Description Usage Arguments Value Examples

View source: R/auc_functions.R

This estimator is computed by re-sampling with replacement (i.e., bootstrap sampling) from the data. The AUC is computed for the learner trained on the full data. The AUC is then computed for the learner trained on each bootstrap sample. The average difference between the full data-trained learner and the bootstrap-trained learner is computed to estimate the bias in the full-data-estimated AUC. The final estimate of AUC is given by the difference in the full-data AUC and the estimated bias.

1 2 |

`Y` |
A numeric vector of outcomes, assume to equal |

`X` |
A |

`B` |
The number of bootstrap samples. |

`learner` |
A wrapper that implements the desired method for building a prediction algorithm. See TODO: ADD DOCUMENTATION FOR WRITING |

`correct632` |
A boolean indicating whether to use the .632 correction. |

`...` |
Other options, not currently used. |

A list with `$auc`

as the bootstrap-corrected AUC estimate

1 2 3 4 5 6 | ```
# simulate data
X <- data.frame(x1 = rnorm(50))
Y <- rbinom(50, 1, plogis(X$x1))
# compute lpo_auc for logistic regression
# use small B for fast run
boot <- boot_auc(Y = Y, X = X, B = 25, learner = "glm_wrapper")
``` |

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

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