Description Usage Arguments Value References See Also Examples

View source: R/cvsl_plot_roc.R

Based on initial code by Alan Hubbard.

1 2 | ```
cvsl_plot_roc(cvsl, Y = cvsl$Y,
title = "CV-SuperLearner cross-validated ROC", digits = 4)
``` |

`cvsl` |
CV.SuperLearner object |

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

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

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

List with the AUC plus standard error and confidence interval.

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1, "L'Ecuyer-CMRG")
# Subset rows to speed up example computation.
row_subset = sample(nrow(Boston), 100)
Boston = Boston[row_subset, ]
X = subset(Boston, select = -chas)
cvsl = CV.SuperLearner(Boston$chas, X[, 1:2], family = binomial(),
cvControl = list(V = 2, stratifyCV = TRUE),
SL.library = c("SL.mean", "SL.glm"))
cvsl_plot_roc(cvsl)
``` |

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

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