jagsModel: Create a JAGS model object

In datacloning/dclone: Data Cloning and MCMC Tools for Maximum Likelihood Methods

Create a JAGS model object

Description

jagsModel 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. This function uses jags.model but keeps track of data cloning information supplied via the data argument. The model argument can also accept functions or 'custommodel' objects.

Usage

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

Arguments

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 custommodel object.
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. 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 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.

An object of class jags is a list of functions that share a common environment, see jags.model for details.

An n.clones attribute is attached to the object when applicable.

Author(s)

Peter Solymos

See Also

Underlying functions: jags.model, update.jags

See example on help page of codaSamples.

Parallel version: parJagsModel

datacloning/dclone documentation built on Sept. 29, 2024, 3:21 p.m.