randomForestInterface: An Interface for randomForest Package's randomForest Function

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

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'
randomForestTrainInterface(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
randomForestTrainInterface(measurements, classes, ..., verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
randomForestTrainInterface(measurements, targets = names(measurements), ...)
  ## S4 method for signature 'randomForest,matrix'
randomForestPredictInterface(forest, test, ...)
  ## S4 method for signature 'randomForest,DataFrame'
randomForestPredictInterface(forest, test, ...,
                                          returnType = c("class", "score", "both"), verbose = 3)
  ## S4 method for signature 'randomForest,MultiAssayExperiment'
randomForestPredictInterface(forest, 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.
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.
forest	A trained random forest classifier, as created by randomForestTrainInterface, which has the same form as the output of randomForest.
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 or predict.randomForest functions.
returnType	Default: "class". Either "class", "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	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 randomForestTrainInterface, the trained random forest. For randomForestPredictInterface, either a factor vector of predicted classes, a matrix of scores for each class, or a table of both the class labels and class scores, depending on the setting of returnType.

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), sep = '')
    rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix), sep = '')
    trainingSamples <- c(1:20, 26:45)
    testingSamples <- c(21:25, 46:50)
    
    trained <- randomForestTrainInterface(genesMatrix[, trainingSamples],
                                          classes[trainingSamples])
    predicted <- randomForestPredictInterface(trained, genesMatrix[, testingSamples])
  }

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.