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• SAS/STAT Procedures A-Z

The MIXED Procedure

• 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
• construct custom hypothesis tests
• construct custom scalar estimates and their confidence limits
• compute least square means and least square mean differences for classification fixed effects
• permits subject and group effects that enable blocking and heterogeneity, respectively
• perform multiple comparison of main effect means
• capacity to handle unbalanced data
• computes Type I, Type II, and Type III tests of fixed effects
• perform sampling-based Bayesian analysis
• perform weighted estimation
• obtain separate analyses on observations in groups
• perform repeated measures analysis
• use ODS create a SAS data set corresponding to any table
• uses ODS Graphics to create graphs as part of its output

For further details see the SAS/STAT User's Guide: The MIXED Procedure
( PDF | HTML )

Examples

• Split-Plot Design
• Repeated Measures
• Plotting the Likelihood
• Known G and R
• Random Coefficients
• Line-Source Sprinkler Irrigation
• Influence in Heterogeneous Variance Model
• Influence Analysis for Repeated Measures Data
• Examining Individual Test Components
• Isotonic Contrasts for Ordered Mean Values

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