setControl: Set up controls for a JAGS segregation ratio model run
In polySegratioMM: Bayesian mixture models for marker dosage in autopolyploids

Description Usage Arguments Value Author(s) See Also Examples

Sets up directives for running JAGS which are subsequently put into a .cmd file. MCMC attributes such as the size of burn in, length of MCMC and thinning may be specified

setControl(model, stem = "test", burn.in = 2000, sample = 5000, thin = 1,
 bugs.file = paste(stem, ".bug", sep = ""),
 data.file = paste(stem, "-data.R", sep = ""),
 inits.file = paste(stem, "-inits.R", sep = ""),
 monitor.var = model$monitor.var, seed=1)

`model`	object of class `modelSegratioMM` specifying model parameters, ploidy etc
`stem`	text to be used as part of `JAGS` .cmd file name
`burn.in`	size of MCMC burn in (Default: 2000)
`sample`	size of MCMC sample (default: 5000)
`thin`	thinning interval between consecutive observations. Thinning may be a scalar or specified for each variable set by specifying a vector (default: 1 or no thinning)
`bugs.file`	name of .bug file
`data.file`	name of R data file
`inits.file`	name of R inits file
`monitor.var`	which variables to be monitored (Default: as per model)
`seed`	seed for JAGS run for Windows only (for unix set seed in `setInits`)

Returns an object of class jagsControl which is a list with components

`jags.code`	Text containing control statements for `JAGS` .cmd file
`model`	object of class `modelSegratioMM` specifying model parameters, ploidy etc
`stem`	text to be used as part of `JAGS` .cmd file name
`burn.in`	size of MCMC burn in (Default: 2000)
`sample`	size of MCMC sample (default: 5000)
`thin`	thinning interval between consecutive observations
`bugs.file`	name of .bug file
`data.file`	name of R data file
`inits.file`	name of R inits file
`monitor.var`	which variables to be monitored
`call`	function call

Peter Baker p.baker1@uq.edu.au

setModel setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

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

## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)

jc <- setControl(x)
print(jc)