Package: cancerclass 1.51.0
Daniel Kosztyla
cancerclass: Development and validation of diagnostic tests from high-dimensional molecular data
The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.
Authors:
cancerclass_1.51.0.tar.gz
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cancerclass.pdf |cancerclass.html✨
cancerclass/json (API)
| # Install 'cancerclass' in R: |
| install.packages('cancerclass', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
On BioConductor:cancerclass-1.49.0(bioc 3.20)cancerclass-1.48.0(bioc 3.19)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
cancermicroarrayclassificationvisualization
Last updated 23 days agofrom:7222a6be28. Checks:OK: 1 NOTE: 4 WARNING: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.4-mac-x86_64 | WARNING | Oct 30 2024 |
| R-4.4-mac-aarch64 | WARNING | Oct 30 2024 |
| R-4.3-win-x86_64 | NOTE | Oct 30 2024 |
| R-4.3-mac-x86_64 | WARNING | Oct 30 2024 |
| R-4.3-mac-aarch64 | WARNING | Oct 30 2024 |
Exports:.initFoocalc.auccalc.rocfilterfitget.dget.d2get.lmget.ntrainilogitloonvalidateplotplot3dpredictpreparesummaryvalidate
Dependencies:binomBiobaseBiocGenerics
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Development and validation of diagnostic tests from high-dimensional molecular data | cancerclass |
| Fitting of a predictor | fit |
| GOLUB DATA | GOLUB GOLUB1 |
| Leave-one-out cross-validation | loo |
| Classification in a multiple random validation protocol in pependence of the number of features used for predictor construction | nvalidate |
| Class "nvalidation" | nvalidation-class |
| Plot Method for 'validation, nvalidation, prediction, predictor' Classes | plot plot,nvalidation-method plot,prediction-method plot,predictor-method plot,validation-method |
| Plot3d method for 'validtion and 'nvalidation' classes | plot3d plot3d,nvalidation-method plot3d,validation-method |
| Predict Method for 'predictor' Class | predict predict,predictor-method predict-methods |
| Class "prediction" | prediction-class |
| Class "predictor" | predictor-class |
| Summary Method for 'prediction' Class | summary summary,prediction-method |
| Classification in a Multiple Random Validation Protocol in Dependence of the Training Set Size | validate |
| Class "validation" | validation-class |