Mixed model with multiple response variables?
On 06/08/2008, at 5:54 AM, 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? 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. Ken