Mixed model with multiple response variables?
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?
You might look at the Oct 2007 R-News article on the subject. But a flexible approach is to use the sem package. David Duffy.
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v