dic.samples: Generate penalized deviance samples

In rjags: Bayesian Graphical Models using MCMC

Generate penalized deviance samples

Description

Function to extract random samples of the penalized deviance from a jags model.

Usage

dic.samples(model, n.iter, thin = 1, type, ...)

Arguments

model	a jags model object
n.iter	number of iterations to monitor
thin	thinning interval for monitors
type	type of penalty to use
...	optional arguments passed to the update method for jags model objects

Details

The dic.samples function generates penalized deviance statistics for use in model comparison. The two alternative penalized deviance statistics generated by dic.samples are the deviance information criterion (DIC) and the penalized expected deviance. These are chosen by giving the values “pD” and “popt” respectively as the type argument.

DIC (Spiegelhalter et al 2002) is calculated by adding the “effective number of parameters” (pD) to the expected deviance. The definition of pD used by dic.samples is the one proposed by Plummer (2002) and requires two or more parallel chains in the model.

DIC is an approximation to the penalized plug-in deviance, which is used when only a point estimate of the parameters is of interest. The DIC approximation only holds asymptotically when the effective number of parameters is much smaller than the sample size, and the model parameters have a normal posterior distribution.

The penalized expected deviance (Plummer 2008) is calculated by adding the optimism (popt) to the expected deviance. The popt penalty is at least twice the size of the pD penalty, and penalizes complex models more severely.

Value

An object of class “dic”. This is a list containing the following elements:

deviance	A numeric vector, with one element for each observed stochastic node, containing the mean deviance for that node
penalty	A numeric vector, with one element for each observed stochastic node, containing an estimate of the contribution towards the penalty
type	A string identifying the type of penalty: “pD” or “popt”

Note

The popt penalty is estimated by importance weighting, and may be numerically unstable.

Author(s)

Martyn Plummer

References

Spiegelhalter, D., N. Best, B. Carlin, and A. van der Linde (2002), Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society Series B 64, 583-639.

Plummer, M. (2002), Discussion of the paper by Spiegelhalter et al. Journal of the Royal Statistical Society Series B 64, 620.

Plummer, M. (2008) Penalized loss functions for Bayesian model comparison. Biostatistics doi: 10.1093/biostatistics/kxm049

See Also

diffdic

rjags documentation built on Sept. 11, 2024, 6:31 p.m.