Package: trinROC 0.7
Reinhard Furrer
trinROC: Statistical Tests for Assessing Trinormal ROC Data
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers. See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.
Authors:
trinROC_0.7.tar.gz
trinROC_0.7.tar.gz(r-4.5-noble)trinROC_0.7.tar.gz(r-4.4-noble)
trinROC_0.7.tgz(r-4.4-emscripten)trinROC_0.7.tgz(r-4.3-emscripten)
trinROC.pdf |trinROC.html✨
trinROC/json (API)
NEWS
| # Install 'trinROC' in R: |
| install.packages('trinROC', repos = 'https://cloud.r-project.org') |
Pkgdown site:https://www.math.uzh.ch
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 months agofrom:da71b33879. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 04 2025 |
| R-4.5-linux | OK | Mar 04 2025 |
| R-4.4-linux | OK | Mar 04 2025 |
Exports:boot.testboxcoxROCemp.vusfindmuroc.edaroc3.testrocsurf.emprocsurf.trintrinROC.testtrinVUS.test
Dependencies:base64encbslibcachemclicolorspacedigestevaluatefansifarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigR6rappdirsRColorBrewerrglrlangrmarkdownsassscalestibbletinytexutf8vctrsviridisLitewithrxfunyaml
Citation
Noll, S., Furrer, R., Reiser, B., and Nakas C. T. (2019). Inference in ROC surface analysis via a trinormal model-based testing approach. Stat, 8(1), e249.
Corresponding BibTeX entry:
@Article{,
title = {Inference in receiver operating characteristic surface
analysis via a trinormal model-based testing approach},
author = {Samuel Noll and Reinhard Furrer and Benjamin Reiser and
Christos T. Nakas},
journal = {Stat},
year = {2019},
volume = {8},
number = {1},
pages = {e249},
doi = {10.1002/sta4.249},
}
Readme and manuals
trinROC
This package helps to assess three-class Receiver Operating Characteristic (ROC) type data. It provides several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis and Gatsonis (2023), ISBN 9781482233704.
Help Manual
| Help page | Topics |
|---|---|
| trinROC: Statistical Tests for Assessing Trinormal ROC Data | trinROC-package trinROC |
| Bootstrap test for three-class ROC data | boot.test |
| Box-Cox transformation on three-class ROC data | boxcoxROC |
| Synthetic data set to investigate three-class ROC data. | cancer |
| Empirical VUS calculation | emp.vus |
| Determine equidistant means of trinormal ROC data simulation | findmu |
| Synthetic small data set to investigate three-class ROC data. | krebs |
| Exploratory data analysis for a three-class ROC marker | roc.eda |
| Statistical test function for computing multiple tests on three-class ROC data | roc3.test |
| Empirical ROC surface plot | rocsurf.emp |
| Trinormal ROC surface plot | rocsurf.trin |
| Trinormal based ROC test | trinROC.test |
| Trinormal VUS test | trinVUS.test |