RFgetModelInfo: Internal information

Description

The function returns internal information about the simulation of a random field and the calculation of covariance functions

Usage

RFgetModel(register, explicite.natscale, show.call=FALSE)
RFgetModelInfo(register, level = 3,
               spConform = RFoptions()$general$spConform,
               which.submodels = c("user", "internal", "both"),
               modelname = NULL)

Arguments

$0,...,21$ or an evaluating function, e.g. RFsimulate. Place where intermediate calculations are stored; The user should only use the registers $0,..,9$ for the user's own purpose.

explicite.natscale

logical. Advanced option. If missing, then the model is returned as stored. If FALSE then any RMnatsc is ignored. If TRUE then any

show.call

logical. If FALSE then the model is shown as interpreted. If TRUE then the user's input including the calling function is returned. See example below.

level

integer [0..5]; level of details, i.e. the higher the number the more details are given. If level >= 10 then the leading internal model is also given (which is, in general, not of interest by the user).

spConform

see RFoptions

which.submodels

Internally, the sub-models are represented in two different ways internal and user. The latter is very close to the model defined by the user.

modelname

string. If modelname is given then it returns the first appearance of the covariance model with name modelname. If meth is given then the model within the method is returned.

Value

List of internal information is returned.

Details

RFgetModelInfo(register, ignore.active=TRUE) is useful for debugging and specialists' need to control the algorithm, see the examples in RFoptions and RFsimulate. If RFoptions()$Storage=FALSE then values of the internal registers are not kept if RFsimulate has been called. Hence RFgetModelInfo cannot provide any information.

Examples

Run this code

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
model <- RMexp(scale=4, var=2) + RMnugget(var=3) + RMtrend(mean=1)
z <- RFsimulate(model, 1:4, grid=TRUE, storing=TRUE)
RFgetModelInfo(RFsimulate, level=1)


RFgetModel(RFsimulate, show.call=FALSE)
RFgetModel(RFsimulate, show.call=TRUE)
FinalizeExample()