Shared Concepts
Stepwise Selection
This section applies to the following procedures: CNTSELECT and SEVSELECT.
METHOD=STEPWISE specifies the stepwise method, which modifies the forward selection technique by allowing effects already in the model to be removed.
Just as you can in forward selection and backward elimination, you can use the SELECT= option to change the criterion that is used to assess effect contributions. You can also use the STOP= option to specify a stopping criterion and use a CHOOSE= option to provide a criterion for selecting among the sequence of models produced. For more information, see the section Forward Selection.
The procedures in this book support further modification to the stepwise method. In the standard stepwise method, no effect can enter the model if removing any effect currently in the model would yield an improved value of the selection criterion. In the modification, you can use the COMPETITIVE option to specify that addition and deletion of effects should be treated competitively. The selection criterion is evaluated for all models that are produced by deleting an effect from the current model or by adding an effect to this model. The action that most improves the selection criterion is the action taken.
Examples of Stepwise Selection Specifications
The following statement requests stepwise selection based on the SBC criterion:
selection method=stepwise;
First, if removing any effect yields a model that has a lower SBC statistic than the current model, then the effect that produces the smallest SBC statistic is removed. If removing any effect increases the SBC statistic, then provided that adding some effect lowers the SBC statistic, the effect that produces the model that has the lowest SBC is added.
The following statement requests the stepwise method where effects enter and leave based on the AIC statistic, but with the following extra check: if any effect to be added or removed yields a model whose SBC statistic is greater than the SBC statistic of the current model, then the stepwise method terminates at the current model.
selection method=stepwise(select=AIC stop=SBC) stophorizon=1;
The following statement requests stepwise selection that is based on the AICC criterion and treats additions and deletions competitively:
selection method=stepwise(select=AICC competitive);
Each step evaluates the AICC statistics that correspond to the removal of any effect in the current model or the addition of any effect to the current model and chooses the addition or removal that produced the minimum value, provided that this minimum is lower than the AICC statistic of the current model.