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Re: st: Gllamm: Convergence not achieved: try with more quadrature points

From   Stas Kolenikov <>
Subject   Re: st: Gllamm: Convergence not achieved: try with more quadrature points
Date   Wed, 22 Sep 2010 08:03:27 -0500

2010/9/22 Kjetil Arne Van Der Wel <>:
> Many thanks for this. Sorry for describing the analysis so poorly. I did
> actually run the command with pweight (I tried both methods), but that did
> not change the situation. Also, each country within the survey provides a
> representative sample of its population. Together, the countries constitutes
> EU-25, and hence, do not represent a greater *population' of countries.
> Furthermore, the countries are not merely administrative borders, but
> political entities employing different policies that have profound impact on
> the lives of their inhabitants. Because of this, I feel it is correct to
> treat them as 'units' in which individuals (and regions) are nested (i.e.
> appropriate entities of variation that can be described with variables)
> rather than 'strata' (which I take to mean as constructed by the researcher,
> such as social class). Sorry if I completely misunderstood you, Steve and
> Stas.

There are several schools of thought in survey statistics. The
hard-core design based paradigm states that uncertainty in the data
and statistics based on them is exclusively due to sampling, and
inference based on the probability spaces generated by randomization
mechanism. Both Steve and I tend to gravitate towards this end of
specturm. On the other end of it, there is model-based inference that
postulates an underlying model for the data generating process, like a
multilevel model with fixed or random effects for strata and PSUs. comes more or less
from this school, and it is a natural place to gravitate for
multilevel people. There is no "truth", there are different
approximations to reality, and depending on the objectives of your
analysis and the tools available to you, one or the other or something
in between might be more appropriate.

> Running this command:
> gllamm depvar indepvar1-4, i(country) pweight(wt) link(logit)
> fam(binom) nip(20) adapt
> leads to the error message, while omitting the pweight works fine. (By the
> way; depvar and all indepvars are binary individual level variables, except
> age. in the next model, continous counry level variables will be added).
> N(i)=188 000
> N(j)=25

That's a sizeable data set. Gllamm might be running into numerical
difficulties with it, I am afraid. How much variation is there in your
weights? If there is substantial variation, then the few (a couple
thousand, in your case) observations dominate the estimation
procedure, and -gllamm- just cannot find a good place for the
remaining ones in the model. If that is the case, you would want to
scale and possibly trim some weights to make the model more stable.

Stas Kolenikov, also found at
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