runSegratioMM {polySegratioMM}R Documentation

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 p.baker1@uq.edu.au

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)

[Package polySegratioMM version 0.5-2 Index]