..cor_sep

Calculate correlation for separable model

internal

Simulating and estimating (regime-switching) Markov chain Gaussian
fields with covariance functions of the Gneiting class (Gneiting 2002)
<doi:10.1198/016214502760047113>. It supports parameter estimation by
weighted least squares and maximum likelihood methods, and produces Kriging
forecasts and intervals for existing and new locations.

Tianxia Jia

mcgf

Markov Chain Gaussian Fields Simulation and Parameter Estimation

..cor_sep function

<dl><dt>nugget</dt>
<dd>The nugget effect \(\in[0, 1]\).</dd>
<dt>c</dt>
<dd>Scale parameter of <code>cor_exp</code>, \(c&gt;0\).</dd>
<dt>gamma</dt>
<dd>Smooth parameter of <code>cor_exp</code>, \(\gamma\in(0, 1/2]\).</dd>
<dt>a</dt>
<dd>Scale parameter of <code>cor_cauchy</code>, \(a&gt;0\).</dd>
<dt>alpha</dt>
<dd>Smooth parameter of <code>cor_cauchy</code>, \(\alpha\in(0, 1]\).</dd>
<dt>h</dt>
<dd>Euclidean distance matrix or array.</dd>
<dt>u</dt>
<dd>Time lag, same dimension as <code>h</code>.</dd></dl>

Arguments

Calculate correlation for separable model — ..cor_sep

<dl>

<dt>nugget</dt>
<dd>The nugget effect \(\in[0, 1]\).</dd>


<dt>c</dt>
<dd>Scale parameter of <code>cor_exp</code>, \(c&gt;0\).</dd>


<dt>gamma</dt>
<dd>Smooth parameter of <code>cor_exp</code>, \(\gamma\in(0, 1/2]\).</dd>


<dt>a</dt>
<dd>Scale parameter of <code>cor_cauchy</code>, \(a&gt;0\).</dd>


<dt>alpha</dt>
<dd>Smooth parameter of <code>cor_cauchy</code>, \(\alpha\in(0, 1]\).</dd>


<dt>h</dt>
<dd>Euclidean distance matrix or array.</dd>


<dt>u</dt>
<dd>Time lag, same dimension as <code>h</code>.</dd>

</dl>

..cor_sep: Calculate correlation for separable model

Description

Usage

Value

Arguments

Details