https://github.com/cran/fields
Tip revision: ca1b621280412ef00fbb00b121ae8782c730a345 authored by Douglas Nychka on 30 October 2021, 12:40:02 UTC
version 13.3
version 13.3
Tip revision: ca1b621
Krig.se.test.Rout.save
R version 4.1.1 (2021-08-10) -- "Kick Things"
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Platform: x86_64-apple-darwin17.0 (64-bit)
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> #
> # fields is a package for analysis of spatial data written for
> # the R software environment.
> # Copyright (C) 2021 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
>
>
> 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", aRange=50)-> out
>
>
> # direct calculation
> Krig.Amatrix( out, x=x0)-> A
> test.for.zero( A%*%ChicagoO3$y, predict( out, x0),tag="Amatrix vs. predict")
Testing: Amatrix vs. predict
PASSED test at tolerance 1e-08
>
> Sigma<- out$sigmahat*Exp.cov( ChicagoO3$x, ChicagoO3$x, aRange=50)
> S0<- out$sigmahat*c(Exp.cov( x0, x0, aRange=50))
> S1<- out$sigmahat*Exp.cov( out$x, x0, aRange=50)
>
> #yhat= Ay
> #var( f0 - yhat)= var( f0) - 2 cov( f0,yhat)+ cov( yhat)
>
> look<- S0 - t(S1)%*% t(A) - A%*%S1 +
+ A%*% ( Sigma + diag(out$tauHat.MLE**2/out$weightsM))%*% t(A)
> #
> #compare to
> # diagonal elements
>
>
> test2<- predictSE( out, x= x0)
> test.for.zero( sqrt(diag( look)), test2,tag="Marginal predictSE")
Testing: Marginal predictSE
PASSED test at tolerance 1e-08
>
> out2<- Krig( ChicagoO3$x, ChicagoO3$y, cov.function = "Exp.cov", aRange=50,
+ lambda=out$lambda)
>
> test2<- predictSE( out2, x= x0)
> test.for.zero( sqrt(diag( look)), test2,tag="Marginal predictSE fixed ")
Testing: Marginal predictSE fixed
PASSED test at tolerance 1e-08
>
> test<- predictSE( out, x= x0, cov=TRUE)
> test.for.zero( look, test,tag="Full covariance predictSE")
Testing: Full covariance predictSE
PASSED test at tolerance 1e-08
>
>
> # 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.")
Testing: Marginal standard Cond. Sim.
PASSED test at tolerance 0.015
>
> 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$vectors)-> hold
> cor(test%*% hold)-> hold2
> # off diagonal elements of correlations -- expected values are zero.
>
> abs(hold2[ col(hold2)> row( hold2)])-> hold3
>
> test.for.zero( mean(hold3), 0, relative=FALSE, tol=.02,
+ tag="Full covariance standard Cond. Sim.")
Testing: Full covariance standard Cond. Sim.
PASSED test at tolerance 0.02
>
>
> # test of sim.Krig.approx.R
> #
> # first create and check a gridded test case.
>
>
> data( ozone2)
> as.image(ozone2$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",
+ aRange=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")
Testing: Amatrix vs. predict
PASSED test at tolerance 1e-08
>
> Sigma<- out$sigmahat*stationary.cov(
+ out$xM, out$xM, aRange=1.0,smoothness=1.0, Covariance="Matern")
>
> S0<- out$sigmahat*stationary.cov(
+ x0, x0, aRange=1.0,smoothness=1.0, Covariance="Matern")
>
> S1<- out$sigmahat*stationary.cov(
+ out$xM, x0, aRange=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$tauHat.MLE**2/out$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)
Testing: Weighted case and exact for ozone2 full
cov
PASSED test at tolerance 1e-08
>
> ########################################################################
> ######### 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$ind]
>
> ##D Krig( xd, y, Covariance="Matern",
> ##D aRange=1.0, smoothness=1.0, weights=weights) -> out
>
>
> ##D xr<- range( dtemp$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)
all done testing predictSE
> options( echo=TRUE)
>
> proc.time()
user system elapsed
4.084 0.188 4.416