Calculate the area uder ROC curve statistic for a given logit model.
Usage
AUROC(actuals, predictedScores)
Arguments
actuals
The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.
predictedScores
The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.
Value
The area under the ROC curve for a given logit model.
Details
For a given actuals and predicted probability scores, the area under the ROC curve shows how well the model performs at capturing the false events and false non-events. An best case model will have an area of 1. However that would be unrealistic, so the closer the aROC to 1, the better is the model.