R: Covariance Model for binary field based on Gaussian field

RMschlather {RandomFields} | R Documentation

## Covariance Model for binary field based on Gaussian field

### Description

`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.

### Usage

    RMschlather(phi, var, scale, Aniso, proj)

### Arguments

`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.

### Details

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

### Value

`RMschlather` returns an object of class `RMmodel`

### Author(s)

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

### See Also

`RPschlather` `RMmodel`, `RFsimulate`,

### Examples

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

    ## This examples considers an extremal Gaussian random field
    ## with Gneiting's correlation function.

    ## first consider the covriance model and its corresponding tail
    ## corrlation 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 tcf
    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 corrlation
    ## function equal to the above tail correlation function.
    z <- RFsimulate(RMschlather(model), x)
    plot(z)

[Package RandomFields version 3.1.50 Index]