ck37r (version 1.0.0)

cvsl_plot_roc: Plot a ROC curve from cross-validated AUC from CV.SuperLearner

Description

Based on initial code by Alan Hubbard.

Usage

cvsl_plot_roc(cvsl, Y = cvsl$Y,
  title = "CV-SuperLearner cross-validated ROC", digits = 4)

Arguments

cvsl

CV.SuperLearner object

Y

Outcome vector if not already included in the SL object.

title

Title to use in the plot.

digits

Digits to use when rounding AUC and CI for plot.

Value

List with the AUC plus standard error and confidence interval.

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, "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_plot_roc(cvsl)