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
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