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#
# fields is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2024 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka, douglasnychka@gmail.com,
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R software environment if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
# or see http://www.r-project.org/Licenses/GPL-2
##END HEADER
mKrigMLEJoint<- function(x, y, weights = rep(1, nrow(x)), Z = NULL,
ZCommon = NULL,
mKrig.args = NULL,
na.rm = TRUE,
cov.function = "stationary.cov",
cov.args = NULL,
cov.params.start = NULL,
optim.args = NULL,
reltol = 1e-6,
parTransform = NULL,
REML = FALSE,
GCV = FALSE,
hessian = FALSE,
iseed = 303,
verbose = FALSE) {
# overwrite basic data to remove NAs this has be done in case distance
# matrices are precomputed (see below)
if( na.rm){
obj<- mKrigCheckXY(x, y, weights, Z, ZCommon, na.rm)
x<- obj$x
y<- obj$y
weights<- obj$weights
Z<- obj$Z
ZCommon<- obj$ZCommon
}
# main way to keep track of parameters to optimize
# lambda is included if lambda.fixed is NULL
# (most of the time one would want to optimize over lambda parameter)
parNames <- names(cov.params.start)
# precompute distance matrix if possible so it only needs to be computed once
supportsDistMat = supportsArg(cov.function, "distMat")
if(supportsDistMat & is.null( cov.args$distMat)) {
#Get distance function and arguments if available
#
Dist.fun= c(cov.args)$Distance
Dist.args=c(cov.args)$Dist.args
#If user left all distance settings NULL, use rdist with compact option.
#Use rdist function by default in general.
#
if(is.null(Dist.fun)) {
Dist.fun = "rdist"
if(is.null(Dist.args))
Dist.args = list(compact=TRUE)
}
distMat = do.call(Dist.fun, c(list(x), Dist.args))
#add precomputed distance matrix to the cov.args
cov.args = c(cov.args, list(distMat=distMat, onlyUpper=TRUE))
}
# these are all the arguments needed to call mKrig except cov.args
# if lambda is then it has been added to mKrig.args
# if lambda.start then it is part of the parameter names and will
# added in the cov.args list
mKrig.args <- c(list(x = x, y = y, weights = weights, Z = Z, ZCommon = ZCommon ),
mKrig.args,
list(cov.function=cov.function)
)
if( verbose){
cat("***** Info from call to mKrigJoint:",fill=TRUE)
cat("Argument names in full mKrig.args: ", fill=TRUE)
print( names(mKrig.args) )
cat("Full cov.args names:\n ", names( cov.args), fill=TRUE)
cat("Parameters to optimze: ", parNames, length( parNames), fill=TRUE)
cat("Starting values (cov.params.start) : ", fill=TRUE)
print( cov.params.start)
}
if(!GCV){
nameCriterion<- ifelse( !REML,
"lnProfileLike.FULL",
"lnProfileREML.FULL" )
}
else{
nameCriterion<-"GCV"
}
if(verbose){
cat("nameCriterion: ", nameCriterion, fill=TRUE)
}
callOptim<- !is.null(cov.params.start) & length(parNames) > 0
##########################################################################
###### main if else block
##########################################################################
if(callOptim){
#########################################################
### actually do something
#set default optim.args if necessary
# abstol is anticipating this is a log likelihood so differencs of 1e-4 are not appreciable
#
# setup control parameters for optim ....
if( length(cov.params.start)==0){
stop("On no! Found zero parameters to optimize!")
}
if(is.null(optim.args)){
# number of step sizes either include lambda as a parameter or not.
ndeps<- rep(log(1.1), length( parNames) )
optim.args = list(method = "BFGS", hessian = hessian,
control=list(fnscale = -1,
ndeps = ndeps,
reltol = reltol,
maxit = 20)
)
}
if( is.null(parTransform)){
# parTransform: log/exp
parTransform<- function( ptemp, inv=FALSE){
if( !inv){ log( ptemp)}
else{
exp(ptemp)
}
}
}
#
capture.evaluations <- NULL
capture.env <- environment()
# call to optim with initial start (default is log scaling )
#
init.start <- parTransform( unlist(c(cov.params.start)), inv=FALSE)
if( verbose){
cat("Transformed starting values ","\n", init.start, fill=TRUE)
