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 appear to make some sense for your problem and data?

SInce your post lacks a signature and your credentials we have no idea of 
your background, which makes it very difficult even to know what reading 
to suggest to you.  But Bill Venables' 'exegeses' paper 
(http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf) may be a good start.
It does explain your comment '3.'.

On Sun, 27 Aug 2006, Amasco Miralisus wrote:

> Hello everybody,
> 
> I have some questions on ANOVA in general and on ANOVA in R particularly.
> I am not Statistician, therefore I would be very appreciated if you answer
> it in a simple way.
> 
> 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
> Type II and Type III sum of squares. Did I get the point?
> 
> 2. Now more specific question. Type II sum of squares is not well suited
> for unbalanced ANOVA designs too (as stated in STATISTICA help), therefore
> the general rule of thumb is to use Anova() function using Type II SS
> only for balanced ANOVA and Anova() function using Type III SS for
> unbalanced ANOVA? Is this correct interpretation?
> 
> 3. I have found a post from John Fox in which he wrote that Type III SS
> could be misleading in case someone use some contrasts. What is this about?
> Could you please advice, when it is appropriate to use Type II and when
> Type III SS? I do not use contrasts for comparisons, just general ANOVA
> with subsequent Tukey post-hoc comparisons.
> 
> Thank you in advance,
> Amasco
> 
>       [[alternative HTML version deleted]]
> 
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-- 
Brian D. Ripley,                  [EMAIL PROTECTED]
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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