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Rmagpie (version 1.28.0)

MicroArray Gene-expression-based Program In Error rate estimation

Description

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.

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Version

Version

1.28.0

License

GPL (>= 3)

Maintainer

Camille Maumet

Last Published

February 15th, 2017

Functions in Rmagpie (1.28.0)

getDataset-methods

getDataset Method to access the attributes of a dataset from an assessment
rankedGenesImg-methods

rankedGenesImg Method to plot the genes according to their frequency in a microarray like image
thresholds-class

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

getResults Method to access the result of one-layer and two-layers cross-validation from an assessment
plotErrorsRepeatedOneLayerCV-methods

plotErrorsRepeatedOneLayerCV Method to plot the estimated error rates in each repeat of a one-layer Cross-validation
assessment-class

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

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

runTwoLayerExtCV: Method to run an external two-layers cross-validation
initialize-methods

Initialize objects of class from Rmagpie
setFeatureSelectionOptions-methods

getFeatureSelectionOptions<- Method to modify the attributes of a featureSelectionOptions from an assessment
findFinalClassifier-methods

findFinalClassifier Method to train and build the final classifier based on an assessment
geneSubsets-class

geneSubsets: A class to handle the sizes of gene susbets to be tested during forward gene selection
plotErrorsFoldTwoLayerCV-methods

plotErrorsFoldTwoLayerCV Method to plot the error rate of a two-layer Cross-validation
vV70genes

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

classifyNewSamples Method to classify new samples for a given assessment
featureSelectionOptions-class

"featureSelectionOptions": A virtual class to store the options of a feature selection
getFeatureSelectionOptions-methods

getFeatureSelectionOptions Method to access the attributes of a featureSelectionOptions from an assessment
setDataset-methods

getDataset<- Method to modify the attributes of a dataset from an assessment
runOneLayerExtCV-methods

runOneLayerExtCV: Method to run an external one-layer cross-validation
finalClassifier-class

finalClassifier: A class to store the final classifier corresponding to an assessment
plotErrorsSummaryOneLayerCV-methods

plotErrorsSummaryOneLayerCV Method to plot the summary estimated error rates of a one-layer Cross-validation
getFinalClassifier-methods

getFinalClassifier Method to access the attributes of a finalClassifier from an assessment