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Finite mixture models (FMMs)

Learn about Finite mixture models.

fmm: prefix for finite mixture models

  • Mixtures of regression models
  • Mixtures of distributions
  • With two, three, four, or more latent classes (components)

Outcome types

  • Continuous, modeled as
    • Linear
    • Truncated
    • Interval
    • Tobit
    • Instrumental variables
  • Binary, modeled as
    • Logistic
    • Probit
    • Complementary log-log
  • Count, modeled as
    • Poisson
    • Negative binomial
    • Truncated Poisson
  • Categorical, modeled as
    • Multinomial logistic
  • Ordinal, modeled as
    • Ordered logistic
    • Ordered probit
  • Survival, modeled as
    • Exponential
    • Weibull
    • Lognormal
    • Loglogistic
    • Gamma
  • Fractional, modeled as
    • Beta
  • Generalized linear models (GLMs)
    • 11 families: Gaussian, Bernoulli, beta, binomial, Poisson, negative binomial, exponential, gamma, lognormal, loglogistic, Weibull
    • 5 links: identity, log, logit, probit, complementary log-log
  • Mixtures of above models
  • Mixtures of above models with a point mass at a single value

Model class membership

  • Predictors of class membership
  • Multinomial logistic model

Starting values

  • EM algorithm
  • Fixed or random starting values
  • Select number of random draws


  • Expected means, probabilities, or counts in each class
  • Expected proportion of population in each class
  • AIC and BIC information criteria
  • Wald tests of linear and nonlinear constraints
  • Likelihood-ratio tests
  • Contrasts
  • Pairwise comparisons
  • Linear and nonlinear combinations of coefficients with SEs and CIs


  • Class membership
  • Posterior class membership
  • Predicted means, probabilities, counts
    • For each latent class
    • Marginal with respect to latent classes
    • Marginal with respect to posterior latent classes
  • Survivor function
  • Density function
  • Distribution function

Postestimation selector

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

Factor variables

  • Automatically create indicators based on categorical variables
  • Form interactions among discrete and continuous variables
  • Include polynomial terms
  • Perform contrasts of categories/levels
Watch Introduction to Factor Variables in Stata tutorials

Marginal analysis

  • Estimated marginal means
  • Marginal and partial effects
  • Average marginal and partial effects
  • Adjusted predictions, means, and effects
  • Works with multiple outcomes simultaneously
  • Contrasts of margins
  • Pairwise comparisons of margins
  • Profile plots
  • Graphs of margins and marginal effects

Additional resources

See tests, predictions, and effects.

See New in Stata 18 to learn about what was added in Stata 18.