https://github.com/cran/RandomFields
Tip revision: 6eca414de4c835af2032db4cae6c05e9cc684529 authored by Martin Schlather on 23 April 2016, 15:04:07 UTC
version 3.1.11
version 3.1.11
Tip revision: 6eca414
RP.Rd
\name{RPprocess}
\alias{RP}
\alias{RPmodel}
\alias{RPmodels}
\alias{RPprocess}
\alias{RPprocesses}
\title{Models for classes of random fields (RP commands)}
\description{
Here, all the 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.}
}
\author{Martin Schlather, \email{schlather@math.uni-mannheim.de}
\url{http://ms.math.uni-mannheim.de/de/publications/software}
}
\keyword{spatial}
\examples{
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 covaraince 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()}
}