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From | SURYADIPTA ROY <sroy9163@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: r-square in -betafit- |
Date | Fri, 18 Jun 2010 08:10:27 -0400 |
Dear Maarten and Nick, Thank you so much for these invaluable comments and suggestions! Regards, Suryadipta. On Fri, Jun 18, 2010 at 4:35 AM, Nick Cox <n.j.cox@durham.ac.uk> wrote: > 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/ > * * 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/