[BioC] unbalanced factorial design

[Francois Collin]

Running lme on a single probe set takes about 20
minutes computing time on my PC.  I'm running R on
windows, which I know can run into memory management
problems, but this problem appears to be completely
cpu bound.  

The model that I'm fitting has repeated measures, 6
Visits per Subject, for 20 Subjects.  Subjects have a
Treatment attribute and I'm interested in the fully
saturated model with Visit, Treatment and interaction
effects.   The call to lme() I use is something like
this:

lme(fixed= Expr ~ Visit:Treatment,
    random= ~ Visit | Subject)

The results appear ok, but it takes 20 minutes to run.
 Am I doing something wrong?  Can you all use lme() on
20,000 probe sets and live to talk about it?

Thanks for any insight into this problem.

-francois


--- Naomi Altman <naomi at stat.psu.edu> wrote:
> I find that the simplest thing to do is to write my
> own function that 
> includes the appropriate call to lme.   That way I
> do not need to worry 
> about grabbing components from complicated objects
> and passing arguments to 
> lme.   I do have to write my own calling function
> for each experiment, but 
> that takes only a few minutes.
> 
> --Naomi
> 
> At 10:15 PM 2/4/2004, Vincent Carey 525-2265 wrote:
> 
> > >
> > > 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.
> >
> >_______________________________________________
> >Bioconductor mailing list
> >Bioconductor at stat.math.ethz.ch
>
>https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
> 
> Naomi S. Altman                               
> 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics                             
> 814-863-7114 (fax)
> Penn State University                        
> 814-865-1348 (Statistics)
> University Park, PA 16802-2111
> 
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