response_curve_bart

This function calculates the response curve (functional responses) using a Bayesian Additive Regression Trees (BART) model.

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

response_curve_bart function

<dl><dt>bart_model</dt>
<dd>A BART model object obtained from fitting BART ('dbarts::bart').</dd>
<dt>data</dt>
<dd>A data frame containing the predictor variables (the design matrix) used in the BART model.</dd>
<dt>predictor_names</dt>
<dd>A character vector containing the names of the predictor variables.</dd></dl>

Arguments

Calculate Response Curve Using BART Model — response_curve_bart

<dl>

<dt>bart_model</dt>
<dd>A BART model object obtained from fitting BART ('dbarts::bart').</dd>


<dt>data</dt>
<dd>A data frame containing the predictor variables (the design matrix) used in the BART model.</dd>


<dt>predictor_names</dt>
<dd>A character vector containing the names of the predictor variables.</dd>

</dl>

response_curve_bart: Calculate Response Curve Using BART Model

Description

Usage

Value

Arguments