is provided.
devtools::install_github("johnbaums/rmaxent")
library(rmaxent)
# Import data, create RasterStack
occ_file <- system.file('ex/bradypus.csv', package='dismo')
occ <- read.table(occ_file, header=TRUE, sep=',')[,-1]
library(raster)
pred_files <- list.files(system.file('ex', package='dismo'), '\\.grd$', full.names=TRUE )
predictors <- stack(pred_files)
# Fit model
library(dismo)
me <- maxent(predictors, occ, factors='biome', args=c('hinge=false', 'threshold=false'))
prediction <- project(me, predictors)
# Plot
library(rasterVis)
library(viridis)
levelplot(prediction$prediction_logistic, margin=FALSE, col.regions=viridis, at=seq(0, 1, len=100),
panel = function(...){
panel.levelplot(...)
sp.points(SpatialPoints(occ), pch = 20, col = 1)
})
# Compare timing (your results may vary)
library(microbenchmark)
timings <- microbenchmark(
rmaxent=pred_rmaxent <- project(me, predictors),
dismo=pred_dismo <- predict(me, predictors),
times=10)
print(timings, signif=2)
# Unit: milliseconds
# expr min lq mean median uq max neval
# rmaxent 66 69 76 71 83 88 10
# dismo 230 240 290 250 290 530 10
all.equal(values(pred_rmaxent$prediction_logistic), values(pred_dismo))
# [1] "Mean relative difference: 0.2073864"
# Examine model
parse_lambdas(me)
# Features with non-zero weights
#
# feature lambda min max type
# (biome==1.0) 1.49227 0 1 categorical
# (biome==2.0) 1.15527 0 1 categorical
# (biome==9.0) 2.33123 0 1 categorical
# (biome==13.0) 1.96180 0 1 categorical
# (biome==14.0) 0.32250 0 1 categorical
# bio1 6.60856 -23 289 linear
# bio16 0.32171 0 2458 linear
# bio17 -4.08709 0 1496 linear
# bio7 -16.18362 62 461 linear
# bio8 1.85855 -66 323 linear
# bio5^2 -2.16296 3721 178084 quadratic
# bio6^2 -5.26415 0 57600 quadratic
# bio7^2 -7.49172 3844 212521 quadratic
# bio8^2 0.12389 0 104329 quadratic
# bio12*bio7 4.15357 0 737464 product
# bio12*bio8 2.44714 -27324 2020366 product
# bio16*bio8 0.01359 -13332 638038 product
# bio17*bio7 0.07878 0 145705 product
# bio5*bio7 -3.27012 8235 162812 product
#
#
# Features with zero weights
#
# feature lambda min max type
# bio12 0 0 7682 linear
# bio5 0 61 422 linear
# bio6 0 -212 240 linear
# Identify which variable is most responsible for decreasing suitability
lim <- limiting(predictors, me)
# Plot
levelplot(lim, col.regions=rainbow,
panel = function(...){
panel.levelplot(...)
sp.points(SpatialPoints(occ), pch = 20, col = 1)
})
# Improved model
me2 <- maxent(predictors, occ, factors='biome', args=c('hinge=false', 'threshold=false', 'betamultiplier=5'))
pred2 <- project(me2, predictors)
# Compare
ic(stack(pred_rmaxent$prediction_raw, pred2$prediction_raw),
occ, list(me, me2))
# n k ll AIC AICc BIC
# layer.1 94 19 -736.2354 1510.471 1520.741 1558.793
# layer.2 94 9 -753.9800 1525.960 1528.103 1548.850