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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
##END HEADER
# test of sreg and related functions
suppressMessages(library(fields))
options(echo=FALSE)
test.for.zero.flag<- 1
set.seed(123)
# Local Kriging - sparse matrix implementation (small example)
#source( "makeBigB.R")
# simple covariance function for implementation
exp_cov <- function(dist){
sigma2<-1
covariance <- sigma2* exp(-dist / 10) # 10 is arbitrary
return(covariance)
}
# -----------------------------
# Define grid and observations
# -----------------------------
m<- 40
n<- 45
nx<- m
ny<- n
M<- 10
dx<- 1
dy<- 1
sigma2<-2.0
np<-3
set.seed( 123)
# locations random but avoid edges
s<- cbind( dx*runif( M, np, (m-(np+1))),
dy* runif( M, np, (n-(np+1)))
)
# random uniform is ok as we just checking agreement
set.seed( 222)
y<- matrix( runif(m*n),m,n)
yUnrolled<- c( y)
#look<- sparseB%*%yUnrolled
look2<- predVar<- rep( NA, M)
theShift<- 0:(2*np-1) - (np - 1)
for( k in 1:M){
#k<- 3
yTemp<- NULL
sTemp<- NULL
i0<- trunc(s[k,1])
j0<- trunc(s[k,2])
for( j in theShift + j0){
for( i in theShift + i0){
#cat( i,j, fill=TRUE)
sTemp<- rbind( sTemp, c(i,j))
yTemp<- c(yTemp,y[i,j])
}
}
Sigma11<- sigma2*exp_cov(rdist(sTemp, sTemp))
Sigma11Inv<- solve(Sigma11)
Sigma21<- sigma2*exp_cov(rdist(rbind(s[k,]), sTemp))
Btest<- Sigma21%*%Sigma11Inv
result<- Sigma21%*%Sigma11Inv%*%yTemp
look2[k]<- result
predVar[k]<- sigma2 - diag(Sigma21%*%Sigma11Inv%*%t(Sigma21 ) )
}
###################################
# test new function
###################################
sparseObj0<- offGridWeights( s, list( x= 1:m, y=1:n),
aRange=10, sigma2=sigma2,
Covariance="Exponential",
np=np)
look5<- sparseObj0$B%*%yUnrolled
test.for.zero( look2, look5 )
test.for.zero(predVar, sparseObj0$predictionVariance )
mKrigObj<- mKrig( s, rnorm( nrow(s)),
sigma2=sigma2, tau=0,
aRange=10)
sparseObj<- offGridWeights( s, list( x= 1:m, y=1:n),
mKrigObject = mKrigObj, np=np
)
look3<- sparseObj$B%*%yUnrolled
test.for.zero( look2, look3 )
test.for.zero(predVar, sparseObj$predictionVariance )
# cheating on mKrig object
fakeObj<- list( args = list( Covariance= "Exponential" ),
summary= c(aRange=10*2.5, sigma2=sigma2)
)
sparseObj1<- offGridWeights( s*2.5,
list( x = (1:m)*2.5,
y = (1:n)*2.5 ),
mKrigObject = fakeObj,
np = np
)
look4<- sparseObj1$B%*%yUnrolled
test.for.zero( look2, look4 )
test.for.zero( sparseObj$predictionVariance,
predVar )
# this switch will just give a warning instead of an error
#sparseObj1<- offGridWeights( s, list( x= 1:4, y=1:4),
# mKrigObject = mKrigObj, np=np, give.warning=TRUE)
cat("all done with off grid weight tests part 1", fill=TRUE)
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