RFgetModelInfo(...) RFgetModelInfo_register(register, level = 1, spConform =
                 RFoptions()$general$spConform, which.submodels =
                 c("user", "internal", "call+user", "call+internal",
                 "user.but.once", "internal.but.once",
                 "user.but.once+jump", "internal.but.once+jump",
                 "all"), modelname = NULL)
RFgetModelInfo_model(model, dim = 1, Time = FALSE,
                     kernel = FALSE, exclude_trend = TRUE, ...)
Registers in "call+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.NAs where
    information on the parameters is requested.TRUE is available.RFgetModelInfo(model) is called a list returned with the
  following elements:
  trans.inv: logical. Whether the model is translation
    invariant (stationary)isotropic: logical. Whether the model is rotation
    invariant (stationary)NAs: is case of an additive model it gives the number
    of NAs in each submodelminmax: a data frame containing information on all
    arguments set toNAspmin,pmax: lower and upper endpoint of the
      parameter values usually found in practicetype: integer; recognized particularities of a parameter;
      an explanation of the values is given after the table, if printed.NAN: the number ofNANs foundmin,max: mathetically valid lower and upper
      endpoints of the parameter valuesomin,omax: logical. IfFALSEthe
      respective mathematical endpoint is includedcol,row: the dimension of the parameter.
      If the parameter is a scalar thencol = row = 1. If it is a
      vector thencol = 1.bayes: currently not used (alwaysFALSE)  
RFgetModelInfo()returns internal information on the
    last call of anRFfunction.RFgetModelInfo(RFfunction)returns internal information on the
    last call ofRFfunction.RFgetModelInfo(RMmodel)returns general information onRMmodelRFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
StartExample()
model <- RMexp(scale=4, var=2) + RMnugget(var=3) + RMtrend(mean=1)
z <- RFsimulate(model, 1:4, storing=TRUE)
RFgetModelInfo()
model <-  RMwhittle(scale=NA, var=NA, nu=NA) + RMnugget(var=NA)
RFgetModelInfo(model)
FinalizeExample()Run the code above in your browser using DataLab