Finite mixture models (FMMs)

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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)

Watch Finite mixture models (FMMs).

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

Inferences

Expected means, probabilities, or counts in each class
Expected proportion of population in each class
Lo–Mendell–Rubin-adjusted likelihood-ratio test New
Vuong–Lo–Mendell–Rubin likelihood-ratio test New
Entropy New
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

Watch Latent class model-comparison statistics. New

Predictions

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

Watch Postestimation selector.

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

The basics
Interactions
More interactions

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 19 to learn about what was added in Stata 19.

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