Description Usage Arguments Value References See Also Examples
Also calculates confidence interval. Based on initial code by Alan Hubbard.
1 | cvsl_auc(cvsl)
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cvsl |
CV.SuperLearner object |
List with cvAUC and ci elements.
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
auc_table.CV.SuperLearner
plot_roc.CV.SuperLearner
ci.cvAUC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | 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)
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