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Topic: Pseudo-R2 for logistic regression
Replies: 1   Last Post: Aug 20, 2013 7:00 PM

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Johann Hibschman

Posts: 5
Registered: 4/16/07
Pseudo-R2 for logistic regression
Posted: Aug 19, 2013 2:48 PM
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Is there anything wrong with using

R^2 = 1 - D(M) / D(M0)

as a rough goodness-of-fit measure for logistic regression models, where
D is the deviance, defined as

D(M) = -2 * (log(L(M)) - log(L(M_saturated))


I'm sure this is very well-trodden territory, but I keep going in
circles in online searches, and I've not found an answer in my
usual reference, Harrell's Regression Model Strategies.

I've not found this precise form quoted anywhere. McFadden's R^2 uses
the straight log(L(M)), without subtracting off the saturated model
version, while Cox & Snell (and Nagelkerke's version) use L(M)^(2/N)
rather than the log.

I'm just starting to read up on the various distinctions here, but I've
seen it claimed[1] that the Cox & Snell version reduces to the OLS
R^2, but I can't see how that's the case, since

L_normal = Prod_i exp((y - yhat)^2)

and L^(2/N) gives something like the harmonic mean of exp((y-yhat)^2),
which is very different from the log, which just gives the straight
arithmetic sum, same as the OLS R^2.

Clearly, the normalizing coefficients cause some problems, but one nice
thing about my measure is that it divides them out by subtrating the
saturated model log-likelihood.

Basically, I've been naively using the above measure, and now I'm
wondering if I'm setting myself up for problems.


ignorant practitioner


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