[R] R-equivalent Stata command: poisson or quasipoisson?

Bill.Venables at csiro.au Bill.Venables at csiro.au
Sun Sep 12 06:36:58 CEST 2010


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

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