fisherDiscriminant {ClassifyR} | R Documentation |
Finds the decision boundary using the training set, and gives predictions for the test set.
## 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), ...)
measurements |
Either a |
classes |
Either a vector of class labels of class |
test |
An object of the same class as |
targets |
If |
... |
Variables not used by the |
returnType |
Default: |
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. |
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"
.
A vector or data.frame
of class prediction information, as long as the number of samples in the test data.
Dario Strbenac
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)