SVC_mle_control  R Documentation 
Set Parameters for SVC_mle
Description
Function to set up control parameters for SVC_mle
.
In the following, we assume the GPbased SVC model to have q GPs which
model the SVCs and p fixed effects.
Usage
SVC_mle_control(...)
## Default S3 method:
SVC_mle_control(
cov.name = c("exp", "sph", "mat32", "mat52", "wend1", "wend2"),
tapering = NULL,
parallel = NULL,
init = NULL,
lower = NULL,
upper = NULL,
save.fitted = TRUE,
profileLik = FALSE,
mean.est = c("GLS", "OLS"),
pc.prior = NULL,
extract_fun = FALSE,
hessian = TRUE,
dist = list(method = "euclidean"),
parscale = TRUE,
...
)
## S3 method for class 'SVC_mle'
SVC_mle_control(object, ...)
Arguments
... 
Further Arguments yet to be implemented

cov.name 
(character(1) )
Name of the covariance function of the GPs. Currently, the following are
implemented: "exp" for the exponential, "sph" for
spherical, "mat32" and "mat52" for Matern class covariance
functions with smoothness 3/2 or 5/2, as well as "wend1" and
"wend2" for Wendland class covariance functions with kappa 1 or 2.

tapering 
(NULL or numeric(1) )
If NULL , no tapering is applied. If a scalar is given, covariance
tapering with this taper range is applied, for all Gaussian processes
modeling the SVC. Only defined for Matern class covariance functions,
i.e., set cov.name either to "exp" , "mat32" , or
"mat52" .

parallel 
(NULL or list )
If NULL , no parallelization is applied. If cluster has been
established, define arguments for parallelization with a list, see
documentation of optimParallel . See Examples.

init 
(NULL or numeric(2q+1+p*as.numeric(profileLik)) )
Initial values for optimization procedure. If NULL is given, an
initial vector is calculated (see Details). Otherwise, the vector is
assumed to consist of qtimes (alternating) range and variance,
the nugget variance and if profileLik = TRUE p mean effects.

lower 
(NULL or numeric(2q+1+p*as.numeric(profileLik)) )
Lower bound for init in optim . Default NULL calculates
the lower bounds (see Details).

upper 
(NULL or numeric(2q+1+p*as.numeric(profileLik)) )
Upper bound for init in optim . Default NULL calculates
the upper bounds (see Details).

save.fitted 
(logical(1) )
If TRUE , calculates the fitted values and residuals after MLE and
stores them. This is necessary to call residuals and
fitted methods afterwards.

profileLik 
(logical(1) )
If TRUE , MLE is done over profile Likelihood of covariance
parameters.

mean.est 
(character(1) )
If profileLik = TRUE , the means have to be estimated seperately for
each step. "GLS" uses the generalized least square estimate while
"OLS" uses the ordinary least squares estimate.

pc.prior 
(NULL or numeric(4) )
If numeric vector is given, penalized complexity priors are applied. The
order is ρ_0, α_ρ, σ_0, α_σ to give some
prior believes for the range and the standard deviation of GPs, such that
P(ρ < ρ_0) = α_ρ, P(σ > σ_0) = α_σ.
This regulates the optimization process. Currently, only supported for
GPs with of Matérn class covariance functions. Based on the idea by
Fulgstad et al. (2018) doi: 10.1080/01621459.2017.1415907.

extract_fun 
(logical(1) )
If TRUE , the function call of SVC_mle stops before
the MLE and gives back the objective function of the MLE as well as all
used arguments. If FALSE , regular MLE is conducted.

hessian 
(logical(1) )
If TRUE , Hessian matrix is computed, see optim. This
required to give the standard errors for covariance parameters and to do
a Wald test on the variances, see summary.SVC_mle .

dist 
(list )
List containing the arguments of dist or
nearest.dist. This controls
the method of how the distances and therefore dependency structures are
calculated. The default gives Euclidean distances in a ddimensional
space. Further editable arguments are p, miles, R , see respective
help files of dist or nearest.dist.

parscale 
(logical(1) )
Triggers parameter scaling within the optimization in optim.
If TRUE , the optional parameter scaling in optim.control in
function SVC_mle is overwritten by the initial value used in
the numeric optimization. The initial value is either computed from the
data or provided by the user, see init argument above or Details
below. Note that we check whether the initial values are unequal to zero.
If they are zero, the corresponding scaling factor is 0.001. If
FALSE , the parscale argument in optim.control is let
unchanged.

object 
(SVC_mle )
The function then extracts the control settings from the function call
used to compute in the given SVC_mle object.

Details
If not provided, the initial values as well as the lower and upper
bounds are calculated given the provided data. In particular, we require
the median distance between observations, the variance of the response and,
the ordinary least square (OLS) estimates, see init_bounds_optim
.
The argument extract_fun
is useful, when one wants to modify
the objective function. Further, when trying to parallelize the
optimization, it is useful to check whether a single evaluation of the
objective function takes longer than 0.05 seconds to evaluate,
cf. Gerber and Furrer (2019) doi: 10.32614/RJ2019030. Platform specific
issues can be sorted out by the user by setting up their own optimization.
Value
A list with which SVC_mle
can be controlled.
Author(s)
Jakob Dambon
See Also
SVC_mle
Examples
control < SVC_mle_control(init = rep(0.3, 10))
# or
control < SVC_mle_control()
control$init < rep(0.3, 10)
# Code for setting up parallel computing
require(parallel)
# exchange number of nodes (1) for detectCores()1 or appropriate number
cl < makeCluster(1, setup_strategy = "sequential")
clusterEvalQ(
cl = cl,
{
library(spam)
library(varycoef)
})
# use this list for parallel argument in SVC_mle_control
parallel.control < list(cl = cl, forward = TRUE, loginfo = TRUE)
# SVC_mle goes here ...
# DO NOT FORGET TO STOP THE CLUSTER!
stopCluster(cl); rm(cl)