calculateDIC {polySegratioMM} | R Documentation |
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)
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 |
A scalar DIC is returned
Peter Baker p.baker1@uq.edu.au
## 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)