posmeansGG {gaga} | R Documentation |
Computes posterior means for the gene expression levels using a GaGa or MiGaGa model.
posmeansGG(gg.fit, x, groups, sel, underpattern) posmeansGG.gagafit(gg.fit, x, groups, sel, underpattern)
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. |
sel |
Numeric vector with the indexes of the genes we want to
draw new samples for (defaults to all genes). If a logical vector is
indicated, it is converted to (1:nrow(x))[sel] . |
underpattern |
Expression pattern assumed to be true (defaults to
last pattern in gg.fit$patterns ). Posterior
means are computed under this pattern. For example, if only the null
pattern that all groups are equal and the full alternative that all
groups are different are considered, underpattern=1 returns
the posterior means under the assumption that groups are different
from each other (underpattern=0 returns the same mean for all
groups). |
The posterior distribution of the mean parameters actually depends on
the gene-specific shape parameter(s), which is unknown. To speed up
computations, a gamma approximation to the shape parameter posterior
is used (see rcgamma
for details) and the shape parameter is
fixed to its mode a posteriori.
Matrix with mean expression values a posteriori, for each selected gene and each group. Genes are in rows and groups in columns.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
fitGG
for fitting GaGa and MiGaGa models,
parest
for computing posterior probabilities of
each expression pattern.