Description Usage Arguments Details Value Examples
View source: R/auc_functions.R
This function computes K-fold cross-validated estimates of the area under the receiver operating characteristics (ROC) curve (hereafter, AUC). This quantity can be interpreted as the probability that a randomly selected case will have higher predicted risk than a randomly selected control.
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Y |
A numeric vector of outcomes, assume to equal |
X |
A |
K |
The number of cross-validation folds (default is |
learner |
A wrapper that implements the desired method for building a prediction algorithm. See TODO: ADD DOCUMENTATION FOR WRITING |
nested_cv |
A boolean indicating whether nested cross validation should
be used to estimate the distribution of the prediction function. Default ( |
nested_K |
If nested cross validation is used, how many inner folds should
there be? Default ( |
parallel |
A boolean indicating whether prediction algorithms should be
trained in parallel. Default to |
max_cvtmle_iter |
Maximum number of iterations for the bias correction
step of the CV-TMLE estimator (default |
cvtmle_ictol |
The CV-TMLE will iterate |
prediction_list |
For power users: a list of predictions made by |
... |
Other arguments, not currently used |
To estimate the AUC of a particular prediction algorithm, K-fold cross-validation is commonly used. The data are partitioned into K distinct groups. The prediction algorithm is developed using K-1 of these groups. In standard K-fold cross-validation, the AUC of this prediction algorithm is estimated using the remaining fold
A list TO DO: More documentation here
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