cubfits(reu13.df.obs, phi.Obs, y, n, nIter = 1000, b.Init = NULL, init.b.Scale = .CF.CONF$init.b.Scale, b.DrawScale = .CF.CONF$b.DrawScale, b.RInit = NULL, p.Init = NULL, p.nclass = .CF.CONF$p.nclass, p.DrawScale = .CF.CONF$p.DrawScale, phi.Init = NULL, init.phi.Scale = .CF.CONF$init.phi.Scale, phi.DrawScale = .CF.CONF$phi.DrawScale, model = .CF.CT$model[1], model.Phi = .CF.CT$model.Phi[1], adaptive = .CF.CT$adaptive[1], verbose = .CF.DP$verbose, iterThin = .CF.DP$iterThin, report = .CF.DP$report)
reu13.df
object, ORFs information.phi.Obs
object, expression with measurement errors.y
object, codon counts.n
object, total codon counts.b
.b
if b.Init = NULL
.b
.model.Phi = "logmixture"
.sigma.Phi
.phi.Init = NULL
.b
and
Phi.b.Mat
for mutation and selection coefficients of b
,
p.Mat
for hyper-parameters, and
phi.Mat
for expected expression values Phi.
All lists are of length nIter / iterThin + 1
and
each element contains the output of each iteration.All lists also can be binded as trace matrices, such as via
do.call("rbind", b.Mat)
yielding a matrix of dimension number of
iterations by number of parameters. Then, those traces can be analyzed
further via other MCMC packages such as coda.
Total number of MCMC iterations is nIter + 1
, but the
outputs may be thinned to nIter / iterThin + 1
iterations.
Temporary result dumping may be controlled by .CF.DP
.
Shah P. and Gilchrist M.A. ``Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift'' Proc Natl Acad Sci USA (2011) 108:10231--10236.
Wallace E.W.J., Airoldi E.M., and Drummond D.A. ``Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data'' Mol Biol Evol (2013) 30(6):1438--1453.
cubappr()
and cubpred()
.
## Not run:
# suppressMessages(library(cubfits, quietly = TRUE))
#
# demo(roc.train, 'cubfits', ask = F, echo = F)
# ## End(Not run)
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