Covariance Model for binary field based on Gaussian field
RMschlather gives
the tail correlation function of the extremal Gaussian
process, i.e.
C(h) = 1 - √{ (1-φ(h)/φ(0)) / 2 }
where φ is the covariance of a stationary Gaussian field.
RMschlather(phi, var, scale, Aniso, proj)
This model yields the tail correlation function of the field
that is returned by RPschlather.
RMschlather returns an object of class RMmodel.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
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)
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