Re: st: GLLAMM for 2-level latent factors not converging

[Nirup Menon <nmenon@gmu.edu>]

Thank you for the prompt advice.
The indicators are likert scale ranging 1-7.
 BTW, in an earlier model, where I had constrained one of the 
regression weights from the level-2 latent to the level-3 latent, 
converged fine. I used the initial values from those results to run this 
model.
I will try other constraints, as you suggested, to look for 
identification issues.
Nirup

On 3/11/2013 10:22 AM, JVerkuilen (Gmail) wrote:
> This sounds like either an identification problem or a Heywood case to
> me. Do you have reason to suppose the model is identified? (i.e., try
> it with simulated data, or you know otherwise). You may have empirical
> unidentification due to needing a correlation to be nonzero but having
> a value too close to 0, too. A Heywood case may be in the offing, too,
> which is created by a seriously misspecified model. Often the best
> diagnosis for this is to look at the observed correlations. If one of
> them is extraordinarily high compared to the rest that is often a
> sign.
>
> Another trick is to really constrain the model a lot by fixing
> loadings to 1 or some other meaningful value and slowly freeing them
> to see where it blows up.
> What is the nature of the indicators? Are they binary?
>
> In order I'd check:
>
> Identification. I suspect you may not have an identified model.
> Empirical underidentification
> Heywood case
>
>
> On Mon, Mar 11, 2013 at 10:05 AM, Nirup Menon <nmenon@gmu.edu> wrote:
> > Hi,
> > I am running gllamm on a data file in which "scene" is nested inside "idno"
> > (please see code below). Each idno has responded to 5 or less scenarios.
> >
> > At idno level, I have 2 latent (reflective) factors made up of 2 and 3
> > indicator (observed) items respectively.
> >
> > At the scene level, I have 1 latent (reflective) factor made up of 2
> > indicator (observed) items.
> >
> > The scene level latent factor is regressed on the two idno level latent
> > factors.
> >
> > I have omitted the random effect from idno to scene for now. The code is:
> >
> > gen scene=_n
> >
> > reshape long y, i(idno scene) j(item)
> >
> > tab item, gen(d)
> >
> > eq load1: d1 d2
> >
> > eq load2: d3 d4 d5
> >
> > eq load3: d6 d7
> >
> > matrix bm1 =(0,1,1\0,0,0\0,0,0)
> >
> > eq f1: x1 x2 x3
> >
> > gllamm y d2-d7, i(scene idno) l(oprob) bmat(bm1) nrf(1 2) eqs(load1 load2
> > load3) geqs(f1) trace nip(5) nocorrel
> >
> > The problem is that the lambdas for d4 and d5 are large positive numbers
> > (around 200), while the regression weight of the factor made up of d3, d4,
> > and d5 on the scene factor is becoming a large negative number (around
> > -300). The variance of the both factors are reasonable. Is this a
> > convergence problem? The "adapt" option is not helping either. Any
> > suggestions?
> > Thanks
> > Nirup

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/