Christian, You need, first to factor() your factors in the data frame P.PA, and then denote the error-terms in aov correctly, as follows:
> 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) > # added code: > P.PA$group=factor(P.PA$group) > P.PA$time=factor(P.PA$time) > P.PA$subject=factor(P.PA$subject) > summary(aov(p.pa~group*time+Error(subject/time),data=P.PA)) Error: subject Df Sum Sq Mean Sq F value Pr(>F) group 1 158.7 158.7 0.1931 0.672 Residuals 8 6576.3 822.0 Error: subject:time Df Sum Sq Mean Sq F value Pr(>F) time 2 1078.07 539.03 7.6233 0.004726 ** group:time 2 216.60 108.30 1.5316 0.246251 Residuals 16 1131.33 70.71 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 On 28-Feb-06, at 4:00 AM, [EMAIL PROTECTED] 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 -- Please avoid sending me Word or PowerPoint attachments. See <http://www.gnu.org/philosophy/no-word-attachments.html> -Dr. John R. Vokey ______________________________________________ R-help@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