[R] Negative Binomial Regression

Ben Bolker bolker at ufl.edu
Tue Jul 29 18:11:26 CEST 2008

Hash: SHA1

Prof Brian Ripley wrote:
| On Tue, 29 Jul 2008, Ben Bolker wrote:
|> jcarmichael <jcarmichael314 <at> gmail.com> writes:
|>> Hello.
|>> I am attempting to duplicate a negative binomial regression in R.
|>> SAS uses
|>> generalized estimating equations for model fitting in the GENMOD
|>> procedure.
|>> proc genmod data=mydata (where=(gender='F'));
|>> by agegroup;
|>> class id gender type;
|>> model count = var1 var2 var3 /dist=NB link=log offset=lregtm;
|>> repeated subject=id /type=exch;
|>> run;
|>> Since my dataset has several observations for each subject, I need the
|>> REPEATED statement in order to indicate dependence among observations
|>> with
|>> the same subject ID and independence amongst those with distinct subject
|>> IDs.  The TYPE statement goes on to specify the structure of the
|>> correlation
|>> matrix to be used (exchangeable in this case).
|>  I would try glmmPQL in the MASS package.  I don't think you
|> can *quite* get negative binomial regression this way, but
|> you can definitely get a quasipoisson model.  I think exchangeable
|> correlation corresponds to correlation=corCompSymm() in your
|> glmmPQL command.
| The problem here is that GLMM and GEE are not fitting the same model --
| in one the coefficients are subject-specific and in the other
| population-average (see MASS4 or Diggle, Liang, Zeger +/- Heagarty).
| There are several R packages for GEE, including gee, yags, geepack.  The
| documentation of geeglm (geepack) claims it can be used with families as
| in glm(), so you could try it with MASS's negative.binomial family.

~  Point taken (although I guess I was pointing the original poster
to a way to do a reasonable analysis, not necessarily to duplicate
the SAS analysis as requested).  Will the negative.binomial family
really work for this, since it seems to require a fixed theta
(overdispersion) parameter?

~  If I very naively do the following:

mf2 <- formula(Weight~Cu*Time+I(Time^2)+I(Time^3))
gee2 <- geeglm(mf2, data=dietox, id=Pig,
~   family=poisson("identity"),corstr="ar1")
gee2 <-

~  gives an error "variance invalid" --

~  so the whole thing would seem to take a bit of troubleshooting

~  (geeglm also gives warnings about non-integer Poisson values -- I
don't know why a Poisson link is being used in this example for
a non-integer Weight value ... ?)

~  Ben Bolker

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