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# Re: st: r-square in -betafit-

 From Maarten buis To statalist@hsphsun2.harvard.edu Subject Re: st: r-square in -betafit- Date Fri, 18 Jun 2010 08:28:24 +0000 (GMT)

```--- On Fri, 18/6/10, SURYADIPTA ROY wrote:
> The -betafit- option does not supply a value of r-square or
> similar measure of goodnees of fit.

It gives you the log likelihood, which means that for model
comparison you can use likelihood ratio statistics or AICs
or BICs.

> I actually followed this FAQ:
> http://www.stata.com/support/faqs/stat/rsquared.html
> and implemented the procedure as suggested by Nick. Here
> are the results:
> It would have been very helpful to get some suggestions if
> this procedure can be relied upon in this case, and if the
> value of calculated r-square here can be compared with the
> OLS r-squared (say).

I would in that case rely more on comparing AICs and BICs
(which are also available after -regress-)

> Also, it would have been very helpful to get some help in
> understanding the difference between the results for
> -proportion- and -xb- following -predict- after -betafit-
> since the mean of the linear prediction (xb = -5.38) is
> found to be wildy beyond (0,1), while the mean of the
> default (i.e. the proportion) is found to be very close to
> the average value of the dependent variable (0.01 vs 0.007).

What -betafit- does is model the mean dependent variable as
invlogit(xb), xb is the linear predictor and invlogit(xb) is
the predicted probability. invlogit(xb) is the function
exp(xb)/(1+exp(xb)). So typically what you are interested
in is the predicted proportion rather than the linear
predictor.

Hope this helps,
Maarten (co-author -betafit-)

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------

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