How does one go about doing a repeated measure regression? The documentation I have on it (Lorch & Myers 1990) says to use linear / (subj x linear) to get your F. However, if I put subject into glm or lm I can't get back a straight error term because it assumes (rightly) that subject is a nominal predictor of some sort. In looking at LME it seems like it just does the right thing here if I enter the random effect the same as when looking for ANOVA like results out of it. But, part of the reason I'm asking is that I wanted to compare the two methods. I suppose I could get it out of aov but isn't that built on lm? I guess what I'm asking is how to calculate the error terms easily with lm.
You need to gain some background. MIXED EFFECTS MODELS in S and S-PLUS by Pinheiro and Bates is a canonical reference for how to do this with R. Chapter 10 of Venables and Ripley's MASS(4th ed.) contains a more compact but very informative overview that may suffice. Other useful references can also be found on CRAN. Bert Gunter Genentech Nonclinical Statistics -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of John Christie Sent: Thursday, May 17, 2007 10:06 AM To: R-help at stat.math.ethz.ch Subject: [R] repeated measures regression How does one go about doing a repeated measure regression? The documentation I have on it (Lorch & Myers 1990) says to use linear / (subj x linear) to get your F. However, if I put subject into glm or lm I can't get back a straight error term because it assumes (rightly) that subject is a nominal predictor of some sort. In looking at LME it seems like it just does the right thing here if I enter the random effect the same as when looking for ANOVA like results out of it. But, part of the reason I'm asking is that I wanted to compare the two methods. I suppose I could get it out of aov but isn't that built on lm? I guess what I'm asking is how to calculate the error terms easily with lm. ______________________________________________ R-help at stat.math.ethz.ch 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.
Hi John, I have collected a few methods for doing this in a very empyrical fashion. I've asked a few questions on r-help about them, and got mixed responses. You can find the archived thread at: http://tolstoy.newcastle.edu.au/R/e2/help/07/05/16660.html The responses and linked resources might be of some interest to you, too... Basically, my understanding is that ANOVA procedures are the most powerful ones, provided you can meet their assumptions. MANOVA procedures do not require sphericity, but your design should be balanced and time intervals should be equally-spaced. Finally, assumptions for lme(r) models are the most forgiving, but their power is also reduced. I may be wrong on my conclusions, though, so I'm looking forward to comments on this, especially on the lme(r) approaches... Regards, -- Marco B On 5/17/07, John Christie <jc at or.psychology.dal.ca> wrote:> > How does one go about doing a repeated measure regression? The > documentation I have on it (Lorch & Myers 1990) says to use linear / > (subj x linear) to get your F. However, if I put subject into glm or > lm I can't get back a straight error term because it assumes > (rightly) that subject is a nominal predictor of some sort. > > In looking at LME it seems like it just does the right thing here if > I enter the random effect the same as when looking for ANOVA like > results out of it. But, part of the reason I'm asking is that I > wanted to compare the two methods. I suppose I could get it out of > aov but isn't that built on lm? I guess what I'm asking is how to > calculate the error terms easily with lm. > > ______________________________________________ > R-help at stat.math.ethz.ch 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. >
Possibly Parallel Threads
- Comparing non nested models with correlation coefficients (inspired from Lorch and Myers )
- Tukey HSD (or other post hoc tests) following repeated measures ANOVA
- Some questions on repeated measures (M)ANOVA & mixed models with lme4
- repeated measures help; disagreement with SPSS
- does lme repeated measures require sphericity?