ENMeval (version 0.3.0)

enmeval_results : An object of class "ENMevaluation"

Description

An example results file based on a call of ENMevaluate (see example).

Usage

data(enmeval_results)

Format

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

Details

The dataset is based on the simulated dataset and call of ENMevaluate shown in the example section below.

Examples

# NOT RUN {
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')
# }
# 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([e-mail], pch=21, bg= [e-mail])

### Niche overlap statistics between model predictions
enmeval_results@overlap
# }

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