ck37r (version 1.0.3)

auc_table.SuperLearner: 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

# S3 method for SuperLearner
auc_table(
  x,
  y = x$Y,
  sort = TRUE,
  null_hypothesis = NULL,
  two_tailed = FALSE,
  lower.tail = TRUE,
  ...
)

Arguments

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.

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

auc_table.CV.SuperLearner plot_roc.SuperLearner ci.cvAUC

Examples

# NOT RUN {
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.glm"))

auc_table(sl, y = Boston$chas)

# }