**[ME] me** -- Introduction to me commands

__Description__

Mixed-effects models are characterized as containing both fixed effects
and random effects. The fixed effects are analogous to standard
regression coefficients and are estimated directly. The random effects
are not directly estimated (although they may be obtained postestimation)
but are summarized according to their estimated variances and
covariances. Random effects may take the form of either random
intercepts or random coefficients, and the grouping structure of the data
may consist of multiple levels of nested groups. As such, mixed-effects
models are also known in the literature as multilevel models and
hierarchical models. Mixed-effects commands fit mixed-effects models for
a variety of distributions of the response conditional on normally
distributed random effects.

__Mixed-effects linear regression__

**mixed** Multilevel mixed-effects linear regression

__Mixed-effects generalized linear model__

**meglm** Multilevel mixed-effects generalized linear model

__Mixed-effects censored regression__

**metobit** Multilevel mixed-effects tobit regression
**meintreg** Multilevel mixed-effects interval regression

__Mixed-effects binary regression__

**melogit** Multilevel mixed-effects logistic regression
**meqrlogit** Multilevel mixed-effects logistic regression (QR
decomposition)
**meprobit** Multilevel mixed-effects probit regression
**mecloglog** Multilevel mixed-effects complementary log-log
regression

__Mixed-effects ordinal regression__

**meologit** Multilevel mixed-effects ordered logistic
regression
**meoprobit** Multilevel mixed-effects ordered probit
regression

__Mixed-effects count-data regression__

**mepoisson** Multilevel mixed-effects Poisson regression
**meqrpoisson** Multilevel mixed-effects Poisson regression (QR
decomposition)
**menbreg** Multilevel mixed-effects negative binomial
regression

__Mixed-effects multinomial regression__

Although there is no **memlogit** command, multilevel mixed-effects
multinomial logistic models can be fit using **gsem**; see **[SEM] example**
**41g**.

__Mixed-effects survival model__

**mestreg** Multilevel mixed-effects parametric survival
model

__Nonlinear mixed-effects regression__

**menl** Nonlinear mixed-effects regression

__Postestimation tools specific to mixed-effects commands__

**estat df** Calculate and display degrees of freedom for
fixed effects
**estat group** Summarize the composition of the nested groups
**estat icc** Estimate intraclass correlations
**estat recovariance** Display the estimated random-effects covariance
matrices
**estat sd** Display variance components as standard
deviations and correlations
**estat wcorrelation** Display model-implied within-cluster correlations
and standard deviations