[R] Negative Binomial Model

(Ted Harding) Ted.Harding at manchester.ac.uk
Wed May 14 11:38:28 CEST 2008


On 14-May-08 09:11:31, Achim Zeileis wrote:
> On Wed, 14 May 2008, Mike Ryckman wrote:
> 
>> Hello,
>>
>> I am trying to run a negative binomial regression model in R and
>> can't get the standard errors to match the output I get from the
>> Stata nbreg command. I've tried a few different options but
>> haven't had much luck. The closest I've found is:
>>
>> gamlss(formula, family = NBI, sigma.formula = ~ 1,data=dataframe)
>>
>> ...But this is still a little off most of the time and pretty far off
>> at
>> other
>> times (compared with the Stata output). The glm.nb from the MASS
>> package
>> produces the correct coefficients, but different (usually very
>> different)
>> standard errors.
>>
>> Could anybody explain this and point me in the right direction? I'd
>> really
>> appreciate it.
> 
> Well, you don't really give us enough information to know (a
> reproducible 
> R example and the desired standard errors from Stata would have been 
> helpful). My guess is that Stata uses some sort of "robust" standard 
> errors, i.e., sandwich standard errors. Try something like:
>    library("MASS")
>    library("sandwich")
>    library("lmtest")
>    fm <- glm.nb(...)
>    coeftest(fm, vcov = sandwich)
> 
> See also the following thread for some more discussion for count data 
> regression in R and Stata:
>    https://stat.ethz.ch/pipermail/r-help/2008-May/161640.html

Better links for following this thread are:

Start:
  https://stat.ethz.ch/pipermail/r-help/2008-May/161591.html

Then click on "Next message:" for the second. Unfortunately,
at that point the thread broke (Paul next responded to the list
as a reply to an off-list message I sent him). So to continue,
next take Achim's URL above:

  https://stat.ethz.ch/pipermail/r-help/2008-May/161640.html

and thereafter continue to click on "Next message:" until the
thread runs out.

That was a very helpful thread, for me!
Best wishes,
Ted.

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Date: 14-May-08                                       Time: 10:38:24
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