[R-sig-ME] submitted for list review: ezBuildME()

[Reinhold Kliegl]

Three comments (which you probably already considered anyway, but it
was not clear from the post):

(1) In general, I would recommend to implement the sequence
drop1()-like, that is start with the full model and check whether
dropping the highest-order interaction significantly reduces the GOF,
and so on. (Of course, in perfectly balanced design it does not
matter, but we rarely have the data in this shape.) I was not sure
whether you want to advocate it as a way to arrive at a minimal model.
If so, then the drop1() approach makes sure that you do not
accidentally delete low-order interactions before you test the
high-order one.

(2) Do you plan some branching for separate tests of main effects or
of interactions of the same order rather than an omnibus test for
removing all main effects or 2-factor or 3-factor interactions? Often,
we expect only one of the the interactions to be significant. In other
words, suppose you have factors A, B, and C. Do you plan to test the
joint effect of A:B, A:C, and B:C or do you perform the tests for each
of the three interactions separately?

(3) There are quite a few side conditions for whether or not the LRT
statistics are conservative or anti-conservative (e.g., Pinheiro &
Bates, 2000). So probably there should be a big "Use at or your own
risk!" message displayed up front. (In my experience, data from
typical psychological experiments with RT as DV are usually fine in
this respect--or at least I have not seen evidence to the contrary.)

Reinhold Kliegl

PS: Many psychologists will love you for this LRT script as a
substitute for their favorite omnibus ANOVA F-test. Fortunately, they
still will have to think about planned comparisons to make sense of
the coefficients for factors.