get_importance: Extract variable importance from model

In spatialRF: Easy Spatial Modeling with Random Forest

Extract variable importance from model

Description

Extracts variable importance scores from models fitted with rf(), rf_repeat(), or rf_spatial().

Usage

get_importance(model)

Arguments

model

Model object from rf(), rf_repeat(), or rf_spatial().

Details

For spatial models (rf_spatial()) with many spatial predictors, this function returns aggregated importance statistics for spatial predictors to improve readability. Non-spatial models return per-variable importance scores directly.

Value

Data frame with columns variable (character) and importance (numeric), sorted by decreasing importance.

See Also

rf(), rf_repeat(), rf_spatial(), plot_importance(), print_importance()

Other model_info: get_evaluation(), get_importance_local(), get_moran(), get_performance(), get_predictions(), get_residuals(), get_response_curves(), get_spatial_predictors(), print.rf(), print_evaluation(), print_importance(), print_moran(), print_performance()

Examples

data(plants_rf)

# Extract variable importance
importance <- get_importance(plants_rf)
head(importance)

# View top 5 most important variables
importance[1:5, ]

spatialRF documentation built on Dec. 20, 2025, 1:07 a.m.