Let's say that location defined a group, and observations may be more similar in a group. You could account for this similarity with the following model.
model1 <-lme(X~CorP,random=~1|location,data=mydata,method="ML") This fits a random intercept model grouped by location. This would assume that the slope of the regression of X on CorP is the same by group, but the group means differ. "CorP" defines the fixed part of the model. random= ~1 defines the random part (in this case just an intercept). The last part specifies the estimation method. I would recommend reading through Pinheiro and Bates. Brady West's Linear Mixed Models also has nice examples. It is difficult (for me) to assess your analysis. For example, I can see how location and subject would be random if you had repeated measures on subjects within location. These concepts take some time to understand. I would recommend working on this problem with someone experienced in this. In your studying, if you come across specific questions about nlme or lme4, there is a mixed model mailing list that is very helpful. One more note, your response takes values between 0 and 1, so you would have to make sure the residuals are behaving ok (read up on diagnostics). Best, Juliet 2009/8/26 細田弘吉 <khos...@med.kobe-u.ac.jp>: > Hi, > I am quite new to R and trying to analyze the following data. I have 28 > controls and 25 patients. I measured X values of 4 different locations > (A,B,C,D) in the brain image of each subject. And X ranges from 0 to 1. > I think "control or patient" is a between subject factor and location is > a within subject factor. So, > > controls: 28 > patients: 25 (unbalanced data set) > respone measure: X values (ranging 0 to 1) > fixed factor: control vs. patient (between subject factor) > random factor: location (level: A,B,C,D ;no order) (within subject factor) > random factor: subjectID 1-53 > > My data looks like this; > > CorP X location subjectID > control 0.708 A 1 > control 0.648 A 2 > patient 0.638 C 3 > control 0.547 D 4 > patient 0.632 B 5 > control 0.723 C 6 > ........... > > I want to know > (a) if there is a significant difference between controls and patients > in X values. > (b) where (A,B,C,D?) the difference is between controls and patients in > X values. (There may be an interaction) > > I constructed linear mixed model with lme as followings; > > (1) model1 <- lme(X ~ CorP*location, random= ~ 1| subjectID, mydata) > > (2) model2 <- lme(X ~ CorP*location, random= ~ location| subjectID, mydata) > > I am not familiar with lme syntax. I'm just wondering which formula > [(1) or (2)] is appropriate for my model to know answers of (a) and (b) > questions. Or may be both of the formulas are wrong. > > I would appreciate it very much if somebody could help me. > > Sincerely, > > Kohkichi Hosoda > > ______________________________________________ > 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. > ______________________________________________ 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.
