cv_bart

This function performs k-fold cross-validation for a Bayesian Additive Regression Trees (BART) model
using presence-absence data and environmental covariate layers. It calculates various performance metrics
for model evaluation.

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

cv_bart function

<dl><dt>data</dt>
<dd>Data frame with a column (named 'pa') indicating presence (1) or absence (0) and columns for the predictor variables.</dd>
<dt>k</dt>
<dd>Integer; number of folds for cross-validation (default is 10).</dd>
<dt>seed</dt>
<dd>Optional; random seed.</dd></dl>

Arguments

Cross-Validation for BART Model — cv_bart

<dl>

<dt>data</dt>
<dd>Data frame with a column (named 'pa') indicating presence (1) or absence (0) and columns for the predictor variables.</dd>


<dt>k</dt>
<dd>Integer; number of folds for cross-validation (default is 10).</dd>


<dt>seed</dt>
<dd>Optional; random seed.</dd>

</dl>

cv_bart: Cross-Validation for BART Model

Description

Usage

Value

Arguments