An object of class "ENMevaluation"
An example results file based on a call of ENMevaluate (see example).
data(enmeval_results)
An object of class 'ENMevaluation' with nine slots:
@ results : data.frame of evaluation metrics
@ predictions : RasterStack of model predictions
@ models: list of MaxEnt model objects (see MaxEnt documentation for details)
@ partition.method: character giving method of data partitioning
@ occ.pts : data.frame of latitude and longitude of occurrence localities
@ occ.grp : data.frame of bins for occurrence localities
@ bg.pts : data.frame of latitude and longitude of background localities
@ bg.grp : data.frame of bins for background localities
@ overlap : matrix of pairwise niche overlap
The dataset is based on the simulated dataset and call of ENMevaluate shown in the example section below.
require(raster) ### Simulated data environmental covariates set.seed(1) r1 <- raster(matrix(nrow=50, ncol=50, data=runif(10000, 0, 25))) r2 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100), byrow=TRUE)) r3 <- raster(matrix(nrow=50, ncol=50, data=rep(1:100, each=100))) r4 <- raster(matrix(nrow=50, ncol=50, data=c(rep(1,1000),rep(2,500)),byrow=TRUE)) values(r4) <- as.factor(values(r4)) env <- stack(r1,r2,r3,r4) ### Simulate occurrence localities nocc <- 50 x <- (rpois(nocc, 2) + abs(rnorm(nocc)))/11 y <- runif(nocc, 0, .99) occ <- cbind(x,y) ## Not run: ### This gives the results that are loaded below: enmeval_results <- ENMevaluate(occ, env, method="block", n.bg=500, categoricals=4, algorithm='maxent.jar') ## End(Not run) data(enmeval_results) enmeval_results ### See table of evaluation metrics enmeval_results@results ### Plot prediction with lowest AICc plot(enmeval_results@predictions[[which (enmeval_results@results$delta.AICc == 0) ]]) points(enmeval_results@occ.pts, pch=21, bg= enmeval_results@occ.grp) ### Niche overlap statistics between model predictions enmeval_results@overlap
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