ck37r (version 1.0.3)

auc_table.CV.SuperLearner: Table of cross-validated AUCs from CV.SuperLearner result

Description

Calculates cross-validated AUC for each learner in the CV.SuperLearner. Also calculates standard-error, confidence interval and p-value. Based on initial code by Alan Hubbard.

Usage

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

Arguments

x

CV.SuperLearner object

y

Outcome vector, if not already added to CV.SL object.

sort

Sort table by order of AUC.

null_hypothesis

Default is the highest AUC from the learners.

two_tailed

Two-failed null hypothesis test? Default FALSE.

lower.tail

Examine only lower tail of test distribution? Default FALSE.

...

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

cvsl_auc plot_roc.SuperLearner ci.cvAUC

Examples

# NOT RUN {
library(SuperLearner)
library(ck37r)

data(Boston, package = "MASS")

set.seed(1)
y = as.numeric(Boston$medv > 23)
cvsl = CV.SuperLearner(Y = y,
                       X = subset(Boston, select = -medv),
                       family = binomial(),
                       cvControl = list(V = 2, stratifyCV = TRUE),
                       SL.library = c("SL.mean", "SL.glm"))
auc_table(cvsl, y = y)

# }