Selection of Differential Variability with Bartlett Statistic

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Description

Ranks features by largest Bartlett statistic and chooses the features which have best resubstitution performance.

Usage

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  ## S4 method for signature 'matrix'
bartlettSelection(expression, classes, ...)
  ## S4 method for signature 'ExpressionSet'
bartlettSelection(expression, datasetName,
                                            trainParams, predictParams, resubstituteParams,
                                            selectionName = "Bartlett Test", verbose = 3)

Arguments

expression

Either a matrix or ExpressionSet containing the training data. For a matrix, the rows are features, and the columns are samples.

classes

A vector of class labels.

...

For the matrix method, variables passed to the ExpressionSet method.

datasetName

A name for the dataset used. Stored in the result.

trainParams

A container of class TrainParams describing the classifier to use for training.

predictParams

A container of class PredictParams describing how prediction is to be done.

resubstituteParams

An object of class ResubstituteParams describing the performance measure to consider and the numbers of top features to try for resubstitution classification.

selectionName

A name to identify this selection method by. Stored in the result.

verbose

A number between 0 and 3 for the amount of progress messages to give. This function only prints progress messages if the value is 3.

Details

The calculation of the test statistic is performed by the bartlett.test function from the stats package.

Value

An object of class SelectResult or a list of such objects, if the classifier which was used for determining resubstitution error rate made a number of prediction varieties.

Author(s)

Dario Strbenac

Examples

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  if(require(sparsediscrim))
  {
    # Samples in one class with differential variability to other class.
    # First 20 genes are DV.
    genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 1)))
    genesMatrix <- cbind(genesMatrix, rbind(sapply(1:25, function(sample) rnorm(20, 9, 5)),
                                            sapply(1:25, function(sample) rnorm(80, 9, 1))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    genesMatrix <- exprs(subtractFromLocation(genesMatrix, 1:ncol(genesMatrix)))
    bartlettSelection(genesMatrix, classes, datasetName = "Example",
                    trainParams = TrainParams(fisherDiscriminant, FALSE, TRUE),
                    predictParams = PredictParams(function(){}, FALSE, getClasses = function(result) result),
                    resubstituteParams = ResubstituteParams(nFeatures = seq(10, 100, 10),
                                                            performanceType = "balanced", better = "lower"))
  }

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