Transformation from Brown-Resnick to Bernoulli
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_{bg}(h) = \cos(π (2Φ(√{γ(h) / 2}) -1) )
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}( 3 / 4 ) }^2 = 1.819746 / 2 = 0.9098728
RMbr2bg(phi, var, scale, Aniso, proj)
RMbr2bg
binary random field RPbernoulli
simulated with RMbr2bg(RMmodel())
has
a uncentered covariance function that equals
the tail correlation function of the max-stable process constructed with this binary random field
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) x <- seq(0, 10, 0.05) z <- RFsimulate(RPschlather(RMbr2eg(model)), x, x) plot(z)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.