Neg Binomial In GEE

SamiC <s.cox.10 <at> aberdeen.ac.uk> writes:

I have been using zuurs book but it only goes as far as poisson and binomial
GEE's.  Initially I fitted a glm with poisson and this was over dispersed. 
Then moved to binomial, but residual patterns are not great (ie variance). 
Looks like some spatial correlation.  However, in the GEE with poisson it
looks like i am stil having issues with over dispersion.

How about using family=quasibinomial?  It has a different variance
structure from NB (var = phi*mu rather than var = mu*(1+phi*mu), and
NB is sometimes preferred because it has a slightly stronger foundation --
the NB parameterized as mu*(1+phi*mu) is in the exponential family
if phi is fixed -- but this is not so important if you are using
GEE anyway.

Also I am getting convergence errors once I have built the model so far. 
Not sure what to do with this either as I have all ready reduced variables
as much as possible and still havent finished model selection (ie. with AIC
and anova test).  

Well, if you have reduced the variables "as much as possible"
you may simply have a problem with not enough/poor quality data.
Sometimes that happens and you just have to simplify your model more
than you would like.

  Remember that if you are going to do model selection (throwing
away variables on the basis of some form of model goodness-of-fit)
then you should *not* make inferences on the basis of the parameters
in the selected model -- e.g. see Harrell's _Regression Modeling
Strategies_.

  Ben Bolker