[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Jeffrey Simons <jsimons@usd.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: RE: multilevel model and gllamm |

Date |
Wed, 22 Sep 2004 08:50:47 -0500 |

I see now. I first understood that both varx and vary are the measurement variables. Then your setup looks correct. There should also be a way of making varz and varw regressors for the latent variable, but whether you want to do that or not depends on your interpretation on whether they affect the random coeffcients, or directly the outcome. Then also the unit correlations would indicate that you are making up a model that is too complicated. All higher constants require lower slopes means that all of your lines, i.e. the curves across all individuals, are passing through the same point in varx / vary plane. Does this seem plausible to you? Does it make sense in terms of your problem? I am unsure of this. I was wondering whether the distribution of the response variable would be problematic. For example, on most (i.e., 84%) of the measurement occasions the individuals are not drinking. Thus, there is an overrepresentation of zero values and then a positively skewed distribution of positive values. Also, I do not have all of the data yet and am using a subset of early data to learn the analysis. Hence there are only 22 level two units (participants) and 1331 level 1 units. I did not receive any convergence diagnostic messages when I ran the analysis. What you mentioned about the model being too complicated makes sense in that when I simplify it then the correlation , though still very high, goes down below 1. For example, here is a simplified model I ran (I realize , now, that I should have used the canonical link, but this gives an idea of what it looks like). Even with simple models however, if I context center the level 1 predictor I seem to be getting a intercept and slope corr of -1. I greatly appreciate your input. gllamm drink30sum C_negafflag1 C_dts ,i(id) family(gamma) link(identity) nrf(2) eqs(cons slope) adapt number of level 1 units = 1331 number of level 2 units = 22 Condition Number = 20.368537 gllamm model log likelihood = -957.30155 ---------------------------------------------------------------------------- -- drink30sum | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+-------------------------------------------------------------- -- C_negafflag1 | -.035851 .0070699 -5.07 0.000 -.0497078 -.0219942 C_dts | .0035777 .0037784 0.95 0.344 -.0038279 .0109833 _cons | 1.325152 .0577773 22.94 0.000 1.21191 1.438393 ---------------------------------------------------------------------------- -- Squared Coefficient of Variation ---------------------------------------------------------------------------- - .15615195 (.00604391) Variances and covariances of random effects ---------------------------------------------------------------------------- - ***level 2 (id) var(1): .06730144 (.02305949) cov(1,2): -.00582862 (.00219853) cor(1,2): -.95641864 var(2): .00055184 (.00033866) ---------------------------------------------------------------------------- - This is also where the model would become computationally truciky and unstable, I suppose: the likelihood is going to be flat in some directions, and the maximizer will stumble over it. Also, Stata's -ml- does not like problems on the boundary when it cannot step further than 1 for correlation coefficients. Did you receive any lack of convergence diagnostic messages along with unit correlations? Stas * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: multilevel model and gllamm***From:*Stas Kolenikov <skolenik@gmail.com>

- Prev by Date:
**Re: st: St: Programming question, local macros** - Next by Date:
**st: RE: Michaelis-Menten and regression** - Previous by thread:
**st: St: Programming question, local macros** - Next by thread:
**Re: st: RE: multilevel model and gllamm** - Index(es):

© Copyright 1996–2021 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |