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]