ClassifyR (version 1.2.4)

naiveBayesKernel: Classification Using A Bayes Classifier with Kernel Density Estimates

Description

Kernel density estimates are fitted to the training data and a naive Bayes classifier is used to classify samples in the test data.

Usage

"naiveBayesKernel"(expression, classes, ...) "naiveBayesKernel"(expression, test, weighted = c("both", "unweighted", "weighted"), minDifference = 0, 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.

weighted

In weighted mode, the difference in densities is summed over all features. If unweighted mode, each features's vote is worth the same. To save computational time, both can be calculated simultaneously.

minDifference

The minimum difference in densities for a feature to be allowed to vote. Can be a vector of cutoffs.

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 list of class prediction information, as long as the number of samples in the test data, or lists of such information, if both weighted and unweighted voting or a range of minDifference values was provided.

Details

If weighted is TRUE, then a sample's predicted class is the class with the largest sum of density estimates, scaled for the number of samples in the training data of each class. Otherwise, when weighted is FALSE, each feature has an equal vote, and votes for the class with the largest density, scaled for class sizes in the training set. For unweighted classification, the score is the number of features which voted for the second class, divided by the features which voted. For weighted classification, the score is the sum of scaled differences in densities.

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))
  naiveBayesKernel(trainMatrix, classes, testMatrix)