Outcomes and regression estimators

Continuous, modeled as


Log linear

Log gamma


Interval-measured (interval-censored)

Left-censored, right-censored, or both (tobit

Binary outcomes, modeled as



Complementary log-log

Count outcomes, modeled as


Negative binomial

Categorical outcomes, modeled as

Multinomial logistic
(via generalized SEM)

Ordered outcomes, modeled as

Ordered logistic

Ordered probit

Survival outcomes, modeled as






Generalized linear models (GLMs)

Seven families: Gaussian, Bernoulli, binomial, gamma, negative binomial, ordinal, Poisson

Five links: identity, log, logit, probit, cloglog

Bayesian estimation

Select from many prior distributions or use default priors

Adaptive MH sampling or Gibbs sampling with linear regression

Postestimation tools for checking convergence, estimating functions of model parameters, computing Bayes factors, and performing interval hypotheses testing

Watch Nonlinear mixed-effects models.

Watch Multilevel tobit and interval regression.

Watch a Tour of multilevel GLMs.


Types of models

Two-, three-, and higher-level models

Nested (hierarchical) models

Crossed models

Mixed models

Balanced and unbalanced designs


Types of effects

Random effects (variance components)

Random intercepts

Random slopes (coefficients)

Fixed effects (fixed coefficients)


Effect covariance structures

Identity—shared variance parameter for specified effects with no covariances

Independent—unique variance parameter for each specified effect with no covariances

Exchangeable—shared variance parameter and single shared covariance parameter for specified effects

Unstructured—unique variance parameter for each specified effect and unique covariance parameter for each pair of effects

Compound—any combination of the above


Residual error structures for linear models




Moving average





Estimation methods

Maximum likelihood (ML)

Restricted maximum likelihood (REML)

Mean-variance or mode-curvature adaptive Gauss–Hermite quadrature

Nonadaptive Gauss–Hermite quadrature

Laplacian approximation

EM method starting values


Small-sample inference in linear models (DDF adjustments)




Repeated-measures ANOVA


Watch Small-sample inference for mixed-effects models.


linear constraints on fixed parameters

linear constraints on variance components


Survey data for linear models

Sampling weights

Weights at each level of model

Cluster–robust SEs allowing for correlated data


Survey data for generalized linear and survival models

Sampling weights

Weights at each level of model

Cluster–robust SEs allowing for correlated data

Support the –svy– prefix for linearized variance estimation including stratification and multistage weights

Watch Multilevel models for survey data in Stata.


Multiple imputation


Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

Watch Postestimation Selector.


Estimates of random effects

BLUPs for linear models

Standard errors of BLUPs for linear models

Empirical Bayes posterior means or posterior modes

Standard errors of posterior modes or means



Predicted outcomes with and without effects

Linear predictions



Density function

Distribution function

Survivor function

Hazard function

Predict marginally with respect to random effects

Pearson, deviance, and Anscombe residuals


Other postestimation analysis

Estimate variance components  

Intraclass correlation coefficients (ICCs), logistic, and probit random-effects models

Linear and nonlinear combinations of coefficients with SEs and CIs

Wald tests of linear and nonlinear constraints

Likelihood-ratio tests

Linear and nonlinear predictions

Summarize the composition of nested groups

Adjusted predictions

AIC and BIC information criteria

Hausman tests


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

Least-squares means

Predictive margins

Adjusted predictions, means, and effects

Works with multiple outcomes simultaneously

Integrates over random effects

Contrasts of margins

Pairwise comparisons of margins

Profile plots

Graphs of margins and marginal effects


Additional resources

Multilevel and Longitudinal Modeling Using Stata, Third Edition (Volumes I and II) by Sophia Rabe-Hesketh and Anders Skrondal

In the spotlight: meglm