I have generated a ROC curve and AUC in graphpad prism as I have continuous data (rather than binary) in both a control and treatment group. The Real Statistics Logistic Regression data analysis tool automatically creates the ROC curve as described above. The formula for calculating the AUC for Example 1 (cell AP19) is =SUM(AP6:AP18). the formula in cell AP8) is shown in Figure 3. The formula for calculating the area for the rectangle corresponding to the p-Pred value in row 8 (i.e. 5 show that the model’s ability to discriminate between success and failure is due to chance.įor Example 1, the AUC is simply the sum of the areas of each of the rectangles in the step function. The closer AUC is to 1 (the maximum value) the better the fit. In fact, the area under the curve (AUC) can be used for this purpose. The higher the ROC curve the better the fit. The actual ROC curve is a step function with the points shown in the figure. The result is shown on the right side of Figure 2. The ROC curve can then be created by highlighting the range AN6:AO18 and selecting Insert > Charts|Scatter and adding the chart and axes titles. these entries for row 8 are calculated via the following formulas:įigure 3 – Selected formulas from Figure 2 Next, create the cumulative values for Failure and Success (columns AL and AM) and then the values of FPR and TPR for each row (columns AN and AO). The result is shown in columns AI, AJ, and AK of Figure 2. This can be done using the SelectCols supplemental function as follows (referring to Figure 1): columns H, I, and L from Figure 1) and sorting these by the p-Pred values. We begin by taking the observed values of success and failures in Logistic Regression summary format together with the calculated p-Pred values (i.e. The first portion of the analysis from Comparing Logistic Regression Models is shown here in Figure 1.įigure 1 – Logistic regression data analysis ExampleĮxample 1: Create the ROC curve for Example 1 of Comparing Logistic Regression Models. See ROC Curve and Classification Table for further information. The ROC Curve is a plot of values of the False Positive Rate (FPR) versus the True Positive Rate (TPR) for all possible cutoff values from 0 to 1.

Graphpad prism 5 logistic regression