Logistic Regression Model Properties

The following properties are available for the logistic regression model:
Name
enables you to specify the name for this model.
Informative missingness
specifies whether the informative missingness algorithm is used. For more information, see Missing Values.
Use variable selection
specifies whether variable selection is performed. For more information, see Variable Selection.
Significance level
specifies the significance level that is required in order for variables to be considered for the model. This property is available only when Use variable selection is selected.
Link function
specifies the link function that is used in the logistic regression. Link functions link the response mean to the linear predictor.
The following link functions are available:
Convergence
  • Override function convergence enables you to manually specify the function convergence value.
  • Value specifies the function convergence value when Override function convergence is selected. When you specify a larger value, the model will converge sooner. This reduces the amount of time spent training the model, but it can create a suboptimal model.
  • Override gradient convergence enables you to manually specify the gradient convergence value.
  • Value specifies the gradient convergence value when Override gradient convergence is selected. When you specify a larger value, the model will converge sooner. This reduces the amount of time spent training the model, but it can create a suboptimal model.
  • Maximum iterations specifies the maximum number of iterations performed during model training. If you specify a relatively small value, you reduce the amount of time spent training the model, but it can create a suboptimal model.
Note: When you specify a gradient convergence or function convergence criterion, it is possible for the model to converge based on an internal convergence criterion before your specified criterion is reached. The reason for convergence is on the Convergence tab of the details table.
Assessment
  • Use default number of bins specifies whether you want to use the default number of bins or to set your own value. By default, measure variables are grouped into 20 bins.
  • Number specifies the number of bins to use when the Use default number of bins property is not selected. You must specify an integer value between 5 and 100.
  • Prediction cutoff specifies the value at which a computed probability is considered an event.
  • Tolerance specifies the tolerance value that is used to determine the convergence of the iterative algorithm that estimates the percentiles. Specify a smaller value to increase the algorithmic precision.
Show diagnostic plots
specifies whether the Residual Plot, Assessment, and Influence Plot windows appear in the model pane.
Copyright © SAS Institute Inc. All Rights Reserved.
Last updated: January 8, 2019