pa_optimal_cutoff

This function calculates the optimal cutoff for presence-absence prediction using a 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

pa_optimal_cutoff function

<dl><dt>y</dt>
<dd>Vector indicating presence (1) or absence (0).</dd>
<dt>x</dt>
<dd>Dataframe with same number of rows as the length of the vector `y` with the covariate values.</dd>
<dt>model</dt>
<dd>A BART model object.</dd>
<dt>seed</dt>
<dd>Random seed for reproducibility.</dd></dl>

Arguments

Optimal Cutoff for Presence-Absence Prediction — pa_optimal_cutoff

<dl>

<dt>y</dt>
<dd>Vector indicating presence (1) or absence (0).</dd>


<dt>x</dt>
<dd>Dataframe with same number of rows as the length of the vector `y` with the covariate values.</dd>


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


<dt>seed</dt>
<dd>Random seed for reproducibility.</dd>

</dl>

pa_optimal_cutoff: Optimal Cutoff for Presence-Absence Prediction

Description

Usage

Value

Arguments