I'm trying to run a 3-way within-subject anova in lme with 3 fixed factors (Trust, Sex, and Freq), but get stuck with handling the random effects. As I want to include all the possible random effects in the model, it would be something more or less equivalent to using aov
> fit.aov <- aov(Beta ~ Trust*Sex*Freq+Error(Subj/(Trust*Sex*Freq)), Model) However I'm not so sure what I should do in lme. Sure > lme(Beta ~ Trust*Sex*Freq, random = ~1|Subj, Model) works fine, but it only models the random effect of the intercept. I tried the following 4 possibilities: > lme(Beta ~ Trust*Sex*Freq, random = pdBlocked(list(pdCompSymm (~Trust-1), pdCompSymm(~Sex-1), pdCompSymm(~Freq-1), pdIdent(~1))), Model) > lme(Beta ~ Trust*Sex*Freq, random = pdBlocked(list(pdCompSymm(~ (Trust*Sex*Freq-Trust:Sex:Freq-1)), pdIdent(~1))), Model) > lme(Beta ~ Trust*Sex*Freq, random = pdBlocked(list(pdCompSymm(~ (Trust*Sex*Freq-1)), pdIdent(~1))), Model) > lme(Beta ~ Trust*Sex*Freq, random = pdBlocked(list(pdCompSymm (~Trust-1), pdCompSymm(~Sex-1), pdCompSymm(~Freq-1), pdCompSymm(~ (Trust-1)*(Sex-1)), pdCompSymm(~(Trust-1)*(Freq-1)), pdCompSymm(~ (Sex-1)*(Freq-1)), pdIdent(~1))), Model) but all failed with the same error message: Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups What am I missing? Saw a similar situation in the archives, but I'm still clueless about the solution: http://tolstoy.newcastle.edu.au/R/e2/help/07/01/8431.html Any help would be highly appreciated. Gang ______________________________________________ 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 and provide commented, minimal, self-contained, reproducible code.