polySegratioMM (version 0.6-2)

setControl: Set up controls for a JAGS segregation ratio model run

Description

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

Usage

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)

Arguments

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 Rdata file

inits.file

name of Rinits 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)

Value

• 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 Rdata file
• inits.file
  name of Rinits file
• monitor.var
  which variables to be monitored
• call
  function call

concept

• segregation ratio
• dominant marker
• autopolyploid

See Also

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

Examples

## 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)