[BioC] unbalanced factorial design

[Vincent Carey 525-2265]

>
> I am not sure about if bioconductor includes any functions for
> mixed-effect models. there are several packages in R handles mixed-effect
> models, the most complete one is nlme.

it is not too difficult to run gene-specific mixed
effects models using the combination of esApply (in
Biobase) and lme (in nlme).  the non-trivial part is
to properly specify the function (esApply parameter FUN)
to invoke through esApply.  the design will be derivable from
information in the phenoData component.  all variables
in phenoData are visible to the FUN for esApply, so the
model formula can be specified fairly naturally, thanks
to the environment manipulations provided in esApply
(by RG).

with appropriately structured experimental designs in
which expression might vary smoothly but nonlinearly
as a function of some design variable, nlme models may
be of interest to fit through esApply as well.

so the question "does bioconductor include functions
for ... modeling" often has a negative answer -- we don't
aim to have functions for all conceivable approaches to
modeling bioinformatic data.  we prefer to have interfaces
that allow existing functions in R to be reused conveniently
and at the option of the analyst, in the bioinformatic context.