variable_importance: Variable importance

In kuenm2: Detailed Development of Ecological Niche Models

Variable importance

Description

Variable importance

Usage

variable_importance(models, modelID = NULL, by_terms = FALSE,
                    parallel = FALSE, ncores = NULL,
                    progress_bar = TRUE, verbose = TRUE)

Arguments

models	an object of class fitted_models returned by the fit_selected() function.
modelID	(character). Default = NULL.
by_terms	(logical) whether to calculate importance by model terms (e.g., bio1, I(bio1^2), hinge(bio1)) instead of aggregating by variable. Default = FALSE.
parallel	(logical) whether to calculate importance in parallel. Default is FALSE.
ncores	(numeric) number of cores to use for parallel processing. Default is NULL and uses available cores - 1. This is only applicable if parallel = TRUE.
progress_bar	(logical) whether to display a progress bar during processing. Default is TRUE.
verbose	(logical) whether to display detailed messages during processing. Default is TRUE.

Value

A data.frame containing the relative contribution of each variable (or term if by_terms = TRUE). An identification for distinct models is added if fitted contains multiple models.

See Also

plot_importance()

Examples

# Example with maxnet
# Import example of fitted_models (output of fit_selected())
data(fitted_model_maxnet, package = "kuenm2")

# Variable importance
imp_maxnet <- variable_importance(models = fitted_model_maxnet)

# Plot
plot_importance(imp_maxnet)

# Example with glm
# Import example of fitted_models (output of fit_selected())
data(fitted_model_glm, package = "kuenm2")

# Variable importance
imp_glm <- variable_importance(models = fitted_model_glm)

# Plot
plot_importance(imp_glm)

kuenm2 documentation built on April 21, 2026, 1:07 a.m.