\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}} } \author{Martin Schlather, \email{schlather@math.uni-mannheim.de} \url{http://ms.math.uni-mannheim.de/de/publications/software}} \seealso{ \command{\link{RPschlather}} \command{\link{RMmodel}}, \command{\link{RFsimulate}}, } \examples{ RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again ## This examples considers an extremal Gaussian random field ## with Gneiting's correlation function. ## first consider the covriance model and its corresponding tail ## corrlation 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 tcf x <- seq(0, 10, if (interactive()) 0.1 else 3) 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 corrlation ## function equal to the above tail correlation function. z <- RFsimulate(RMschlather(model), x) plot(z) \dontshow{FinalizeExample()} } \keyword{spatial} \keyword{models}