RandomFields (version 2.0.71)

soil: Soil data of North Bavaria, Germany

Description

Soil physical and chemical data collected on a field in the Weissenstaedter Becken, Germany

Usage

data(soil)

source

The data were collected by Wolfgang Falk, Soil Physics Group, http://www.geo.uni-bayreuth.de/bodenphysik/Welcome.html, University of Bayreuth, Germany.

Details

For technical reasons some of the data were obtained as differences of two measurements (which are not available anymore). Therefore, some of the data have negative values.

References

Falk, W. (2000) Kleinskalige raeumliche Variabilitaet von Lachgas und bodenchemischen Parameters [Small Scale Spatial Variability of Nitrous Oxide and Pedo-Chemical Parameters], Master thesis, University of Bayreuth, Germany.

Examples

################################################################
##                                                            ##
##         a geostatistical analysis that demonstrates        ##
##         features of the package `RandomFields'             ##
##                                                            ##
################################################################

# options(warn=2)

data(soil)
names(soil)
pts <- soil[,c(1,2)]
d <- soil$moisture

## define some graphical parameters first
close.screen(close.screen())
maxbin <- max(dist(pts)) / 2
(zlim <- range(d))
cn <- 100
colour <- rainbow(cn)
par(bg="white",cex=1, cex.lab=1.4, cex.axis=1.4, mar=c(4.3,4.3,0.8,0.8))
lu.x <- min(soil$x)
lu.y <- max(soil$y)
y <- x <- seq(min(soil$x), max(soil$x), l=121) 

## ... and a certain appearance of the legend
my.legend <- function(lu.x, lu.y, zlim, col, cex=1) {
  ## uses already the legend code of R-1.3.0
  cn <- length(col)
  filler <- vector("character", length=(cn-3)/2)
  legend(lu.x, lu.y, y.i=0.03, x.i=0.1, 
         legend=c(format(zlim[2], dig=2), filler,
         format(mean(zlim), dig=2), filler,
         format(zlim[1], dig=2)),
         lty=1, col=rev(col),cex=cex)
}

## plot the data first
plot(pts, col=colour[1+(cn-1)*((d-zlim[1])/diff(zlim))], pch=16,
     xlab="x [cm]", ylab="y [cm]", cex.axis=1.5, cex.lab=1.5)
my.legend(lu.x, lu.y, zlim=zlim, col=colour, cex=1.5)## empirical variogram
ev <- EmpiricalVariogram(pts, data=d, grid=FALSE,
                         bin=c(-1,seq(0,maxbin,l=30)))

## NOTE: the next command requires a selection of the
##       model by mouse klicks in the graphics window
##       see help("Showmodels") for further detailsby.eye <- ShowModels(x=x, y=y, emp=ev, col=colour, Zlim=zlim,
                     Mean=mean(d), me="ci")
## fit parameters of the whittlematern model by MLEfit <- fitvario(x=pts, data=d, model="whittle", par=rep(NA,5),
                mle.m="ml", cross.m=NULL)
str(fit)

## plot the fitted model and the empirical variogram
plot(ev$c, ev$emp.var, ylim=c(0,11), ylab="variogram", xlab="lag")
gx <- seq(0.001, max(ev$c), l=100)
if(!is.null(by.eye)) lines(gx, Variogram(gx, model=by.eye)) 
lines(gx, Variogram(gx, model=fit$ml$model), col=2)
lines(gx, Variogram(gx, model=fit$plain$model), col=3)
lines(gx, Variogram(gx, model=fit$sqrt.nr$model), col=4)
lines(gx, Variogram(gx, model=fit$sd.inv$model), col=6, lty=2)
legend(120, 4, c("empirical", "by eye", "ML", "lsq", "sqrt(n) lsq",
               "sd^-1 lsq"),
       lty=c(-1, rep(1, 5)), pch=c(1, rep(-1, 5)),
       col=c(1, 1, 2, 3, 4, 6), cex=1.4)
## map of expected valuesk <- Kriging("O", x=x, y=y, grid=TRUE, model=fit$ml$model, given=pts, data=d)
par(mfrow=c(1,2))
plot(pts, col=colour[1+(cn-1)*((d-zlim[1])/diff(zlim))],
     pch=16, xlab="x [cm]", ylab="y [cm]")
my.legend(lu.x, lu.y, zlim=zlim, col=colour, cex=1)
image(x, y, k, col=colour, zlim=zlim, xlab="x [cm]", ylab="y [cm]")
par(bg="white")## what is the probability that at no point of the
## grid given by x and y the moisture is greater than 24 percent?RFparameters(Print=1, CE.force=FALSE, CE.trials=3, CE.useprimes=TRUE)
cs <- CondSimu("O", x=x, y=y, grid=TRUE, model=fit$ml$model, given=pts,
               data=d, n=10) # better n=100 or n=1000
par(mfcol=c(2,3))
plot(pts, col=colour[1+(cn-1)*((d-zlim[1])/diff(zlim))], pch=16,
     xlab="x [cm]", ylab="y [cm]", cex.axis=1.5, cex.lab=1.5)
my.legend(lu.x, lu.y, zlim=zlim, col=colour, cex=0.5)
image(x, y, k, col=colour, zlim=zlim, xlab="x [cm]", ylab="y [cm]")
for (i in 1:4)
  image(x, y, cs[, , i], col=colour, zlim=zlim,
        xlab="x [cm]", ylab="y [cm]")mean(apply(cs<=24, 3, all)) ## about 40 percent ...