Outcomes and regression estimators
Continuous, modeled as
Linear
Log linear
Log gamma
Nonlinear
Interval-measured (interval-censored)
Left-censored, right-censored, or both (tobit
Binary outcomes, modeled as
Logistic
Probit
Complementary log-log
Count outcomes, modeled as
Poisson
Negative binomial
Categorical outcomes, modeled as
Multinomial logistic
(via generalized SEM)
Ordered outcomes, modeled as
Ordered logistic
Ordered probit
Survival outcomes, modeled as
Exponential
Weibull
Lognormal
Loglogistic
Gamma
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
Independent
Exchangeable
Autoregressive
Moving average
Banded
Toeplitz
Unstructured
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)
Kenward–Roger
Satterthwaite
ANOVA
Repeated-measures ANOVA
Residual
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
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
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), 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
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