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Re: st: Re: Measuring Models/pseudo R-squared


From   Paul Millar <paul.millar@shaw.ca>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re: Measuring Models/pseudo R-squared
Date   Wed, 06 Apr 2005 13:11:39 -0600

This is an interesting article. The pre measure examined there uses the mean of the dependent variable as the standard for comparison. The ssc command -pre- uses the mode for categorical variables. It would be interesting to see how that measure performs and to see how it performs across model types.

- Paul

At 12:16 PM 06/04/2005, you wrote:

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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