MicroArray Gene-expression-based Program In Error rate estimation


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Documentation for package ‘Rmagpie’ version 1.26.0

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A C F G I P R S T V

-- A --

assessment-class assessment: A central class to perform one and two layers of external cross-validation on microarray data

-- C --

classifyNewSamples classifyNewSamples Method to classify new samples for a given assessment
classifyNewSamples-method classifyNewSamples Method to classify new samples for a given assessment
classifyNewSamples-methods classifyNewSamples Method to classify new samples for a given assessment

-- F --

featureSelectionOptions-class "featureSelectionOptions": A virtual class to store the options of a feature selection
finalClassifier-class finalClassifier: A class to store the final classifier corresponding to an assessment
findFinalClassifier findFinalClassifier Method to train and build the final classifier based on an assessment
findFinalClassifier-method findFinalClassifier Method to train and build the final classifier based on an assessment
findFinalClassifier-methods findFinalClassifier Method to train and build the final classifier based on an assessment

-- G --

geneSubsets-class geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getClassifierName assessment: A central class to perform one and two layers of external cross-validation on microarray data
getClassifierName-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getClassifierName<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getClassifierName<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getDataset getDataset Method to access the attributes of a dataset from an assessment
getDataset-method getDataset Method to access the attributes of a dataset from an assessment
getDataset-methods getDataset Method to access the attributes of a dataset from an assessment
getDataset<- getDataset<- Method to modify the attributes of a dataset from an assessment
getDataset<--method getDataset<- Method to modify the attributes of a dataset from an assessment
getDataset<--methods getDataset<- Method to modify the attributes of a dataset from an assessment
getFeatureSelectionMethod-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getFeatureSelectionOptions getFeatureSelectionOptions Method to access the attributes of a featureSelectionOptions from an assessment
getFeatureSelectionOptions-method getFeatureSelectionOptions Method to access the attributes of a featureSelectionOptions from an assessment
getFeatureSelectionOptions-methods getFeatureSelectionOptions Method to access the attributes of a featureSelectionOptions from an assessment
getFeatureSelectionOptions<- getFeatureSelectionOptions<- Method to modify the attributes of a featureSelectionOptions from an assessment
getFeatureSelectionOptions<--method getFeatureSelectionOptions<- Method to modify the attributes of a featureSelectionOptions from an assessment
getFeatureSelectionOptions<--methods getFeatureSelectionOptions<- Method to modify the attributes of a featureSelectionOptions from an assessment
getFinalClassifier getFinalClassifier Method to access the attributes of a finalClassifier from an assessment
getFinalClassifier-method getFinalClassifier Method to access the attributes of a finalClassifier from an assessment
getFinalClassifier-methods getFinalClassifier Method to access the attributes of a finalClassifier from an assessment
getGenesFromBestToWorst finalClassifier: A class to store the final classifier corresponding to an assessment
getGenesFromBestToWorst-method finalClassifier: A class to store the final classifier corresponding to an assessment
getMaxSubsetSize geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getMaxSubsetSize-method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getMaxSubsetSize<- geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getMaxSubsetSize<--method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getModels finalClassifier: A class to store the final classifier corresponding to an assessment
getModels-method finalClassifier: A class to store the final classifier corresponding to an assessment
getNoFolds1stLayer assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds1stLayer-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds1stLayer<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds1stLayer<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds2ndLayer assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds2ndLayer-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds2ndLayer<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoFolds2ndLayer<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoModels geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getNoModels-method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getNoOfRepeats assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoOfRepeats-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoOfRepeats<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoOfRepeats<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getNoThresholds thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getNoThresholds-method thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getNoThresholds<- thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getNoThresholds<--method thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getOptionValues thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getOptionValues-method "featureSelectionOptions": A virtual class to store the options of a feature selection
getOptionValues-method thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getOptionValues<- thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getOptionValues<--method thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid
getResult1LayerCV assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult1LayerCV-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult1LayerCV<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult1LayerCV<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult2LayerCV assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult2LayerCV-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult2LayerCV<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResult2LayerCV<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getResults getResults Method to access the result of one-layer and two-layers cross-validation from an assessment
getResults-method getResults Method to access the result of one-layer and two-layers cross-validation from an assessment
getResults-methods getResults Method to access the result of one-layer and two-layers cross-validation from an assessment
getSpeed geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSpeed-method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSpeed<- geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSpeed<--method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSubsetsSizes geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSubsetsSizes-method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSubsetsSizes<- geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSubsetsSizes<--method geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
getSvmKernel assessment: A central class to perform one and two layers of external cross-validation on microarray data
getSvmKernel-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getSvmKernel<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getSvmKernel<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getTypeFoldCreation assessment: A central class to perform one and two layers of external cross-validation on microarray data
getTypeFoldCreation-method assessment: A central class to perform one and two layers of external cross-validation on microarray data
getTypeFoldCreation<- assessment: A central class to perform one and two layers of external cross-validation on microarray data
getTypeFoldCreation<--method assessment: A central class to perform one and two layers of external cross-validation on microarray data

