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Variable importance

Source: R/variable_importance.R
variable_importance.Rd

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)
#> 
#> Calculating variable contribution for model 1 of 2
#> 
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#> 
#> Calculating variable contribution for model 2 of 2
#> 
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# 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)
#> 
#> Calculating variable contribution for model 1 of 1
#> 
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# Plot
plot_importance(imp_glm)