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help fmmlc
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Postestimation for finite mixture models

        fmmlc [ , savec savep ]


Description

    fmmlc is a postestimation command for fmm. fmm is a user-written command
    (Deb 2008) that fits finite mixture regression models. Being called after
    fmm, fmmlc provides a convenient overview of latent class posterior
    probabilities, classification of subjects based on most likely latent
    class membership, average posterior probabilities by latent class, the
    quality of a classification (entropy), and the information criteria AIC,
    BIC, and a sample size adjusted BIC.

Options 

    savec saves the categorical latent variable based on most likely latent
        class membership. When using this option, a variable with the name
        -_class_1- will be saved. If this variable already exists, a variable
        with the name -_class_2- will be saved, and so on.

    savep saves latent class posterior probabilities. When using this option,
        predicted posterior probabilities are saved as variables. The number
        of variables corresponds to the number of components from the mixture
        model and are indexed accordingly.

Technical note 

    The entropy measure implemented in fmmlc uses formula 24 in Ramaswamy et
    al. (1993). Calculation of the Akaike information criterion (AIC) follows
    Akaike (1987). In the calculation of the Bayesian information criterion
    (BIC, Schwartz 1978), the number of observations from the previous fmm
    run is used (also see: bic_note). The sample size adjusted BIC follows
    Sclove's (1987) suggestion to replace the number of observations (n) with
    ((n+2)/24).

 
Saved results 

    fmmlc saves the following in r():

    Scalars   
      r(AIC)         Akaike information criterion
      r(BIC)         Bayesian information criterion
      r(BIC2)        Sample size adjusted BIC
      r(entropy)     Entropy measure

    Matrix    
      r(app)         Average posterior probabilities

Examples

    . webuse nlsw88

    . gen lnwage=log(wage)

    . fmm lnwage tenure age, components(2) mix(normal)

    . fmmlc

    . fmm lnwage tenure age, components(3) mix(normal)

    . fmmlc, savec savep


References 

    Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.

    Deb, P. (2008). FMM: Stata module to estimate finite mixture models.
    Statistical Software Components, Boston College Department of Economics, 
    http://econpapers.repec.org/RePEc:boc:bocode:s456895.

    Ramaswamy, V., DeSarbo, W., Reibstein, D., and Robinson, W. (1993).  An
    empirical pooling approach for estimating marketing mix elasticities with
    PIMS data. Marketing Science, 12, 103–124.

    Schwartz, G. (1978). Estimating the dimension of a model. The Annals of
    Statistics, 6, 461–464.

    Sclove, L. (1987). Application of model-selection criteria to some
    problems in multivariate analysis. Psychometrika, 52, 333–343.


Author

    Joerg Luedicke, Yale University, Department of Psychology, New Haven, USA

    email: joerg.luedicke1@gmail.com


Also see