Parameter estimates and posterior probabilities of differential expression for GaGa and MiGaGa model
Description
Obtains parameter estimates and posterior probabilities of
differential expression after a GaGa or MiGaGa model has been fit with
the function fitGG
.
Usage
1  parest(gg.fit, x, groups, burnin, alpha=.05)

Arguments
gg.fit 
GaGa or MiGaGa fit (object of type 
x 

groups 
If 
burnin 
Number of MCMC samples to discard. Ignored if

alpha 
If 
Details
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 hyperparameters.
To compute posterior probabilities of differential expression the hyperparameters are fixed to
their estimated value, i.e. not averaged over MCMC iterations.
Value
An object of class gagafit
, with components:
parest 
Hyperparameter estimates. 
mcmc 
Object of class 
lhood 
For 
nclust 
Number of clusters. 
patterns 
Object of class 
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). 
Author(s)
David Rossell
References
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
See Also
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.
Examples
1 2 3 4 5 6 7 8 9  #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

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