kernelName

Name of the 1d kernel in a composite 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

kernelName function

<dl> <dt>object</dt>
<dd>A covariance kernel.</dd> <dt>...</dt>
<dd>Arguments for methods.</dd></dl>

Arguments

Name of the One-Dimensional Kernel in a Composite Kernel Object — kernelName

<dl>

 <dt>object</dt>
<dd>A covariance kernel.</dd>

 <dt>...</dt>
<dd>Arguments for methods.</dd>

</dl>

kernelName: Name of the One-Dimensional Kernel in a Composite Kernel Object

Description

Usage

Value

Arguments