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

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

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

 

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