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

[David Afshartous]

Note sure how to fit the model in R, but two good references are:

Fieuws & Verbeke (2006). "Pairwise fitting of mixed models for the joint
modeling of multivariate longitudinal profiles," Biometrics, 62, 424-431.

Fieuws et al. (2008). "Predicting renal graft failure using multivariate
longitudinal profiles," Biostatistics, 9, 419-431.


On 8/5/08 3:54 PM, "Gang Chen" <gangchen6 at gmail.com> 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?
> 
> Also, if I want to find out which response variables (among the 10
> measurements) are statistically significant in terms of acting as
> indicators for group difference, what kind of statistical analysis
> would help me sort them out?
> 
> Thanks in advance,
> Gang
> 
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