extract_noNA_cov_values

This function extracts covariate values for species occurrences, excluding NA values.

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

extract_noNA_cov_values function

<dl><dt>data</dt>
<dd>A data frame containing species occurrence data with columns x/long (first column) and y/lat (second column).</dd>
<dt>covariate_layers</dt>
<dd>A list of raster layers representing covariates.</dd>
<dt>predictor_variables</dt>
<dd>Variables to select from all the layers.</dd></dl>

Arguments

Extract Non-NA Covariate Values — extract_noNA_cov_values

<dl>

<dt>data</dt>
<dd>A data frame containing species occurrence data with columns x/long (first column) and y/lat (second column).</dd>


<dt>covariate_layers</dt>
<dd>A list of raster layers representing covariates.</dd>


<dt>predictor_variables</dt>
<dd>Variables to select from all the layers.</dd>

</dl>

extract_noNA_cov_values: Extract Non-NA Covariate Values

Description

Usage

Value

Arguments

Details