fit_bart_model

This function fits a Bayesian Additive Regression Trees (BART) model using
presence/absence data and environmental covariate layers.

A user-friendly 'shiny' application for Bayesian machine
learning analysis of marine species distributions. GLOSSA (Global
Ocean Species Spatio-temporal 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

fit_bart_model function

<dl><dt>y</dt>
<dd>A numeric vector indicating presence (1) or absence (0).</dd>
<dt>x</dt>
<dd>A data frame with the same number of rows as the length of the vector `y`, containing the covariate values.</dd>
<dt>seed</dt>
<dd>An optional integer value for setting the random seed for reproducibility.</dd>
<dt>...</dt>
<dd>Additional arguments passed to `dbarts::bart()`.</dd></dl>

Arguments

Fit a BART Model Using Environmental Covariate Layers — fit_bart_model

<dl>

<dt>y</dt>
<dd>A numeric vector indicating presence (1) or absence (0).</dd>


<dt>x</dt>
<dd>A data frame with the same number of rows as the length of the vector `y`, containing the covariate values.</dd>


<dt>seed</dt>
<dd>An optional integer value for setting the random seed for reproducibility.</dd>


<dt>...</dt>
<dd>Additional arguments passed to `dbarts::bart()`.</dd>

</dl>

fit_bart_model: Fit a BART Model Using Environmental Covariate Layers

Description

Usage

Value

Arguments