R: Tail correlation function of the Brown-Resnick process

Strokorb's Functions {RandomFields} | R Documentation

## Tail correlation function of the Brown-Resnick process

### Description

The models define various shape functions for max-stable processes for a given tail correlation function

### Usage

    RMm2r(phi)
    RMm3b(phi)
    RMmps(phi)

### Arguments

`phi` | a model for a tail correlation function belonging to the Gneiting class H_d

### Details

`RMm2r` used with `RPsmith` defines a monotone shape function that corresponds to a tail correlation function belonging to Gneiting's class H_d. Currently, the function is implemented for dimensions 1 and 3. Called as such it returns the corresponding monotone function.

`RMm3b` used with `RPsmith` defines balls with random radius that corresponds to a tail correlation function belonging to Gneiting's class H_d. Currently, the function is implemented for dimensions 1 and 3. (Note that in Strokorb et al. (2014) the density function for twice the radius is considered.) Called as such it returns the corresponding density function for the radius of the balls.

`RMmps` used with `RPsmith` defines random hyperplane polygons that corresponds a tail correlaton function belonging to Gneiting's class H_d. It currently only allows for `RMbrownresnick(RMfbm(alpha=1))` and dimension 2. Called as such it returns the tcf defined by the submodel – this definition may change in future.

### Value

object of class `RMmodel`

### Author(s)

Martin Schlather, schlather@math.uni-mannheim.de http://ms.math.uni-mannheim.de/de/publications/software

### References

- Strokorb, K. (2013) Properties of the Extremal Coefficient Functions. Univ. Goettingen. PhD thesis.

- Strokorb, K., Ballani, F. and Schlather, M. (2014) In Preparation.

### See Also

`RFsimulate`, `RMmodel`.

### Examples

    RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
    ##                   RFoptions(seed=NA) to make them all random again

    model <- RMbrownresnick(RMfbm(alpha=1.5, s=0.2))
    plot(RMm2r(model))

    x <- seq(0, 10, 0.005)
    z <- RFsimulate(RPsmith(RMm2r(model), xi=0), x)
    plot(z, type="p", pch=20)

[Package RandomFields version 3.1.50 Index]