Methods that are specific to certain covariance models
This model determines that the (Gaussian) random field should be modelled by a particular method that is specific to the given covariance model.
RPspecific(phi, boxcox)
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 trend model 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
field after the field has been simulated.
Hence, when var is random, then for each realization
of the Gaussian field (for n>1 in RFsimulate)
a new realization of var is used.
Further, new coordinates are created where the old coordinates
have been divided 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.
Note that RPspecific applies only to the first model or
operator in the argument phi.
RPspecific returns an object of class RMmodel.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
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.
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set ## RFoptions(seed=NA) to make them all random again ## example for implicit 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 explicit use: every simulation has a different variance model <- RPspecific(RMS(var=unif(min=0, max=100), RMgauss())) x <- seq(0,50,0.02) plot(RFsimulate(model, x=x, n=4), ylim=c(-15,15))
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