Would it improve things if "type" were a continuous variable rather than categorical? I chose words at the extreme ends of a valence rating scale but I still have the raw valence ratings for each word.
On Sat, Feb 7, 2009 at 12:02 PM, Dieter Menne <dieter.me...@menne-biomed.de> wrote: > Mike Lawrence <mike <at> thatmike.com> writes: > > Thanks for the excellent reproducible sample set! > >> I'm most interested in the interaction between color and type, but I >> know that there is likely an effect of word. Yet since word is not >> completely crossed with type, simply adding it to an aov() won't work. >> A colleague recommended I look into lme() but so far I can't figure >> out the proper call. > > Without word, it would be > > summary(lme(rt~type*color, data=a,random=~1|id)) > > With the interaction, the extreme would be > summary(lme(rt~type*color*word, data=a,random=~1|id)) > > or, less extreme > > summary(lme(rt~type*color+color:word, data=a,random=~1|id)) > > but all these fail because of the rather degenerate structure > of you data set. While lmer in package lme4 allows for a wider > set of solutions, I currently do not see how it could help, > but I might be wrong with p=0.5. > > > word happy joy sad grumpy > type color > positive white 93 90 0 0 > red 90 88 0 0 > green 88 87 0 0 > negative white 0 0 88 95 > red 0 0 91 85 > green 0 0 88 88 >> > >> Another issue is whether to collapse across repetition before running >> the stats, particularly since errors will leave unequal numbers of >> observations per cell if it's left in. > > That's one of the points where you have little to bother with the lme > approach. Collapsing would give equal weights to unequal numbers of > repeat, and might of minor importance when not too extreme, though. > > Dieter > > > set.seed(1) > a=rbind( > cbind( > type='positive' > ,expand.grid( > id=1:10 > ,color=c('white','red','green') > ,word=c('happy','joy') > ,repetition = 1:10 > ) > ) > ,cbind( > type='negative' > ,expand.grid( > id=1:10 > ,color=c('white','red','green') > ,word=c('sad','grumpy') > ,repetition = 1:10 > ) > ) > ) > > #add some fake rt data > a$rt=rnorm(length(a[,1])) > > #And because people make errors sometimes: > a$error = rbinom(length(a[,1]),1,.1) > > #remove error trials because they're not psychologically interesting: > a=a[a$error==0,] > > library(nlme) > ftable(a[,c(1,3,4)]) > summary(lme(rt~type*color, data=a,random=~1|id)) > > ______________________________________________ > 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. > -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University www.thatmike.com Looking to arrange a meeting? Check my public calendar: http://www.thatmike.com/mikes-public-calendar ~ Certainty is folly... I think. ~ ______________________________________________ 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.