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# fields is a package for analysis of spatial data written for
# the R software environment .
# Copyright (C) 2018
# University Corporation for Atmospheric Research (UCAR)
# Contact: Douglas Nychka, nychka@ucar.edu,
# National Center for Atmospheric Research,
# PO Box 3000, Boulder, CO 80307-3000
#
# 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.
suppressMessages(library(fields))
# tests of predictSE
# against direct linear algebra
#options( echo=FALSE)
test.for.zero.flag<- 1
x0<- expand.grid( c(-8,-4,0,20,30), c(10,8,4,0))
Krig( ChicagoO3$x, ChicagoO3$y, cov.function = "Exp.cov", theta=50)-> out
# direct calculation
Krig.Amatrix( out, x=x0)-> A
test.for.zero( A%*%ChicagoO3$y, predict( out, x0),tag="Amatrix vs. predict")
Sigma<- out$rhohat*Exp.cov( ChicagoO3$x, ChicagoO3$x, theta=50)
S0<- out$rhohat*c(Exp.cov( x0, x0, theta=50))
S1<- out$rhohat*Exp.cov( out$x, x0, theta=50)
#yhat= Ay
#var( f0 - yhat)= var( f0) - 2 cov( f0,yhat)+ cov( yhat)
look<- S0 - t(S1)%*% t(A) - A%*%S1 +
A%*% ( Sigma + diag(out$shat.MLE**2/out$weightsM))%*% t(A)
#
#compare to
# diagonal elements
test2<- predictSE( out, x= x0)
test.for.zero( sqrt(diag( look)), test2,tag="Marginal predictSE")
out2<- Krig( ChicagoO3$x, ChicagoO3$y, cov.function = "Exp.cov", theta=50,
lambda=out$lambda)
test2<- predictSE( out2, x= x0)
test.for.zero( sqrt(diag( look)), test2,tag="Marginal predictSE fixed ")
test<- predictSE( out, x= x0, cov=TRUE)
test.for.zero( look, test,tag="Full covariance predictSE")
# simulation based.
set.seed( 333)
sim.Krig( out, x0,M=4e3)-> test
var(test)-> look
predictSE( out, x=x0)-> test2
mean( diag( look)/ test2**2)-> look2
test.for.zero(look2, 1.0, tol=1.5e-2, tag="Marginal standard Cond. Sim.")
predictSE( out, x=x0, cov=TRUE)-> test2
# multiply simulated values by inverse square root of covariance
# to make them white
eigen( test2, symmetric=TRUE)-> hold
hold$vectors%*% diag( 1/sqrt( hold$values))%*% t( hold$vectors)-> hold
cor(test%*% hold)-> hold2
# off diagonal elements of correlations -- expected values are zero.
abs(hold2[ col(hold2)> row( hold2)])-> hold3
test.for.zero( mean(hold3), 0, relative=FALSE, tol=.02,
tag="Full covariance standard Cond. Sim.")
# test of sim.Krig.approx.R
#
# first create and check a gridded test case.
data( ozone2)
as.image(ozone2$y[16,], x= ozone2$lon.lat, ny=24, nx=20,
na.rm=TRUE)-> dtemp
#
# A useful disctrtized version of ozone2 data
x<- dtemp$xd
y<- dtemp$z[ dtemp$ind]
weights<- dtemp$weights[ dtemp$ind]
Krig( x, y, Covariance="Matern",
theta=1.0, smoothness=1.0, weights=weights) -> out
set.seed(234)
ind0<- cbind( sample( 1:20, 5), sample( 1:24, 5))
x0<- cbind( dtemp$x[ind0[,1]], dtemp$y[ind0[,2]])
# an inline check plot(out$x, cex=2); points( x0, col="red", pch="+",cex=2)
# direct calculation as backup ( also checks weighted case)
Krig.Amatrix( out, x=x0)-> A
test.for.zero( A%*%out$yM, predict( out, x0),tag="Amatrix vs. predict")
Sigma<- out$rhohat*stationary.cov(
out$xM, out$xM, theta=1.0,smoothness=1.0, Covariance="Matern")
S0<- out$rhohat*stationary.cov(
x0, x0, theta=1.0,smoothness=1.0, Covariance="Matern")
S1<- out$rhohat*stationary.cov(
out$xM, x0, theta=1.0,smoothness=1.0, Covariance="Matern")
#yhat= Ay
#var( f0 - yhat)= var( f0) - 2 cov( f0,yhat)+ cov( yhat)
look<- S0 - t(S1)%*% t(A) - A%*%S1 +
A%*% ( Sigma + diag(out$shat.MLE**2/out$weightsM) )%*% t(A)
test<- predictSE( out, x0, cov=TRUE)
test.for.zero( c( look), c( test), tag="Weighted case and exact for ozone2 full
cov", tol=1e-8)
########################################################################
######### redo test with smaller grid to speed things up
#cat("Conditional simulation test -- this takes some time", fill=TRUE)
# redo data set to smaller grid size
##D N1<-4
##D N2<-5
##D as.image(ozone2$y[16,], x= ozone2$lon.lat, ny=N2, nx=N1,
##D na.rm=TRUE)-> dtemp
#
# A useful discretized version of ozone2 data
##D xd<- dtemp$xd
##D y<- dtemp$z[ dtemp$ind]
##D weights<- dtemp$weights[ dtemp$ind]
##D Krig( xd, y, Covariance="Matern",
##D theta=1.0, smoothness=1.0, weights=weights) -> out
##D xr<- range( dtemp$x)
##D yr<- range( dtemp$y)
##D M1<-N1
##D M2<- N2
##D glist<- list( x=seq( xr[1], xr[2],,M1) , y=seq( yr[1], yr[2],,M2))
##D set.seed( 233)
# with extrap TRUE this finesses problems with
# how NAs are handled in var below
##D sim.Krig.approx( out, grid= glist, M=3000, extrap=TRUE)-> look
##D predictSE( out, make.surface.grid( glist))-> test
##D look2<- matrix( NA, M1,M2)
##D for( k in 1:M2){
##D for ( j in 1:M1){
##D look2[j,k] <- sqrt(var( look$z[j,k,], na.rm=TRUE)) }
##D }
##D test.for.zero( 1-mean(c(look2/test), na.rm=TRUE), 0, relative=FALSE,
##D tol=.001, tag="Conditional simulation marginal se for grid")
cat("all done testing predictSE ", fill=TRUE)
options( echo=TRUE)
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