runSegratioMM: Run a Bayesian mixture model for marker dosage with minimal...
In polySegratioMM: Bayesian Mixture Models for Marker Dosage in Autopolyploids

Description Usage Arguments Value Author(s) See Also Examples

Given segregation ratios and a ploidy level, a mixture model is constructed with default priors and initial values and JAGS run to produce an MCMC sample for statistical inference. Returns an object of S3 class runJagsWrapper

runSegratioMM(seg.ratios, model, priors = setPriors(model),
 inits = setInits(model, priors), jags.control =
 setControl(model, stem, burn.in = burn.in, sample = sample, thin = thin),
 burn.in = 2000, sample = 5000, thin = 1, stem = "test", fix.one = TRUE,
 print = TRUE, plots = TRUE, print.diagnostics = TRUE,
 plot.diagnostics = TRUE, run.diagnostics.later=FALSE )

`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
`model`	object of class `modelSegratioMM` specifying model parameters, ploidy etc
`priors`	object of class `priorsSegratioMM` indicating priors that are “vague”, “strong” or “specified”
`inits`	A list of initial values usually produced by `setInits`
`jags.control`	Object of class `jagsControl` containing MCMC burn in, sample and thinning as well as relavant files for BUGS commands, inits and data
`burn.in`	size of MCMC burn in (Default: 2000)
`sample`	size of MCMC sample (default: 5000)
`thin`	thinning interval between consecutive observations (default: 1 or no thinning)
`stem`	text to be used as part of `JAGS` .cmd file name
`fix.one`	Logical to fix the dosage of the observation closest to the centre of each component on the logit scale. This can greatly assist with convergence (Default: `TRUE`)
`print`	logical for printing monitoring and summary information (default: TRUE)
`plots`	logical to plotting MCMC posterior distributions (default: TRUE)
`print.diagnostics`	logical for printing disagnostic statistics (default: TRUE)
`plot.diagnostics`	logical for diagnostic plots (default: TRUE)
`run.diagnostics.later`	should diagnostics be run later which may help if there are convergence problems (Default: FALSE)

Returns object of class runJagsWrapper with components

`seg.ratios`	Object of class `segRatio` contains the segregation ratios for dominant markers
`model`	object of class `modelSegratioMM` specifying model parameters, ploidy etc
`priors`	Object of class `priorsSegratioMM` specifying prior distributions
`inits`	A list of initial values usually produced by `setInits`
`jags.control`	Object of class `jagsControl` containing MCMC burn in, sample and thinning as well as relavant files for BUGS commands, inits and data
`stem`	text to be used as part of `JAGS` .cmd file name and other files
`fix.one`	Logical to fix the dosage of the observation closest to the centre of each component on the logit scale. This can greatly assist with convergence (Default: `TRUE`)
`run.jags`	object of class `runJAGS` produced by running JAGS
`mcmc.mixture`	Object of type `segratioMCMC` produced by `coda` usually by using `readJags`
`diagnostics`	list containing various diagnostic summaries and statistics produced by `coda`
`summary`	summaries of posterior distributions of model parameters
`doses`	object of class `dosagesMCMC` containing posterior probabilities of dosages for each marker dosage and allocated dosages
`DIC`	Deviance Information Critereon

Peter Baker p.baker1@uq.edu.au

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits and diagnosticsJagsMix

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)

## End(Not run)