fit.gg {gaga} | R Documentation |
Fits GaGa or MiGaGa hierarchical models, either via a fully Bayesian approach or via maximum likelihood.
fit.gg(x, groups, patterns, nclust = 1, method = "Bayes", B = 1000, priorpar, parini, trace = TRUE)
x |
ExpressionSet , data frame or matrix
containing the gene expression measurements used to fit the model. |
groups |
If x is of type ExpressionSet ,
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. |
patterns |
Matrix indicating which groups are put together under each pattern, i.e. the hypotheses to consider for each gene. Defaults to two hypotheses: null hypothesis of all groups being equal and full alternative of all groups being different. |
nclust |
Number of clusters in the MiGaGa model. nclust
corresponds to the GaGa model. |
method |
method=='Bayes' fits a fully Bayesian model via
MCMC posterior sampling. method=='EBayes' finds
maximum-likelihood estimates via the expectation-maximization
algorithm. For nclust>1 only Bayes is currently implemented. |
B |
Number of MCMC iterations. Ignored if method=='EBayes' . |
priorpar |
List with prior parameter values. It must have
components a.alpha0,b.alpha0,a.nu,b.nu,a.balpha,b.balpha,a.nualpha,b.nualpha,p.probclus
and p.probpat . If missing they are set to non-informative
values that are usually reasonable for RMA and GCRMA normalized data. |
parini |
List with components a0 , nu ,
balpha , nualpha , probclus and probpat
indicating the starting values for the hyper-parameters. If not
specified, a method of moments estimate is used. |
trace |
For trace==TRUE the progress of the model fitting
routine is printed. |
The Bayesian fit uses an approximation to sample faster from the
posterior distribution of the gamma shape parameters. This
approximation is implemented in rcgamma
.
An object of class gagafit
, with components
parest |
Hyper-parameter estimates. Only returned if method=='EBayes' , for method=='Bayes' one must call the function parest after fit.gg |
mcmc |
Object of class mcmc with posterior draws for hyper-parameters. Only returned if method=='Bayes' . |
lhood |
For method=='Bayes' it is the log-likelihood evaluated at each MCMC iteration. For method=='EBayes' it is the log-likelihood evaluated at the maximum. |
nclust |
Same as input argument. |
patterns |
Same as input argument, converted to object of class gagahyp . |
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
parest
to estimate hyper-parameters and compute
posterior probabilities after a GaGa or MiGaGa
fit. findgenes
to find differentially expressed
genes. classpred
to predict the group that a new sample
belongs to.
#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 <- fit.gg(x,groups,patterns,method='EBayes') #gg <- parest(gg,x,groups) #gg # #gg.bay <- fit.gg(x,groups,patterns,method='Bayes',B=1000) #plot(gg.bay$mcmc) #gg.bay <- parest(gg.bay,x,groups,burnin=100) #gg.bay