Package: ClassifyR 3.11.4
Dario Strbenac
ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing
The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.
Authors:
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ClassifyR.pdf |ClassifyR.html✨
ClassifyR/json (API)
NEWS
| # Install 'ClassifyR' in R: |
| install.packages('ClassifyR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sydneybiox/classifyr/issues
Pkgdown:https://sydneybiox.github.io
- classes - Asthma RNA Abundance and Patient Classes
- clinical - METABRIC Clinical Data
- interactors - Human Reference Interactome
- measurements - Asthma RNA Abundance and Patient Classes
On BioConductor:ClassifyR-3.11.3(bioc 3.21)ClassifyR-3.10.0(bioc 3.20)
Last updated 22 hours agofrom:c1585acb96. Checks:OK: 1 NOTE: 4 WARNING: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 07 2024 |
| R-4.5-win-x86_64 | NOTE | Dec 07 2024 |
| R-4.5-linux-x86_64 | NOTE | Dec 07 2024 |
| R-4.4-win-x86_64 | NOTE | Dec 07 2024 |
| R-4.4-mac-x86_64 | WARNING | Dec 07 2024 |
| R-4.4-mac-aarch64 | WARNING | Dec 07 2024 |
| R-4.3-win-x86_64 | NOTE | Dec 07 2024 |
| R-4.3-mac-x86_64 | WARNING | Dec 07 2024 |
| R-4.3-mac-aarch64 | WARNING | Dec 07 2024 |
Exports:actualOutcomeallFeatureNamesavailablebubblePlotcalcCostsAndPerformancecalcCVperformancecalcExternalPerformancechosenFeatureNamesClassifyResultcolCoxTestscrissCrossPlotcrissCrossValidatecrossValidateCrossValParamsdistributionedgesToHubNetworksFeatureSetCollectionfeatureSetSummaryfinalModelflowchartinteractorDifferencesModellingParamsmodelsperformanceperformancePlotperformanceTableplotFeatureClassesprecisionPathwaysPredictprecisionPathwaysTrainpredictionsPredictParamsprepareDatarankingPlotROCplotrunTestrunTestssampleNamessamplesMetricMapsamplesSplitsselectionPlotSelectParamsshowsplitsTestInfostrataPlottotalPredictionsTrainParamsTransformParamstunedParameters
Dependencies:abindannotateAnnotationDbiaskpassbackportsBHBiobaseBiocBaseUtilsBiocGenericsBiocParallelBiostringsbitbit64blobbootbroomcachemcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11crayoncurlDBIDelayedArrayDerivdoBydplyrfansifarverfastmapformatRFormulafutile.loggerfutile.optionsgenefiltergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggpubrggrepelggsciggsignifggupsetgluegridExtragtablehttrIRangesisobandjsonliteKEGGRESTlabelinglambda.rlatticelifecyclelme4magrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrMultiAssayExperimentmunsellnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplogrplyrpngpolynompurrrquantregR6rangerRColorBrewerRcppRcppEigenreshape2rlangRSQLiterstatixS4ArraysS4VectorsscalessnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXMLxtableXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Asthma RNA Abundance and Patient Classes | asthma classes measurements |
| List Available Feature Selection and Classification Approaches | available |
| Various Functions for Evaluating Precision Pathways | bubblePlot bubblePlot.PrecisionPathways calcCostsAndPerformance flowchart flowchart.PrecisionPathways strataPlot strataPlot.PrecisionPathways summary.PrecisionPathways |
| Add Performance Calculations to a ClassifyResult Object or Calculate for a Pair of Factor Vectors | calcCVperformance calcCVperformance,ClassifyResult-method calcExternalPerformance calcExternalPerformance,factor,factor-method calcExternalPerformance,factor,tabular-method calcExternalPerformance,Surv,numeric-method calcPerformance performanceTable |
| Container for Storing Classification Results | actualOutcome actualOutcome,ClassifyResult-method allFeatureNames allFeatureNames,ClassifyResult-method chosenFeatureNames chosenFeatureNames,ClassifyResult-method ClassifyResult ClassifyResult,DataFrame,character,characterOrDataFrame-method ClassifyResult,DataFrame,character-method ClassifyResult-class features features,ClassifyResult-method finalModel finalModel,ClassifyResult-method models models,ClassifyResult-method performance performance,ClassifyResult-method predictions predictions,ClassifyResult-method sampleNames sampleNames,ClassifyResult-method show,ClassifyResult-method totalPredictions totalPredictions,ClassifyResult-method tunedParameters tunedParameters,ClassifyResult-method |
| A function to perform fast or standard Cox proportional hazard model tests. | colCoxTests |
