moose_preds

Locations at which to predict moose counts and presence in Alaska, USA.

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

moose_preds function

An <code>sf</code> object with 100 rows and 3 columns.<ul>
<li>elev: The elevation.</li>
<li>strat: A factor representing strata (used for sampling). Can take values <code>L</code> and <code>M</code>.</li>
<li>geometry: <code>POINT</code> geometry representing coordinates in an Alaska
Albers projection (EPSG: 3338). Distances between points are in meters.</li>
</ul>

Format

Locations at which to predict moose counts and presence in Alaska, USA — moose_preds

Locations at which to predict moose counts and presence in Alaska, USA

moose_preds: Locations at which to predict moose counts and presence in Alaska, USA

Description

Usage

Arguments

Format