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

[Nick Cox <njcoxstata@gmail.com>]

I think we need complete clarity and considerable caution here.

Your previous post claimed that you are using an exact replica of
Sophia [Rabe-Hesketh]'s model, except that you changed something. I
don't know these models and so cannot judge whether your change was
trivial or substantive, but on the face of it one of those statements
is wrong or at least confusing.

Are you using exactly the same dataset as Sophia used? That is a
crucial detail.

I certainly agree that exactly the same model on exactly the same
dataset should produce the same results now with -gllamm- as in 2002,
and if not there should be an explanation why. -gllamm- has changed
and Stata has changed, meanwhile, and no one can be confident with
large complicated programs that something might not have been broken.

I don't know how much experience you have in Stata programming, but I
have some. There are certainly programs of mine in the public domain
that might not converge with particular datasets; I've had that
experience myself and typically conclude from graphical evidence that
I was trying to get a cat to pretend it was a dog, and that was a bad
idea. With your kind of model such checks are, as I understand it,
typically not available.

It's my impression that Sophia gets far more requests for -gllamm-
support than she can possibly handle. That's a tough call all round.
She's not an active member of Statalist.
Nick
njcoxstata@gmail.com


On 24 May 2013 01:51, Kyle Fluegge <fluegge.1@buckeyemail.osu.edu> wrote:
> The notable problem is that this is not my model, exactly. I have simulated the minimum number of variables to make it run. This is the model provided by Rabe-Hesketh and colleagues at Stata User Group Meeting in Maastricht, May 2002. Thus, not being able to replicate it may or may not signify a broader problem here. Hopefully, if others who have attempted to run it have noted similar problems, they can speak up within this list to contribute their alterations to the code I have provided or to provide incentive for Rabe-Hesketh and colleagues to perhaps clarify their 2002 work in a more general sense. The latter is what I think is really needed. I have not seen this model used in the literature (or at least from what I have read; there are probably papers out there somewhere), which may lend credibility to the fact that the gllamm simply cannot estimate a model like this, contrary to what Rabe-Hesketh and colleagues have proclaimed. Thank you for your assistance.
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
> Sent: Thursday, May 23, 2013 8:36 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Rabe-Hesketh's gllamm: multivariate multilevel dropout model
>
> 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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