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From   Stas Kolenikov <>
Subject   Re: st: GLLAMM versus XTMEPOISSON
Date   Tue, 12 Feb 2013 11:03:50 -0600


these are the parameters of the Cholesky decomposition of the
variance-covariance matrix. The lns are the natural logs of the
diagonal elements, and atr is the (hyperbolic?) arctan of the
correlation. -gllamm- produces appropriately transformed coefficients
in the output, but I don't think the matrix of the random effect
appears in the output or saved -e()- results.

Try running -xtmepoisson- using -gllamm-'s results as starting values,
and vice versa. May be you are finding local optima. Also, try
changing the number of integration points, that's a crucial parameter
in numeric integration of mixed models.

-- Stas Kolenikov, PhD, PStat (SSC)  ::
-- Senior Survey Statistician, Abt SRBI  ::  work email kolenikovs at
srbi dot com
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer

On Tue, Feb 12, 2013 at 9:48 AM, Ana Cecilia Montes Vinas
<> wrote:
> Dear statalisters
> I'm estimating a Multilevel poisson regression using xtmepoisson and gllamm  commands. I have two types of characteristics, firm individual characteristics (x1) and sector characteristics (x2). However, when we use gllamm and Xtemepoisson we obtain contradictory results, in particular, x1 is negative with xtmepoisson, and positive  with gllamm.
> eq ri: cons
> eq rc: x1
> matrix a = e(b)
> gllamm y x1 x2, family(poisson) link(log) i(ciiu) nrf(2) eqs(ri  rc) from(a) eform adapt
> xtmepoisson y x1 x2 || ciiu:x1, irr cov(unstructured)
> Adicionally when i extract the e(b) matrix, i get 3 things called lns1_1_1, lns1_1_2, atr1_1_1_2, and i'm not sure if they are the variances and covariaces of the random effects.
> Thank you
> Ana C
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