dosagesJagsMix {polySegratioMM} | R Documentation |
Computes and returns estimated dosages under specified model using posterior probabilities derived from mcmc chains by the proportion of samples in each dosage class.
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)
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 |
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 |
Peter Baker p.baker1@uq.edu.au
## 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) dumpData(sr, x) inits <- setInits(x,x2) dumpInits(inits) writeJagsFile(x, x2, stem="test") ## Not run: ## 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)