HPLMIXED
The HPLMIXED procedure is a highperformance version of the MIXED procedure in SAS/STAT software. The HPLMIXED 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 in SAS/STAT software; the
generalization is that the data are permitted to exhibit correlation and nonconstant variability. Therefore, the mixed linear model provides you with the flexibility of modeling not only
the means of your data (as in the standard linear model) but also their variances and covariances. PROC HPLMIXED runs in either singlemachine mode or distributed mode. The procedure
enables you to do the following:
 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
 partial correlation

 Restricted Maximum Likelihood (REML) and Maximum Likelihood (ML)
 uses sparse matrix approach
 fits 20 covariance structures including
 variance components
 compound symmetry (exchangeable)
 unstructured correlation
 AR(1) and ARMA(1,1)

 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
 computes Type I, Type II, and Type III tests of fixed effects
 performs weighted estimation
 specify performance options
 supports BY group processing, which allows 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 HPLMIXED Procedure
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Examples