The REGSELECT Procedure
PROC REGSELECT Features
The main features of the REGSELECT procedure are as follows:
Model specification
supports multiple parameterizations for classification effects
supports any degree of interaction (crossed effects) and nested effects
supports a hierarchy among effects
supports partitioning of data into training, validation, and testing roles
provides a FREQ statement for grouped analysis
provides a WEIGHT statement for weighted analysis
Selection control
provides multiple effect-selection methods
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
Display and output
produces output data tables that contain predicted values, residuals, studentized residuals, confidence limits, and influence statistics
uses ODS Graphics to create model selection plots as part of its output. For more information about ODS Graphics, see the section ODS Graphics.
The REGSELECT procedure supports the following effect-selection methods. For a more detailed description of these methods, see the section SELECTION Statement in Chapter 3: Shared Concepts.
Forward selection starts with no effects in the model and adds effects.
Backward elimination starts with all effects in the model and deletes effects.
Stepwise regression is similar to forward selection, except that effects already in the model do not necessarily stay there.
Forward-swap selection is an extension of the forward selection method. Before any addition step, PROC REGSELECT makes all pairwise swaps of one effect in the model and one effect 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.
Least angle regression, 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.
LASSO adds and deletes parameters based on a version of ordinary least squares in which the sum of the absolute regression coefficients is constrained. PROC REGSELECT also supports adaptive LASSO selection, in which weights are applied to each of the parameters in forming the LASSO constraint.
Hybrid versions of the LAR and LASSO methods are also supported. They use LAR or LASSO to select the model, but they estimate the regression coefficients by ordinary weighted least squares.
Because the REGSELECT procedure runs on CAS, it also does the following:
enables you to run on a cluster of machines that distribute the data and the computations
enables you to run in single-machine mode on CAS
exploits all the available cores and concurrent threads. For information about how PROC REGSELECT uses threads, see the section Multithreading in Chapter 3: Shared Concepts.