The one function you need | Look at it | Get the numbers out | Where to go next | Citation
Package: precrec 0.24.0
Takaya Saito
precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics) curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Authors:
precrec_0.24.0.tar.gz
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precrec_0.24.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
precrec/json (API)
| # Install 'precrec' in R: |
| install.packages('precrec', repos = c('https://evalclass.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/evalclass/precrec/issues
Pkgdown/docs site:https://evalclass.github.io
Datasets:
- B1000 - Balanced data with 1000 positives and 1000 negatives.
- B500 - Balanced data with 500 positives and 500 negatives.
- C3N150 - Multiclass sample with three classes.
- IB1000 - Imbalanced data with 1000 positives and 10000 negatives.
- IB500 - Imbalanced data with 500 positives and 5000 negatives.
- M2N50F5 - 5-fold cross validation sample.
- P10N10 - A small example dataset with several tied scores.
Last updated from:bbe270215f. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 164 | ||
| linux-devel-x86_64 | OK | 207 | ||
| source / vignettes | OK | 198 | ||
| linux-release-arm64 | OK | 194 | ||
| linux-release-x86_64 | OK | 188 | ||
| macos-release-arm64 | OK | 203 | ||
| macos-release-x86_64 | OK | 435 | ||
| macos-oldrel-arm64 | OK | 295 | ||
| macos-oldrel-x86_64 | OK | 575 | ||
| windows-devel-arm64 | OK | 442 | ||
| windows-devel-x86_64 | OK | 245 | ||
| windows-release-arm64 | OK | 434 | ||
| windows-release-x86_64 | OK | 208 | ||
| windows-oldrel-x86_64 | OK | 192 | ||
| wasm-release | OK | 114 |
Exports:aucauc_bootauc_ciauc_delongauc_diffaverage_precisionbest_cutoffclassification_reportcreate_sim_samplesevalmodformat_nfoldformat_pointsjoin_labelsjoin_scoresmetric_curvemetric_tablemmdatapartpaucprbeprob_metricsprob_metrics_ci
Dependencies:backportscheckmateclicpp11data.tablefarverggplot2gluegridExtragtableisobandlabelinglifecycleR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert a curves and points object to a data frame | as.data.frame as.data.frame.aucroc as.data.frame.mmcurves as.data.frame.mmpoints as.data.frame.mmxycurves as.data.frame.mscurves as.data.frame.mspoints as.data.frame.msxycurves as.data.frame.smcurves as.data.frame.smpoints as.data.frame.smxycurves as.data.frame.sscurves as.data.frame.sspoints as.data.frame.ssxycurves |
| Convert a curves and points object to a data.table | as.data.table as.data.table.aucroc as.data.table.mmcurves as.data.table.mmpoints as.data.table.mscurves as.data.table.mspoints as.data.table.smcurves as.data.table.smpoints as.data.table.sscurves as.data.table.sspoints |
| Retrieve a data frame of AUC scores | auc auc.aucs |
| Bootstrap AUCs from one test set | auc_boot |
| Calculate CIs of ROC and precision-recall AUCs | auc_ci auc_ci.aucboot auc_ci.aucdelong auc_ci.aucs |
| DeLong's standard error for the ROC AUC | auc_delong |
| Compare AUCs between models | auc_diff auc_diff.aucboot auc_diff.aucdelong |
| Plot performance evaluation metrics with ggplot2 | autoplot autoplot.mmcurves autoplot.mmpoints autoplot.mmxycurves autoplot.mscurves autoplot.mspoints autoplot.msxycurves autoplot.smcurves autoplot.smpoints autoplot.smxycurves autoplot.sscurves autoplot.sspoints autoplot.ssxycurves |
| Calculate the average precision | average_precision average_precision.aucs |
| Balanced data with 1000 positives and 1000 negatives. | B1000 |
| Balanced data with 500 positives and 500 negatives. | B500 |
| Choose an operating point | best_cutoff |
| Multiclass sample with three classes. | C3N150 |
| Classification report | classification_report |
| Create random samples for simulations | create_sim_samples |
| Evaluate models and calculate performance evaluation metrics | evalmod |
| Create n-fold cross validation dataset from data frame | format_nfold |
| Reconstruct per-instance data from a table of curve points | format_points |
| Convert a curves and points object to a data frame for ggplot2 | fortify fortify.mmcurves fortify.mmpoints fortify.mmxycurves fortify.mscurves fortify.mspoints fortify.msxycurves fortify.smcurves fortify.smpoints fortify.smxycurves fortify.sscurves fortify.sspoints fortify.ssxycurves |
| Imbalanced data with 1000 positives and 10000 negatives. | IB1000 |
| Imbalanced data with 500 positives and 5000 negatives. | IB500 |
| Join observed labels of multiple test datasets into a list | join_labels |
| Join scores of multiple models into a list | join_scores |
| 5-fold cross validation sample. | M2N50F5 |
| Draw one evaluation metric against another | metric_curve |
| Get every evaluation metric at every cutoff | metric_table |
| Reformat input data for performance evaluation calculation | mmdata |
| A small example dataset with several tied scores. | P10N10 |
| Calculate partial AUCs | part part.mmcurves part.mscurves part.smcurves part.sscurves |
| Retrieve a data frame of pAUC scores | pauc pauc.aucs |
| Plot performance evaluation metrics | plot plot.mmcurves plot.mmpoints plot.mmxycurves plot.mscurves plot.mspoints plot.msxycurves plot.smcurves plot.smpoints plot.smxycurves plot.sscurves plot.sspoints plot.ssxycurves |
| Calculate the precision-recall break-even point | prbe prbe.curve_info |
| precrec: A package for computing accurate ROC and Precision-Recall curves | precrec-package precrec |
| Print the summary of a precrec object | print print.aucroc print.beval_info print.classification_report print.curve_info print.mdat print.xycurve_info |
| Calculate the Brier score, the RMSE and the log loss of predicted probabilities | prob_metrics |
| Calculate CIs of the Brier score and the log loss | prob_metrics_ci |