calculateDIC function

Compute DIC for fitted mixture model

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

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

Returns

A scalar DIC is returned

References

• G Celeaux et. al.(2006) Deviance Information Criteria for Missing Data Models Bayesian Analysis 4 23pp
• D Spiegelhalter et. el.(2002) Bayesian measures of model complexity and fit JRSS B 64 583--640

Author(s)

Peter Baker p.baker1@uq.edu.au

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)