Description Usage Arguments Value References Examples

Plots the ROC curve for a single learner from a SuperLearner object, defaulting to the minimum estimated risk learner. Based on code by Alan Hubbard.

1 2 3 | ```
sl_plot_roc(sl, Y = sl$Y, learner = which.min(sl$cvRisk),
title = paste0("SuperLearner cross-validated ROC: ",
names(sl$cvRisk)[learner]), digits = 4)
``` |

`sl` |
SuperLearner object |

`Y` |
Outcome vector if not already included in the SL object. |

`learner` |
Which learner to plot - defaults to minimum risk learner. |

`title` |
Title to use in the plot. |

`digits` |
Digits to use when rounding AUC and CI for plot. |

List with plotted AUC & CI, table of AUC results for all learners, and the name of the best learner.

LeDell, E., Petersen, M., & van der Laan, M. (2015). Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electronic journal of statistics, 9(1), 1583.

Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/

Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics, 21(20), 3940-3941.

van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml

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ck37r documentation built on June 4, 2017, 1:02 a.m.

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