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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 sampling-based 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 SAS/STAT User's Guide: The MIXED Procedure
( PDF | HTML )

Examples