GetNeg2loglikelihoodTaper

compute the negative 2 log likelihood based on theta

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

GetNeg2loglikelihoodTaper function

<dl><dt>theta</dt>
<dd><code>(numeric vector)</code> a vector with parameters values.</dd>
<dt>par.pos</dt>
<dd><code>(list)</code> par.pos list from getDesignMatrix.</dd>
<dt>ref_taper</dt>
<dd><code>(S4)</code> spam object based on a compact-supported covariance function.</dd>
<dt>locs</dt>
<dd><code>(matrix)</code> spatial location matrix.</dd>
<dt>x_covariates</dt>
<dd><code>(data.frame)</code> design matrix.</dd>
<dt>smooth.limits</dt>
<dd><code>(numeric vector)</code> smooth.limits.</dd>
<dt>cholS</dt>
<dd><code>(S4)</code> Cholesky object from spam.</dd>
<dt>z</dt>
<dd><code>(numeric vector)</code> a vector of observed values.</dd>
<dt>n</dt>
<dd><code>(numeric)</code> dim(z)[1].</dd>
<dt>lambda</dt>
<dd><code>(numeric vector)</code> a vector including lambda.Sigma, lambda.betas, lambda.reg (in this order)</dd>
<dt>safe</dt>
<dd><code>(TRUE/FALSE)</code> if <code>TRUE</code> returns a large pre-defined value under Cholesky errors. Default <code>TRUE</code>.</dd></dl>

Arguments

Author

GetNeg2loglikelihoodTaper — GetNeg2loglikelihoodTaper

<dl>

<dt>theta</dt>
<dd><code>(numeric vector)</code> a vector with parameters values.</dd>


<dt>par.pos</dt>
<dd><code>(list)</code> par.pos list from getDesignMatrix.</dd>


<dt>ref_taper</dt>
<dd><code>(S4)</code> spam object based on a compact-supported covariance function.</dd>


<dt>locs</dt>
<dd><code>(matrix)</code> spatial location matrix.</dd>


<dt>x_covariates</dt>
<dd><code>(data.frame)</code> design matrix.</dd>


<dt>smooth.limits</dt>
<dd><code>(numeric vector)</code> smooth.limits.</dd>


<dt>cholS</dt>
<dd><code>(S4)</code> Cholesky object from spam.</dd>


<dt>z</dt>
<dd><code>(numeric vector)</code> a vector of observed values.</dd>


<dt>n</dt>
<dd><code>(numeric)</code> dim(z)[1].</dd>


<dt>lambda</dt>
<dd><code>(numeric vector)</code> a vector including lambda.Sigma, lambda.betas, lambda.reg (in this order)</dd>


<dt>safe</dt>
<dd><code>(TRUE/FALSE)</code> if <code>TRUE</code> returns a large pre-defined value under Cholesky errors. Default <code>TRUE</code>.</dd>

</dl>

GetNeg2loglikelihoodTaper: GetNeg2loglikelihoodTaper

Description

Usage

Value

Arguments

Author