parest {gaga} | R Documentation |
Obtains parameter estimates and posterior probabilities of
differential expression after a GaGa or MiGaGa model has been fit with
the function fitGG
.
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