Simulation of overdispersion in LGM PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
 Daniel Seddig posted on Sunday, September 25, 2011 - 12:27 pm
I am MC-simulating overdispersed count data (nb) generated from a quadratic growth curve model of an outcome with 5 timepoints. The specification of the model-implied outcome variances will result in dispersion-parameter values that are equal to the specified variances. Given the restriction of the model-implied outcome intercepts to zero and the NegBin2 Variance-Function (mu_i+alpha*mu_i^2) it not obvious to me why this has to happen.



i BY y1-y5@1;
s BY y1@0 y2@1 y3@2 y4@3 y5@4;
q BY y1@0 y2@1 y3@4 y4@9 y5@16;

[y1-y5*0]; !!!!!!!!!!
y1*3.000; !!!!!!!!!!
y2*5.000; !!!!!!!!!!
y3*5.000; !!!!!!!!!!
y4*3.000; !!!!!!!!!!
y5*3.000; !!!!!!!!!!

[i*0.5 s*0.25 q*-0.2];

i WITH s*-0.3;
i WITH q*-0.05;
s WITH q*0;

Results in:

Y1 3.000
Y2 5.000
Y3 5.000
Y4 3.000
Y5 3.000

Thank you!
 Linda K. Muthen posted on Sunday, September 25, 2011 - 3:33 pm
With a count variable, the name of the count variable refers to the dispersion parameter, alpha, not the variance. So when you say y1*3 you are giving the alpha parameter value.
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