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.model
Sequential jagsModel
See example on help page of parCodaSamples
.