Modeling and Multivariate Methods - SAS

Chapter 4 Fitting Stepwise Regression Models 139
The Stepwise Report
Table 4.3 Description of the Stepwise Regression Control Panel
Stopping Rule • P-value Threshold uses p-values (significance levels) to enter and
remove effects from the model. Two other options appear when P-value
Threshold is chosen: Prob to Enter is the maximum p-value that an
effect must have to be entered into the model during a forward step.
Prob to Leave is the minimum p-value that an effect must have to be
removed from the model during a backward step.
• Minimum AICc uses the minimum corrected Akaike Information
Criterion to choose the best model.
• Minimum BIC uses the minimum Bayesian Information Criterion to
choose the best model.
• Max Validation RSquare uses the maximum R-square from the
validation set to choose the best model. This is available only when a
validation column is used, and the validation column has two or three
distinct values. For more information about validation, see “Using
Validation” on page 152.
• Max K-Fold RSquare uses the maximum R-square from K-fold cross
validation to choose the best model. This is available only when K-Fold
cross validation is used. For more information about validation, see
“Using Validation” on page 152.
Direction
Choose how effects enter and leave the model:
• Forward brings in the regressor that most improves the fit, given that
term is significant at the level specified by Prob to Enter. See “Forward
Selection Example” on page 141.
• Backward removes the regressor that affects the fit the least, given that
term is not significant at the level specified in Prob to Leave. See
“Backward Selection Example” on page 142.
• Mixed alternates the forward and backward steps. It includes the most
significant term that satisfies Prob to Enter and removes the least
significant term satisfying Prob to Leave. It continues removing terms
until the remaining terms are significant and then it changes to the
forward direction.