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From |
Stas Kolenikov <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: multilevel model and gllamm |

Date |
Wed, 22 Sep 2004 10:42:02 -0400 |

Then at least theoretically you should first attempt to model the overdispersion at zero, like in zero-inflated Poisson models, or in Tobit and related models, and then the remaining stuff. I am not sure if that is easily done with -gllamm-. You would then need to play around with mixed responses and -fv- option, probably. I have never explored this so far. Sorry to leave you confused more than you were when you first posted this :)) Stas > 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) > ---------------------------------------------------------------------------- > - > -- Stas Kolenikov http://stas.kolenikov.name * * 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/

**References**:**st: RE: multilevel model and gllamm***From:*Jeffrey Simons <[email protected]>

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