evaluation_metrics

Computes a set of performance metrics (e.g., AUC, TSS, CBI) based on observed and predicted values.

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

evaluation_metrics function

<dl><dt>df</dt>
<dd>A data.frame with columns: `observed` (0/1), `predicted` (0/1), `probability` (numeric).</dd>
<dt>na.rm</dt>
<dd>Logical. Whether to remove rows with NA values.</dd>
<dt>method</dt>
<dd>Correlation method for CBI ("spearman", "pearson", or "kendall").</dd></dl>

Arguments

Evaluation metrics for model predictions — evaluation_metrics

<dl>

<dt>df</dt>
<dd>A data.frame with columns: `observed` (0/1), `predicted` (0/1), `probability` (numeric).</dd>


<dt>na.rm</dt>
<dd>Logical. Whether to remove rows with NA values.</dd>


<dt>method</dt>
<dd>Correlation method for CBI ("spearman", "pearson", or "kendall").</dd>

</dl>

evaluation_metrics: Evaluation metrics for model predictions

Description

Usage

Value

Arguments