Re: st: Factor variable notation vs. hand made dummy vars

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 10:41 AM 2/6/2012, Brendan Halpin wrote:
> To put the "why" back one step, the immediate reason is evident from the
> output
>
> | . logit for mpg d2-d5
> |
> | note: d2 != 0 predicts failure perfectly
> |       d2 dropped and 8 obs not used
> |
> | [...]
> |
> | . logit for mpg ib1.rep78
> |
> | note: 1.rep78 != 0 predicts failure perfectly
> |       1.rep78 dropped and 2 obs not used
> |
> | note: 2.rep78 != 0 predicts failure perfectly
> |       2.rep78 dropped and 8 obs not used
> |
> | note: 5.rep78 omitted because of collinearity
> |
> | [...]
>
> You end up fitting different models on different data.
>
> The question is now why do the formulations behave differently, and
> which is the better default?

To clarify my last answer, my guess is that in the vast majority of 
cases it won't matter which approach you use. But, this particular 
example is problematic because of the very small category Ns and the 
perfect prediction issues. If you are in a situation where it 
matters, you may want to recode the problematic variable (e.g. 
combine categories to dichotomize it) or consider an alternative 
technique, such as -exlogistic-, which, as the manual says, "produces 
more-accurate inference in small samples because it does not depend 
on asymptotic results and exlogistic can better deal with one-way 
causation, such as the case where all females are observed to have a 
positive outcome."


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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EMAIL:  Richard.A.Williams.5@ND.Edu
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