[R] repeated measures ANOVA

[Gabor Grothendieck]

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 <vokey at uleth.ca> 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, r-help-request at stat.math.ethz.ch 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
>
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