dosagesJagsMix: Compute dosages under specified Bayesian mixture model

In polySegratioMM: Bayesian Mixture Models for Marker Dosage in Autopolyploids

Compute dosages under specified Bayesian mixture model

Description

Computes and returns estimated dosages under specified model using posterior probabilities derived from mcmc chains by the proportion of samples in each dosage class.

Usage

dosagesJagsMix(mcmc.mixture, jags.control, seg.ratio, chain = 1,
max.post.prob = TRUE, thresholds = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95,
0.99), print = FALSE, print.warning = TRUE, index.sample = 20)

Arguments

mcmc.mixture	Object of type segratioMCMC produced by coda usually by using readJags
jags.control	Object of class jagsControl for setting up JAGS command file
seg.ratio	Object of class segRatio contains the segregation ratios for dominant markers and other information such as the number of dominant markers per individual
chain	Which chain to use when compute dosages (Default: 1)
max.post.prob	Logical for producing dose allocations based on the maximum posterior probability (Default: TRUE)
thresholds	Numeric vector of thresholds for allocating dosages when the posterior probabilty to a particular dosage class is above the threshold
print	Logical indicating whether or not to print intermediate results (Default: FALSE)
print.warning	Logical to print warnings if there is more than one marker with the maximum posterior probability
index.sample	Numeric vector indicating which markers to print if print is TRUE. If index.sample is of length 1 then a random sample of size index.sample is selected

Value

An object of class dosagesMCMC is returned with components:

p.dosage	Matrix of posterior probabilities of dosages for each marker dosage
dosage	Matrix of allocated dosages based on posterior probabilities. The columns correspond to different 'thresholds' and if requested, the last column is allocated on basis of max.post
thresholds	vector of cutoff probabilities for dosage class
chain	Chain used to compute dosages
max.post	maximum dosage posterior probabilties for each marker
index.sample	Numeric vector indicating which markers to print if print is TRUE. If index.sample is of length 1 then a random sample of size index.sample is selected

Author(s)

Peter Baker [e-mail]

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model, priors, inits etc and write files for JAGS
x <- setModel(3,8)
x2 <- setPriors(x)

## Not run: 
## write control files for JAGs run
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
writeJagsFile(x, x2, stem="test")

## run JAGS
small <- setControl(x, burn.in=200, sample=500)
writeControlFile(small)
rj <- runJags(small)  ## just run it
print(rj)

## read mcmc chains and produce dosage allocations
xj <- readJags(rj)
dd <- dosagesJagsMix(xj, small, sr)
print(dd)

## End(Not run)

polySegratioMM documentation built on Feb. 25, 2026, 5:08 p.m.