Description Usage Arguments Value References See Also Examples
View source: R/plot_roc.CV.SuperLearner.R
Based on initial code by Alan Hubbard.
1 2 3 4 5 6 7 8 9 10 11 |
x |
CV.SuperLearner object |
y |
Outcome vector if not already included in the SL object. |
learner |
Which learner to plot - defaults to highest AUC learner. |
title |
Title to use in the plot. |
subtitle |
Optional plot subtitle. |
digits |
Digits to use when rounding AUC and CI for plot. |
show_plot |
If TRUE print the ggplot, otherwise just return in list. |
... |
Any additional unused arguments, due to the auc_table generic. |
List with the AUC plus standard error and confidence interval.
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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"))
plot_roc(cvsl)
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