predict_bart

This function makes predictions using a Bayesian Additive Regression Trees (BART) model
on a stack of environmental covariates ('SpatRaster').

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

predict_bart function

<dl><dt>bart_model</dt>
<dd>A BART model object obtained from fitting BART using the 'dbarts' package.</dd>
<dt>layers</dt>
<dd>A SpatRaster object containing environmental covariates for prediction.</dd>
<dt>cutoff</dt>
<dd>An optional numeric cutoff value for determining potential presences. If NULL, potential presences and absences will not be computed.</dd></dl>

Arguments

Make Predictions Using a BART Model — predict_bart

<dl>

<dt>bart_model</dt>
<dd>A BART model object obtained from fitting BART using the 'dbarts' package.</dd>


<dt>layers</dt>
<dd>A SpatRaster object containing environmental covariates for prediction.</dd>


<dt>cutoff</dt>
<dd>An optional numeric cutoff value for determining potential presences. If NULL, potential presences and absences will not be computed.</dd>

</dl>

predict_bart: Make Predictions Using a BART Model

Description

Usage

Value

Arguments