Mplus Users Guide v6.. - Muthén & Muthén

Examples: Mixture Modeling With Longitudinal DataMODEL:OUTPUT:%OVERALL%i s q | u1@0 u2@.1 u3@.2 u4@.3 u5@.4 u6@.5u7@.6 u8@.7;s-q@0;i s ON x;c ON x;TECH1 TECH8;The difference between this part of the example and the first part is thata growth mixture model (GMM) for a count outcome using a negativebinomial model is estimated instead of a zero-inflated Poisson model.The negative binomial model estimates a dispersion parameter for eachof the outcomes (Long, 1997; Hilbe, 2007).The COUNT option is used to specify which dependent variables aretreated as count variables in the model and its estimation and which typeof model is estimated. The nb in parentheses following u1-u8 indicatesthat a negative binomial model will be estimated. The dispersionparameters for each of the outcomes are held equal across classes as thedefault. The dispersion parameters can be referred to using the names ofthe count variables. An explanation of the other commands can befound in the first part of this example and in Example 8.1.EXAMPLE 8.6: GMM WITH A CATEGORICAL DISTALOUTCOME USING AUTOMATIC STARTING VALUES ANDRANDOM STARTSTITLE: this is an example of a GMM with acategorical distal outcome using automaticstarting values and random startsDATA: FILE IS ex8.6.dat;VARIABLE: NAMES ARE y1–y4 u x;CLASSES = c(2);CATEGORICAL = u;ANALYSIS: TYPE = MIXTURE;MODEL:%OVERALL%i s | y1@0 y2@1 y3@2 y4@3;i s ON x;c ON x;OUTPUT: TECH1 TECH8;211