fit.gpr.sim.model {bdpopt}R Documentation

Fit A Gaussian Process Regression Function

Description

fit.gpr method for objects of class sim.model.

Usage

## S3 method for class 'sim.model'
fit.gpr(model, start, gr = TRUE, method = "L-BFGS-B",
lower = 0, upper = Inf, control = list())

Arguments

model

A model object obtained as the return value from eval.on.grid.

start

Start value passed on to optim when performing the marginal likelihood optimisation to find appropriate values for the hyperparameters for the GPR regression function.

gr

Set to TRUE if gradient information should be passed to optim. If false, optim uses a finite difference approximation of the gradient when performing the optimisation of the hyperparameters.

method

The optimisation method to be used by optim. One of "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN" or "Brent".

lower

A numeric, atomic vector containing the lower limits for the hyperparameters. The first entry is for the standard deviation parameter and the remaining entries are for the length parameters. If supplied, all elements must be >= 0.

upper

A numeric, atomic vector containing the upper limits for the hyperparameters. The first entry is for the standard deviation parameter and the remaining entries are for the length parameters.

control

A list of control parameters passed on to optim.

Details

See fit.gpr for further documentation.


[Package bdpopt version 1.0-1 Index]