runSegratioMM {polySegratioMM}

Run a Bayesian mixture model for marker dosage with minimal effort

Description

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

Usage

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 )

Arguments

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)

Value

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

Author(s)

Peter Baker [e-mail]

See Also

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits and diagnosticsJagsMix

Examples

## 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)