I have written a R-package called fbroc for the fast bootstrap ROC curve analysis. To install package fbroc on your R installation, please use
install.packages("fbroc") or go to CRAN. If you want to check out the current development version, go to GitHub.
Start quickly with fbroc
To get familiar with fbroc and to learn what it can do, please use the shiny application.
- Very fast bootstrapping of ROC curves.
- Visualization of confidence regions for the ROC curve.
- Analysis of the AUC including confidence intervals.
- Analysis of TPR and FPR.
fbroc can calculate 100000 bootstrap iteration for 1000 samples in under 4 seconds. The exact performance depends on your PC.
y <- rep(c(TRUE, FALSE), each = 500) x <- rnorm(1000) + y system.time(boot.roc(x, y, n.boot = 100000)) User System elapsed 3.59 0.05 3.64
- Analysis of paired ROC curves to compare two classifiers.
- Help with finding a cutoff optimized for a specific application.
Instructions for the Shiny application
You can get started right away, using the example data set included with fbroc.
Import your own data
- Use a tab-delimited text file as input (Excel can save tables in this format).
- The text file must include a column with numerical values. Higher values are assumed to be associated with the positive class.
- It must also include a column with the classes. Denote the positive class with
TRUEand the negative class with