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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: r-square in -betafit- |

Date |
Fri, 18 Jun 2010 09:35:50 +0100 |

I am another co-author of -betafit- (SSC) and author of the FAQ referred to. I see no great harm in computing a R-square measure as an extra descriptive measure. How useful and reliable it is will depend on the science of what you are doing and how far it makes sense as a summary, which is best judged graphically by considering a plot of observed vs fitted. Wanting to go further, if you do, in terms of formal inference with R-square would in my judgement be a bad idea. As Maarten indicates, the machinery supplied by -betafit- is superior for that purpose. Nick n.j.cox@durham.ac.uk Maarten buis --- 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: r-square in -betafit-***From:*SURYADIPTA ROY <sroy9163@gmail.com>

**References**:**st: r-square in -betafit-***From:*SURYADIPTA ROY <sroy9163@gmail.com>

**Re: st: r-square in -betafit-***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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