ENMeval (version 2.0.5)

evalplot.grps: Partition group plots

Description

Plot occurrence partition groups over an environmental predictor raster.

Usage

evalplot.grps(
  e = NULL,
  envs,
  pts = NULL,
  pts.grp = NULL,
  ref.data = "occs",
  pts.size = 1.5,
  return.tbl = FALSE
)

Arguments

e

ENMevaluation object

envs

SpatRaster: environmental predictor variable used to build the models in "e"

pts

matrix / data frame: coordinates for occurrence or background data

pts.grp

numeric vector: partition groups corresponding to data in "pts"

ref.data

character: plot occurrences ("occs") or background ("bg"), with default "occs"

pts.size

numeric: custom point size for ggplot

return.tbl

boolean: if TRUE, return the data frames used to make the ggplot instead of the plot itself

Details

This function serves as a quick way to visualize occurrence or background partitions over the extent of an environmental predictor raster. It can be run with an existing ENMevaluation object, or alternatively with occurrence or background coordinates and the corresponding partitions.

Examples

if (FALSE) {
library(terra)
library(ENMeval)
occs <- read.csv(file.path(system.file(package="predicts"), 
                           "/ex/bradypus.csv"))[,2:3]
envs <- rast(list.files(path=paste(system.file(package="predicts"), 
                                   "/ex", sep=""), pattern="tif$", full.names=TRUE))
bg <- as.data.frame(predicts::backgroundSample(envs, n = 10000))
names(bg) <- names(occs)

parts <- get.block(occs, bg, orientation = "lat_lon")

# now, plot the partition groups for occurrence and background points
evalplot.grps(envs = envs, pts = occs, pts.grp = parts$occs.grp)
evalplot.grps(envs = envs, pts = bg, pts.grp = parts$bg.grp)

# you can also plot with an ENMevaluation object
ps <- list(orientation = "lat_lon")
e <- ENMevaluate(occs, envs, bg, 
                 tune.args = list(fc = c("L","LQ"), rm = 1:3), 
                 partitions = "block", partition.settings = ps, 
                 algorithm = "maxnet", categoricals = "biome", 
                 parallel = TRUE)

evalplot.grps(e = e, envs = envs, ref.data = "occs")
evalplot.grps(e = e, envs = envs, ref.data = "bg")
}