caribou

datasets

Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.

Michael Dumelle

spmodel

Spatial Statistical Modeling and Prediction

Matt Higham

Ryan A. Hill

Michael Mahon

Jay M. Ver Hoef

caribou function

A <code>tibble</code> with 30 rows and 5 columns:<ul>
<li>water: A factor representing whether water was added. Takes values
<code>N</code> (no water added) and <code>Y</code> (water added).</li>
<li>tarp: A factor representing tarp cover. Takes values <code>clear</code>
(a clear tarp), <code>shade</code> (a shade tarp), and <code>none</code> (no tarp).</li>
<li>z: The percentage of nitrogen.</li>
<li>x: The x-coordinate.</li>
<li>y: The y-coordinate.</li>
</ul>

Format

A caribou forage experiment — caribou

A caribou forage experiment

caribou: A caribou forage experiment

Description

Usage

Arguments

Format

References