varVec

Generic function returning a variance vector

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

varVec function

<dl> <dt>object</dt>
<dd>
 
Covariance kernel object.
</dd> <dt>X</dt>
<dd></dd>A matrix with \(d\) columns, where \(d\) is the number of inputs
 of the covariance kernel. The \(n\) rows define a set of sites or
 locations.
 <dt>...</dt>
<dd>
 
Other arguments for methods.
</dd></dl>

Arguments

Generic function returning a variance vector

Generic Function: Variance of Gaussian Process at Specific Locations — varVec

<dl>

 <dt>object</dt>
<dd>
 
Covariance kernel object.
</dd>

 <dt>X</dt>
<dd>

A matrix with \(d\) columns, where \(d\) is the number of inputs
 of the covariance kernel. The \(n\) rows define a set of sites or
 locations.
</dd>

 <dt>...</dt>
<dd>
 
Other arguments for methods.
</dd>

</dl>

varVec: Generic Function: Variance of Gaussian Process at Specific Locations

Description

Usage

Value

Arguments