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From |
Nirup Menon <nmenon@gmu.edu> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Mon, 11 Mar 2013 11:15:01 -0400 |

Thank you for the prompt advice. The indicators are likert scale ranging 1-7.

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

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**Follow-Ups**:**Re: st: GLLAMM for 2-level latent factors not converging***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**References**:**st: GLLAMM for 2-level latent factors not converging***From:*Nirup Menon <nmenon@gmu.edu>

**Re: st: GLLAMM for 2-level latent factors not converging***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

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