[R-sig-ME] Mixed model with multiple response variables?

[Andrew Robinson]

Hi Gang,

I suggest that you ask yourself whether or not the correlation between
the response variables is of inferential interest in the subject
matter.

If not, then analyze them separately, correct for multiple tests
somehow, and check the correlation of the residuals.  If the residuals
are correlated then a more efficient estimate would be possible using
e.g. a relation to seemingly unrelated regression.  If the residuals
are uncorrelated then I think that you can keep the separate analyses.

If you want to try to model the correlations between the response
variables in an otherwise mixed-effects framework, some nice work was
done by Daniel Hall (U of Georgia) on forestry data, published in
Biometrics, if I recall correctly.  I also tried out some ideas in a
2004 article published in the Canadian Journal of Forest Research. 

Cheers,

Andrew

> On Tue, 5 Aug 2008, Gang Chen wrote:
> 
> Hi,
> 
> I have a data set collected from 10 measurements (response variables)
> on two groups (healthy and patient) of subjects performing 4 different
> tasks. In other words there are two fixed factors (group and task),
> and 10 response variables. I could analyze the data with aov() or
> lme() in package nlme for each response variable separately, but since
> most likely there are correlations among the 10 response variables,
> would it be more meaningful to run a MANOVA? However manova() in R
> seems not to allow an error term in the formula. What else can I try
> for this kind of multivariate mixed model?
> 

-- 
Andrew Robinson  
Department of Mathematics and Statistics            Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia         Fax: +61-3-8344-4599
http://www.ms.unimelb.edu.au/~andrewpr
http://blogs.mbs.edu/fishing-in-the-bay/