maxent_example.R

require("sp")
require("rgdal")
require("dismo")

# only run if the maxent.jar file is available, in the right folder
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')

# checking if maxent can be run (normally not part of your script)
if (file.exists(jar) & require(rJava)) {
  
  # get predictor variables
  fnames <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''), pattern='grd', full.names=TRUE )
  predictors <- stack(fnames)
  #plot(predictors)
  
  # file with presence points
  occurence <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
  occ <- read.table(occurence, header=TRUE, sep=',')[,-1]
  
  # witholding a 20% sample for testing 
  fold <- kfold(occ, k=5)
  occtest <- occ[fold == 1, ]
  occtrain <- occ[fold != 1, ]
  
  # fit model, biome is a categorical variable
  me <- maxent(predictors, occtrain, factors='biome')
  
  # see the maxent results in a browser:
  # me
  
  # use "args"
  # me2 <- maxent(predictors, occtrain, factors='biome', args=c("-J", "-P"))
  
  # plot showing importance of each variable
  plot(me)
  
  # response curves
  # response(me)
  
  # predict to entire dataset
  r <- predict(me, predictors) 
  
  # with some options:
  # r <- predict(me, predictors, args=c("outputformat=raw"), progress='text', 
  #      filename='maxent_prediction.grd')
  
  plot(r)
  points(occ)
  
  #testing
  # background data
  bg <- randomPoints(predictors, 1000)
  
  #simplest way to use 'evaluate'
  e1 <- evaluate(me, p=occtest, a=bg, x=predictors)
  
  # alternative 1
  # extract values
  pvtest <- data.frame(extract(predictors, occtest))
  avtest <- data.frame(extract(predictors, bg))
  
  e2 <- evaluate(me, p=pvtest, a=avtest)
  
  # alternative 2 
  # predict to testing points 
  testp <- predict(me, pvtest) 
  head(testp)
  testa <- predict(me, avtest) 
  
  e3 <- evaluate(p=testp, a=testa)
  e3
  threshold(e3)
  
  plot(e3, 'ROC')
  
}