Mplus Users Guide v6.. - Muthén & Muthén

Examples: Regression And Path Analysisused to represent individuals who are able to assume values of zero andabove and individuals who are unable to assume any value except zero.This approach allows the estimation of the probability of being in eachclass and the posterior probabilities of being in each class for eachindividual.TITLE: this is an example of a negative binomialmodel for a count dependent variable withtwo covariatesDATA: FILE IS ex3.8b.dat;VARIABLE: NAMES ARE u1-u6 x1-x4;USEVARIABLES ARE u1 x1 x3;COUNT IS u1 (nb);MODEL: u1 ON x1 x3;The difference between this part of the example and the first part is thata regression for a count outcome using a negative binomial model isestimated instead of a zero-inflated Poisson model. The negativebinomial model estimates a dispersion parameter for each of theoutcomes (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 indicates thata negative binomial model will be estimated. The dispersion parametercan be referred to using the name of the count variable. An explanationof the other commands can be found in the first part of this example andin Example 3.1.EXAMPLE 3.9: RANDOM COEFFICIENT REGRESSIONTITLE: this is an example of a random coefficientregressionDATA: FILE IS ex3.9.dat;VARIABLE: NAMES ARE y x1 x2;CENTERING = GRANDMEAN (x1 x2);ANALYSIS: TYPE = RANDOM;MODEL: s | y ON x1;s WITH y;y s ON x2;29