NSCtrainInterface: Interface for 'pamr.train' Function from 'pamr' CRAN Package

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

Description

Restructures variables from ClassifyR framework to be compatible with pamr.train definition.

Usage

  ## S4 method for signature 'matrix'
NSCtrainInterface(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
NSCtrainInterface(measurements, classes, ..., verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
NSCtrainInterface(measurements, targets = names(measurements), ...)

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.
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.
...	Variables not used by the matrix nor the MultiAssayExperiment method which are passed into and used by the DataFrame method or extra arguments passed to pamr.train.
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

This function is an interface between the ClassifyR framework and pamr.train.

Value

A list with elements as described in pamr.train.

Author(s)

Dario Strbenac

See Also

pamr.train for the function that was interfaced to.

Examples

  if(require(pamr))
  {
    # Samples in one class with differential expression to other class.
    genesMatrix <- sapply(1:25, function(geneColumn) c(rnorm(100, 9, 1)))
    genesMatrix <- cbind(genesMatrix, sapply(1:25, function(geneColumn)
                                 c(rnorm(75, 9, 1), rnorm(25, 14, 1))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    
    NSCtrainInterface(genesMatrix, classes)
  }

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