On Mon, Mar 23, 2009 at 2:35 PM, Lawrence Hanser <lhan...@gmail.com> wrote: > Dear Colleagues, > I have what Roger Kirk (Experimental Design: Procedures for the Behavioral > Sciences, 1968) refers to as a randomized block factorial design. The anova > table would look like this: > > df > A 3 > Subj/A 103 (error term for A) > B 23 > A*B 69 > B*Subj/A 2369 (error term for B and A*B)
> Subjects are nested within A and give a response for each B. If y is the > dependent variable, is this the correct lmer specification for the above, > where ID is the variable name for Subj: > lmer(y ~ A + B + A*B + (A|ID)) If, as you say, subjects are nested within levels of A, then I don't think you want a random effects term of the form (A | ID). I understand what you say to mean that each subject is exposed to one and only one level of factor A so trying to fit a random effect for the levels of A within each subject doesn't make sense. Trying to understand model specifications for lmer according to the degrees of freedom for each term is probably not the best approach. > Am I barking up the right tree? I can also fit: > > aov(y ~ A + B + A*B + ID) > then I have to do some hand calculations to use ID as the error term for A. > The residual (really B*ID) is the correct error term for B and A*B. > > Thanks, > > Larry > > [[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. > ______________________________________________ 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.
