parJagsModel is used to create an object representing a
Bayesian graphical model, specified with a BUGS-language description
of the prior distribution, and a set of data.parJagsModel(cl, name, file, data=sys.frame(sys.parent()),
inits, n.chains = 1, n.adapt=1000, quiet=FALSE)makeCluster, or
an integer. It can also be NULL,
see snowWrapper.
Size of the cluster must file can be a readable text-mode connection,
or a complete URL. It can be also a function or a
data corresponding to node arrays used in
file are taken to represent the values of observed nodes
in the modelInitialization on
help page of jags.model). If omitted,
initial values will be generated automatiadapt for details. If n.adapt = 0 then no
adaptation takes place.TRUE then messages generated during compilation
will be suppressed. Effect of this argument is not visible on the
master process.parJagsModel returns an object inheriting from class jags
which can be used to generate dependent samples from the posterior
distribution of the parameters. These jags models are
residing on the workers, thus updating/sampling is possible.Length of cl must be equal to or greater than n.chains.
RNG seed generation takes place first on the master,
and chains then initialized on
each worker by distributing inits and single chained models.
An object of class jags is a list of functions that share a
common environment, see jags.model for details.
Data cloning information is attached to the returned
object if data argument has n.clones attribute.
jags.modelSequential jagsModel
See example on help page of parCodaSamples.