Capabilities

The GLMSELECT Procedure

PROC GLMSELECT performs effect selection in the framework of general linear models. The main features are as follows:

Model Specification
- supports different parameterizations 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 constructed effects including spline and multimember effects
Selection Control
- provides multiple effect selection methods including the following:
  - forward selection
  - backward elimination
  - stepwise regression
  - least angle regression (LAR)
  - least absolute shrinkage and selection operator (LASSO)
  - hybrid versions of LAR and LASSO
- 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
- provides leave-one-out and k-fold cross validation
- supports data resampling and model averaging
Display and Output
- produces graphical representation of selection process
- produces output data sets containing predicted values and residuals
- produces an output data set containing the design matrix
- produces macro variables containing selected models
- supports parallel processing of BY groups
- supports multiple SCORE statements

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

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