Documentation of major changings
This man page documents some major changings in RandomFields.
log Gauss field corrected (has been a log log Gauss field)
RMconstant is now called RMfixcov
Conditional simulation: several severe typos corrected.
S4 objects
RandomFields is now based on S4 objects using the package sp. The functions accept both sp objects 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 with RM, distribution families
with RR, processes with RP, user
functions with RF
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
than the functions in version 2. E.g., RFsimulate can 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 as options.
RFgui is an instructive interface based on tcl/tk,
replacing the former ShowModels
Inference for Gaussian random fields
RFfit has undergone a major revision. E.g.:
(i) estimation of random effect models with spatial covariance structure
(ii) automatic estimation of 10 and more arguments in multivariate and/or space-time models
RFvariogram is now based on an fft algorithm
if the data are on a grid, even allowing for missing values.
RFratiotest has been added.
Processes
Modelling of maxstable processes has been enhanced, including
(i) the simulation of Brown-Resnick processes
(ii) initial support of tail correlation functions;
Further processes chi2 processes, compound Poisson processes, binary processes added.
Models
the formula notation for 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 be included which will be extended to Bayesian modelling in future.
Martin Schlather, schlather@math.uni-mannheim.de, https://www.wim.uni-mannheim.de/schlather/
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, 0.1) model <- RMexp() plot(RFsimulate(model, x)) ## S4 plot(RFsimulate(model, x, spConform=FALSE)) ## no class
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