On Sun, Apr 3, 2011 at 3:10 AM, peter dalgaard <pdalgd@gmail.com> wrote:

>
> On Apr 3, 2011, at 09:24 , Krishna Kirti Das wrote:
>
> > I have a three-way unbalanced ANOVA that I need to calculate (fixed
> effects
> > plus interactions, no random effects). But word has it that aov() is good
> > only for balanced designs. I have seen a number of different
> recommendations
> > for working with unbalanced designs, but they seem to differ widely (car,
> > nlme, lme4, etc.). So I would like to know what is the best or most usual
> > way to go about working with unbalanced designs and extracting a reliable
> > ANOVA table from them in R?
>
> Actually, without random effects, aov() is not too crazy, but you might as
> well use plain lm(). In both cases, the main point is that you need to be
> aware that there is no such thing as "the" ANOVA table: Sums of squares will
> depend on the order of testing, and there is nothing to do about that
> (except getting balanced data).
>
> Pragmatically, I'd test the three-factor interaction, then use drop1() on a
> model with two-factor interactions, if nothing glaringly obvious pops up,
> try reduction to additive model and then use drop1() again. Obviously, if
> significant interactions appear, you cannot just remove them and need to
> investigate what they mean.
>
> That helps. And I've been looking for something like drop1() for a while
now.

Thank you.

Sincerely,

Krishna

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