MLOGITROC: Stata module to calculate multiclass ROC Curves and AUC from Multinomial Logistic Regression

mlogitroc generates multiclass ROC curves for classification accuracy based on multinomial logistic regression using mlogit. The algorithm begins by running mlogit B=100 times using bootstrapped records for each run while the original class labels are intact. Class prediction is then performed for records not sampled during bootstrapping, and accuracy for the left out records is determined as the fraction of correct class membership predictions. This results in B=100 realizations of the accuracy for the alternative distribution. Next, B=100 mlogit runs are made again, but this time after shuffling class labels of all records prior to modeling, which results in B=100 realizations of null accuracy. Smoothed probability distributions are obtained for the B=100 alternative and null accuracy values using kernel density estimation (KDE, Gaussian kernel) to obtain 100 smoothed realizations for alternative and null accuracy. The false positive rate (FPR), true positive rate (TPR), and area under the curve (AUC) are determined from the smooth pdfs derived from KDE. Twoway scatter plots of the smoothed pdfs are constructed, followed by plotting the ROC curve.