[BioC] unbalanced factorial design

[Kenny Ye]

believe that it is CPU bound, although the algorithm in nlme is very
computational intensive. but i was surprised that it takes 20 minutes for
such a small problem. you might want to try SAS PROC MIXED and see how
long it takes. If you send me your Rdata file, i will try it on my
machine.


Kenny


On Wed, 25 Feb 2004, Francois Collin wrote:

> 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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> > Bioconductor at stat.math.ethz.ch
> >
> https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
> 
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