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

From   Steve Samuels <>
Subject   Re: st: Gllamm: Convergence not achieved: try with more quadrature points
Date   Wed, 22 Sep 2010 09:54:58 -0400



To Stas's comments, I would only add:

In my post, I used the technical sampling term "strata": units which
together constitute the population. In your data, countries are
formally strata.

To get an idea of what the probability weighted estimates will look
like, set up the survey design approach:

svyset _n [pweight= wt1], strata(country)
svy: logit [your model, which can include country-level indicators and

Standard errors will be valid for the individual-level covariates. The
validity is "design validity", based on the possibly false assumption
that observations within country were sampled independently, and not
in multiple-stages or clusters. For true design validity, you would
need to add information about the sample design within each country
(strata, first-stage clusters or "primary sampling units")

Another possibility: try HLM, which will do multi-level models with
survey data and give design-based estimates of standard errors.


Steven J. Samuels

On Wed, Sep 22, 2010 at 9:03 AM, Stas Kolenikov <> wrote:
> 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
> Small print: I use this email account for mailing lists only.
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