sulfate_preds

Locations at which to predict sulfate atmospheric deposition in the conterminous 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

sulfate_preds function

An <code>sf</code> object with 197 rows and 1 column.<ul>
<li>geometry: <code>POINT</code> geometry representing coordinates in a
Conus Albers projection (EPSG: 5070).</li>
</ul>

Format

Locations at which to predict sulfate atmospheric deposition in the conterminous USA — sulfate_preds

Locations at which to predict sulfate atmospheric deposition in the conterminous USA

sulfate_preds: Locations at which to predict sulfate atmospheric deposition in the conterminous USA

Description

Usage

Arguments

Format

References