The HPREG Procedure
SELECTION <options> ;
The SELECTION statement performs variable selection. The statement is fully documented in the section SELECTION Statement in Chapter 3: Shared Statistical Concepts.
The HPREG procedure supports the following variable selection methods in the METHOD= option in the SELECTION statement:
- NONE
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No model selection.
- FORWARD
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The forward selection method starts with no effects in the model and adds effects.
- BACKWARD
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The backward elimination method starts with all effects in the model and deletes effects.
- STEPWISE
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The stepwise regression method is similar to the FORWARD method except that effects already in the model do not necessarily stay there.
- FORWARDSWAP
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The forward-swap selection method is an extension of the forward selection method. Before any addition step, PROC HPREG makes all pairwise swaps of effects in and out of the current model that improve the selection criterion. When the selection criterion is R square, this method is the same as the MAXR method in the REG procedure in SAS/STAT software.
- LAR
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The least angle regression method, like forward selection, starts with no effects in the model and adds effects. The parameter estimates at any step are “shrunk” when compared to the corresponding least squares estimates. If the model contains classification variables, then these classification variables are split. See the SPLIT option in the CLASS statement for details.
- LASSO
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The lasso method adds and deletes parameters based on a version of ordinary least squares where the sum of the absolute regression coefficients is constrained. If the model contains classification variables, then these classification variables are split. See the SPLIT option in the CLASS statement for details.
The DETAILS=ALL and DETAILS=STEPS options produce the “ANOVA,” “Fit Statistics,” and “Parameter Estimates” tables, which provide information about the model that is selected at each step of the selection process.