Maxent Variable Importance
Shows the percent contribution and permutation importance of the environmental variables used to train the model.
maxentVarImp(model)
model |
SDMmodel or SDMmodelCV object trained using the "Maxent" method. |
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.
A data frame with the variable importance.
Sergio Vignali
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
pattern = "grd", full.names = TRUE)
predictors <- raster::stack(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")
# Train a Maxent model
# The next line checks if Maxent is correctly configured but you don't need
# to run it in your script
if (dismo::maxent(silent = TRUE)) {
model <- train(method = "Maxent", data = data, fc = "l")
maxentVarImp(model)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.