fisherDiscriminant: Classification Using Fisher's LDA

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description

Finds the decision boundary using the training set, and gives predictions for the test set.

Usage

  ## S4 method for signature 'matrix'
fisherDiscriminant(measurements, classes, test, ...)
  ## S4 method for signature 'DataFrame'
fisherDiscriminant(measurements, classes, test, returnType = c("class", "score", "both"),
                   verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
fisherDiscriminant(measurements, test, targets = names(measurements), ...)

Arguments

measurements	Either a matrix, DataFrame or MultiAssayExperiment containing the training data. For a matrix, the rows are features, and the columns are samples.
classes	Either a vector of class labels of class factor of the same length as the number of samples in measurements or if the measurements are of class DataFrame a character vector of length 1 containing the column name in measurement is also permitted. Not used if measurements is a MultiAssayExperiment object.
test	An object of the same class as measurements with no samples in common with measurements and the same number of features as it.
targets	If measurements is a MultiAssayExperiment, the names of the data tables to be used. "clinical" is also a valid value and specifies that integer variables from the clinical data table will be used.
...	Variables not used by the matrix nor the MultiAssayExperiment method which are passed into and used by the DataFrame method.
returnType	Default: "class". Either "class", "score", or "both". Sets the return value from the prediction to either a vector of class labels, score for a sample belonging to the second class, as determined by the factor levels, or both labels and scores in a data.frame.
verbose	Default: 3. 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

Unlike ordinary LDA, Fisher's version does not have assumptions about the normality of the features.

Data tables which consist entirely of non-numeric data cannot be analysed. If measurements is an object of class MultiAssayExperiment, the factor of sample classes must be stored in the DataFrame accessible by the colData function with column name "class".

Value

A vector or data.frame of class prediction information, as long as the number of samples in the test data.

Author(s)

Dario Strbenac

Examples

  trainMatrix <- matrix(rnorm(1000, 8, 2), ncol = 10)
  classes <- factor(rep(c("Poor", "Good"), each = 5))
  
  # Make first 30 genes increased in value for poor samples.
  trainMatrix[1:30, 1:5] <- trainMatrix[1:30, 1:5] + 5
  
  testMatrix <- matrix(rnorm(1000, 8, 2), ncol = 10)
  
  # Make first 30 genes increased in value for sixth to tenth samples.
  testMatrix[1:30, 6:10] <- testMatrix[1:30, 6:10] + 5
  
  fisherDiscriminant(trainMatrix, classes, testMatrix)

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.