Dear Statalist,
I have a question regarding the goodness of fit measures available for
poisson models based on survey data.
I've tried three commands to fit the same poisson model and find that
Stata is not consistent in the post-estimation options offered after
these three different commands. I can't find an explanation for the
difference in the manuals (though perhaps I've missed something).
I started by fitting a poisson model to the survey data using
-svy:poisson-. After this command Stata does not allow -estat gof- or
the prediction of any residuals (anscombe, deviance etc).
My data are stratified, which is why I'm using -svy:poisson- and not
-poisson- with the cluster and weight options. However, if I ignore the
strata and use -poisson- with the weight and cluster options then Stata
allows -estat gof-. If I use -glm-, again with the weight and cluster
options and ignoring the stratification, I can obtain the anscombe,
pearson and deviance residuals.
Is there is something about the stratification of the data that makes
it difficult, or inappropriate, to calculate the residuals and the
goodness of fit test? I'm under the impression that weights and
clusters are more of a problem in this respect.
Is it appropriate to calculate residuals/gof following glm or poisson
with the weight and cluster options? Is -svy:poisson- too strict, or
are -glm- and -poisson- too lenient?
I've not found anything in the literature so would be grateful for any
help.
Thanks,
Emma
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