Order
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
- Ordinal, modeled as
- Ordered logistic
- Ordered probit
- Survival, modeled as
- Exponential
- Weibull
- Lognormal
- Loglogistic
- Gamma
- Fractional, modeled as
- 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
- 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
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
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.
