Package: sigFeature 1.25.0
Pijush Das Developer
sigFeature: sigFeature: Significant feature selection using SVM-RFE & t-statistic
This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.
Authors:
sigFeature_1.25.0.tar.gz
sigFeature_1.25.0.zip(r-4.5)sigFeature_1.25.0.zip(r-4.4)sigFeature_1.25.0.zip(r-4.3)
sigFeature_1.25.0.tgz(r-4.4-any)sigFeature_1.25.0.tgz(r-4.3-any)
sigFeature_1.25.0.tar.gz(r-4.5-noble)sigFeature_1.25.0.tar.gz(r-4.4-noble)
sigFeature_1.25.0.tgz(r-4.4-emscripten)sigFeature_1.25.0.tgz(r-4.3-emscripten)
sigFeature.pdf |sigFeature.html✨
sigFeature/json (API)
NEWS
| # Install 'sigFeature' in R: |
| install.packages('sigFeature', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
- ExampleRawData - Example dataset to test the performance of the sigFeature package.
- featsweepSigFe - Processed output data after using the function named "sigCVError()".
- featureRankedList - Processed output data after using the function named "svmrfeFeatureRanking()".
- results - Processed output data after using the function named "sigFeature.enfold()".
- sigfeatureRankedList - Processed output data after using the function named "sigFeature()".
On BioConductor:sigFeature-1.25.0(bioc 3.21)sigFeature-1.24.0(bioc 3.20)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
featureextractiongeneexpressionmicroarraytranscriptionmrnamicroarraygenepredictionnormalizationclassificationsupportvectormachine
Last updated 2 months agofrom:ac23f1c3c6. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 04 2024 |
| R-4.5-win | WARNING | Dec 04 2024 |
| R-4.5-linux | WARNING | Dec 04 2024 |
| R-4.4-win | WARNING | Dec 04 2024 |
| R-4.4-mac | WARNING | Dec 04 2024 |
| R-4.3-win | WARNING | Dec 04 2024 |
| R-4.3-mac | WARNING | Dec 04 2024 |
Exports:PlotErrorssigCVErrorsigFeaturesigFeature.enfoldsigFeatureFrequencysigFeaturePvaluesvmrfeFeatureRankingWritesigFeature
Dependencies:abindaskpassBHBiobaseBiocGenericsBiocManagerBiocParallelbiocViewsbitopsclassclicodetoolscolorspacecpp11crayoncurlDelayedArraye1071farverformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluegraphgtablehttrIRangesjsonlitelabelinglambda.rlatticelifecycleMASSMatrixMatrixGenericsmatrixStatsmimemunsellnlmeopensslopenxlsxpheatmapproxyR6RBGLRColorBrewerRcppRCurlrlangRUnitS4ArraysS4VectorsscalessnowSparseArraySparseMstringiSummarizedExperimentsysUCSC.utilsviridisLiteXMLXVectorzipzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Example dataset to test the performance of the sigFeature package. | ExampleRawData |
| Processed output data after using the function named "sigCVError()". | featsweepSigFe |
| Processed output data after using the function named "svmrfeFeatureRanking()". | featureRankedList |
| Plot the mean CV errors. | PlotErrors |
| Processed output data after using the function named "sigFeature.enfold()". | results |
| Mean external cross validation (k-fold) error calculation. | sigCVError |
| Significant Feature Selection by using SVM-RFE & t-statistic. | sigFeature |
| Significant feature selection with k-fold data. | sigFeature.enfold |
| Arrange the features on the basis of frequency. | sigFeatureFrequency |
| Find the p-value of those ranked features by using t-statistic | sigFeaturePvalue |
| Processed output data after using the function named "sigFeature()". | sigfeatureRankedList |
| R implementation of the SVM-RFE algorithm for binary classification problems | svmrfeFeatureRanking |
| Write the features and sample IDs. | WritesigFeature |
