fit.gpr {bdpopt} | R Documentation |
Fit a GPR regression function to the estimated expected utility values
obtained through simulation via JAGS by calling
eval.on.grid
. This is a generic function for S3 objects.
fit.gpr(model, start, gr = TRUE, method = "L-BFGS-B", lower = 0, upper = Inf, control = list())
model |
A model object obtained as the return value from |
start |
Start value passed on to |
gr |
Set to |
method |
The optimisation method to be used by |
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 |
The fitting operation consists of maximising the marginal likelihood of the hyperparameters for a GPR model based on a squared-exponential covariance model. This is done by minimising a function proportional to the negative marginal likelihood. The number of hyperparameters for this model equals 1 + the number of decision variables of the decision model. The first hyperparameter is a standard deviation and the rest consists of a length parameter for each decision dimension.
The optimisation strategy depends on the value of method
. If
"L-BFGS-B"
is used, then the arguments lower
and
upper
are passed on as specified to optim
as the lower and upper
limits for the optimisation of the hyperparameters. If any other value
is provided for method
, then optim
will be used to
minimise a function defined to be equal to the objective function when
the hyperparameter argument x
satisfies x >= lower
,
x <= upper
and equal to Inf
otherwise. The actual lower
and upper limits passed to optim
in this latter case are
-Inf
and Inf
, respectively.
A new simulation model object constructed from the object given as the
first argument and the GPR regression results. The updated components in
the new object are model$regression.fun
and
model$gpr.hyper.params
. See sim.model
for a
description of these components.
Sebastian Jobjörnsson jobjorns@chalmers.se