HPMIXED Procedure
The HPMIXED procedure uses a number of specialized highperformance 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 (LSmeans) 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