[R] contr.sum, model summaries and `missing' information

[Christophe Rhodes]

Christophe Rhodes <csr21 at cantab.net> writes:

> I have a dataset with a response variable and multiple factors with more
> than two levels, which I have been fitting using lm() or glm().  In
> these fits, I am generally more interested in deviations from the global
> mean than I am in comparing to a "control" group, so I use contr.sum()
> as the factor contrasts.  I think I'm happy to interpret the
> coefficients in the model summary as the effect of a particular factor
> level on the deviation from the overall mean; I'm not after a highly
> rigorous treatment of these coefficients and their standard errors, but
> rather using them as suggestive of further things to investigate.
>
> [...]
>
> As far as I can tell, models m1 and m2 are semantically equivalent.  Is
> there a straightforward way of extracting the standard error and
> t-statistic for the `redundant' comparison directly from m1?  I'd rather
> not have to fit two linear models if I can fit just one.

Did I post this to the wrong list?  I'm still very much interested in
any answer, or a redirection to a more appropriate forum...

Thanks,

Christophe