NSCpredictInterface {ClassifyR}R Documentation

Interface for pamr.predict Function from pamr CRAN Package

Description

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

Usage

  ## S4 method for signature 'pamrtrained,matrix'
NSCpredictInterface(trained, test, ...)
  ## S4 method for signature 'pamrtrained,DataFrame'
NSCpredictInterface(trained, test, classes = NULL, ..., verbose = 3)  
  ## S4 method for signature 'pamrtrained,MultiAssayExperiment'
NSCpredictInterface(trained, test, targets = names(test), ...)

Arguments

trained

An object of class pamrtrained.

test

An object of the same class as measurements with no samples in common with measurements used in the training stage and the same number of features as it. Also, if a DataFrame, the class column must be absent.

classes

Either NULL or a character vector of length 1, specifying the column name to remove.

targets

If test 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 optional settings that are passed to pamr.predict.

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.predict. It selects the highest threshold that gives the minimum error rate in the training data.

Value

A factor of predicted classes for the test data.

Author(s)

Dario Strbenac

See Also

pamr.predict 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))
    
    fit <- NSCtrainInterface(genesMatrix[, c(1:20, 26:45)], classes[c(1:20, 26:45)])
    NSCpredictInterface(fit, genesMatrix[, c(21:25, 46:50)])
  }

[Package ClassifyR version 2.0.10 Index]