Shows the percent contribution and permutation importance of the environmental variables used to train the model.
Arguments
- model
SDMmodel or SDMmodelCV object trained using the "Maxent" method.
Details
When an SDMmodelCV object is passed to the function, the output is the average of the variable importance of each model trained during the cross validation.
Examples
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
pattern = "grd",
full.names = TRUE)
predictors <- terra::rast(files)
# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background
# Create SWD object
data <- prepareSWD(species = "Virtual species",
p = p_coords,
a = bg_coords,
env = predictors,
categorical = "biome")
#> ℹ Extracting predictor information for presence locations
#> ✔ Extracting predictor information for presence locations [37ms]
#>
#> ℹ Extracting predictor information for absence/background locations
#> ✔ Extracting predictor information for absence/background locations [63ms]
#>
# Train a Maxent model
# The next line checks if Maxent is correctly configured but you don't need
# to run it in your script
model <- train(method = "Maxent",
data = data,
fc = "l")
maxentVarImp(model)
#> Variable Percent_contribution Permutation_importance
#> 1 bio1 48.4267 0.0000
#> 2 biome 36.7933 7.0174
#> 3 bio8 7.3643 21.7752
#> 4 bio6 2.6993 56.4936
#> 5 bio7 2.5507 0.0000
#> 6 bio16 1.2838 0.0000
#> 7 bio17 0.5727 0.0000
#> 8 bio12 0.2764 14.2192
#> 9 bio5 0.0328 0.4946