simdata

A data set containing the necessary components to fit the nonstationary
spatial model, simulated from the true model.

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

simdata function

A list with the following objects:<dl class="dl-horizontal">
 <dt>sim.locations</dt><dd>A matrix of longitude/latitude coordinates of the
 simulated locations.</dd>
 <dt>mc.locations</dt><dd>A matrix of longitude/latitude coordinates of the
 mixture component locations.</dd>
 <dt>mc.kernel</dt><dd>A three-dimensional array, containing the true 2 x 2
 kernel covariance matrices for each mixture component location.</dd>
 <dt>kernel.ellipses</dt><dd>A three-dimensional array, containing the true 2 x 2
 kernel covariance matrices for each simulated location.</dd>
 <dt>sim.data</dt><dd>A matrix of the simulated data; each of the ten columns
 correspond to an independent and identically distribured replicate.</dd>
 <dt>lambda.w</dt><dd>Scalar; the value of the tuning parameter used in the
 weight function.</dd>
 <dt>holdout.index</dt><dd>Vector; indicates which of the simulated locations
 should be used in the hold-out sample.</dd></dl>

simdata: Simulated nonstationary dataset

Description

Usage

Arguments

Format