Re: st: RE: save iterations of xtmelogit - need to break/pause

[Jeph Herrin <stata@spandrel.net>]

To the other good tips offered here, I would add another. If you try to estimate your model as a linear probability 
model (that is, use -xtmixed- instead of -xtmelogit-) you will likely get a quick sense of whether you have too many 
levels and which ones they are. -xtmixed- typically converges much more quickly, and will indicate whether some of your 
random effects are essentially zero.

Generally, while it is frustrating to abandon 6 days of calculations, the slow convergence likely indicates that you 
headed down the wrong path.

hth,
Jeph


On 5/9/2013 8:17 AM, JVerkuilen (Gmail) wrote:
> On Thu, May 9, 2013 at 1:11 AM, tshmak <tshmak@hku.hk> wrote:
> > Hi Ann,
> >
> > Despite your 160,000 individuals, I am rather unsure you have enough data
> > to fit a model with 4 random effects, if that's what you mean by 4 levels.
> > Since it's a logistic regression, perhaps a better measure of how much data
> > you have is the smaller of the number of 1's and 0's. There's probably huge
> > uncertainty over the variance of the random effects, and Stata is having a
> > hard time converging.
>
> 100% agree. Everyone always says "I have X thousands of data
> points!?!?!?" but in a multilevel model the Ns at each level matter.
> With logistic it's even worse, just as Tim noted, because it's
> essentially the minimal number of events or non-events that matter.
> These can become quite small.
>
>
> > My suggestion is:
> > 1. Try and fit a simpler model. Perhaps some of the random effects have
> > only a handful number of different values. Perhaps consider treating them as
> > fixed-effects?
>
> Yes, start with the random intercept model and build upwards towards
> the model you want.
>
>
>
> > 2. Consider doing it in WinBUGS/OpenBUGS. If you use informative priors,
> > you can pretty much fit anything. You can also use it to tell you whether
> > your model should be fit in Stata. If using different uninformative priors
> > gives you very different results, you probably shouldn't use Stata for your
> > model, and your best bet is either simplify it or use a subjective Bayesian
> > approach.
>
> Agreed, but that's no light undertaking. I suspect the amount of
> effort it would take would be substantial. An alternative would be to
> look at MCMCglmm in R, which is a nice program but has some quirks in
> the interface.
>
> Jay
> --
> JVVerkuilen, PhD
> jvverkuilen@gmail.com
>
> "They were careless people, Tom and Daisy - they smashed up things and
> creatures and then retreated back into their money of their vast
> carelessness, or whatever it was that kept them together, and let other
> people clean up the mess they had made." -- F. Scott Fitzgerald
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