help fmmlc

Postestimation for finite mixture models

fmmlc [ , savec savep ]


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.


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


. 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


Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317332.

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, 103124.

Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461464.

Sclove, L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333343.


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

email: joerg.luedicke1@gmail.com

Also see