Variable importance
variable_importance.RdVariable importance
Usage
variable_importance(models, modelID = NULL, parallel = FALSE, ncores = NULL,
progress_bar = TRUE, verbose = TRUE)Arguments
- models
an object of class
fitted_modelsreturned by thefit_selected() function.- modelID
(character). Default = NULL.
- 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. An
identification for distinct models is added if fitted contains multiple
models.
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)
