Description Usage Format Details Examples
An example results file based on a call of ENMevaluate (see example).
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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.
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### 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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