Function to extract random samples of the penalized deviance from
a jags model.
dic.samples(model, n.iter, thin = 1, type, ...)a jags model object
number of iterations to monitor
thinning interval for monitors
type of penalty to use
optional arguments passed to the update method for jags model objects
An object of class ``dic''. This is a list containing the following elements:
A numeric vector, with one element for each observed stochastic node, containing the mean deviance for that node
A numeric vector, with one element for each observed stochastic node, containing an estimate of the contribution towards the penalty
A string identifying the type of penalty: ``pD'' or ``popt''
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.
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