-- I --

initialize-method Initialize objects of class from Rmagpie

-- P --

plotErrorsFoldTwoLayerCV plotErrorsFoldTwoLayerCV Method to plot the error rate of a two-layer Cross-validation
plotErrorsFoldTwoLayerCV-method plotErrorsFoldTwoLayerCV Method to plot the error rate of a two-layer Cross-validation
plotErrorsFoldTwoLayerCV-methods plotErrorsFoldTwoLayerCV Method to plot the error rate of a two-layer Cross-validation
plotErrorsRepeatedOneLayerCV plotErrorsRepeatedOneLayerCV Method to plot the estimated error rates in each repeat of a one-layer Cross-validation
plotErrorsRepeatedOneLayerCV-method plotErrorsRepeatedOneLayerCV Method to plot the estimated error rates in each repeat of a one-layer Cross-validation
plotErrorsRepeatedOneLayerCV-methods plotErrorsRepeatedOneLayerCV Method to plot the estimated error rates in each repeat of a one-layer Cross-validation
plotErrorsSummaryOneLayerCV plotErrorsSummaryOneLayerCV Method to plot the summary estimated error rates of a one-layer Cross-validation
plotErrorsSummaryOneLayerCV-method plotErrorsSummaryOneLayerCV Method to plot the summary estimated error rates of a one-layer Cross-validation
plotErrorsSummaryOneLayerCV-methods plotErrorsSummaryOneLayerCV Method to plot the summary estimated error rates of a one-layer Cross-validation

-- R --

rankedGenesImg rankedGenesImg Method to plot the genes according to their frequency in a microarray like image
rankedGenesImg-method rankedGenesImg Method to plot the genes according to their frequency in a microarray like image
rankedGenesImg-methods rankedGenesImg Method to plot the genes according to their frequency in a microarray like image
runOneLayerExtCV runOneLayerExtCV: Method to run an external one-layer cross-validation
runOneLayerExtCV-method runOneLayerExtCV: Method to run an external one-layer cross-validation
runOneLayerExtCV-methods runOneLayerExtCV: Method to run an external one-layer cross-validation
runTwoLayerExtCV runTwoLayerExtCV: Method to run an external two-layers cross-validation
runTwoLayerExtCV-method runTwoLayerExtCV: Method to run an external two-layers cross-validation
runTwoLayerExtCV-methods runTwoLayerExtCV: Method to run an external two-layers cross-validation

-- S --

show-method show Display the object, by printing, plotting or whatever suits its class

-- T --

thresholds-class thresholds: A class to handle the thresholds to be tested during training of the Nearest Shrunken Centroid

-- V --

vV70genes vV70genes: van't Veer et al. 70 best genes in an object of class dataset.