Or use gl which directly forms a factor: group <- gl(2, 5, 30) time <- gl(3, 10) subject <- gl(10, 1, 30)
On 2/28/06, John Vokey <[EMAIL PROTECTED]> wrote: > 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 > ______________________________________________ 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