R: Method to simulate the Nugget effect

Independent Variables {RandomFields} | R Documentation

## 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`

### 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.

### Value

`RPnugget` returns an object of class `RMmodel`

### Author(s)

Martin Schlather, schlather@math.uni-mannheim.de

### 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)

[Package RandomFields version 3.1.50 Index]