Hi Ullrich, >> # what does '~1 | Subj/Cond' mean?
It is equivalent to your aov() error structure [ ... +Error(Subj/Cond) ]. It gives you a set of random intercepts, one for each level of your nesting structure. ## To get some idea of what's being done it helps to have a continuous covariate in your model. ## So look at this example: mod1 <- lme(distance ~ age, Orthodont, random = ~ 1 | Subject) ## random intercepts, parallel slopes mod2 <- lme(distance ~ age, Orthodont, random = ~ age | Subject) ## random intercepts, separate slopes plot(augPred(mod1, primary=~age)) ## parallel slopes plot(augPred(mod2, primary=~age)) ## separate slopes In my first post I used the example I did because it's used in V&R's MASS (: 283--286), where the equivalence of the two methods is illustrated. So there really can be no argument about whether lme() can do what aov() does. But lme() [and the newer, still somewhat "hard-hat" version, lmer()] can do a whole lot more than aov() can do. So use lme() [or lmer() if you can't get lme() to converge]. Also, you would be much better off using the multcomp package to do TukeyHSD. Amongst its other advantages are that you can use not only the methods it offers for adjusting p-values, but everything offered in p.adjust(). ?p.adjust. HTH, Mark. Ullrich Ecker wrote: > > Great, thanks a lot, guys! > > Only thing is I now have two working versions, that yield *slightly* > different results. > > > ACCaov <- aov(Acc ~ Cond + Error(Subj/Cond), WMU3C) > ACClme <- lme(Acc ~ Cond, random = ~1 | Subj/Cond, WMU3C) # what does > '~1 | Subj/Cond' mean? > summary(glht(ACClme, linfct=mcp(Cond="Tukey"))) > > yielding > > Linear Hypotheses: > Estimate Std. Error z value p value > 2 - 1 == 0 0.392560 0.027210 14.427 <1e-05 *** > 3 - 1 == 0 0.400372 0.027210 14.714 <1e-05 *** > 3 - 2 == 0 0.007812 0.025442 0.307 0.95 > > > and > > > ACCaov <- aov(Acc ~ Cond + Error(Subj/Cond), WMU3C) > ACCaov2 <- aov(terms(Acc ~ Subj + Cond, WMU3C)) # gives same result > as 1st aov, but yields aov not aovlist > ACCtukey <- TukeyHSD(ACCaov2, "Cond"); ACCtukey > > yielding > > $Cond > diff lwr upr p adj > 2-1 0.3637756 0.29889404 0.42865721 0.0000000 > 3-1 0.3715881 0.30670654 0.43646971 0.0000000 > 3-2 0.0078125 -0.05329192 0.06891692 0.9504659 > > > I am trying to work my way through this (so I'm one of the good guys, > at least I'm trying to understand my stats... ;-)), but any hint to > what solution may be more appropriate to my problem would be much > appreciated. > > Cheers, > > Ullrich > > > > At 03:16 PM 30/05/2008, you wrote: > >>Hi Ullrich, >> >> >> The model is >> >> RT.aov <- aov(RT~Cond + Error(Subj/Cond), WMU3C) >> >> I understand that TukeyHSD only works with an aov object, but that >> >> RT.aov is an aovlist object. >> >>You want to use lme() in package nlme, then glht() in the multcomp package. >>This will give you multiplicity adjusted p-values and confidence intervals. >> >>## Example >>require(MASS) ## for oats data set >>require(nlme) ## for lme() >>require(multcomp) ## for multiple comparison stuff >> >>Aov.mod <- aov(Y ~ N + V + Error(B/V), data = oats) >>Lme.mod <- lme(Y ~ N + V, random = ~1 | B/V, data = oats) >> >>summary(Aov.mod) >>anova(Lme.mod) >> >>summary(Lme.mod) >>summary(glht(Lme.mod, linfct=mcp(V="Tukey"))) >> >>HTH, Mark. >> >> >>Ullrich Ecker wrote: >> > >> > Hi everyone, >> > >> > I am fairly new to R, and I am aware that others have had this >> > problem before, but I have failed to solve the problem from previous >> > replies I found in the archives. >> > >> > As this is such a standard procedure in psychological science, there >> > must be an elegant solution to this...I think. >> > >> > I would much appreciate a solution that even I could understand... ;-) >> > >> > >> > Now, I want to calculate a post-hoc test following up a within-subjects >> > ANOVA. >> > >> > The dv is reaction time (RT), there is a 3-level Condition factor >> > (Cond; within-subjects), a number of subjects (Subj), and the >> > dataframe is called WMU3C. >> > >> > The model is >> > >> > > RT.aov <- aov(RT~Cond + Error(Subj/Cond), WMU3C) >> > >> > I understand that TukeyHSD only works with an aov object, but that >> > RT.aov is an aovlist object. >> > >> > > class(RT.aov) >> > [1] "aovlist" "listof" >> > >> > I've tried to work around it using the "maiz" example in the MMC >> > documentation of the HH package (a solution previously recommended), >> > but I couldn't get it to work: My best shot was to calculate another >> > aov avoiding the error term (I don't see how this could be a feasible >> > approach, but that's how I understood the MMC example) and a contrast >> > vector (contrasting conditions 2 and 3): >> > >> > I have to admit that I don't quite understand what I'm doing here >> > (not that you couldn't tell) >> > >> > > RT2.aov <- aov(terms(RT~Subj*Cond, WMU3C)) >> > > Cond.lmat <- c(0,1,-1) >> > > Tukey <- glht.mmc(RT2.aov, focus = "Cond", focus.lmat = Cond.lmat) >> > >> > yielding >> > >> > Error in mvt(lower = carg$lower, upper = carg$upper, df = df, corr = >> > carg$corr, : >> > NA/NaN/Inf in foreign function call (arg 6) >> > In addition: Warning message: >> > In cov2cor(covm) : diagonal has non-finite entries >> > >> > > Tukey >> > height >> > >> > >> > >> > Thank you very much for your help! >> > >> > Ullrich >> > >> > >> > Dr Ullrich Ecker >> > Postdoctoral Research Associate >> > Cognitive Science Laboratories >> > School of Psychology (Mailbag M304) >> > Room 211 Sanders Building >> > University of Western Australia >> > 35 Stirling Hwy >> > Crawley WA 6009 >> > Australia >> > Office: +61 8 6488 3266 >> > Mobile: +61 4 5822 0072 >> > Fax: +61 8 6488 1006 >> > E-mail: [EMAIL PROTECTED] >> > Web: www.cogsciwa.com >> > [[alternative HTML version deleted]] >> > >> > ______________________________________________ >> > 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. >> > >> > >> >>-- >>View this message in context: >>http://www.nabble.com/Tukey-HSD-%28or-other-post-hoc-tests%29-following-repeated-measures-ANOVA-tp17508294p17553029.html >>Sent from the R help mailing list archive at Nabble.com. >> >>______________________________________________ >>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. > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/Tukey-HSD-%28or-other-post-hoc-tests%29-following-repeated-measures-ANOVA-tp17508294p17559307.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
