st: Re: Measuring Models/pseudo R-squared

["Christer Thrane" <ch-thran@online.no>]

Alfred DeMaris wrote a paper on the performances of the various measures of 
pseudo-R-squared a few years back.

DeMaris, A. (2002)  Explained Variance in Logistic Regression. A Mote Carlo 
Study of Proposed Measures.  Sociological Methods & Research  31, 27-74.



Christer

----- Original Message ----- 
From: "Paul Millar" <paul.millar@shaw.ca>
To: <statalist@hsphsun2.harvard.edu>
Sent: Wednesday, April 06, 2005 8:04 PM
Subject: st: Measuring Models



> Most of the various kinds of pseudo-R2s are attempts at providing the 
> equivalent of the "variance explained" interpretation of the OLS R2.  The 
> other interpretation of R2 is the proportional reduction in errors when 
> predicting the dependent variable or PRE.  This is a measure of the 
> predictive capability of the model and can be calculated for other models 
> as well - the ssc post-estimation command -pre- will calculate this for 
> common model types (logit, ologit, mogit, poisson and the like).  Some 
> don't like it because for some models it can actually be negative if the 
> model is worse than predicting the mode (for example with logit or probit 
> models that model a rare phenomenon).  Nevertheless, I think it is useful 
> to know how the model improves prediction capability - this might in fact 
> be one of the more important measures of a model, yet it doesn't seem to 
> be widely used.
>
> I prefer the plain old Pseudo-R2 (the proportional improvement in the 
> log-likelihood) for pseudo-R2s, since it is available for all models and 
> is easily calculated and understood.  It is somewhat analogous to the pre, 
> in that it measures the improvement of the log-likelihood instead of the 
> reduction of errors.
>
> - Paul Millar
> Sociology
> University of Calgary
>
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