Order
Multilevel mixed-effects models
Whether the groupings in your data arise in a nested fashion (students nested
in schools and schools nested in districts) or in a nonnested fashion (regions
crossed with occupations), you can fit a multilevel model to account for the
lack of independence within these groups. Fit models for continuous, binary,
count, ordinal, and survival outcomes. Estimate variances of random intercepts
and random coefficients. Compute intraclass correlations. Predict random
effects. Estimate relationships that are population averaged over the random
effects. And much more.
Outcomes and regression estimators
- Continuous, modeled as
- 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
- 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
Types of models
- Two-, three-, and higher-level models
- Nested (hierarchical) models
- Crossed models
- Mixed models
- Balanced and unbalanced designs
Types of effects
- Random intercepts
- Random coefficients (slopes)
- Variances of random effects (variance components)
- Fixed effects (regression 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
Error (residual) structures for linear models
- Independent
- Exchangeable
- Autoregressive
- Moving average
- Exponential
- Banded
- Toeplitz
- Flexible
- 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
See New in Stata 18 to learn about what was added in Stata 18.
