\name{soil}
\docType{data}
\alias{soil}
\title{Soil data of North Bavaria, Germany}
\usage{data(soil)}
\description{
 Soil physical and chemical data collected on a field in the
 Weissenstaedter Becken, Germany
}
\format{
 This data frame contains the following columns:
 \describe{
 \item{x.coord}{x coordinates given in cm}
 \item{y.coord}{y coordinates given in cm}
 \item{nr}{number of the samples, which were taken in this order}
 \item{moisture}{moisture content [Kg/Kg * 100\%]}
 \item{NO3.N}{nitrate nitrogen [mg/Kg]}
 \item{Total.N}{total nitrogen [mg/Kg]}
 \item{NH4.N}{ammonium nitrogen [mg/Kg]}
 \item{DOC}{dissolved organic carbon [mg/Kg]}
 \item{N20N}{nitrous oxide [mg/Kg dried substance]}
 }
}
\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.
}
\source{
 The data were collected by Wolfgang Falk,
 Soil Physics Group,
 \url{http://www.geo.uni-bayreuth.de/bodenphysik/Welcome.html},
 University of Bayreuth, Germany.
}
\references{
 Falk, W. (2000)
 \emph{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{
set.seed(0)
% library(RandomFields, lib="~/TMP");
% source("~/R/RF/RandomFields/R/rf.R")
################################################################
## ##
## a geostatistical analysis that demonstrates ##
## features of the package `RandomFields' ##
## ##
################################################################
\dontshow{\dontrun{

data(soil)
str(soil)
soil <- RFspatialPointsDataFrame(
 coords = soil[, c("x.coord", "y.coord")],
 data = soil[, c("moisture", "NO3.N", "Total.N",
 "NH4.N", "DOC", "N20N")],
 RFparams=list(vdim=6, n=1)
 )
% RFempiricalvariogram(data=soil["Total.N"])

## plot the data first
colour <- rainbow(100)
plot(soil["moisture"], col=colour)

x <- seq(min(soil@coords[, 1]), max(soil@coords[, 1]), l=121)

## fit by eye
RFgui.model <-  RFgui(soil["moisture"]) 
\dontshow{if (!interactive()) RFgui.model <- RMexp()}

## fit by ML
model <- ~1 + RMplus(RMwhittle(scale=NA, var=NA, nu=NA), RMnugget(var=NA))
fit <- RFfit(model, data=soil["moisture"])
plot(fit, fit.method=c("ml", "plain", "sqrt.nr", "sd.inv"),
     model = RFgui.model, col=1:8)


## Kriging ...
k <- RFinterpolate(fit["ml"], x=x, y=x, grid=TRUE, data=soil["moisture"])
plot(x=k, col=colour)
plot(x=k, y=soil["moisture"], col=colour)


## what is the probability that at no point of the
## grid given by x and y the moisture is greater than 24 percent?
% works well since fit$ml:nugget==0!!!!
cs <- RFsimulate(model=fit["ml"], x=x, y=x, data=soil["moisture"], n=50)
plot(cs, col=colour)
plot(cs, y=soil["moisture"], col=colour)
Print(mean(apply(as.matrix(cs@data) <= 24, 2, all))) ## about 40 percent
##...

}
}
}
\keyword{datasets}