activates all of the display options.

displays between-class correlations.

displays between-class covariances. The between-class covariance matrix equals the between-class SSCP matrix divided by , where is the number of observations and is the number of classes. The between-class covariances should be interpreted in comparison with the total-sample and within-class covariances, not as formal estimates of population parameters.

displays the between-class SSCP matrix.

specifies the data set to be analyzed. The data set can be an ordinary SAS data set or one of several specially structured data sets created by statistical procedures available with SAS/STAT software. These specially structured data sets include TYPE=CORR, COV, CSSCP, and SSCP. If the DATA= option is omitted, the procedure uses the most recently created SAS data set.

includes the first variables in the VAR statement in every model. By default, INCLUDE=0.

specifies the maximum number of macro variables with independent variable lists to create. By default, MAXMACRO=100. PROC STEPDISC saves the list of selected variables in a macro variable, &_StdVar. Suppose your input variable list consists of x1-x10; then &_StdVar would be set to x1 x3 x4 x10 if, for example, the first, third, fourth, and tenth variables were selected for the model. This list can be used, for example, in a subsequent procedure’s VAR statement as follows:

var &_stdvar;

With BY processing, one macro variable is created for each BY group, and the macro variables are indexed by the BY-group number. The MAXMACRO= option can be used to either limit or increase the number of these macro variables in processing data sets with many BY groups. The macro variables are created as follows:

specifies the maximum number of steps. By default, MAXSTEP= two times the number of variables in the VAR statement.

specifies the method used to select the variables in the model. The BACKWARD method specifies backward elimination, FORWARD specifies forward selection, and STEPWISE specifies stepwise selection. By default, METHOD=STEPWISE.

displays pooled within-class correlations (partial correlations based on the pooled within-class covariances).

displays pooled within-class covariances.

specifies the partial R square for adding variables in the forward selection mode, where .

specifies the partial R square for retaining variables in the backward elimination mode, where .

displays the pooled within-class corrected SSCP matrix.

suppresses the displayed output from each step.

displays simple descriptive statistics for the total sample and within each class.

specifies the singularity criterion for entering variables, where . PROC STEPDISC precludes the entry of a variable if the squared multiple correlation of the variable with the variables already in the model exceeds . With more than one variable already in the model, PROC STEPDISC also excludes a variable if it would cause any of the variables already in the model to have a squared multiple correlation (with the entering variable and the other variables in the model) exceeding . By default, SINGULAR= 1E–8.

specifies the significance level for adding variables in the forward selection mode, where . The default value is 0.15.

specifies the significance level for retaining variables in the backward elimination mode, where . The default value is 0.15.

specifies that the first variables in the VAR statement be used to begin the selection process. When you specify METHOD=FORWARD or METHOD=STEPWISE, the default value is 0; when you specify METHOD=BACKWARD, the default value is the number of variables in the VAR statement.

displays total-sample and pooled within-class standardized class means.

specifies the number of variables in the final model. The STEPDISC procedure stops the selection process when a model with n variables is found. This option applies only when you specify METHOD=FORWARD or METHOD=BACKWARD. When you specify METHOD=FORWARD, the default value is the number of variables in the VAR statement; when you specify METHOD=BACKWARD, the default value is 0.

displays total-sample correlations.

displays total-sample covariances.

displays the total-sample corrected SSCP matrix.

displays within-class correlations for each class level.

displays within-class covariances for each class level.

displays the within-class corrected SSCP matrix for each class level.