}
# and now the heavy lifting ...
# optimize over (some) covariance parameters and possibly lambda
# wrapping in try allows for error catching
optimResults <-try(
do.call(optim, c(list(par=init.start),
list(mKrigJointTemp.fn),
optim.args,
list( parNames = parNames,
parTransform = parTransform,
mKrig.args = mKrig.args,
cov.args = cov.args,
capture.env = capture.env,
REML = REML,
GCV = GCV,
verbose = verbose)
)
)
)
# catch error in optim and return
if( is(optimResults, "try-error") ){
cat("Error in call to optim", fill=TRUE)
out =list( summary = NA,
lnLikeEvaluations = capture.evaluations[-1,],
init.start= init.start,
optim.args= optim.args
)
return( out)
}
# reformat the optim results
lnLikeEvaluations <- capture.evaluations[-1,] # first row is just NAs
if( verbose){
cat("Captured evaluations from optim: ", fill=TRUE)
print(lnLikeEvaluations)
}
#ind<- which(lnLikeEvaluations[ , nameCriterion]
# == optimResults$value )
#ind<- max(ind)
optim.counts <- optimResults$counts
parOptimum<- parTransform(optimResults$par, inv=TRUE)
names( parOptimum)<- parNames
# update parameter start values with converged ones
# note that lambda may be part of this
cov.params.final<- as.list(parOptimum)
}
##########################################################################
###### end if block
##########################################################################
else{
# no optimization required, just setup for a single evaluation
cov.params.final <- NULL
lnLike.eval <- NA
optimResults <- NULL
optim.counts <- NA
parOptimum <- NULL
lnLikeEvaluations<- NULL
if( verbose){
cat("fast return in mKrigMLEJoint", fill=TRUE)
}
}
#########################################################
### just evaluate
### at final parameters or if a fast return
### and also find the trace and GCV
#########################################################
cov.args.final<- c( cov.args, cov.params.final)
mKrigCallingArgs<- c(mKrig.args, iseed = iseed,
verbose=verbose,
cov.args.final)
if( verbose){
cat("**** Final call in mKrigMLEJoint", fill=TRUE)
cat( names(mKrigCallingArgs ), sep=",", fill=TRUE)
cat("aRange", mKrigCallingArgs$cov.args$aRange,fill=TRUE)
print( mKrigCallingArgs$aRange)
}
fastObject <- do.call("mKrig", mKrigCallingArgs )$summary
#########################################################
summary <- c( fastObject, optim.counts,
parOptimum
)
out =list( summary = summary,
pars.MLE = parOptimum ,
parTransform = parTransform,
optimResults = optimResults ,
lnLikeEvaluations = lnLikeEvaluations)
return(out)
}
# Define the objective function as a tricksy call to mKrig
# if y is a matrix of replicated data sets use the log likelihood for the complete data sets
mKrigJointTemp.fn <- function(parameters,
mKrig.args, cov.args, parTransform, parNames,
REML=FALSE,
GCV= FALSE,
verbose = verbose,
capture.env) {
verbose<- FALSE # turn off verbose
# optimization is over a transformed scale ( so need to back transform for mKrig)
tPars<- parTransform( parameters, inv=TRUE)
names( tPars)<- parNames
#get all this eval's covariance arguments using the input parameters
cov.args.temp = c(cov.args, tPars)
if( verbose){
cat("pars in call to objective function:", fill=TRUE)
print(tPars )
}
# NOTE: FULL refers to estimates collapsed across the replicates if Y is a matrix
# NOTE cov.args.temp can also include lambda as a component.
# due to the flexibility in arguments to mKrig ( I.e. the ... argument)
# any covariance arguments in cov.args.temp are matched to existing mKrig arguments
# in particular lambda, the remaining unmatched arguments are assumed to be for the
# covariance function and used within mKrig in the call to cov.fun
#
# Don't find approx trace of A for log likelihood evaluations.
mKrig.argsTemp<- mKrig.args
if( !GCV){
mKrig.argsTemp$find.trA<- FALSE
}
# the summary vector from the mKrig object has everything we need ...
hold <- do.call("mKrig", c(mKrig.argsTemp,cov.args.temp) )$summary
# add this evalution to an object (i.e. here a matrix) in the calling frame
temp.eval <- get("capture.evaluations", envir=capture.env)
assign("capture.evaluations",
rbind(temp.eval,
c(parTransform(parameters, inv=TRUE), hold)
),
envir = capture.env)
if( !GCV){
objectiveFunction <- ifelse(REML,
hold["lnProfileREML.FULL"],
hold["lnProfileLike.FULL"])
}
else{
objectiveFunction <- -1* hold["GCV"]
}
# cat("objective Function",objectiveFunction, fill=TRUE)
return( objectiveFunction)
}
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