Gang If you want to analyze all variables simultaneously and account for some correlational structure among the different response variables, then the best strategy is to pre-whiten the data and then use lmer. The methods for pre-whitening are described in detail in Pinhiero and Bates in the GLS chapter.
-----Original Message----- From: [EMAIL PROTECTED] on behalf of Gang Chen Sent: Tue 8/5/2008 2:26 PM To: [EMAIL PROTECTED] Subject: [R] Mixed model with multiple response variables? 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
