Re: st: -glm- vs -xtgee,corr(independent)-

[Jeph Herrin <junk@spandrel.net>]

I agree with Joseph Coveney, in that for the -glm- and -xtgee-
results to match, I had to specify -cluster()- for -glm-; the
-irls- option didn't seem to matter.

thanks,

Jeph


Joseph Coveney wrote:
> Yulia Marchenko wrote:
>
> Jeph Herrin <junk@spandrel.net> asks:
> > I'm trying to understand why, contrary to the documentation, these two do
> > not
> > give the same results:
> >
> > . glm   rate exposed, family(binomial) link(logit) robust
> > . xtgee rate exposed, family(binomial) link(logit) robust /*
> >                         */corr(independent) i(id)
> >
> > The -glm- converges nicely, the -xtgee- gives an error:
> >
> > estimates diverging (missing predictions)
> > r(430);
> >
> > One feature is that the dependent variable is a proportion, not 0/1;
> For the parameter estimates from -glm- to be equivalent to those
> from -xtgee-,
> the -irls- option needs to be specified with -glm-.  This is because, by
> default, -glm- uses maximum likelihood optimization to obtain parameter
> estimates.
>
> I would like to see Jeph's data to determine why -xtgee- did not converge.
> I
> have emailed him privately and will report any conclusions to the list.
>
> --------------------------------------------------------------------------------
>
> I didn't realize that -xtgee- can accept a proportion for the response
> variable with the binomial family as can -glm-.
>
> In the dummy example below, correspondence between -xtgee- and -glm- seems
> to have more to do with using -cluster()- with -glm- than with using the 
> IRLS
> algorithm.
>
> Joseph Coveney
>
> clear
> set more off
> set seed `=date("2006-08-22", "ymd")'
> set obs 200
> generate byte treatment = uniform() > 0.5
> generate byte repetition = ceil(uniform() * 6)
> generate int pid = _n
> expand repetition
> generate byte positive = uniform() > 0.5
> xi i.repetition
> xtgee positive treatment _I*, i(pid) family(binomial) ///
>   link(logit) corr(independent) robust nolog
> glm positive treatment _I*, family(binomial) link(logit) ///
>   robust irls nolog
> glm positive treatment _I*, cluster(pid) family(binomial) ///
>   link(logit) nolog
> exit
>
>
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