simulPar

Generic function to draw random values for the parameters of a
 covariance kernel object.

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

simulPar function

<dl> <dt>object</dt>
<dd></dd>A covariance kernel.
 <dt>nsim</dt>
<dd></dd>Number of drawings.
 <dt>seed</dt>
<dd></dd>Seed for the random generator.
 <dt>...</dt>
<dd></dd>Other arguments for methods.
</dl>

Arguments

Generic function to draw random values for the parameters of a
 covariance kernel object.

Generic function: Draw Random Values for the Parameters of a
  Covariance Kernel — simulPar

<dl>

 <dt>object</dt>
<dd>

A covariance kernel.
</dd>

 <dt>nsim</dt>
<dd>

Number of drawings.
</dd>

 <dt>seed</dt>
<dd>

Seed for the random generator.
</dd>

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

Other arguments for methods.
</dd>

</dl>

simulPar: Generic function: Draw Random Values for the Parameters of a Covariance Kernel

Description

Usage

Value

Arguments

Details