rjags-package: Bayesian graphical models using MCMC

Description Details Author(s) References

Description

The rjags package provides an interface from R to the JAGS library for Bayesian data analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples from the posterior distribution of the parameters.

Details

JAGS is a clone of BUGS (Bayesian analysis Using Gibbs Sampling). See Lunn et al (2009) for a history of the BUGS project. Note that the rjags package does not include a copy of the JAGS library: you must install this separately. For instructions on downloading JAGS, see the home page at http://mcmc-jags.sourceforge.net.

To fully understand how JAGS works, you need to read the JAGS User Manual. The manual explains the basics of modelling with JAGS and shows the functions and distributions available in the dialect of the BUGS language used by JAGS. It also describes the command line interface. The rjags package does not use the command line interface but provides equivalent functionality using R functions.

Analysis using the rjags package proceeds in steps:

  1. Define the model using the BUGS language in a separate file.

  2. Read in the model file using the jags.model function. This creates an object of class “jags”.

  3. Update the model using the update method for “jags” objects. This constitutes a ‘burn-in’ period.

  4. Extract samples from the model object using the coda.samples function. This creates an object of class “mcmc.list” which can be used to summarize the posterior distribution. The coda package also provides convergence diagnostics to check that the output is valid for analysis (see Plummer et al 2006).

Author(s)

Martyn Plummer

References

Lunn D, Spiegelhalter D, Thomas A, Best N. (2009) The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28:3049-67.

Plummer M, Best N, Cowles K, Vines K (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, 6:7-11.


rjags documentation built on Nov. 6, 2019, 5:07 p.m.