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HPREG Procedure


The HPREG procedure is a high-performance procedure that provides model fitting and model building for ordinary linear least squares models. The models supported are standard independently and identically distributed general linear models, which can contain main effects that consist of both continuous and classification variables and interaction effects of these variables. The procedure offers extensive capabilities for customizing the model selection with a wide variety of selection and stopping criteria, from traditional and computationally efficient significance-level-based criteria to more computationally intensive validation-based criteria. The following are highlights of the HPREG procedure's features:

  • supports GLM and reference parameterization for classification effects
  • supports any degree of interaction (crossed effects) and nested effects
  • supports hierarchy among effects
  • supports partitioning of data into training, validation, and testing roles
  • supports a FREQ statement for grouped analysis
  • supports a WEIGHT statement for weighted analysis
  • provides multiple effect-selection methods
  • enables selection from a very large number of effects (tens of thousands)
  • offers selection of individual levels of classification effects
  • provides effect selection based on a variety of selection criteria
  • provides stopping rules based on a variety of model evaluation criteria
  • supports stopping and selection rules based on external validation and leave-one-out cross validation
  • produces output data sets that contain predicted values, residuals, studentized residuals, confidence limits, and influence statistics

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

Examples