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

In ck37/ckTools: Chris Kennedy's R Toolkit

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

Description

Based on initial code by Alan Hubbard.

Usage

## S3 method for class 'CV.SuperLearner'
plot_roc(
  x,
  y = x$Y,
  learner = NULL,
  title = "CV-SuperLearner cross-validated ROC",
  subtitle = NULL,
  digits = 4,
  show_plot = TRUE,
  ...
)

Arguments

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.

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"))
plot_roc(cvsl)

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