The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.
|Author||Dario Strbenac, John Ormerod, Graham Mann, Jean Yang|
|Date of publication||None|
|Maintainer||Dario Strbenac <firstname.lastname@example.org>|
bartlettSelection: Selection of Differential Variability with Bartlett Statistic
calcPerformance: Add Performance Calculations to a ClassifyResult object
classifyInterface: Interface for PoiClaClu Package's Classify Function
ClassifyResult-class: Container for Storing Classification Results
distribution: Get Frequencies of Feature Selection and Sample Errors
DMDselection: Selection of Differential Distributions with Differences in...
edgeRselection: Feature Selection Based on Differential Expression for...
errorMap: Plot a Grid of Sample Error Rates
fisherDiscriminant: Classification Using Fisher's LDA
functionOrList-class: Union of Functions and List of Functions
getLocationsAndScales: Calculate Location and Scale
KolmogorovSmirnovSelection: Selection of Differential Distributions with...
KullbackLeiblerSelection: Selection of Differential Distributions with Kullback Leibler...
leveneSelection: Selection of Differential Variability with Levene Statistic
likelihoodRatioSelection: Selection of Differential Distributions with Likelihood Ratio...
limmaSelection: Selection of Differentially Expressed Features
medianDifferenceSelection: Selection of Differential Expression by Comparing Differences...
mixmodels: Selection of Differential Distributions with Mixtures of...
naiveBayesKernel: Classification Using A Bayes Classifier with Kernel Density...
nearestShrunkenCentroidPredictInterface: Interface for 'pamr.predict' Function from 'pamr' CRAN...
nearestShrunkenCentroidSelectionInterface: Interface for 'pamr.listgenes' Function from 'pamr' CRAN...
nearestShrunkenCentroidTrainInterface: Interface for 'pamr.train' Function from 'pamr' CRAN Package
pamrtrained-class: Trained pamr Object
performancePlot: Plot Performance Measures for Various Classifications
plotFeatureClasses: Plot Density and Scatterplot for Genes By Class
PredictParams-class: Parameters for Classifier Prediction
previousSelection: Automated Selection of Previously Selected Features
rankingPlot: Plot Pair-wise Overlap of Ranked Features
ResubstituteParams-class: Parameters for Resubstitution Error Calculation
ROCplot: Plot Receiver Operating Curve Graphs for Classification...
runTest: Perform a Single Classification
runTests: Reproducibly Run Various Kinds of Cross-Validation
selectionPlot: Plot Pair-wise Overlap or Selection Size Distribution of...
SelectParams-class: Parameters for Feature Selection
SelectResult-class: Container for Storing Feature Selection Results
subtractFromLocation: Subtract All Feature Measurements from Location
TrainParams-class: Parameters for Classifier Training
TransformParams-class: Parameters for Data Transformation