get_importance_local: Extract local variable importance from model

In spatialRF: Easy Spatial Modeling with Random Forest

Extract local variable importance from model

Description

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

Usage

get_importance_local(model)

Arguments

model

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

Details

Local importance measures how much each predictor contributes to predictions for individual observations, unlike global importance which summarizes contributions across all observations. This can reveal spatial or contextual patterns in variable influence.

Value

Data frame with one row per observation and one column per predictor variable. Each cell contains the local importance score for that variable at that observation.

See Also

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

Other model_info: get_evaluation(), get_importance(), 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 local importance scores
local_imp <- get_importance_local(plants_rf)

# View structure: rows = observations, columns = variables
dim(local_imp)
head(local_imp)

# Find which variable is most important for first observation
colnames(local_imp)[which.max(local_imp[1, ])]

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