checkX

Generic function to check the compatibility of a design matrix with a
 covariance 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

checkX function

<dl> <dt>object</dt>
<dd></dd>A covariance kernel object.
 <dt>X</dt>
<dd></dd>A design matrix.
 <dt>...</dt>
<dd></dd>Other arguments for methods.
 
</dl>

Arguments

Generic function: Check the Compatibility of a Design Matrix with a
  Given Covariance Object — checkX

<dl>

 <dt>object</dt>
<dd>

A covariance kernel object.
</dd>

 <dt>X</dt>
<dd>

A design matrix.
</dd>

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

Other arguments for methods.
</dd>

 
</dl>

checkX: Generic function: Check the Compatibility of a Design Matrix with a Given Covariance Object

Description

Usage

Value

Arguments

See Also