Preparation | Introduction | Scenario A: Biomarker assessment | Scenario B: Prediction model evaluation | References
Package: cases 0.2.0
Max Westphal
cases: Stratified Evaluation of Subgroup Classification Accuracy
Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).
Authors:
cases_0.2.0.tar.gz
cases_0.2.0.zip(r-4.7-any)cases_0.2.0.zip(r-4.6-any)cases_0.2.0.zip(r-4.5-any)
cases_0.2.0.tgz(r-4.6-any)cases_0.2.0.tgz(r-4.5-any)
cases_0.2.0.tar.gz(r-4.7-any)cases_0.2.0.tar.gz(r-4.6-any)
cases_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cases/json (API)
| # Install 'cases' in R: |
| install.packages('cases', repos = c('https://maxwestphal.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/maxwestphal/cases/issues
Pkgdown/docs site:https://maxwestphal.github.io
Datasets:
- data_wdbc - Breast Cancer Wisconsin (Diagnostic) Data Set
Last updated from:4eb54811e3. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 217 | ||
| source / vignettes | OK | 237 | ||
| linux-release-x86_64 | OK | 195 | ||
| macos-release-arm64 | OK | 155 | ||
| macos-oldrel-arm64 | OK | 195 | ||
| windows-devel | OK | 96 | ||
| windows-release | OK | 152 | ||
| windows-oldrel | OK | 165 | ||
| wasm-release | OK | 142 |
Exports:%>%categorizecomparedefine_contrastdraw_datadraw_data_lfcdraw_data_prbdraw_data_rocevaluategenerate_instance_lfcgenerate_instance_rocprocess_instancevisualize
Dependencies:ADGofTestbindatabootclasscliclustercodetoolscolorspacecopulacorrplotcpp11dplyre1071extraDistrfarvergenericsggplot2gluegslgtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmultcompmvtnormnumDerivpcaPPpillarpkgconfigproxypsplineR6RColorBrewerRcppRcppArmadillorlangS7sandwichscalesstabledistsurvivalTH.datatibbletidyselectutf8vctrsviridisLitewithrzoo
Real-world example: biomarker assessment and prediction model evaluation
Last update: 2025-01-04
Started: 2022-08-06
R package cases: overview
Preparation | Important functions | categorize() | compare() | evaluate() | draw_data() | Common workflows | Multiple testing for co-primary endpoints | Specification of hypotheses | Comparison vs. confidence regions | Real data example | References
Last update: 2024-07-17
Started: 2022-08-06
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| 'cases' package | cases-package cases |
| Categorize continuous values | categorize |
| Compare predictions and labels | compare |
| Create an AR(1) correlation matrix | cormat_ar1 |
| Create an equicorrelation matrix | cormat_equi |
| Breast Cancer Wisconsin (Diagnostic) Data Set | data_wdbc |
| Define a contrast (matrix) to specify exact hypothesis system | define_contrast |
| Generate binary data | draw_data |
| Generate binary data (LFC model) | draw_data_lfc |
| Sample binary data (single sample) | draw_data_prb |
| Generate binary data (ROC model) | draw_data_roc |
| Evaluate the accuracy of multiple (candidate) classifiers in several subgroups | evaluate |
| Generate data sets under least favorable parameter configurations | generate_instance_lfc |
| Generate data sets under realistic parameter configurations | generate_instance_roc |
| Analyze simulated synthetic datasets. | process_instance |
| Visualize evaluation results | visualize |