RMschlather: Covariance Model for binary field based on Gaussian field
In RandomFields: Simulation and Analysis of Random Fields

Description Usage Arguments Details Value See Also Examples

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.

1	RMschlather(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.

This model yields the tail correlation function of the field that is returned by RPschlather.

RMschlather returns an object of class RMmodel.

RPschlather, RMmodel, RFsimulate.

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)