unscaledCorr

Dist.mat

Correlation function specification used for g(.). It can be
either "pow.ex" or "matern".

corrModel

Power parameter used in powered exponential kernel function. It
must be 0&lt;gamma&lt;=2.

gamma

Smoothness parameter of the Matern class. It must be a positive
value.

Calculate an unscaled NSGP correlation matrix

Functionalities for modelling functional data with
multidimensional inputs, multivariate functional data, and non-separable
and/or non-stationary covariance structure of function-valued processes.
In addition, there are functionalities for functional regression models where
the mean function depends on scalar and/or functional covariates and
the covariance structure depends on functional covariates.
The development version of the package can be found on
<https://github.com/gpfda/GPFDA-dev>.

Evandro Konzen

GPFDA

Gaussian Process for Functional Data Analysis

unscaledCorr function

<dl><dt>Dist.mat</dt>
<dd>Distance matrix</dd>
<dt>corrModel</dt>
<dd>Correlation function specification used for g(.). It can be
either "pow.ex" or "matern".</dd>
<dt>gamma</dt>
<dd>Power parameter used in powered exponential kernel function. It
must be 0&lt;gamma&lt;=2.</dd>
<dt>nu</dt>
<dd>Smoothness parameter of the Matern class. It must be a positive
value.</dd></dl>

Arguments

Calculate an unscaled NSGP correlation matrix — unscaledCorr

<dl>

<dt>Dist.mat</dt>
<dd>Distance matrix</dd>


<dt>corrModel</dt>
<dd>Correlation function specification used for g(.). It can be
either "pow.ex" or "matern".</dd>


<dt>gamma</dt>
<dd>Power parameter used in powered exponential kernel function. It
must be 0&lt;gamma&lt;=2.</dd>


<dt>nu</dt>
<dd>Smoothness parameter of the Matern class. It must be a positive
value.</dd>

</dl>

unscaledCorr: Calculate an unscaled NSGP correlation matrix

Description

Usage

Value

Arguments

References

Examples