Dear Henrik, As you discovered, entering the covariate age additively into the between-subject model doesn't prevent Anova() from reporting tests for the interactions between age and the within-subjects factors. I'm not sure why you would want to do so, but you could simply ignore these tests.
I hope this helps, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of Henrik Singmann > Sent: July-21-12 1:29 PM > To: r-h...@stat.math.ethz.ch > Subject: [R] car::Anova - Can it be used for ANCOVA with repeated- > measures factors. > > Dear list, > > I would like to run an ANCOVA using car::Anova with repeated measures > factors, but I can't figure out how to do it. My (between-subjects) > covariate always interacts with my within-subject factors. > As far as I understand ANCOVA, covariates usually do not interact with > the effects of interest but are simply additive (or am I wrong here?). > > More specifically, I can add a covariate as a factor to the between- > subjects part when fitting the MLM that behaves like expected (i.e., > does not interact with the other factors), but when calling Anova on > the model, I don't know how I can specify the between-within design > (i.e., which parts of the model should interact with the repeated > measures factors). > > As far as I understand it, neither the idesign, icontrasts or imatrix > arguments, nor the linearHypothesis function can specify the within- > between design (as far as I get it they all specify the within or > intra-subject design, see John Fox's slides from User 2011: > http://web.warwick.ac.uk/statsdept/useR- > 2011/TalkSlides/Contributed/17Aug_1705_FocusV_4-Multivariate_1- > Fox.pdf). > > If this it is not possible using car::Anova, is there another way to > achiebve what I want or is it plainly wrong? > I have the feeling that using R's "New Functions for Multivariate > Analysis" (Dalgaard, 2007, R News) this could be possible, but some > advice on how, would be greatly appreciated, as this does not seem to > be the most straight forward way. > > Below is an example using the car::OBrienKaiser dataset adding an age > covariate. The example is merely an adoption from ?Anova with miniml > changes and includes e.g. age:phase:hour which I don't want to have. > > Note that I posted this question to stackoverflow two days ago > (http://stackoverflow.com/q/11567446/289572) and did not receive any > responses. Please excuse my "crossposting", but I think R-help may be > the better place. > > Best, > Henrik > > PS: I know that the posting guide says "No questions about contributed > packages" but there are some questions about car on R-help, so I > thought this would be the correct place. > > ###### Example follows ##### > > require(car) > set.seed(1) > > n.OBrienKaiser <- within(OBrienKaiser, age <- sample(18:35, size = 16, > replace = TRUE)) > > phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)), > levels=c("pretest", "posttest", "followup")) hour <- ordered(rep(1:5, > 3)) idata <- data.frame(phase, hour) > > mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5, post.1, post.2, > post.3, post.4, post.5, > fup.1, fup.2, fup.3, fup.4, fup.5) ~ treatment * gender + > age, data=n.OBrienKaiser) (av.ok <- Anova(mod.ok, idata=idata, > idesign=~phase*hour, type = 3)) > > # Type II Repeated Measures MANOVA Tests: Pillai test statistic > # Df test stat approx F num Df den Df > Pr(>F) > # (Intercept) 1 0.971 299.9 1 9 > 0.000000032 *** > # treatment 2 0.492 4.4 2 9 > 0.04726 * > # gender 1 0.193 2.1 1 9 > 0.17700 > # age 1 0.045 0.4 1 9 > 0.53351 > # treatment:gender 2 0.389 2.9 2 9 > 0.10867 > # phase 1 0.855 23.6 2 8 > 0.00044 *** > # treatment:phase 2 0.696 2.4 4 18 > 0.08823 . > # gender:phase 1 0.079 0.3 2 8 > 0.71944 > # age:phase 1 0.140 0.7 2 8 > 0.54603 > # treatment:gender:phase 2 0.305 0.8 4 18 > 0.53450 > # hour 1 0.939 23.3 4 6 > 0.00085 *** > # treatment:hour 2 0.346 0.4 8 14 > 0.92192 > # gender:hour 1 0.286 0.6 4 6 > 0.67579 > # age:hour 1 0.262 0.5 4 6 > 0.71800 > # treatment:gender:hour 2 0.539 0.6 8 14 > 0.72919 > # phase:hour 1 0.663 0.5 8 2 > 0.80707 > # treatment:phase:hour 2 0.893 0.3 16 6 > 0.97400 > # gender:phase:hour 1 0.700 0.6 8 2 > 0.76021 > # age:phase:hour 1 0.813 1.1 8 2 > 0.56210 > # treatment:gender:phase:hour 2 1.003 0.4 16 6 > 0.94434 > # --- > # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > > -- > Dipl. Psych. Henrik Singmann > PhD Student > Albert-Ludwigs-Universität Freiburg > http://www.psychologie.uni-freiburg.de/Members/singmann > > ______________________________________________ > R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
