Christian, One thing that may help with the data you provide is to make sure that group, time, and subject are indeed factors. group <- factor(group) time <- factor(time) subject <- factor(subject)
Running your analyses in both SPSS 13.0 and R.2.2.1 (the R sessions were ran in win xp and ubuntu/linux), gave the following results: 1) SPSS time: F(2,16) = 7.623,p <.005. 2) When I ran your code, the aov piece gave a singularity warning, while the lmer bit gave a false convergence message. I believe that in your case, the code should be: aov(p.pa~time*group + Error(subject)) or aov(p.pa~time*group + Error(subject + subject:time) They both give identical results When following the "nlme way", your code is correct and should give the same results as in spss, or aov. I was also stuck in the "lmer way", even when I changed the code to: lmer(p.pa~time*group + (time|subject). Perhaps, another list member, or Prof. Bates could provide more info on this one? IKD On Mon, February 27, 2006 17:15, Christian Gold wrote: > Dear list members: > > I have the following data: > group <- rep(rep(1:2, c(5,5)), 3) > time <- rep(1:3, rep(10,3)) > subject <- rep(1:10, 3) > p.pa <- c(92, 44, 49, 52, 41, 34, 32, 65, 47, 58, 94, 82, 48, 60, 47, > 46, 41, 73, 60, 69, 95, 53, 44, 66, 62, 46, 53, 73, 84, 79) > P.PA <- data.frame(subject, group, time, p.pa) > > The ten subjects were randomly assigned to one of two groups and > measured three times. (The treatment changes after the second time > point.) > > Now I am trying to find out the most adequate way for an analysis of > main effects and interaction. Most social scientists would call this > analysis a repeated measures ANOVA, but I understand that mixed-effects > model is a more generic term for the same analysis. I did the analysis > in four ways (one in SPSS, three in R): > > 1. In SPSS I used "general linear model, repeated measures", defining a > "within-subject factor" for the three different time points. (The data > frame is structured differently in SPSS so that there is one line for > each subject, and each time point is a separate variable.) > Time was significant. > > 2. Analogous to what is recommended in the first chapter of Pinheiro & > Bates' "Mixed-Effects Models" book, I used > library(nlme) > summary(lme ( p.pa ~ time * group, random = ~ 1 | subject)) > Here, time was NOT significant. This was surprising not only in > comparison with the result in SPSS, but also when looking at the graph: > interaction.plot(time, group, p.pa) > > 3. I then tried a code for the lme4 package, as described by Douglas > Bates in RNews 5(1), 2005 (p. 27-30). The result was the same as in 2. > library(lme4) > summary(lmer ( p.pa ~ time * group + (time*group | subject), P.PA )) > > 4. The I also tried what Jonathan Baron suggests in his "Notes on the > use of R for psychology experiments and questionnaires" (on CRAN): > summary( aov ( p.pa ~ time * group + Error(subject/(time * group)) ) ) > This gives me yet another result. > > So I am confused. Which one should I use? > > Thanks > > Christian > > > > > -- > ____________________________ > Dr. Christian Gold, PhD > http://www.hisf.no/~chrisgol > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > -- Ioannis C. Dimakos, Ph.D. University of Patras Department of Elementary Education Patras, GR-26500 GREECE http://www.elemedu.upatras.gr/dimakos/ http://yannishome.port5.com/ -- Ioannis C. Dimakos, Ph.D. University of Patras Department of Elementary Education Patras, GR-26500 GREECE http://www.elemedu.upatras.gr/dimakos/ http://yannishome.port5.com/ ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
