seal

Estimated harbor-seal trends from abundance data in southeast 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

seal function

A <code>sf</code> object with 149 rows and 2 columns:<ul>
<li>log_trend: The log of the estimated harbor-seal trends from abundance data.</li>
<li>stock: A seal stock factor with two levels: 8 and 10. The factor levels indicate the
type of seal stock (i.e., type of seal). Stocks 8 and 10 are two distinct stocks
(out of 13 total stocks) in southeast Alaska.</li>
<li>geometry: <code>POLYGON</code> geometry representing polygons in an Alaska
Albers projection (EPSG: 3338).</li>
</ul>

Format

Estimated harbor-seal trends from abundance data in southeast Alaska, USA — seal

Estimated harbor-seal trends from abundance data in southeast Alaska, USA

seal: Estimated harbor-seal trends from abundance data in southeast Alaska, USA

Description

Usage

Arguments

Format

References