RandomFields (version 3.1.16)

Specific: 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.

Value

RPspecific returns an object of class RMmodel

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 projection has been performed.

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

• RMmppplus
• RMtrend

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

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))
}