Hadley,

Thanks for pointing me to some good articles. Unfortunately, I have already
read Holger's and my main concern is computational efficiency. The buzzword
on this list regarding efficient code is "vectorization". I am, frankly,
surprised that there is a way to vectorize analysis of complex models but
not to extract p values from them. Dieter's reply points one towards using
lapply, which in my experience allows for compact code but not an increase
in efficiency (one of Holger's examples demonstrates this). Anyway, I cannot
see how to go from Holger's fairly simple examples to one that involves a
complex model with several factors and interactions.

Limma, which does provide p values if contrasts are used, is blindingly fast
but I believe Gordon Smyth has hard-coded most of this excellent package in
C. I was hoping to achieve something similar without the use of the
moderated t-statistics that Limma uses.

Looks like I am stuck using loops with mcapply. Thank goodness for my
Corei7!

Mark

Mark W. Kimpel MD  ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine

15032 Hunter Court, Westfield, IN  46074

(317) 490-5129 Work, & Mobile & VoiceMail
(317) 399-1219 Skype No Voicemail please


On Sun, Mar 7, 2010 at 2:08 PM, hadley wickham <h.wickham@gmail.com> wrote:

> Hi Mark,
>
> If efficiency is a concern you might want to read "Computing Thousands
> of Test Statistics Simultaneously in R" by Holger Schwender and Tina
> Müller, http://stat-computing.org/newsletter/issues/scgn-18-1.pdf.
>
> If you just want to do it, see the examples in
> http://had.co.nz/plyr/plyr-intro-090510.pdf.
>
> Hadley
>
> On Sun, Mar 7, 2010 at 7:03 PM, Mark Kimpel <mwkimpel@gmail.com> wrote:
> > Is it possible to vectorize anova over the output of a vectorized lm?  I
> > have a gene expression matrix with each row being a gene and columns for
> > samples. There are several factors with interactions. I can get p values
> by
> > looping over the matrix with lm and anova, but I would like to make this
> as
> > computationally efficient as possible. I am able to vectorize the lm
> > command, but when I try to use anova on the resultant model object I get
> > just one anova result.
> >
> > Is what I want to do possible? And, yes, I am quite conversant with Limma
> > and other BioC packages, I have my reasons for wanting to use lm and
> anova.
> >
> > Thanks,
> >
> > Mark
> > Mark W. Kimpel MD  ** Neuroinformatics ** Dept. of Psychiatry
> > Indiana University School of Medicine
> >
> > 15032 Hunter Court, Westfield, IN  46074
> >
> > (317) 490-5129 Work, & Mobile & VoiceMail
> > (317) 399-1219 Skype No Voicemail please
> >
> >        [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>
>
> --
> Assistant Professor / Dobelman Family Junior Chair
> Department of Statistics / Rice University
> http://had.co.nz/
>

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