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


From   Kyle Fluegge <fluegge.1@buckeyemail.osu.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: Rabe-Hesketh's gllamm: multivariate multilevel dropout model
Date   Thu, 23 May 2013 23:53:55 +0000

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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