cvsl_auc: Calculate cross-validated AUC from CV.SuperLearner result

In ck37/ckTools: Chris Kennedy's R Toolkit

Calculate cross-validated AUC from CV.SuperLearner result

Description

Also calculates confidence interval. Based on initial code by Alan Hubbard.

Usage

cvsl_auc(cvsl)

Arguments

cvsl	CV.SuperLearner object

Value

List with cvAUC and ci elements.

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.CV.SuperLearner ci.cvAUC

Examples

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_auc(cvsl)

ck37/ckTools documentation built on April 29, 2023, 11:47 p.m.