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\name{Extremal t}
\alias{RPopitz}
\alias{extremal t}
\alias{extremal t process}
\title{Extremal t process}
\description{
\command{RPopitz} defines an extremal t process.
}
\usage{
RPopitz(phi, xi, mu, s, alpha)
}
\arguments{
\item{phi}{an \command{\link{RMmodel}};
covariance model for a standardized
Gaussian random field, or the field itself.
}
\item{xi,mu,s}{the extreme value index, the location parameter and the
scale parameter, respectively, of the generalized extreme value
distribution. See Details.
}
\item{alpha}{originally referred to the \eqn{\alpha}-Frechet marginal
distribution, see the original literature for details.
}
}
\details{\GEV
}
\me
\references{
\itemize{
\item
Davison, A.C., Padoan, S., Ribatet, M. (2012).
Statistical modelling of spatial extremes.
\emph{Stat. Science} \bold{27}, 161-186.
\item
Opitz, T. (2012) A spectral construction of the extremal t process.
\emph{arxiv} \bold{1207.2296}.
}
}
\seealso{
\command{\link{RMmodel}},
\command{\link{RPgauss}},
\command{\link{maxstable}},
\command{\link{maxstableAdvanced}}.
}
\keyword{spatial}
\examples{\dontshow{StartExample()}
## sorry, does not work savely yet
\dontshow{\dontrun{
RFoptions(seed=0, xi=0)
## seed=0: *ANY* simulation will have the random xseed 0; set
## RFoptions(seed=NA) to make them all random again
## xi=0: any simulated max-stable random field has extreme value index 0
x <- seq(0, 2, 0.01)
model <- RPopitz(RMgauss(), alpha=2)
z1 <- RFsimulate(model, x)
plot(z1, type="l")
}}
\dontshow{FinalizeExample()}}
|
