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\name{RMschlather}
\alias{RMschlather}
\title{Covariance Model for binary field based on Gaussian field}
\description{
\command{RMschlather} gives
the tail correlation function of the extremal Gaussian
process, i.e.
\deqn{C(h) = 1 - \sqrt{ (1-\phi(h)/\phi(0)) / 2 }}
where \eqn{\phi} is the covariance of a stationary Gaussian field.
}
\usage{
RMschlather(phi, var, scale, Aniso, proj)
}
\arguments{
\item{phi}{covariance function of class \code{\link[=RMmodel-class]{RMmodel}}.}
\item{var,scale,Aniso,proj}{optional arguments; same meaning for any
\command{\link{RMmodel}}. If not passed, the above
covariance function remains unmodified.}
}
\details{
This model yields the tail correlation function of the field
that is returned by \command{\link{RPschlather}}.
}
\value{
\command{\link{RMschlather}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\me
\seealso{
\command{\link{RPschlather}},
\command{\link{RMmodel}},
\command{\link{RFsimulate}}.
}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
## This example considers an extremal Gaussian random field
## with Gneiting's correlation function.
## first consider the covariance model and its corresponding tail
## correlation function
model <- RMgneiting()
plot(model, model.tail.corr.fct=RMschlather(model), xlim=c(0, 5))
## the extremal Gaussian field with the above underlying
## correlation function that has the above tail correlation function
x <- seq(0, 10, 0.1)
z <- RFsimulate(RPschlather(model), x)
plot(z)
## Note that in RFsimulate R-P-schlather was called, not R-M-schlather.
## The following lines give a Gaussian random field with correlation
## function equal to the above tail correlation function.
z <- RFsimulate(RMschlather(model), x)
plot(z)
\dontshow{FinalizeExample()}}
\keyword{spatial}
\keyword{models}
|
