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

Description Usage Arguments Details Value Note References See Also Examples

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

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

`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.

This model yields the covariance function of the field that is returned by RPbernoulli.

RMbernoulli returns an object of class RMmodel.

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

Ballani, Schlather

RPbernoulli, RMmodel, RFsimulate.

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")