getModelLists

Returns a list of parameter vectors for each of the aspects.

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions.
Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics.
An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Federico Blasi

cocons

Covariate-Based Covariance Functions for Nonstationary Spatial
Modeling

Reinhard Furrer

getModelLists function

<dl><dt>theta</dt>
<dd><code>(numeric vector)</code> a vector of length p, where p is the number of parameters for
each of the models.</dd>
<dt>par.pos</dt>
<dd><code>(list)</code> a list detailing in which position of each aspect the elements
of theta should be placed. Expected to be par.pos output of getDesignMatrix.</dd>
<dt>type</dt>
<dd><code>(character)</code> whether parameters are related to a classical parameterization ('classic') or
a difference parameterization 'diff' . Default set to 'diff'.</dd></dl>

Arguments

Author

Builds the necessary input for building covariance matrices — getModelLists

<dl>

<dt>theta</dt>
<dd><code>(numeric vector)</code> a vector of length p, where p is the number of parameters for
each of the models.</dd>


<dt>par.pos</dt>
<dd><code>(list)</code> a list detailing in which position of each aspect the elements
of theta should be placed. Expected to be par.pos output of getDesignMatrix.</dd>


<dt>type</dt>
<dd><code>(character)</code> whether parameters are related to a classical parameterization ('classic') or
a difference parameterization 'diff' . Default set to 'diff'.</dd>

</dl>

getModelLists: Builds the necessary input for building covariance matrices

Description

Usage

Value

Arguments

Author