parest: Parameter estimates and posterior probabilities of...
In gaga: GaGa hierarchical model for high-throughput data analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

Obtains parameter estimates and posterior probabilities of differential expression after a GaGa or MiGaGa model has been fit with the function fitGG.

1	parest(gg.fit, x, groups, burnin, alpha=.05)

`gg.fit`	GaGa or MiGaGa fit (object of type `gagafit`, as returned by `fitGG`).
`x`	`ExpressionSet`, `exprSet`, data frame or matrix containing the gene expression measurements used to fit the model.
`groups`	If `x` is of type `ExpressionSet` or `exprSet`, `groups` should be the name of the column in `pData(x)` with the groups that one wishes to compare. If `x` is a matrix or a data frame, `groups` should be a vector indicating to which group each column in x corresponds to.
`burnin`	Number of MCMC samples to discard. Ignored if `gg.fit` was fit with the option `method=='EBayes'`.
`alpha`	If `gg.fit` was fit with the option `method=='Bayes'`, `parest` also computes `1-alpha` posterior credibility intervals.

If gg.fit was fit via MCMC posterior sampling (option method=='Bayes'), parest discards the first burnin iterations and uses the rest to obtain point estimates and credibility intervals for the hyper-parameters. To compute posterior probabilities of differential expression the hyper-parameters are fixed to their estimated value, i.e. not averaged over MCMC iterations.

An object of class gagafit, with components:

`parest`	Hyper-parameter estimates.
`mcmc`	Object of class `mcmc` with posterior draws for hyper-parameters. Only returned if `method=='Bayes'`.
`lhood`	For `method=='Bayes'` it is the posterior mean of the log-likelihood. For `method=='EBayes'` it is the log-likelihood evaluated at the maximum.
`nclust`	Number of clusters.
`patterns`	Object of class `gagahyp` indicating which hypotheses (expression patterns) were tested.
`pp`	Matrix with posterior probabilities of differential expression for each gene. Genes are in rows and expression patterns are in columns (e.g. for 2 hypotheses, 1st column is the probability of the null hypothesis and 2nd column for the alternative).

David Rossell

Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.

fitGG to fit a GaGa or MiGaGa model, findgenes to find differentially expressed genes and posmeansGG to obtain posterior expected expression values. classpred performs class prediction.

#Not run
#library(EBarrays); data(gould)
#x <- log(exprs(gould)[,-1])  #exclude 1st array
#groups <- pData(gould)[-1,1]
#patterns <- rbind(rep(0,3),c(0,0,1),c(0,1,1),0:2) #4 hypothesis
#gg <- fitGG(x,groups,patterns,method='EBayes')
#gg
#gg <- parest(gg,x,groups)
#gg