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#
# fields is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2022 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka, douglasnychka@gmail.edu,
#
# 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
spatialProcessSetDefaults<- function( x, cov.function,
cov.args,
cov.params.start,
parGrid,
mKrig.args,
extraArgs=NULL,
gridN=5,
verbose=FALSE)
{
## convenient defaults for GP fitting.
## and also sort what starting parameter values are provided
#
# aRange and lambda are handled specially because are almost always
# estimated and this will simplify the call in this top level function
#
if( is.null( cov.function)){
cov.function <- 'stationary.cov'
if( is.null(cov.args) ){
cov.args<- list()
}
if( is.null(cov.args$Covariance )){
cov.args$Covariance<- "Matern"
if( is.null(cov.args$smoothness )
& is.null(cov.params.start$smoothness )
& is.null(parGrid$smoothness) ){
cov.args$smoothness<- 1.0
}
}
}
# overwrite the default choices if some are passed as ...
# (some R arcania!)
if( !is.null( extraArgs)){
if(!is.null(cov.args)){
ind<- match( names(cov.args), names(extraArgs) )
cov.args <- c( cov.args[is.na(ind)], (extraArgs) )
}
else{
cov.args <- list(extraArgs)
}
}
# check for duplicate arguments in starting values and fixed values
covArgsNames <- names(cov.args)
covStartNames<-names(cov.params.start)
covParGridNames<- names( parGrid)
#print( covParGridNames)
if( length( intersect( covArgsNames,covStartNames))>0){
cat("A problem with duplicate parameters:", fill=TRUE)
cat("Names cov.args:", fill=TRUE)
print(covArgsNames)
cat("Names cov.params.start :", fill=TRUE)
print(covStartNames)
stop("parameters must either have starting values ( in cov.params.start list)
or be specified as a covariance function argument (in cov.args list) ")
}
if( verbose){
cat("Updated and passed cov.args", fill=TRUE)
print( cov.args)
}
noLambda<- is.null( cov.args$lambda) & is.null(cov.params.start$lambda)
noARange<- is.null( cov.args$aRange) & is.null(cov.params.start$aRange)
makeDefaultGrid<- (noLambda | noARange) & is.null(parGrid)
# easy default search grid if lambda and/or aRange ahave not been specified
if( makeDefaultGrid ){
if( noLambda){
lGrid<- 10**seq( -4, .5, length.out= gridN)
}
if( noARange){
minX<- apply( x, 2, min)
maxX<- apply( x, 2, max)
xCorners<- rbind( minX,
maxX)
if( is.null( cov.args$Distance)){
dMax<-rdist( rbind(xCorners[1,]), rbind(xCorners[2,]))
}
else{
dMax<- do.call(cov.args$Distance, list(
x1= rbind(xCorners[1,]),
x2= rbind(xCorners[2,]))
)
}
dMax<- c( dMax)
aGrid<- seq( .1*dMax, .7*dMax, length.out= gridN)
}
# now create parGrid
if( noLambda & !noARange){
parGrid<- data.frame( lambda= lGrid)
}
if( noLambda & noARange){
parGrid<- expand.grid( lambda= lGrid, aRange = aGrid)
}
if( !noLambda & noARange){
parGrid<- data.frame( aRange= aGrid)
}
}
# CASE 0 is to evaluate at fixed lambda and aRange
# and there are no other parameters to optimize over.
if( !is.null( cov.args$lambda) &
!is.null( cov.args$aRange) &
is.null( cov.params.start)
){
CASE<- 0
}
#CASE 1 is to find MLEs using starting values provided a grid has not been
# supplied for an initial grid search.
if( !is.null(cov.params.start) & is.null(parGrid) ){
CASE<- 1
}
if( !is.null(parGrid) ){
CASE<- 2
}
# linear fixed model if not specified.
if( is.null(mKrig.args)){
mKrig.args<- list( m=2)
}
# don't find eff df for optimization
if( is.null(mKrig.args$find.trA) ){
if( (CASE >=3)){
mKrig.args<- c( mKrig.args, list(find.trA = FALSE))
}
else{
mKrig.args<- c( mKrig.args, list(find.trA = TRUE))
}
}
#
# tuck in starting value for lambda if missing
#
out<-
list(
cov.function = cov.function,
cov.args = cov.args,
mKrig.args = mKrig.args,
CASE = CASE,
parGrid = parGrid
)
return(
out
)
}
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