ClassifyR (version 1.2.4)

fisherDiscriminant: Classification Using Fisher's LDA

Description

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

Usage

"fisherDiscriminant"(expression, classes, ...) "fisherDiscriminant"(expression, test, returnType = c("label", "score", "both"), 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.

...

Unused variables from the matrix method passed to the ExpressionSet method.

test

Either a matrix or ExpressionSet containing the test data.

returnType

Either "label", "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

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.

Value

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

Details

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

Examples

  trainMatrix <- matrix(rnorm(1000, 8, 2), ncol = 10)
  trainMatrix[1:30, 1:5] <- trainMatrix[1:30, 1:5] + 5 # Make first 30 genes D.E.
  testMatrix <- matrix(rnorm(1000, 8, 2), ncol = 10)
  testMatrix[1:30, 6:10] <- testMatrix[1:30, 6:10] + 5 # Make first 30 genes D.E.
  classes <- factor(rep(c("Poor", "Good"), each = 5))
  fisherDiscriminant(trainMatrix, classes, testMatrix)