variable_importance

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).

A user-friendly 'shiny' application for Bayesian machine
learning analysis of marine species distributions. GLOSSA (Global
Species Spatiotemporal Analysis) uses Bayesian Additive Regression
Trees (BART; Chipman, George, and McCulloch (2010)
<doi:10.1214/09-AOAS285>) to model species distributions with
intuitive workflows for data upload, processing, model fitting, and
result visualization. It supports presence-absence and presence-only
data (with pseudo-absence generation), spatial thinning,
cross-validation, and scenario-based projections. GLOSSA is designed
to facilitate ecological research by providing easy-to-use tools for
analyzing and visualizing marine species distributions across
different spatial and temporal scales.

Jorge Mestre-Tomás

glossa

User-Friendly 'shiny' App for Bayesian Species Distribution
Models

Alba Fuster-Alonso

variable_importance function

<dl><dt>bart_model</dt>
<dd>A BART model object.</dd>
<dt>cutoff</dt>
<dd>A numeric threshold for converting predicted probabilities into presence-absence.</dd>
<dt>n_repeats</dt>
<dd>An integer indicating the number of times to repeat the permutation for each variable.</dd>
<dt>seed</dt>
<dd>An optional seed for random number generation.</dd></dl>

Arguments

Variable Importance in BART Model — variable_importance

<dl>

<dt>bart_model</dt>
<dd>A BART model object.</dd>


<dt>cutoff</dt>
<dd>A numeric threshold for converting predicted probabilities into presence-absence.</dd>


<dt>n_repeats</dt>
<dd>An integer indicating the number of times to repeat the permutation for each variable.</dd>


<dt>seed</dt>
<dd>An optional seed for random number generation.</dd>

</dl>

variable_importance: Variable Importance in BART Model

Description

Usage

Value

Arguments