..cor_stat

Calculate correlation for the general stationary 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_stat function

<dl><dt>cor_base</dt>
<dd>An array of base cross-correlation matrices.</dd>
<dt>lagrangian</dt>
<dd>Lagrangian model, <code>none</code>, <code>lagr_tri</code>, or <code>lagr_askey</code>.</dd>
<dt>lambda</dt>
<dd>Weight of the Lagrangian term, \(\lambda\in[0, 1]\).</dd>
<dt>v1</dt>
<dd>Prevailing wind, u-component.</dd>
<dt>v2</dt>
<dd>Prevailing wind, v-component.</dd>
<dt>k</dt>
<dd>Scale parameter of \(\|\boldsymbol v\|\), \(k&gt;0\). Default is 2.</dd>
<dt>h1</dt>
<dd>Horizontal distance matrix or array.</dd>
<dt>h2</dt>
<dd>Vertical distance matrix or array, same dimension as <code>h1</code>.</dd>
<dt>u</dt>
<dd>Time lag, same dimension as <code>h1</code>.</dd></dl>

Arguments

Calculate correlation for the general stationary model — ..cor_stat

<dl>

<dt>cor_base</dt>
<dd>An array of base cross-correlation matrices.</dd>


<dt>lagrangian</dt>
<dd>Lagrangian model, <code>none</code>, <code>lagr_tri</code>, or <code>lagr_askey</code>.</dd>


<dt>lambda</dt>
<dd>Weight of the Lagrangian term, \(\lambda\in[0, 1]\).</dd>


<dt>v1</dt>
<dd>Prevailing wind, u-component.</dd>


<dt>v2</dt>
<dd>Prevailing wind, v-component.</dd>


<dt>k</dt>
<dd>Scale parameter of \(\|\boldsymbol v\|\), \(k&gt;0\). Default is 2.</dd>


<dt>h1</dt>
<dd>Horizontal distance matrix or array.</dd>


<dt>h2</dt>
<dd>Vertical distance matrix or array, same dimension as <code>h1</code>.</dd>


<dt>u</dt>
<dd>Time lag, same dimension as <code>h1</code>.</dd>

</dl>

..cor_stat: Calculate correlation for the general stationary model

Description

Usage

Value

Arguments

Details