Package: Rmagpie 1.69.0
Camille Maumet
Rmagpie: MicroArray Gene-expression-based Program In Error rate estimation
Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.
Authors:
Rmagpie_1.69.0.tar.gz
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Rmagpie_1.69.0.tgz(r-4.6-any)Rmagpie_1.69.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Rmagpie/json (API)
| # Install 'Rmagpie' in R: |
| install.packages('Rmagpie', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- vV70genes - VV70genes: van't Veer et al. 70 best genes in an object of class dataset.
On BioConductor:Rmagpie-1.69.0(bioc 3.24)Rmagpie-1.68.0(bioc 3.23)
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:01c0eac9e0. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | ERROR | 204 | ||
| linux-devel-x86_64 | NOTE | 210 | ||
| source / vignettes | OK | 192 | ||
| linux-release-x86_64 | NOTE | 210 | ||
| macos-release-arm64 | NOTE | 98 | ||
| macos-oldrel-arm64 | NOTE | 149 | ||
| windows-devel | NOTE | 108 | ||
| windows-release | NOTE | 106 | ||
| windows-oldrel | NOTE | 102 | ||
| wasm-release | OK | 98 |
Exports:classifyNewSamplesfindFinalClassifiergetClassifierNamegetClassifierName<-getDatasetgetDataset<-getFeatureSelectionOptionsgetFeatureSelectionOptions<-getFinalClassifiergetGenesFromBestToWorstgetMaxSubsetSizegetMaxSubsetSize<-getModelsgetNoFolds1stLayergetNoFolds1stLayer<-getNoFolds2ndLayergetNoFolds2ndLayer<-getNoModelsgetNoOfRepeatsgetNoOfRepeats<-getNoThresholdsgetNoThresholds<-getOptionValuesgetOptionValues<-getResult1LayerCVgetResult2LayerCVgetResultsgetSpeedgetSpeed<-getSubsetsSizesgetSubsetsSizes<-getSvmKernelgetSvmKernel<-getTypeFoldCreationgetTypeFoldCreation<-initializeplotErrorsFoldTwoLayerCVplotErrorsRepeatedOneLayerCVplotErrorsSummaryOneLayerCVrankedGenesImgrunOneLayerExtCVrunTwoLayerExtCVshow
Dependencies:BiobaseBiocGenericsclassclustere1071genericskernlablatticeMASSMatrixpamrproxysurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| assessment: A central class to perform one and two layers of external cross-validation on microarray data | assessment-class getClassifierName getClassifierName,assessment-method getClassifierName<- getClassifierName<-,assessment-method getFeatureSelectionMethod,assessment-method getNoFolds1stLayer getNoFolds1stLayer,assessment-method getNoFolds1stLayer<- getNoFolds1stLayer<-,assessment-method getNoFolds2ndLayer getNoFolds2ndLayer,assessment-method getNoFolds2ndLayer<- getNoFolds2ndLayer<-,assessment-method getNoOfRepeats getNoOfRepeats,assessment-method getNoOfRepeats<- getNoOfRepeats<-,assessment-method getResult1LayerCV getResult1LayerCV,assessment-method getResult1LayerCV<- getResult1LayerCV<-,assessment-method getResult2LayerCV getResult2LayerCV,assessment-method getResult2LayerCV<- getResult2LayerCV<-,assessment-method getSvmKernel getSvmKernel,assessment-method getSvmKernel<- getSvmKernel<-,assessment-method getTypeFoldCreation getTypeFoldCreation,assessment-method getTypeFoldCreation<- getTypeFoldCreation<-,assessment-method |
| classifyNewSamples Method to classify new samples for a given assessment | classifyNewSamples classifyNewSamples,assessment-method classifyNewSamples-methods |
| "featureSelectionOptions": A virtual class to store the options of a feature selection | featureSelectionOptions-class getOptionValues,featureSelectionOptions-method |
| finalClassifier: A class to store the final classifier corresponding to an assessment | finalClassifier-class getGenesFromBestToWorst getGenesFromBestToWorst,finalClassifier-method getModels getModels,finalClassifier-method |
| findFinalClassifier Method to train and build the final classifier based on an assessment | findFinalClassifier findFinalClassifier,assessment-method findFinalClassifier-methods |
| geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection | geneSubsets-class getMaxSubsetSize getMaxSubsetSize,geneSubsets-method getMaxSubsetSize<- getMaxSubsetSize<-,geneSubsets-method getNoModels getNoModels,geneSubsets-method getSpeed getSpeed,geneSubsets-method getSpeed<- getSpeed<-,geneSubsets-method getSubsetsSizes getSubsetsSizes,geneSubsets-method getSubsetsSizes<- getSubsetsSizes<-,geneSubsets-method |
| getDataset Method to access the attributes of a dataset from an assessment | getDataset getDataset,assessment-method getDataset-methods |
| getFeatureSelectionOptions Method to access the attributes of a featureSelectionOptions from an assessment | getFeatureSelectionOptions getFeatureSelectionOptions,assessment-method getFeatureSelectionOptions-methods |
| getFinalClassifier Method to access the attributes of a finalClassifier from an assessment | getFinalClassifier getFinalClassifier,assessment-method getFinalClassifier-methods |
| getResults Method to access the result of one-layer and two-layers cross-validation from an assessment | getResults getResults,assessment-method getResults-methods |
| Initialize objects of class from Rmagpie | initialize,assessment-method initialize,geneSubsets-method initialize,thresholds-method |
| plotErrorsFoldTwoLayerCV Method to plot the error rate of a two-layer Cross-validation | plotErrorsFoldTwoLayerCV plotErrorsFoldTwoLayerCV,assessment-method plotErrorsFoldTwoLayerCV-methods |
| plotErrorsRepeatedOneLayerCV Method to plot the estimated error rates in each repeat of a one-layer Cross-validation | plotErrorsRepeatedOneLayerCV plotErrorsRepeatedOneLayerCV,assessment-method plotErrorsRepeatedOneLayerCV-methods |
| plotErrorsSummaryOneLayerCV Method to plot the summary estimated error rates of a one-layer Cross-validation | plotErrorsSummaryOneLayerCV plotErrorsSummaryOneLayerCV,assessment-method plotErrorsSummaryOneLayerCV-methods |
| rankedGenesImg Method to plot the genes according to their frequency in a microarray like image | rankedGenesImg rankedGenesImg,assessment-method rankedGenesImg-methods |
| runOneLayerExtCV: Method to run an external one-layer cross-validation | runOneLayerExtCV runOneLayerExtCV,assessment-method runOneLayerExtCV-methods |
| runTwoLayerExtCV: Method to run an external two-layers cross-validation | runTwoLayerExtCV runTwoLayerExtCV,assessment-method runTwoLayerExtCV-methods |
| getDataset<- Method to modify the attributes of a dataset from an assessment | getDataset<- getDataset<-,assessment-method getDataset<--methods |
| getFeatureSelectionOptions<- Method to modify the attributes of a featureSelectionOptions from an assessment | getFeatureSelectionOptions<- getFeatureSelectionOptions<-,assessment-method getFeatureSelectionOptions<--methods |
| show Display the object, by printing, plotting or whatever suits its class | show,assessment-method show,cvErrorRate-method show,cvErrorRate2ndLayer-method show,errorRate1stLayerCV-method show,errorRate2ndLayerCV-method show,finalClassifier-method show,frequencyGenes-method show,frequencyTopGenePerOneModel-method show,geneSubsets-method show,result2LayerCV-method show,resultRepeated1LayerCV-method show,resultRepeated2LayerCV-method show,resultSingle1LayerCV-method show,selectedGenes-method show,selectedGenes1stLayerCV-method show,selectedGenes2ndLayerCV-method show,selectedGenesPerOneOption-method show,thresholds-method |
| thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid | getNoThresholds getNoThresholds,thresholds-method getNoThresholds<- getNoThresholds<-,thresholds-method getOptionValues getOptionValues,thresholds-method getOptionValues<- getOptionValues<-,thresholds-method thresholds-class |
| vV70genes: van't Veer et al. 70 best genes in an object of class dataset. | vV70genes |