R: Covariance Model for binary field based on a Gaussian field

RMbernoulli {RandomFields} | R Documentation

## Covariance Model for binary field based on a Gaussian field

### Description

`RMbernoulli` gives the centered correlation function of a binary field, obtained by thresholding a Gaussian field.

### Usage

    RMbernoulli(phi, threshold, correlation, centred, var, scale, Aniso, proj)

### Arguments

`phi` | covariance function of class `RMmodel`.
`threshold` | real valued threshold, see `RPbernoulli`. Currently only `threshold=0.0` is possible. Default: 0.
`correlation` | logical. If `FALSE` the corresponding covariance function is returned Default: TRUE.

.

`centred` | logical. If `FALSE` the uncentred covariance is returned. Default: TRUE.
`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 covariance function of the field that is returned by `RPbernoulli`

### Value

`RMbernoulli` returns an object of class `RMmodel`.

### Note

Previous to version 3.0.33 the covariance function was returned, not the correlation function

### Author(s)

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

### References

Ballani, Schlather

### See Also

`RPbernoulli` `RMmodel`, `RFsimulate`,

### Examples

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

    threshold <- 0
    x <- seq(0, 5, 0.02)
    GaussModel <- RMgneiting()

    n <- 1000
    z <- RFsimulate(RPbernoulli(GaussModel, threshold=threshold), x=x, n=n)
    plot(z)

    model <- RMbernoulli(RMgauss(), threshold=threshold, correlation=FALSE)
    plot(model, xlim=c(0,5))
    z1 <- as.matrix(z)
    estim.cov <- apply(z1, 1, function(x) cov(x, z1[1,]))
    points(coordinates(z), estim.cov, col="red")

[Package RandomFields version 3.1.50 Index]