USprecip97

A data set containing the annual precipitation for 1270 locations
in 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

USprecip97 function

A data frame with the following variables:<dl class="dl-horizontal">
 <dt>longitude</dt><dd>Longitude of the monitoring site.</dd>
 <dt>latitude</dt><dd>Latitude of the monitoring site.</dd>
 <dt>annual.ppt</dt><dd>Annual precipitation for the monitoring site, in
 millimeters.</dd>
 <dt>log.annual.ppt</dt><dd>Annual precipitation for the monitoring site, in
 log millimeters.</dd></dl>

USprecip97: Annual precipitation measurements from the western United States, 1997

Description

Usage

Arguments

Format