View source: R/auc_table.CV.SuperLearner.R
auc_table.CV.SuperLearner | R Documentation |
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.
## S3 method for class 'CV.SuperLearner'
auc_table(
x,
y = x$Y,
sort = TRUE,
null_hypothesis = NULL,
two_tailed = FALSE,
lower.tail = TRUE,
...
)
x |
CV.SuperLearner object |
y |
Outcome vector, if not already added to CV.SL object. |
sort |
Sort table by order of AUC. |
null_hypothesis |
Default is the highest AUC from the learners. |
two_tailed |
Two-failed null hypothesis test? Default FALSE. |
lower.tail |
Examine only lower tail of test distribution? Default FALSE. |
... |
Any additional unused arguments, due to the auc_table generic. |
Dataframe table with auc, se, ci, and p-value (null hypothesis = 0.5)
LeDell, E., Petersen, M., & van der Laan, M. (2015). Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electronic journal of statistics, 9(1), 1583.
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: visualizing classifier performance in R. Bioinformatics, 21(20), 3940-3941.
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
cvsl_auc
plot_roc.SuperLearner
ci.cvAUC
library(SuperLearner)
library(ck37r)
data(Boston, package = "MASS")
set.seed(1)
y = as.numeric(Boston$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)
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