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Visualize variable importance scores

Source: R/plot_importance.R
plot_importance.Rd

Creates a visualization of variable importance scores from models fitted with rf(), rf_repeat(), or rf_spatial(). For single-run models (rf(), rf_spatial()), displays points ordered by importance. For repeated models (rf_repeat()), displays violin plots showing the distribution of importance scores across model repetitions.

Usage

plot_importance(
  model,
  fill.color = viridis::viridis(100, option = "F", direction = -1, alpha = 1, end = 0.9),
  line.color = "white",
  verbose = TRUE
)

Arguments

model

Model fitted with rf(), rf_repeat(), or rf_spatial(). Alternatively, a data frame with variable importance scores (for internal use only).

fill.color

Character vector of colors or a function generating a color palette. Accepts hexadecimal codes (e.g., c("#440154FF", "#21908CFF", "#FDE725FF")) or palette functions (e.g., viridis::viridis(100)). For single-run models, creates a continuous gradient. For repeated models, assigns discrete colors to variables. Default: viridis::viridis(100, option = "F", direction = -1, alpha = 1, end = 0.9).

line.color

Character string specifying the color of point borders (single-run models) or violin plot outlines (repeated models). Default: "white".

verbose

Logical. If TRUE, prints the plot to the graphics device. Default: TRUE.

Value

ggplot object that can be further customized or saved. The plot displays variable importance on the x-axis and variable names on the y-axis, ordered by importance (highest at top).

Details

This function creates different visualizations depending on the model type:

Single-run models (rf(), rf_spatial() without repetitions):

  • Displays points showing the importance value for each variable

  • Variables ordered top-to-bottom by importance (most important at top)

  • Point color represents importance magnitude using a continuous gradient

Repeated models (rf_repeat(), rf_spatial() with repetitions):

  • Displays violin plots showing the distribution of importance across repetitions

  • Variables ordered top-to-bottom by median importance (most important at top)

  • The median line within each violin shows the center of the distribution

  • Width of violin reflects the density of importance values at each level

  • Each variable receives a distinct fill color

Importance metric:

The x-axis shows permutation importance, which measures the increase in prediction error when a variable's values are randomly shuffled. Higher values indicate more important variables. Importance is computed on out-of-bag (OOB) samples, providing an unbiased estimate of variable contribution.

Spatial predictors:

In rf_spatial() models, all spatial predictors (MEMs or PCA factors) are grouped into a single category labeled "spatial_predictors" to simplify comparison with non-spatial predictors.

Note on violin plots:

Violin plots display kernel density estimates. The median line shown is the median of the density estimate, which may differ slightly from the actual data median. However, variables are always ordered by the true median importance to ensure accurate ranking.

Cross-validated importance:

This function does not plot results from rf_importance(). For cross-validated importance plots, access model$importance$cv.per.variable.plot after running rf_importance().

See also

print_importance(), get_importance(), rf_importance()

Other visualization: plot_evaluation(), plot_moran(), plot_optimization(), plot_residuals_diagnostics(), plot_response_curves(), plot_response_surface(), plot_training_df(), plot_training_df_moran(), plot_tuning()

Examples


if(interactive()){

data(plants_rf, plants_rf_spatial)

# Plot importance from Random Forest model
plot_importance(plants_rf)

# Plot importance from Spatial Random Forest model
plot_importance(plants_rf_spatial)

}