R: Simulation of Binary Random Fields

RPbernoulli {RandomFields} | R Documentation

## Simulation of Binary Random Fields

### Description

Indicator or binary field which has the value 1, if an underfield field exceeds a given threshold, 0 otherwise.

### Usage

    RPbernoulli(phi, stationary_only, threshold)

### Arguments

`phi` | the `RMmodel`. Either a model for a process or a covariance model must be specified. In the latter case, a Gaussian process `RPgauss` is tacitely assumed.
`stationary_only` | optional arguments; same meaning as for `RPgauss`. It is ignored if the submodel is a process definition.
`threshold` | real valued. `RPbernoulli` returns 1 if value of the random field given by `phi` is equal to or larger than the value of `threshold`, and 0 otherwise. In the multivariate case, a vector might be given. If the threshold is not finite, then the original field is returned. `threshold` defaults value is 0.

### Details

`RPbernoulli` can applied to any field. If only a covariance model is given, a Gaussian field is simulated as underlying field.

### Value

The function 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

`Auxiliary RMmodels`, RP, `RMbernoulli`

### Examples

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

    x <- seq(0, 10, 0.1)
    model <- RPbernoulli(RMexp(), threshold=0)
    z <- RFsimulate(model, x, x, n=4)
    plot(z)

[Package RandomFields version 3.1.50 Index]