Re: st: problem with GLLAMM and a bernoulli mixed model

[Stas Kolenikov <skolenik@gmail.com>]

David Pacheco reported some difficulties in getting convergent
solutions in -gllamm- with binary dependent variable factor analysis
(no covariates) model.

I suspect you might have (empirical) identification problems with this
model. If you don't really have variability at the second level, then
setting the variance parameter to 1 will send your loadings to
infinity. Are your likelihoods the same for different models? You
would want to try a likelihood ratio against the simple -probit-
model, and you would probably want to run -xtprobit, re- with your
data, just to see what comes out (it additionally imposes constraint
of equal loadings of different items, but if that is at least
approximately true, then you will get an estimate of the factor
variance from it to gauge how far it is from zero).

You would probably want to put in different intercepts for both
-gllamm- and -xtmixed- models using

tabulate items, gen( item_dummy )
gllamm response item_dummy*, nocons ...

if you have varying probabilities of success in different items. Thus
far, you have imposed an implicit constraint of equal probabilities,
and -gllamm- might be trying to accommodate that with wildly varying
factor loadings, the only thing you allowed to vary in the model.

On Wed, Mar 9, 2011 at 10:40 AM, David Pacheco <pacheco.david@gmail.com> wrote:
> Hello,
>
> I'm seeking suggestions about a problem with GLLAMM. I've been working
> with a specific and simple Bernoulli mixed model: link probit;
> binomial family; 2 levels;  I don’t have covariate in any level; and
> in the second level I have only one latent factor with normal
> distribution and std=1 plus its factor loading. In general the model
> is very simple, with 3 parameters. I've used this code:
>
> **************!
> gen cons=1
> eq fech1: cons   ***this allow me to create the equation for the
> latent variable that only have a factor loading
> constraint def 1 [fec1_1]cons = 1   *** this constraint the std=1 for
> the normal factor
> gllamm df_cuotas, i(fecha_n) link(probit) family(binom)
> denom(n_cuotas) eqs(fech1) constr(1) frload(1)   **** where df_cuotas
> is the response and I don't have covariate
> **************
>
> The model looks very simple, but when I've tried with different number
> of integration points (like nip(8), ... nip(20), nip(40), etc ) plus
> the traditional or adaptative cuadrature, the solution for the factor
> loading change a lot, so is very sensitive to the cuadrature setting.
>
> After that, I've tried to add start values to, maybe, neutralize this
> sensitivity to the cuadrature setting. I've used like start values a
> skew solution that I know for this model, in this way:
>
> **************!
> matrix list e(b)   *** for see the structure of the parameter matrix
>
> Stata show me this:
>
> e(b)[1,3]
>     df_cuotas:    fec1_1l:     fec1_1:
>         _cons        cons        cons
> y1           0         1.1          .5
>
> copy a=e(b)      *** to copy the structure of the parameter matrix
> matrix a[1,1]= -1.1 *** replace the values on matrix "a" with my initial values
> matrix a[1,2]= 0.016
> matrix a[1,3]= 1
> **************
>
> Thus, I've run the following code:
>
> **************
> gllamm df_cuotas , i(fecha_n) link(probit) family(binom)
> denom(n_cuotas) eqs(fech1) constr(1) frload(1) from(a)
> **************
>
> but Stata send me the error:
>
> ******
> initial vector: extra parameter df_cuotas:_cons found
> specify skip option if necessary
> (error occurred in ML computation)
> (use trace option and check correctness of initial model)
> ******
>
> However, the parameter "df_cuotas:_cons" exist in the model and in the
> matrix e(b). I thought that I had to delete the parameter "
> fec1_1:cons" from the matrix "a" of initial values, because this is
> the std. of the latent variable that I've constrained to 1.
> Nevertheless, Stata send me the same error.
>
> My questions:
>
> 1) Is something wrong on my code or is a common problem in GLLAMM, and
> in this kind of models, the sensitivity of loading factor to the
> cuadrature setting?...because with every number of integration point
> that I've tried the solution of the factor loading has changed a lot
>
> 2)  What’s  wrong in my code or in my matrix of initial values, when I
> try to use "from"?
>
> Any suggestions would be very much appreciated!
>
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>



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

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