getScale

Centers and scale the design matrix.

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

getScale function

<dl><dt>x</dt>
<dd><code>(S4) or (matrix)</code> a coco object, or a n x p matrix with covariate information to introduce,
where the first column is a column of ones.</dd>
<dt>mean.vector</dt>
<dd><code>(numeric vector)</code> if provided, it centers covariates based on this information.</dd>
<dt>sd.vector</dt>
<dd><code>(numeric vector)</code> if provided, it scales covariates based on this information.</dd></dl>

Arguments

Author

Fast and simple standardization for the design matrix. — getScale

<dl>

<dt>x</dt>
<dd><code>(S4) or (matrix)</code> a coco object, or a n x p matrix with covariate information to introduce,
where the first column is a column of ones.</dd>


<dt>mean.vector</dt>
<dd><code>(numeric vector)</code> if provided, it centers covariates based on this information.</dd>


<dt>sd.vector</dt>
<dd><code>(numeric vector)</code> if provided, it scales covariates based on this information.</dd>

</dl>

getScale: Fast and simple standardization for the design matrix.

Description

Usage

Value

Arguments

Author