npar,covMan-method

npar,covTS-method

Number of parameters for a covariance kernel object.

npar-methods

Gaussian process regression with an emphasis on kernels.
Quantitative and qualitative inputs are accepted. Some pre-defined
kernels are available, such as radial or tensor-sum for
quantitative inputs, and compound symmetry, low rank, group kernel
for qualitative inputs. The user can define new kernels and
composite kernels through a formula mechanism. Useful methods
include parameter estimation by maximum likelihood, simulation,
prediction and leave-one-out validation.

Olivier Roustant

kergp

Gaussian Process Laboratory

npar-methods function

<dl> <dt>object</dt>
<dd></dd>An object with S4 class corresponding to a covariance kernel. 
 <dt>...</dt>
<dd></dd>Not used yet.

 
</dl>

Arguments

Number of Parameters for a Covariance Kernel Object — npar-methods

<dl>

 <dt>object</dt>
<dd>

An object with S4 class corresponding to a covariance kernel. 
</dd>

 <dt>...</dt>
<dd>

Not used yet.
</dd>
 
</dl>

npar-methods: Number of Parameters for a Covariance Kernel Object

Description

Usage

Value

Arguments

See Also