RMbr2eg: Transformation from Brown-Resnick to Gauss
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value References See Also Examples

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

1	RMbr2eg(phi, var, scale, Aniso, proj)

`phi`	covariance function of class `RMmodel`.
`var,scale,Aniso,proj`	optional arguments; same meaning for any `RMmodel`. If not passed, the above covariance function remains unmodified.

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

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.

maxstableAdvanced, RMbr2bg, RMmodel, RMm2r, RPbernoulli, RPbrownresnick, RPschlather.

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)