st: problem with GLLAMM and a bernoulli mixed model

[David Pacheco <pacheco.david@gmail.com>]

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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