DIC: Deviance information criterion

In spatcounts: Spatial count regression

Description

Deviance information criterion without standardizing constant.

Usage

DIC(Yin, fm.X, region, regmodel, burnin = 1)

Arguments

Yin	response vector of length n.
fm.X	formula for mean design.
region	region of each observation.
regmodel	the output of model fits obtained by using 'est.sc'.
burnin	number of steps dropped from the chain to allow for a burn-in phase. Defaults to 1.

Value

DIC	deviance information criterion.
mean.deviance	posterior mean of deviance.
p.D	effective number of parameters.

References

Gelman, Andrew, John B. Carlin, Hall S. Stern, and Donald B. Rubin (2003). Bayesian Data Analysis, Second Edition. Chapman \& Hall/CRC.

Spiegelhalter, David J.; Nicola G. Best, Bradley P. Carlin, and Angelika van der Linde (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B. 64 (4), 583-639.

Examples

data(sim.Yin)
data(sim.fm.X)
data(sim.region)
data(sim.gmat)
data(sim.nmat)

poi <- est.sc(sim.Yin, ~1+sim.fm.X[,1]+sim.fm.X[,2], sim.region, 
model="Poi", sim.gmat, sim.nmat, 3)
DIC.poi <- DIC(sim.Yin, ~1+sim.fm.X[,1]+sim.fm.X[,2], sim.region, poi)

spatcounts documentation built on May 29, 2017, 11:04 p.m.