[R-sig-ME] Mixed model with multiple response variables?
ken at kjbeath.com.au
Wed Aug 6 10:47:44 CEST 2008
On 06/08/2008, at 5:54 AM, Gang Chen wrote:
> 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?
This looks like a multilevel model, with your measurements nested
within subject. The difference to typical models is that each outcome
will need a different variance, which I think is possible in LME.
A GEE might work (as an alternative to multilevel GEE which should)
using the robust SE to cope with the model misspecification.
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