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\name{RPbernoulli}
\alias{RPbernoulli}
\title{Simulation of Binary Random Fields}
\description{
Indicator or binary field which
has the value 1, if an underfield field
exceeds a given threshold, 0
otherwise.
}
\usage{
RPbernoulli(phi, stationary_only, threshold)
}
\arguments{
\item{phi}{the \command{\link{RMmodel}}.
Either a model for a process or a covariance model must be
specified.
In the latter case, a Gaussian process \command{\link{RPgauss}} is
tacitely assumed.}
\item{stationary_only}{optional arguments; same meaning as for
\command{\link{RPgauss}}. It is ignored if the submodel
is a process definition.}
\item{threshold}{real valued.
\command{\link{RPbernoulli}} returns \eqn{1}
if value of the random field given by \code{phi} is equal
to or larger than the value of \code{threshold}, and \eqn{0}
otherwise. In the multivariate case, a vector might be given.
If the threshold is not finite, then the original field is returned.
\code{threshold} default value is 0.
}
}
\value{
The function returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\details{
\command{\link{RPbernoulli}} can be applied to any field. If only
a covariance model is given, a Gaussian field is simulated as
underlying field.
}
\me
\seealso{
\command{\link{Auxiliary RMmodels}},
\link{RP},
\command{\link{RMbernoulli}}.
}
\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 <- RPbernoulli(RMexp(), threshold=0)
z <- RFsimulate(model, x, x, n=4)
plot(z)
model <- RPbernoulli(RPbrownresnick(RMexp(), xi=1), threshold=1)
z <- RFsimulate(model, x, x, n=4)
plot(z)
\dontshow{FinalizeExample()}}
|
