https://github.com/cran/RandomFields
Tip revision: e994a4415e67fa60cbfd3f208aaab20872521c0b authored by Martin Schlather on 14 February 2019, 21:02:19 UTC
version 3.3
version 3.3
Tip revision: e994a44
RMexp.Rd
\name{RMexp}
\alias{RMexp}
\title{Exponential Covariance Model}
\description{
\command{\link{RMexp}} is a stationary isotropic covariance model whose
corresponding covariance function only depends
on the distance \eqn{r \ge 0}{r \ge 0} between
two points and is given by
\deqn{C(r) = e^{-r}.}{C(r) = exp(-r).}
}
\usage{
RMexp(var, scale, Aniso, proj)
}
\arguments{
\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 is a special case of the Whittle covariance model (see \command{\link{RMwhittle}}) if \eqn{\nu=\frac{1}{2}}{\nu=0.5} and of the symmetric stable family (see \command{\link{RMstable}}) if \eqn{\nu = 1}{\nu=1}. Moreover,
it is the continuous-time analogue of the first order autoregressive time
series covariance structure.
The exponential covariance function is a normal scale mixture.
}
\value{
\command{\link{RMexp}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.
}
\references{
Covariance model
\itemize{
\item Gelfand, A. E., Diggle, P., Fuentes, M. and Guttorp,
P. (eds.) (2010) \emph{Handbook of Spatial Statistics.}
Boca Raton: Chapman & Hall/CRL.
}
Tail correlation function
\itemize{
\item Strokorb, K., Ballani, F., and Schlather, M. (2014)
Tail correlation functions of max-stable processes: Construction
principles, recovery and diversity of some mixing max-stable
processes with identical TCF.
\emph{Extremes}, \bold{} Submitted.
}
}
\me
\seealso{
\command{\link{RMwhittle}},
\command{\link{RMstable}},
\command{\link{RMmodel}},
\command{\link{RFsimulate}},
\command{\link{RFfit}}.
}
\keyword{spatial}
\keyword{models}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
model <- RMexp()
x <- seq(0, 10, 0.02)
plot(model)
plot(RFsimulate(model, x=x))
\dontshow{FinalizeExample()}}