cubpred(reu13.df.obs, phi.Obs, y, n, reu13.df.pred, y.pred, n.pred, 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, phi.pred.Init = NULL, 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
to be trained.phi.Obs
to be trained.y
to be trained.n
to be trained.reu13.df
to be predicted.y
to be predicted.n
to be predicted.b
.b
if b.Init = NULL
.b
.model.Phi = "logmixture"
.sigma.Phi
.phi.Init = NULL
.phi.DrawScale
but for predicted set.b
and Phi.b.Mat
for mutation and selection coefficients of b
,
p.Mat
for hyper-parameters,
phi.Mat
for expected expression values Phi, and
phi.pred.Mat
for predictive expression values Phi.
All lists have nIter / iterThin + 1
elements,
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.
cubfits()
and cubappr()
.
## Not run:
# suppressMessages(library(cubfits, quietly = TRUE))
#
# demo(roc.pred, 'cubfits', ask = F, echo = F)
# ## End(Not run)
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