plot_cv_folds_points

Plot cross-validation fold assignments

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

plot_cv_folds_points function

<dl><dt>data</dt>
<dd>Dataframe with columns: 'decimalLongitude', 'decimalLatitude', 'pa' and 'fold'.</dd>
<dt>polygon</dt>
<dd>An sf object representing the inverted study area.</dd></dl>

Arguments

Plot cross-validation fold assignments — plot_cv_folds_points

<dl>

<dt>data</dt>
<dd>Dataframe with columns: 'decimalLongitude', 'decimalLatitude', 'pa' and 'fold'.</dd>


<dt>polygon</dt>
<dd>An sf object representing the inverted study area.</dd>

</dl>

plot_cv_folds_points: Plot cross-validation fold assignments

Description

Usage

Value

Arguments