Thank you Ben,
From the article, the purpose of the author's methodology is to
better handle heteroskedasticity (due, in part, to Jensen's
inequality). Either way, I'll try both, and see how they compare, as
I'd like the R estimation to match the Stata one.
Thanks again for your insight,
Cheers,
Wil
On Sep 12, 2010, at 12:58 PM, Ben Bolker wrote:
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
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