classpred: Predict the class that a new sample belongs to.
In gaga: GaGa hierarchical model for high-throughput data analysis

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

Computes the posterior probability that a new sample belongs to each group and classifies it into the group with highest probability.

1	classpred(gg.fit, xnew, x, groups, prgroups, ngene=100)

`gg.fit`	GaGa or MiGaGa fit (object of type `gagafit`, as returned by `fitGG`).
`xnew`	Expression levels of the sample to be classified. Only the subset of the genes indicated by ngene is used.
`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.
`prgroups`	Vector specifying prior probabilities for each group. Defaults to equally probable groups.
`ngene`	Number of genes to use to build the classifier. Genes with smaller probability of being equally expressed are selected first.

The classifier weights each gene according to the posterior probability that it is differentially expressed. Hence, adding genes that are unlikely to be differentially expressed does not affect the performance of the classifier, but it does increase the computational cost. All computations are performed by fixing the hyper-parameters to their estimated value (posterior mean if model was fit with method=='Bayes' or maximum likelihood estimate is model was fit with method=='EBayes').

List with the following elements:

`d`	Numeric value indicating the group that the new sample is classified into, i.e. where the maximum in `posgroups` is.
`posgroups`	Vector giving the posterior probability that the `xnew` belongs to each of the groups.

David Rossell

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

fitGG, parest

#Not run. Example from the help manual
#library(gaga)
#set.seed(10)
#n <- 100; m <- c(6,6)
#a0 <- 25.5; nu <- 0.109
#balpha <- 1.183; nualpha <- 1683
#probpat <- c(.95,.05)
#xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha)
#
#ggfit <- fitGG(xsim$x[,c(-6,-12)],groups,patterns=patterns,nclust=1)
#ggfit <- parest(ggfit,x=xsim$x[,c(-6,-12)],groups,burnin=100,alpha=.05)
#
#pred1 <- classpred(ggfit,xnew=xsim$x[,6],x=xsim$x[,c(-6,-12)],groups)
#pred2 <- classpred(ggfit,xnew=xsim$x[,12],x=xsim$x[,c(-6,-12)],groups)
#pred1
#pred2