R: An Interface for e1071 Package's Support Vector Machine...

SVMinterface {ClassifyR}

R Documentation

An Interface for e1071 Package's Support Vector Machine Classifier.

Description

SVMtrainInterface generates a trained SVM classifier and SVMpredictInterface uses it to make predictions on a test data set.

Usage

  ## S4 method for signature 'matrix'
SVMtrainInterface(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
SVMtrainInterface(measurements, classes, ..., verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
SVMtrainInterface(measurements, targets = names(measurements), ...)
  ## S4 method for signature 'svm,matrix'
SVMpredictInterface(model, test, ...)
  ## S4 method for signature 'svm,DataFrame'
SVMpredictInterface(model, test, classes = NULL, verbose = 3)
  ## S4 method for signature 'svm,MultiAssayExperiment'
SVMpredictInterface(model, test, targets = names(test), ...)

Arguments

`measurements`	Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples. If of type `DataFrame`, the data set is subset to only those features of type `integer`.
`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. Also, if a `DataFrame`, the `class` column must be absent.
`targets`	If `measurements` is a `MultiAssayExperiment`, the names of the data tables to be used. `"clinical"` is also a valid value and specifies that numeric variables from the clinical data table will be used.
`model`	A fitted model as returned by `SVMtrainInterface`.
`...`	Variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method (e.g. `verbose`) or options that are used by the `svm` function.
`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

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

For SVMtrainInterface, a trained SVM classifier of type svm. For SVMpredictInterface, a result of type factor, as created by e1071's predict method for trained SVM models.

Author(s)

Dario Strbenac

Examples

  if(require(e1071))
  {
    # Genes 76 to 100 have differential expression.
    genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
    genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample)
                                      c(rnorm(75, 9, 2), rnorm(25, 14, 2))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix))
    rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))
    trainingSamples <- c(1:20, 26:45)
    testingSamples <- c(21:25, 46:50)
    
    classifier <- SVMtrainInterface(genesMatrix[, trainingSamples],
                                     classes[trainingSamples], kernel = "linear")
    SVMpredictInterface(classifier, genesMatrix[, testingSamples])
  }

[Package ClassifyR version 2.0.10 Index]