RandomFields (version 3.0.32)

Major Revisions: Documentation of major changings

Description

Major Revision: changings from Version 2 to Version 3

• S4 objects
  • RandomFieldsis now based on S4 objects using the packagesp. The functions accept bothspobjects and simple objects as used in version 2. See also above.
• Documentation
  • each model has now its own man page;
  • classes of models and functions are bundled in several pages: Covariance models start withRM, distribution families withRR, processes withRP, user functions withRF
  • the man pages of several functions are split into two parts: (i) a beginners man page which includes a link to (ii) man pages for advanced users
• Interfaces
  • The interfaces become simpler, at the same time more powerful then the functions in version 2. E.g.,RFsimulatecan perform unconditional simulation, conditional simulation and random imputing.
  • Only those arguments are kept in the functions that are considered as being absolutely necessary. All the other arguments can be included asoptions.
  • RFguiis an instructive interface based on tcl/tk, replacing the formerShowModels
• Inference for Gaussian random fields
  • RFfithas undergone a major revision. E.g.: (i) estimation random effects model with spatial covariance structure

    (ii) automatic estimation of 10 and more arguments in multivariate and/or space-time models

  • RFempiricalvariogramis now based on an fft algorithm if the data are on a grid, even allowing for missing values.
  • RFratiotesthas been added.
• Processes
  • Maxstable processes
  modelling ofmaxstable processeshas been enhanced, including (i) the simulation of Brown-Resnick processes (ii) initial support oftail correlation functions;
• Further processes

chi2 processes, compound Poisson processes, binary processes added.

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Models

• theformula notationfor linear models may now be defined
• Novel, user friendly definition of the covariance models

Multivariate and vector valued random fields are now fully included The user may now define his own functions, to some extend. The trend allows for much more flexibility Distributions may now included which will be extended to Baysian modelling in future.

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

## S4 vs S3 x <- seq(0, 10, if (interactive()) 0.1 else 2) model <- RMexp() plot(RFsimulate(model, x)) ## S4 plot(RFsimulate(model, x, spConform=FALSE)) ## S3

FinalizeExample()