Learn R Programming
dclone (version 1.5-0)

parJagsModel: Create a JAGS model object on parallel workers

Description

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.

Usage

parJagsModel(cl, name, file, data=sys.frame(sys.parent()), inits, n.chains = 1,
n.adapt=1000, quiet=FALSE)

Arguments

cl
a cluster object created by the snow package. Length must correspond to n.chains.
name
variable name for the model to be assigned on the workers.
file
the name of the file containing a description of the model in the JAGS dialect of the BUGS language. Alternatively, file can be a readable text-mode connection, or a complete URL. It can be also a function or a
data
a list or environment containing the data. Any numeric objects in data corresponding to node arrays used in file are taken to represent the values of observed nodes in the model
inits
optional specification of initial values in the form of a list or a function (see Initialization below). If omitted, initial values will be generated automatically. It is an error to supply an initial value for an observed node.
n.chains
the number of parallel chains for the model
n.adapt
the number of iterations for adaptation. See adapt for details. If n.adapt = 0 then no adaptation takes place.
quiet
if TRUE then messages generated during compilation will be suppressed.

Value

  • parJagsModel returns an object inheriting from class jags which can be used to generate dependent samples from the posterior distribution of the parameters.

    Length of cl must equal n.chains. Initialization 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.

See Also

jags.model, jagsModel

See example on help page of parCodaSamples.