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HPMIXED Procedure


The HPMIXED procedure uses a number of specialized high-performance techniques to fit linear mixed models with variance component structure. The following are highlights of the HPMIXED procedure's features:

  • specifically designed to cope with estimation problems involving:
    • linear mixed models with thousands of levels for the fixed and/or random effects
    • linear mixed models with hierarchically nested fixed and/or random effects, possibly with hundreds or thousands of levels at each level of the hierarchy
  • enables you to specify a linear mixed model with variance component structure, to estimate the covariance parameters by restricted maximum likelihood, and to perform confirmatory inference in such models
  • computes appropriate standard errors for all specified estimable linear combinations of fixed and random effects, and corresponding t and F tests
  • permits subject and group effects that enable blocking and heterogeneity, respectively
  • provides a mechanism for obtaining custom hypothesis tests
  • computes least squares means (LS-means) of fixed effects
  • perform weighted estimation
  • supports BY group processing, which enables you tp obtain separate analyses on grouped observations
  • creates a data set that contains predicted values and residual diagnostics
  • creates a SAS data set that corresponds to any output table

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

Examples