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Re: st: Rabe-Hesketh's gllamm: multivariate multilevel dropout model


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Rabe-Hesketh's gllamm: multivariate multilevel dropout model
Date   Fri, 24 May 2013 01:36:10 +0100

The short answer is likely to be that you are doing nothing wrong that
we can identify for you.

-gllamm- (SSC) is a very general, indeed highly versatile, command
that is more like a family of commands. However, many of the models it
covers are difficult to fit -- or conversely many of the models are
often applied to data that aren't suitable. Where to put the blame is
an open and delicate matter. Naturally it is usually impossible to be
clear about suitability before trying a fit, but
having correct syntax is not a guarantee of anything but having correct syntax.

People who are familiar with your kind of model may well be able to
add more specific comments. Means of binary variables being very near
0 or very near 1 can be problematic.

The recent thread starting here has other advice, some specific:

http://www.stata.com/statalist/archive/2013-05/msg00665.html

Nick
njcoxstata@gmail.com


On 24 May 2013 00:53, Kyle Fluegge <fluegge.1@buckeyemail.osu.edu> wrote:
> Dear Statalisters,
>
> I am attempting to model a multivariate multilevel dropout model with gllamm. The data set is in long form, with response vector including both binary and continuous data. As for notation, x_i1 is a dichotomous variable predicting the continuous outcome, i1 is variable denoting records within the substantive model, i2 is variable denoting records within the dropout/selection model (probit), y0_i2do is variable referring to concurrent continuous outcome's impact on dropout, and y1_i2 is lagged variable referring to previous continuous outcome's impact on current dropout. The model syntax is below (it is an exact replica of Rabe-Hesketh's dropout model):
>
> gllamm resp x_i1 i1 y0_i2d0 i2 y1_i2, i(t id) eqs(eta1_1 eta2_1) nocons  /*
> */ family(gauss binom) fv(var) link(ident probit) lv(var) bmatrix(B) geqs(f1_1) frload(1) constr(1/5)/*
> */ nats nip(7) adapt trace
>
> When running this model, it is not converging and produces errors that "numerical derivatives are approximate" and "flat or discontinuous region encountered". I am curious to know what I am doing wrong. The only thing that I have changed from Rabe-Hesketh's model in the link is that x_i1 is a dichotomous explanatory variable (and that is because the model will not run without an "x"). Everything else is exactly the same. Why is this not running? I have contacted the authors of gllamm, who have not responded. Has anyone else been able to run this model as Rabe-Hesketh et al. have written and had success?
>
> Sincerely,
>   kyle
>
>
>
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