ck37r (version 1.0.0)

sl_auc_table: Table of cross-validated AUCs from SuperLearner result

Description

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.

Usage

sl_auc_table(sl, Y = sl$Y, sort = T)

Arguments

sl

CV.SuperLearner object

Y

Outcome vector, if not already added to SL object.

sort

Sort table by order of AUC.

Value

Dataframe table with auc, se, ci, and p-value (null hypothesis = 0.5).

References

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

See Also

cvsl_auc sl_plot_roc ci.cvAUC

Examples

library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
sl = SuperLearner(Boston$chas, subset(Boston, select = -chas), family = binomial(),
                 SL.library = c("SL.mean", "SL.glmnet"))

sl_auc_table(sl, Y = Boston$chas)