Transformation from Brown-Resnick to Gauss
This function can be used to model a max-stable process based on a binary field, with the same extremal correlation function as a Brown-Resnick process
C_{eg}(h) = 1 - 2 (1 - 2 Φ(√{γ(h) / 2}) )^2
Here, Φ is the standard normal distribution function, and γ is a semi-variogram with sill
4(erf^{-1}(1/√ 2))^2 = 2 * [Φ^{-1}( [1 + 1/√ 2] / 2)]^2 = 4.425098 / 2 = 2.212549
RMbr2eg(phi, var, scale, Aniso, proj)
RMbr2eg
The extremal Gaussian model RPschlather
simulated with RMbr2eg(RMmodel())
has
tail correlation function that equals
the tail correlation function of Brown-Resnick process with
variogram RMmodel
.
Note that the reference paper is based on the notion of the (genuine) variogram, whereas the package RandomFields is based on the notion of semi-variogram. So formulae differ by factor 2.
object of class RMmodel
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
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. Extremes, Submitted.
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again model <- RMexp(var=1.62 / 2) binary.model <- RPbernoulli(RMbr2bg(model)) x <- seq(0, 10, 0.05) z <- RFsimulate(RPschlather(binary.model), x, x) plot(z)
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