MIXED Procedure
The MIXED procedure fits a variety of mixed linear models to data and enables you to use these fitted models to make statistical inferences
about the data. A mixed linear model is a generalization of the standard linear model used in the GLM procedure, the generalization being
that the data are permitted to exhibit correlation and nonconstant variability. The mixed linear model, therefore, provides you with the
flexibility of modeling not only the means of your data (as in the standard linear model) but their variances and covariances as well.
The following are highlights of the MIXED procedure's features:
 fits general linear models with fixed and random effects under the assumption
that the data are normally distributed. The types of models include:
 simple regression
 multiple regression
 analysis of variance for balanced or unbalanced data
 analysis of covariance
 response surface models
 weighted regression
 polynomial regression
 multivariate analysis of variance (MANOVA)
 partial correlation
 repeated measures analysis of variance
 fits covariance structures including:
 variance components
 compound symmetry
 unstructured
 AR(1) and (ARMA(1,1,)
 Toeplitz
 spatial
 general linear
 factor analytic
 offers six estimation methods for the covariance parameters including:
 Restricted Maximum Likelihood (REML)
 Maximum Likelihood (ML)
 Method of Moments
 MIVQUE0
 Type I
 Type II
 Type III

 uses PROC GLM  type syntax by using MODEL, RANDOM, and REPEATED statements
for model specification and CONTRAST, ESTIMATE, and LSMEANS statements for inferences
 provides appropriate standard errors for all specified estimable linear combinations
of fixed and random effects, and corresponding t and F tests
 enables you to construct custom hypothesis tests
 enables you to construct custom scalar estimates and their confidence limits
 computes least square means and least square mean differences for classification fixed effects
 permits subject and group effects that enable blocking and heterogeneity, respectively
 performs multiple comparison of main effect means
 accommodates unbalanced data
 computes Type I, Type II, and Type III tests of fixed effects
 performs samplingbased Bayesian analysis
 performs weighted estimation
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details see the MIXED Procedure
Examples