Wil M Contreras Arbaje <wil.contreras <at> gmail.com> writes: > > Thanks Bill! > > Not asking for help with Stata at all, on the contrary: the article > mentioned using Stata to fit the model described earlier, and I wasn't > sure how to do the same in R (which is what I've used since college). > > Thanks again, I'll play around a bit glmRob, see what happens (though > it's slightly worrisome that I won't be able to obtain similar > results, if only for 'contrast'). > > Cheers, > > Wil
I find it very hard to tell from Stata's help page, but my best guess would be that the previously mentioned Stata command is more or less equivalent to R's quasipoisson -- the 'robust' specification seems to apply only to the standard error calculation, not to the fitting process. What's unclear about 'robust' is that in other (least-squares fitting) contexts in Stata, it means 'Huber-White sandwich estimators', i.e. estimators that are robust to heteroscedasticity. I suppose this is more general (but also more data-hungry) than the simple expedient of scaling the standard errors by a single estimated overdispersion parameter. The best thing, of course, would be to try a test case in both systems. Or it seems that http://www.stata.com/bookstore/lrm.html (chapter 9) would be helpful. (I checked the stata list archives for 'quasipoisson' and found only a post from the author ...) Somewhat heretically, I prefer polycultures to monocultures; I like R for many reasons, but I'm glad that there are other systems out there with independent implementations and different sets of advantages and drawbacks. > > On Sep 12, 2010, at 12:36 AM, <Bill.Venables <at> csiro.au> <Bill.Venables <at> csiro.au > > wrote: > > > In R, the glm families poisson and quasipoisson will give you the > > same estimates. Their standard errors will (usually) be different, > > though, and family = quasipoisson does not give you an AIC (since it > > does not maximise a true likelihood; it uses quasi-likelihood > > estimation). > > > > I hope you are not asking this list for help with Stata. We've never > > heard of it. It looks to me, though, that what you are doing below > > is fitting a robust poisson glm. If so, it is something different > > again. There is a package 'robust' which has a glmRob() fitting > > function in it that may do something similar, but there is so much > > tweaking allowed with robust fits the chance of getting the same > > result as with some other system (or even with R if you do it again, > > mostly) is effectively zero. > > > > Tip: use R and forget the others. It makes life so much easier all > > round. > > > > > > -----Original Message----- > > From: r-help-bounces <at> r-project.org [mailto:r-help-bounces <at> r-project.org > > ] On Behalf Of Wil M Contreras Arbaje > > Sent: Sunday, 12 September 2010 11:27 AM > > To: r-help <at> r-project.org > > Subject: [R] R-equivalent Stata command: poisson or quasipoisson? > > > > Hello R-help, > > > > According to a research article that covers the topic I'm analyzing, > > in Stata, a Poisson pseudo-maximum-likelihood (PPML) estimation can be > > obtained with the command > > > > poisson depvar_ij ln(indepvar1_ij) ln(indepvar2_ij) ... > > ln(indepvarN_ij), robust > > > > I looked up Stata help for the command, to understand syntax and such: > > > > www.stata.com/help.cgi?poisson > > > > Which simply says that the command fits a Poisson regression of depvar > > on indepvars. However, in my google-searching, I noticed that pseudo- > > maximum-likelihood estimation is sometimes called 'quasi-maximum,' and > > that R has a "quasipoisson" family that seems to allow for > > overdispersion. So, am I missing something, or should I specify > > "quasipoisson" when implementing this estimation? > > > > Thanks a lot! > > > > Cheers, > > > > > > Wil > > > > ______________________________________________ > > R-help <at> r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
