modelOutputGrad: Compute derivatives with respect to params of model outputs.

In gptk: Gaussian Processes Tool-Kit

Description

Compute derivatives with respect to params of model outputs.

Usage

  modelOutputGrad(model, X, dim)

Arguments

model	the model structure for which gradients are computed.
X	input locations where gradients are to be computed.
dim	the dimension of the model for which gradients are required.

Details

g <- modelOutputGrad(model, X) gives the gradients of the outputs from the model with respect to the parameters for a given set of inputs.

g <- modelOutputGrad(model, X, dim) gives the gradients of the outputs from the model with respect to the parameters for a given set of inputs.

Value

g	gradients of the model output with respect to the model parameters for the given input locations. The size of the returned matrix is of dimension number of data x number of parameters x number of model outputs (which maintains compatability with NETLAB).

See Also

modelLogLikelihood.

Examples

## missing

Example output

Loading required package: Matrix
Loading required package: fields
Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.1-1 (2017-07-02) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: 'spam'

The following objects are masked from 'package:base':

    backsolve, forwardsolve

Loading required package: maps

gptk documentation built on May 2, 2019, 3:27 p.m.