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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
• Quantile regression New
• Nonlinear
• Multivariate

Constraints

• 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

Multiple imputation

Postestimation Selector

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

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

Predictions

• Predicted outcomes with and without effects
  • Linear predictions
  • Probabilities
  • Counts
  • 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) after logistic , probit , and 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

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 Mixed-Effects Reference Manual
• Bayesian Analysis Reference Manual
• Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, Volumes I and II by Sophia Rabe-Hesketh and Anders Skrondal
• In the spotlight: meglm
• In the spotlight: Nonlinear multilevel mixed-effects models
• Multilevel/mixed models using Stata training course
• Multilevel mixed-effects models flyer