R: Interface for 'pamr.predict' Function from 'pamr' CRAN...

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

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]

Interface for pamr.predict Function from pamr CRAN Package