calculateDIC {polySegratioMM}R Documentation

Compute DIC for fitted mixture model

Description

Computes and returns the Deviance Information Critereon (DIC) as suggested by Celeaux et al (2006) as their DIC$_4$ for Bayesian mixture models

Usage

calculateDIC(mcmc.mixture, model, priors, seg.ratios, chain=1, print.DIC=FALSE)

Arguments

mcmc.mixture Object of type segratioMCMC produced by coda usually by using readJags
model object of class modelSegratioMM specifying model parameters, ploidy etc
priors Object of class priorsSegratioMM
seg.ratios Object of class segRatio contains the segregation ratios for dominant markers and other information such as the number of dominant markers per individual
chain Which chain to use when compute dosages (Default: 1)
print.DIC Whether to print DIC

Value

A scalar DIC is returned

Author(s)

Peter Baker p.baker1@uq.edu.au

References

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model, priors, inits etc and write files for JAGS
x <- setModel(3,8)
x2 <- setPriors(x)
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
writeJagsFile(x, x2, stem="test")

## Not run: 
## run JAGS
small <- setControl(x, burn.in=200, sample=500)
writeControlFile(small)
rj <- runJags(small)  ## just run it
print(rj)

## read mcmc chains and print DIC
xj <- readJags(rj)
print(calculateDIC(xj, x, x2, sr))
## End(Not run)

[Package polySegratioMM version 0.5-2 Index]