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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benkeser/predtmle documentation built on May 20, 2019, 5:41 p.m.

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