Compute the AUC on the OOB samples of the bagging procedure for the binomial family. The true and false positive rates are also returned and could be helpfull for plotting the ROC curves.
Usage
bag.aucoob(bag_pltr, xdata, Y.name)
Arguments
bag_pltr
The output of the function bagging.pltr
xdata
The learning dataset containing the dependent variable, the confounding variables and the predictors variables
Y.name
The name of the binary dependent variable
Details
The thresshold values used for computing the AUC are defined when building the bagging predictor. see bagging.pltr for the convenient parameterization.
Value
A list of 4 elements
AUCOOB
the AUC computed on OOB samples of the Bagging procedure
TPR
the true positive rate for several thresshold values
FPR
the false positive rate for several thresshold values
OOB
the Out Of Bag error for each thresshold value
Note
The plot of the ROC curve is straighforward using the TPR and FPR obtained with the function bag.aucoob
Author(s)
Cyprien Mbogning
References
Mbogning, C., Perdry, H., Broet, P.: A Bagged partially linear tree-based regression procedure for prediction and variable selection (submitted 2014)