--- Mike Lacy <[email protected]> wrote:
> I would suggest instead approaching this specification problem by
> looking at the relative increase in the pseudo-R^2 value associated
> with moving to a non-proportional odds model. My own experiments on
> using such measures to address the related problem of variable choice
> ordinal logit models shows that one measures is about as good as the
> next. (see my comment in
> http://www.stata.com/statalist/archive/2008-03/msg00249.html for a
> brief discussion of this point and a citation.) Now, I admit that
> there is a problem in knowing exactly how big a *relative* change in
> R^2 (10%?) warrants a more complicated model, but I don't think this
> is worse than to p-values as the sole arbiter.
If you want to go along this route, I would personally be more
confortable with comparing BICs rather than pseudo R^2s, as BICs where
actually designed for comparing across models and there is an
approximate justification for what kind of difference is large. For
those who care: they can be turned into an approxemation of the
posterior odds ratio of one model versus the other given uniform
priors, see (Raftery 1995)
-- Maarten
Raftery, Adrian E. (1995). Bayesian model selection in social research
(with Discussion). Sociological Methodology, 25, 111-196.
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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