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Re: PseudoR2 for logistic regression
Posted:
Aug 21, 2013 5:09 PM


Rich Ulrich <rich.ulrich@comcast.net> writes:
> On Mon, 19 Aug 2013 13:48:48 0500, Johann Hibschman > <jhibschman@gmail.com> wrote: > >>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((yyhat)^2), > > ... geometric mean?
Er, yes. Thanks, I should have caught that before posting.
> I don't immediately see any problem with your invention, > and it looks like a fairly natural choice.
Poking around in Regression Modeling Strategies, I eventually found just that measure as eq 9.54, with references to two papers by Korn & Simon. I'll look those up, though I miss no longer having a university journal subscription to my name.
> I have never spent time trying to deal with pseudo Rsquared > in reports  for cases with marginal pvalues, I've been > satisfied to figure for myself what "real" Rsquared would > give the same pvalue for the same d.f.
I'm just using it as a first cut when comparing candidate predictive models, not as something to report. It has the advantage that I understand what it's measuring and am confident that it represents the cost function seen by the model.
> Ray Koopman might have comments when he returns, if > he has just been gone for the summer. > > Some years ago, Frank Harrell used to read these > usenet groups, and I helped popularize the criticisms of > stepwise regressions that he posted here. I think you > might get a response more useful than mine if you send > your question to him.
I'll try to track down those Korn & Simon references [1, 2] and see if that answers my question. Otherwise, I'll give that a try. Usenet is pretty quiet these days, but it's just easier than monkeying around with, e.g., http://stats.stackexchange.com [3]
Thanks, Johann
[1] Korn & Simon. Measures of explained variation for survival data. Statistics in Medicine, 9:487503, 1990.
[2] Korn & Simon. Explained residual variation, explained risk, and goodness of fit. American Statistician, 45:201206, 1991.
[3] http://stats.stackexchange.com/questions/11676/pseudorsquaredformulaforglms



