Package: raptools 1.24.0
Daniel Perez Vicencio
raptools: Risk Assessment Plot and Reclassification Metrics
Assessing the comparative performance of two logistic regression models or results of such models or classification models. Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) <doi:10.2215/CJN.09590911> and Pencina et al. (2008) <doi:10.1002/sim.2929>.
Authors:
raptools_1.24.0.tar.gz
raptools_1.24.0.zip(r-4.7-any)raptools_1.24.0.zip(r-4.6-any)raptools_1.24.0.zip(r-4.5-any)
raptools_1.24.0.tgz(r-4.6-any)raptools_1.24.0.tgz(r-4.5-any)
raptools_1.24.0.tar.gz(r-4.7-any)raptools_1.24.0.tar.gz(r-4.6-any)
raptools_1.24.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
raptools/json (API)
| # Install 'raptools' in R: |
| install.packages('raptools', repos = c('https://researchverse.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/researchverse/raptools/issues
Pkgdown/docs site:https://researchverse.github.io
- data_class - Simple data set with classifications
- data_risk - Simple data set with risk predictions
Last updated from:9087c18140. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 138 | ||
| source / vignettes | OK | 453 | ||
| linux-release | OK | 140 | ||
| macos-release | OK | 147 | ||
| macos-oldrel | OK | 179 | ||
| windows-devel | OK | 90 | ||
| windows-release | OK | 93 | ||
| windows-oldrel | OK | 86 | ||
| wasm-release | OK | 108 |
Exports:anova_glmCI.classNRICI.raplotextract_NRI_CIextractCIggcalibrateggcalibrate_BAggcalibrate_originalggcontributeggdecisionggprerecggrapggrocstatistics.classNRIstatistics.raplot
Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11data.tabledigestdplyrevaluatefarverfastmapfontawesomeforcatsforeignFormulafsgenericsggplot2ggrepelgluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmemoisemimemultcompmvtnormnlmennetpillarpkgconfigpolsplinepracmapROCpurrrquantregR6rappdirsRColorBrewerRcpprlangrmarkdownrmsrpartrstudioapiS7sandwichsassscalesSparseMstringistringrsurvivalTH.datatibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The function anova_glm() returns the Chi^2 and degrees of freedom for each variable & the same was anova.rms() does from lrm() in the rms package. | anova_glm |
| Statistical metrics and confidence intervals for classes | CI.classNRI |
| Statistical metrics with confidence intervals | CI.raplot |
| Simple data set with classifications | data_class |
| Simple data set with risk predictions | data_risk |
| Extract NRI confidence intervals | extract_NRI_CI |
| Extract confidence interval | extractCI |
| The Calibration plot | ggcalibrate |
| The Bland-Altman style Calibration plot | ggcalibrate_BA |
| The Original Calibration plot | ggcalibrate_original |
| The Contribution plot | ggcontribute |
| The Decision curve | ggdecision |
| The Precision-Recall plot | ggprerec |
| The Risk Assessment Plot | ggrap |
| The ROC plot | ggroc |
| List meta data | meta.rap |
| Reclassification metrics with classes (ordinals) as inputs | statistics.classNRI |
| Statistical metrics | statistics.raplot |
| List risk assessment metrics | summary.rap |