[R] ss's are incorrect from aov with multiple factors (EXAMPLE!)

[John Christie]

On Saturday, July 12, 2003, at 07:40  AM, Peter Dalgaard BSA wrote:

> factor, and no, you should not expect otherwise. The various SS in the
> full analysis are distance measures in 24-dim space, whereas in the
> aggregated analysis you get a distance in 12-space. The relation is
> that every value entering in the b and s:b terms will be duplicated in
> the former, hence the SS is twice as big.
>
> This is standard procedure, and R does the same as e.g. Genstat in
> this respect. It is also necessary to ensure that the residual MS are
> comparable, e.g. that you can test for a significant s:b random effect
> by comparing with the residual MS to that of the s:a:b stratum.

OK, perhaps I need a little help then.  Suppose I do an interaction 
plot of a*b and I want to see what it looks like with 95%CI error bars. 
  Following Loftus & Masson (1995) there would be one of two ways. I 
could generate an error bar for the main effect I was interested in and 
stress in the description that the error bars only apply across that 
main effect.  I take it from what you have said that I would collapse 
the data in order to generate a proper error bar for only one effect.  
Or, I could generate one from a weighted average of the MSE from a, b, 
and a:b.  The question I have is, would I get each of the main effects 
in that from separate analyses?

BTW, Statview seems to generate the same MSE for me whether I collapse 
the data or not.