Basic Analysis and Graphing - SAS

226 Performing Simple Logistic Regression Chapter 7
Additional Examples of Logistic Regression
The ROC curve is a graphical representation of the relationship between false-positive and true-positive
rates. A standard way to evaluate the relationship is with the area under the curve, shown below the plot in
the report. In the plot, a yellow line is drawn at a 45 degree angle tangent to the ROC Curve. This marks a
good cutoff point under the assumption that false negatives and false positives have similar costs.
Related Information
• “Example of ROC Curves” on page 230
Save Probability Formula
The Save Probability Formula option creates new data table columns. These data table columns save the
following:
• formulas for linear combinations (typically called logits) of the x factor
• prediction formulas for the response level probabilities
• a prediction formula that gives the most likely response
Inverse Prediction
Inverse prediction is the opposite of prediction. It is the prediction of x values from given y values. But in
logistic regression, instead of a y value, you have the probability attributed to one of the Y levels. This feature
only works when there are two response categories (a binary response).
The Fit Model platform also has an option that gives an inverse prediction with confidence limits. The
Modeling and Multivariate Methods book gives more information about inverse prediction.
Related Information
• “Example of Inverse Prediction Using the Crosshair Tool” on page 231
• “Example of Inverse Prediction Using the Inverse Prediction Option” on page 232
Additional Examples of Logistic Regression
This section contains additional examples using logistic regression.
Example of Ordinal Logistic Regression
This example uses the AdverseR.jmp sample data table to illustrate an ordinal logistic regression. Suppose
you want to model the severity of an adverse event as a function of treatment duration value.
1. Open the AdverseR.jmp sample data table.
2. Right-click on the icon to the left of ADR SEVERITY and change the modeling type to ordinal.
3. Select Analyze > Fit Y by X.