MicroArray Gene-expression-based Program In Error rate estimation

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Documentation for package ‘Rmagpie’ version 1.65.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Code demos. Use demo() to run them.

Help Pages

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.