| A function to plot the output of the crissCrossValidate function. | crissCrossPlot |
| A function to perform pairwise cross validation | crissCrossValidate |
| Cross-validation to evaluate classification performance. | crossValidate crossValidate,data.frame-method crossValidate,DataFrame-method crossValidate,matrix-method crossValidate,MultiAssayExperiment-method, crossValidate,MultiAssayExperimentOrList-method predict.trainedByClassifyR train.data.frame train.DataFrame train.list train.matrix train.MultiAssayExperiment |
| Parameters for Cross-validation Specification | CrossValParams CrossValParams-class |
| Get Frequencies of Feature Selection or Sample-wise Predictive Performance | distribution distribution,ClassifyResult-method |
| Convert a Two-column Matrix or Data Frame into a Hub Node List | edgesToHubNetworks |
| Container for Storing A Collection of Sets | FeatureSetCollection FeatureSetCollection,list-method FeatureSetCollection-class length,FeatureSetCollection-method show,FeatureSetCollection-method [,FeatureSetCollection,numeric,missing,ANY-method [[,FeatureSetCollection,ANY,missing-method |
| Transform a Table of Feature Abundances into a Table of Feature Set Abundances. | featureSetSummary featureSetSummary,DataFrame-method featureSetSummary,matrix-method featureSetSummary,MultiAssayExperiment-method |
| Human Reference Interactome | HuRI interactors |
| Convert Individual Features into Differences Between Binary Interactors Based on Known Sub-networks | interactorDifferences interactorDifferences,DataFrame-method interactorDifferences,matrix-method interactorDifferences,MultiAssayExperiment-method |
| METABRIC Clinical Data | clinical METABRICclinical |
| Parameters for Data Modelling Specification | ModellingParams ModellingParams-class |
| Plot Performance Measures for Various Classifications | performancePlot performancePlot,ClassifyResult-method performancePlot,list-method |
| Plot Density, Scatterplot, Parallel Plot or Bar Chart for Features By Class | plotFeatureClasses plotFeatureClasses,DataFrame-method plotFeatureClasses,matrix-method plotFeatureClasses,MultiAssayExperiment-method |
| Precision Pathways for Sample Prediction Based on Prediction Confidence. | precisionPathwaysPredict precisionPathwaysPredict,PrecisionPathways,MultiAssayExperimentOrList-method precisionPathwaysTrain precisionPathwaysTrain,MultiAssayExperimentOrList-method |
| Parameters for Classifier Prediction | PredictParams PredictParams,characterOrFunction-method PredictParams,missing-method PredictParams-class show,PredictParams-method |
| Convert Different Data Classes into DataFrame and Filter Features | prepareData prepareData,data.frame-method prepareData,DataFrame-method prepareData,list-method prepareData,matrix-method prepareData,MultiAssayExperiment-method |
| Plot Pair-wise Overlap of Ranked Features | rankingPlot rankingPlot,ClassifyResult-method rankingPlot,list-method |
| Plot Receiver Operating Curve Graphs for Classification Results | ROCplot ROCplot,ClassifyResult-method ROCplot,list-method |
| Perform a Single Classification | runTest runTest,DataFrame-method runTest,matrix-method runTest,MultiAssayExperiment-method |
| Reproducibly Run Various Kinds of Cross-Validation | runTests runTests,DataFrame-method runTests,matrix-method runTests,MultiAssayExperiment-method |
| Plot a Grid of Sample-wise Predictive Metrics | samplesMetricMap samplesMetricMap,ClassifyResult-method samplesMetricMap,list-method samplesMetricMap,matrix-method |
| Split Sample Indexes into Training and Test Partitions for Cross-validation Taking Into Account Classes. | samplesSplits samplesSplits,CrossValParams-method samplesSplits,numeric-method splitsTestInfo |
| Plot Pair-wise Overlap, Variable Importance or Selection Size Distribution of Selected Features | selectionPlot selectionPlot,ClassifyResult-method selectionPlot,list-method |
| Parameters for Feature Selection | SelectParams SelectParams,characterOrList-method SelectParams,missing-method SelectParams-class show,SelectParams-method |
| Parameters for Classifier Training | show,TrainParams-method TrainParams TrainParams,characterOrFunction-method TrainParams,missing-method TrainParams-class |
| Parameters for Data Transformation | show,TransformParams-method TransformParams TransformParams,ANY-method TransformParams,character-method TransformParams-class |
