variable_importance: Variable Importance in BART Model

In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models

Variable Importance in BART Model

Description

This function computes the variable importance scores for a fitted BART (Bayesian Additive Regression Trees) model using a permutation-based approach. It measures the impact of each predictor variable on the model's performance by permuting the values of that variable and evaluating the change in performance (F-score is the performance metric).

Usage

variable_importance(bart_model, y, x, cutoff = 0, n_repeats = 10, seed = NULL)

Arguments

bart_model	A BART model object.
y	Vector indicating presence (1) or absence (0).
x	Dataframe with same number of rows as the length of the vector 'y' with the covariate values.
cutoff	A numeric threshold for converting predicted probabilities into presence-absence.
n_repeats	An integer indicating the number of times to repeat the permutation for each variable.
seed	An optional seed for random number generation.

Value

A data frame where each column corresponds to a predictor variable, and each row contains the variable importance scores across permutations.

glossa documentation built on June 8, 2025, 1:20 p.m.