[BioC] Re: Manova nuances

Liaw, Andy andy_liaw at merck.com
Fri Nov 21 14:12:49 MET 2003


> From: Stephen P. Baker [mailto:stephen.baker at umassmed.edu] 
> 
> Principle component analyses should reduce your data array to 
  ^^^^^^^^^
  Principal

> as many independent components as you have samples, and  for 
> each sample get a score for each dimension.  These will have 
> the same total information as the original data.  These can 
> then be analysed separately with univariate anova but since 
> these are "orthogonal" analyses, multiple comparisons 
> adjustments would not be needed.

The analysis you described is quite different than MANOVA, so 
the conclusion/interpretation would be quite different, too.
MANOVA treats the data as coming from multivariate normal
distribution, and tests whether all groups have the same
mean vector.  What you described is n (number of samples) ANOVA
analyses that gives n p-values.

Cheers,
Andy
Andy Liaw, PhD
Biometrics Research      PO Box 2000, RY33-300     
Merck Research Labs           Rahway, NJ 07065
mailto:andy_liaw at merck.com        732-594-0820



> -.- -.. .---- .--. ..-.
> Stephen P. Baker, MScPH , PhD(ABD)                      (508) 856-2625
> Senior Biostatistician
> (775) 254-4885 fax
> Academic Computing Services
> Lecturer in Biostatistics , Graduate School of Biomedical 
> Sciences University of Massachusetts Medical School
> 55 Lake Avenue North                          
> stephen.baker at umassmed.edu
> Worcester, MA 01655  USA
> --------------------------------------------------------------
> --------------
> ----
> Date: Fri, 21 Nov 2003 00:18:54 -0500
> From: "Michael Benjamin" <msb1129 at bellsouth.net>
> Subject: [BioC] Manova nuances
> To: <bioconductor at stat.math.ethz.ch>
> Message-ID: <003401c3afee$f7eff000$7a05fea9 at amd>
> Content-Type: text/plain; charset="US-ASCII"
> 
> 
> Anybody here using manova?  It's powerful and pretty fast, 
> but I'm finding that you can't have more variables than 
> samples (limits its applicability to microarray research).  
> Is there any way around this? Assume
> 
> dim(eset)
> 
> 1200 35
> 
> transeset<-t(eset)
> fit<-manova(transeset ~ categories)
> summary(fit)
> 
> There is probably a complicated mathematical truth that 
> underlies this limitation--if anybody can shed some light, 
> that would be great.
> 
> Also, if anyone knows of a quick, free multivariate tool that 
> summarizes all the tests into a single test statistic, that 
> would be much appreciated.
> 
> Regards,
> Michael Benjamin, MD
> Emory University
> Winship Cancer Institute
> 
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