Fast Algorithms to Bootstrap Receiver Operating Characteristics Curves

Implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second.



title: "Readme for fbroc" author: "Erik Peter" date: "2016-06-20" output: md_document: variant: markdown_github ---


fbroc is intended for the fast bootstrapping of ROC curves, so that the package can be used for simulation studies and shiny applications. It allows for the analysis and comparison of ROC curves. To achieve the necessary performance all critical algorithms are implemented in C++. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second. To try out the package you can visit the package website. A shiny interface for this package is hosted there.

To install:

  • latest released version: install.packages("fbroc")
  • latest development version:
    1. install and load package devtools
    2. install_github("erikpeter/fbroc")

News


title: "NEWS" author: "Erik Peter" date: "Saturday, 18th June, 2015" output: html_document


  • Partial AUCs over both TPR and FPR ranges can be calculated
  • You can now adjust text size for plots
  • In the ROC plot the (partial) AUC can now optionally be shown instead of confidence regions
  • The location of the text showing the performance in the ROC plot has been shifted downwards and to the left
  • Fixed broken plot function for single ROC curves when showing a metric
  • Allows the comparison of two paired ROC curves
  • Bad defaults caused plotting to fail with a large number of negative samples
  • perf.roc is now deprecated. Use the new S3 generic perf instead
  • conf.roc is now deprecated. Use the new S3 generic conf instead
  • fixed a off-by-one pointer error
  • Allow uncached bootstrap of the ROC curve to avoid memory issues, this now the new default
  • New performance metrices: TPR at fixed FPR and FPR at fixed TPR
  • Stand-alone function to find thresholds calculate.thresholds was removed. To calculate thresholds please call boot.roc and look at list item roc of the outpot
  • Smarter default for the number of steps in conf.roc
  • Smarter default for the number of bins in plot.fbroc.perf
  • Completely refactored C++ code for improved maintability
  • Function boot.tpr.at.fpr now works properly
  • For duplicated predictions not all relevant thresholds were found reliably, this was fixed
  • Initial release

Reference manual

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install.packages("fbroc")

0.4.0 by Erik Peter, a year ago


http://www.epeter-stats.de/roc-curve-analysis-with-fbroc/


Report a bug at http://github.com/erikpeter/fbroc/issues


Browse source code at https://github.com/cran/fbroc


Authors: Erik Peter [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp

Depends on ggplot2, methods, stats, utils

Linking to Rcpp


See at CRAN