R: An Interface for randomForest Package's randomForest Function

randomForestInterface {ClassifyR}

R Documentation

An Interface for randomForest Package's randomForest Function

Description

A random forest classifier builds multiple decision trees and uses the predictions of the trees to determine a single prediction for each test sample.

Usage

  ## S4 method for signature 'matrix'
randomForestInterface(measurements, classes, test, ...)
  ## S4 method for signature 'DataFrame'
randomForestInterface(measurements, classes, test, ..., verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
randomForestInterface(measurements, targets = names(measurements), 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.
`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 (e.g. `verbose`) or options which are accepted by the `randomForest` 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

An object of type randomForest. The predictions of the test set samples are stored in the list element named "predicted" of the "test" element.

Author(s)

Dario Strbenac

Examples

  if(require(randomForest))
  {
    # 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))
    selected <- rownames(genesMatrix)[91:100]
    trainingSamples <- c(1:20, 26:45)
    testingSamples <- c(21:25, 46:50)
    
    classified <- randomForestInterface(genesMatrix[, trainingSamples],
                                        classes[trainingSamples],
                                        genesMatrix[, testingSamples], ntree = 10)
    classified[["test"]][["predicted"]]
  }

[Package ClassifyR version 2.0.10 Index]