RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
\dontrun{
auswertung <- function(simu, threshold=2) {
below <- simu <= threshold
freq <- mean(below[1, ])
Print(freq, exp(-1/threshold)) ## univariate kontrolle
both <- t(below) & below[1, ]
plot(x, 2-log(colMeans(both)) / log(freq))
## alle 3 Linien ergeben das Gleiche:
lines(x, RFcov(model=RRbrownresnick(model), x), col="yellow")
lines(x, RFcov(model=RMextremalgauss(RMbr2eg(model)), x), col="red", lty=2)
lines(x, RFcov(model=RMbinarygauss(RMbr2bg(model)), x), col="blue", lty=3)
}
model <- RMgauss(var= 4)
x <- seq(0, 5, 0.1)
z <- seq(0, 10, 0.1)
binary.model <- RPbernoulli(model)
bild <- RFsimulate(RPschlather(binary.model), z, z, grid=TRUE)
plot(bild)
simu <- RFsimulate(RPschlather(model), x, grid=TRUE, n=5000, # 1000000,
spC=FALSE)
auswertung(simu)
bild <- RFsimulate(RPschlather(RMbr2eg(model)), z, z, grid=TRUE)
plot(bild)
simu <- RFsimulate(RPschlather(RMbr2eg(model)), x, grid=TRUE,
spC=FALSE, n=5000) # n= 1000000,
auswertung(simu)
binary.model <- RPbernoulli(RMgaussprocess(RMbr2bg(model)))
bild <- RFsimulate(RPschlather(binary.model), z, z, grid=TRUE)
plot(bild)
simu <- RFsimulate(RPschlather(RMbr2bg(model)), x, grid=TRUE,
n=5000, # 1000000,
spC=FALSE, maxstable.maxGauss=5)
auswertung(simu)
model <- RMgauss()
BR <- RFsimulate(RPbrownresnick(model), x, x, grid=TRUE)
}
FinalizeExample()
Run the code above in your browser using DataLab