US.prediction.locs

A matrix with two columns containing a fine grid of locations for
which to make a filled-in prediction map for the western United States.

datasets

Fits convolution-based nonstationary
Gaussian process models to point-referenced spatial data. The nonstationary
covariance function allows the user to specify the underlying correlation
structure and which spatial dependence parameters should be allowed to
vary over space: the anisotropy, nugget variance, and process variance.
The parameters are estimated via maximum likelihood, using a local
likelihood approach. Also provided are functions to fit stationary spatial
models for comparison, calculate the Kriging predictor and standard errors,
and create various plots to visualize nonstationarity.

Mark D Risser

convoSPAT

Convolution-Based Nonstationary Spatial Modeling

Mark D. Risser

US.prediction.locs function

A matrix with two columns:<dl class="dl-horizontal">
 <dt>Column 1</dt><dd>Longitude of the prediction grid.</dd>
 <dt>Column 2</dt><dd>Latitude of the prediction grid.</dd></dl>

US.prediction.locs: Prediction locations for the western United States

Description

Usage

Arguments

Format