I cannot resist a very brief entry into this old and seemingly
immortal issue, but I will be very brief, I promise!
Amasco Miralisus suggests:
As I understood form R FAQ, there is disagreement among Statisticians
which SS to use

Amasco,
In general it is dangerous to attempt to interpret a main effect that
is included in an interaction, regardless of wether or not the
interaction is significant. If you want to make a valid inference about
a main effect it is safest to do so after dropping any interaction that
contains the

Hello,
First of all, I would like to thank everybody who answered my
question. Every post has added something to my knowledge of the topic.
I now know why Type III SS are so questionable.
As I understood form R FAQ, there is disagreement among Statisticians
which SS to use

Dear Amasco,
Again, I'll answer briefly (since the written source that I previously
mentioned has an extensive discussion):
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Amasco
Miralisus
Sent: Monday, August 28, 2006 2:21 PM
To:

I think this starts from the position of a batch-oriented package.
In R you can refit models with update(), add1() and drop1(), and
experienced S/R users almost never use ANOVA tables for unbalanced
designs. Rather than fit a pre-specified set of sub-models, why not fit
those sub-models that

Dear Amasco,
A complete explanation of the issues that you raise is awkward in an email,
so I'll address your questions briefly. Section 8.2 of my text, Applied
Regression Analysis, Linear Models, and Related Methods (Sage, 1997) has a
detailed discussion.
(1) In balanced designs, so-called Type

1. First of all, more general question. Standard anova() function for lm()
or aov() models in R implements Type I sum of squares (sequential), which
is not well suited for unbalanced ANOVA. Therefore it is better to use
Anova() function from car package, which was programmed by John Fox to use