\name{getPosteriors}
\alias{getPosteriors}
\title{An internal function in the baySeq package for calculating
  posterior likelihoods given likelihoods of the data.}
\description{
  For likelihoods of the data given a set of models, this function
  calculates the posterior likelihoods of the models given the data. An
  internal function of baySeq, which should not in general be called by
  the user.
}
\usage{
getPosteriors(ps, prs, estimatePriors = FALSE, maxit = 100, accuracy = 1e-5)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{ps}{A matrix containing likelihoods of the data for each count
    (rows) under each model (columns).}
  \item{prs}{(Initial) prior probabilities for each of the models.}
  \item{estimatePriors}{Should the prior probabilities on each of the
    groups be estimated by bootstrap from the data? Defaults to FALSE.}
  \item{maxit}{What is the maximum number of iterations that should be
    tried if we are bootstrapping prior probabilities from the data?}
  \item{accuracy}{How small should the difference in estimated priors be
    before we stop bootstrapping.}
}

\details{
  An internal function, that will not in general be called by the
  user. It takes the log-likelihoods of the data given the models being
  tested and returns the posterior likelihoods of the models. An initial
  estimate for the prior likelihoods of the models is required but can
  be iteratively re-estimated from the data by taking the mean of the
  posterior likelihoods across all data.
}
\value{
  A list containing
  posteriors: estimated posterior likelihoods of the model for each
  count (log-scale)
  priors: estimated (or given) prior probabilities of the model
}
\references{Hardcastle T.J., and Kelly, K (2009). Empirical Bayesian
  methods for differential expression in count data. In submission.}
\author{Thomas J. Hardcastle}

\seealso{\code{\link{getLikelihoods}}}

\examples{

# Simulate some log-likeihoods of data given models (each model
# describes one column of the 'ps' object).
ps <- log(rbind(
                cbind(runif(10000, 0, 0.1), runif(10000, 0.3, 0.9)),
                cbind(runif(10000, 0.4, 0.9), runif(1000, 0, 0.2))))

# get posterior log-likelihoods of model, estimating prior likelihoods
# of each model from the data.

pps <- getPosteriors(ps, prs <- c(0.5, 0.5), estimatePriors = TRUE)

pps$priors

pps$posteriors[,1:10]

}

\keyword{models}