| 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