RandomFields (version 3.1.16)

Independent Variables: Method to simulate the Nugget effect

Description

Method to simulate the Nugget effect.

Usage

RPnugget(phi, boxcox, tol, vdim)

Arguments

phi

object of class RMmodel; specifies the covariance model to be simulated. The only possible model for phi is RMnugget.

boxcox

the one or two parameters of the box cox transformation. If not given, the globally defined parameters are used. see RFboxcox for Details.

tol

points at a distance less than or equal to nugget.tol are considered as being identical. This strategy applies to the simulation method and the covariance function itself. Hence, the covariance function is only positive definite if nugget.tol=0.0. However, if the anisotropy matrix does not have full rank and nugget.tol=0.0 then, the simulations are likely to be odd. The value of nugget.tol should be of order $1e-15$. Default: 0.0

vdim

positive integer; the model is treated vdim-variate, vdim=1 (default) corresponds to a univariate random field. Mostly, the value of vdim is set automatically.

Default is that it takes the value of the submodel phi

Value

RPnugget returns an object of class RMmodel

Details

This method only allows RMnugget as a submodel.

The method also allows for zonal nugget effects. Only there the argument tol becomes important. For the zonal nugget effect, the anisotropy matrix Aniso should be given in RMnugget. There, only the kernal of the matrix is important.

References

• Schlather, M. (1999) An introduction to positive definite functions and to unconditional simulation of random fields. Technical report ST 99-10, Dept. of Maths and Statistics, Lancaster University.

See Also

Gaussian, RP, RPcoins, RPhyperplane, RPspectral, RPtbm.

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
model <- RMnugget()
z <- RFsimulate(model=model, 0:10, 0:10, n=4)
plot(z)

model <- RPnugget(RMnugget(var=0.01, Aniso=matrix(nc=2, rep(1,4))))
z <- RFsimulate(model=model, 0:10, 0:10, n=4)
plot(z)