RR.Rd
\name{Distribution Families}
\alias{RR}
\alias{RRmodel}
\alias{RRmodels}
\title{
Distribution families (RR commands)
}
\description{
Distribution families to specify random parameters in the model definition.
}
\note{
The allowance of random parameters is a very recent, developing
feature of \pkg{RandomFields}.
Future changings of the behaviour are not unlikely.
}
\details{
See \link{Bayesian Modelling} for a less technical introduction to
hierarchical modelling.
When simulating Gaussian random fields, the random parameters
are drawn only once at the very beginning.
So, if the argument \code{n} in \command{\link{RFsimulate}}
is greater than \code{1} then \code{n} simulations conditional
on a single realization of the random parameters are
performed. See the examples below.
There are (simple) multivariate versions and additional versions to the
distributions families implemented. Further, \bold{any} distribution
family defined in R can be used, see the examples below.
These functions will allow for Bayesian modelling. (Future project).
}
\section{Implemented models}{
\tabular{ll}{
\command{\link{RRdeterm}} \tab no scattering
\cr
\command{\link{RRdistr}} \tab families of distributions transferred
from R
\cr
\command{\link{RRgauss}} \tab a (multivariate) Gaussian random variable
\cr
\command{\link{RRloc}} \tab modification of location and scale
\cr
\command{\link{RRspheric}} \tab random scale for
the \command{\link{RMball}} to simulate \command{\link{RMspheric}}, etc.
%on lower
% dimensions by \command{\link{RPcoins}} and \command{\link{RPpoisson}}
\cr
\command{\link{RRunif}} \tab a (multivariate) uniform random variable
\cr
}
}
\note{
A further random element is \command{\link{RMsign}}, which is an
operator on shape functions. As an exception its name starts with
\code{RM} and not with \code{RR}.
}
\me
\seealso{
\link{RC}, \link{RF}, \link{RM}, \link{RP}, \command{\link{Other models}},
\command{\link{RFdistr}},
\code{\link[=RMmodelgenerator-class]{RMmodelgenerator}}, \link{R.}
}
\keyword{spatial}
\keyword{distribution}
\examples{\dontshow{StartExample()}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
## RFoptions(seed=NA) to make them all random again
## here, the scale is given by an exponential variable:
model <- RMgauss(scale=exp())
for (i in 1:4) {
RFoptions(seed = i)
# each leads to a simulation with a different scale parameter
plot(model) ## random
plot(RFsimulate(model, x=seq(0,10,0.1)))
readline("press return")
}
# but here, all 4 simulations have the same (but random) scale:
plot(RFsimulate(model, x=seq(0,10,0.1), n=4))
## hierarchical models are also possible:
## here, the scale is given by an exponential variable whose
## rate is given by a uniform variable
model <- RMgauss(scale=exp(rate=unif()))
plot(model)
plot(RFsimulate(model, x=seq(0,10,0.1)))
## HOWEVER, the next model is deterministic with scale \code{e=2.718282}.
model <- RMgauss(scale=exp(1))
plot(model)
plot(RFsimulate(model, x=seq(0,10,0.1)))
\dontshow{FinalizeExample()}}