R: Methods that are specific to certain covariance models

Specific {RandomFields} | R Documentation

## Methods that are specific to certain covariance models

### Description

This model determines that the (Gaussian) random field should be modelled by a particular method that is specific to the given covariance model.

### Usage

    RPspecific(phi, boxcox)

### Arguments

`phi` | object of class `RMmodel`; specifies the covariance model to be simulated.
`boxcox` | the one or two parameters of the box cox transformation. If not given, the globally defined parameters are used. see `RFboxcox` for Details.

### Details

`RPspecific` is used for specific algorithms or specific features for simulating certain covariance functions

- `RMplus` is able to simulate separately the fields given by its summands. This is necessary, e.g., when a `RMtrend` is involved.

- `RMmult` for Gaussian random fields only. `RMmult` simulates the random fields of all the components and multiplies them. This is repeated several times and averaged.

- `RMS` Then, for instance, `sqrt(var)` is multiplied onto the (Gaussian) random fields after the field has been simulated. Hence, when `var` is random, then, for each realisation of the Gaussian field (for `n>1` in `RFsimulate`) a new realisation of `var` is used.

  Further, new coordinates are created where the old coordinates have been devided by the `scale` and/or multiplied with the `Aniso` matrix or a `proj`ection has been performed.

  `RPspecific(RMS())` is called internally when the user wants to simulate `Aniso`tropic fields with isotropic methods, e.g. `RPtbm`.

- `RMmppplus`

- `RMtrend`

Note that `RPspecific` applies only to the first model or operator in argument `phi`.

### Value

`RPspecific` 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.

### Examples

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

    ## example for implicite use
    model <- RMgauss(var=10, s=10) + RMnugget(var=0.1)
    plot(model)
    plot(RFsimulate(model=model, 0:10, 0:10, n=4))
    ## The following function shows the internal structure of the model.
    ## In particular, it can be seen that RPspecific is applied to RMplus.
    RFgetModelInfo(level=0, which="internal")

    ## example for explicite used
    model <- RPspecific(RMS(var=unif(min=0, max=10), RMgauss()))
    x <- seq(0,10,0.02)
    n <- 10

    for (i in 1:n) {
      readline(paste("Simulation no.", i, ": press return", sep=""))
      plot(RFsimulate(model, x=x, n=6, seed=i), ylim=c(-5,5))
    }

[Package RandomFields version 3.1.50 Index]