cubappr(reu13.df.obs, phi.pred.Init, 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.pred.DrawScale = .CF.CONF$phi.pred.DrawScale, model = .CF.CT$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, temporarily initial of expression
without measurement errors.y
object, codon counts.n
object, total codon counts.b
.b
if b.Init = NULL
.b
.model.Phi = "logmixture"
.sigma.Phi
.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.
nIter + 1
, but the
outputs may be thinned to nIter / iterThin + 1
iterations. Temporary result dumping may be controlled by .CF.DP
.
cubfits()
and cubpred()
.
## Not run:
# suppressMessages(library(cubfits, quietly = TRUE))
#
# demo(roc.appr, 'cubfits', ask = F, echo = F)
# ## End(Not run)
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