Description Usage Arguments Value Author(s) See Also Examples
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
1 2 3 4 5 6 | 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 |
model |
object of class |
priors |
object of class |
inits |
A list of initial values usually produced by |
jags.control |
Object of class |
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 |
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: |
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 |
model |
object of class |
priors |
Object of class |
inits |
A list of initial values usually produced by |
jags.control |
Object of class |
stem |
text to be used as part of |
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: |
run.jags |
object of class |
mcmc.mixture |
Object of type |
diagnostics |
list containing various diagnostic summaries and
statistics produced by |
summary |
summaries of posterior distributions of model parameters |
doses |
object of class |
DIC |
Deviance Information Critereon |
Peter Baker p.baker1@uq.edu.au
setPriors
setInits
expected.segRatio
segRatio
setControl
dumpData
dumpInits
and
diagnosticsJagsMix
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## 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)
|
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