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\name{RPprocess}
\alias{RP}
\alias{RPmodel}
\alias{RPmodels}
\alias{RPprocess}
\alias{RPprocesses}
\title{Models for classes of random fields (RP commands)}
\description{
Here, all classes of random fields are described that can be
simulated.
}
\section{Implemented processes}{
\tabular{ll}{
Gaussian Random Fields \tab see \link{Gaussian}\cr
Max-stable Random Fields \tab see \link{Maxstable}\cr
Other Random Fields
\tab \link[=RPbernoulli]{Binary field} \cr
\tab \link[=RPchi2]{chi2 field}\cr
\tab \link[=RPpoisson]{composed Poisson} (shot noise, random coin) \cr
\tab \link[=RPt]{t field}\cr
}
}
\seealso{
\link{RC}, \link{RR}, \link{RM}, \link{RF}, \link{R.}
}
\me
\keyword{spatial}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
x <- seq(0, 10, 0.1)
model <- RMexp()
## a Gaussian field with exponential covariance function
z <- RFsimulate(model, x)
plot(z)
## a binary field obtained as a thresholded Gaussian field
b <- RFsimulate(RPbernoulli(model), x)
plot(b)
sum( abs((z@data$variabl1 >=0 ) - b@data$variable1)) == 0 ## TRUE,
## i.e. RPbernoulli is indeed a thresholded Gaussian process
\dontshow{FinalizeExample()}}
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