Description Usage Arguments Value References See Also Examples
View source: R/auc_table.SuperLearner.R
Calculates cross-validated AUC for each learner in the SuperLearner. Also calculates standard-error, confidence interval and p-value. Based on initial code by Alan Hubbard.
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x |
SuperLearner object |
y |
Outcome vector, if not already added to SL object. |
sort |
Sort table by order of AUC. |
null_hypothesis |
If NULL (default), use the highest observed AUC. |
two_tailed |
Two-failed null hypothesis test? Default FALSE. |
lower.tail |
Examine lower tail of test distribution? Default TRUE. |
... |
Any additional unused arguments, due to the auc_table generic. |
Dataframe table with auc, se, ci, and p-value (null hypothesis = 0.5)
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
auc_table.CV.SuperLearner
plot_roc.SuperLearner
ci.cvAUC
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