Types of models
- multilevel models
- hierarchical models
- mixed models
- two-, three-, and multi-way random-effects models
- crossed random effects
Types of effects
- random effects (variance components)
- random intercepts
- random coefficients
- fixed effects
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
Other features
- factor notation for specifying effects
- allow unbalanced designs and unbalanced panels
- EM method starting values
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Estimation
- maximum likelihood (ML)
- restricted maximum likelihood (REML)
Predictions
- best linear unbiased predictions (BLUPs) of any or all effects
- BLUPs of fitted values
- residuals and standardized residuals
Postestimation analysis
- 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
- information criteria — AIC and BIC
- marginal effects and elasticities with SEs and CIs
- Hausman tests
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