runSegratioMM {polySegratioMM} | R Documentation |
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)