runTest: Perform a Single Classification

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

Perform a Single Classification

Description

For a data set of features and samples, the classification process is run. It consists of data transformation, feature selection, classifier training and testing.

Usage

## S4 method for signature 'matrix'
runTest(measurementsTrain, outcomeTrain, measurementsTest, outcomeTest, ...)

## S4 method for signature 'DataFrame'
runTest(
  measurementsTrain,
  outcomeTrain,
  measurementsTest,
  outcomeTest,
  crossValParams = CrossValParams(),
  modellingParams = ModellingParams(),
  characteristics = S4Vectors::DataFrame(),
  ...,
  verbose = 1,
  .iteration = NULL
)

## S4 method for signature 'MultiAssayExperiment'
runTest(measurementsTrain, measurementsTest, outcomeColumns, ...)

Arguments

measurementsTrain	Either a matrix, DataFrame or MultiAssayExperiment containing the training data. For a matrix or DataFrame, the rows are samples, and the columns are features.
...	Variables not used by the matrix nor the MultiAssayExperiment method which are passed into and used by the DataFrame method or passed onwards to prepareData.
outcomeTrain	Either a factor vector of classes, a Surv object, or a character string, or vector of such strings, containing column name(s) of column(s) containing either classes or time and event information about survival. If column names of survival information, time must be in first column and event status in the second.
measurementsTest	Same data type as measurementsTrain, but only the test samples.
outcomeTest	Same data type as outcomeTrain, but for only the test samples.
crossValParams	An object of class CrossValParams, specifying the kind of cross-validation to be done, if nested cross-validation is used to tune any parameters.
modellingParams	An object of class ModellingParams, specifying the class rebalancing, transformation (if any), feature selection (if any), training and prediction to be done on the data set.
characteristics	A DataFrame describing the characteristics of the classification used. First column must be named "charateristic" and second column must be named "value". Useful for automated plot annotation by plotting functions within this package. Transformation, selection and prediction functions provided by this package will cause the characteristics to be automatically determined and this can be left blank.
verbose	Default: 1. A number between 0 and 3 for the amount of progress messages to give. A higher number will produce more messages as more lower-level functions print messages.
.iteration	Not to be set by a user. This value is used to keep track of the cross-validation iteration, if called by runTests.
outcomeColumns	If measurementsTrain is a MultiAssayExperiment, the names of the column (class) or columns (survival) in the table extracted by colData(data) that contain(s) the samples' outcome to use for prediction.

Details

This function only performs one classification and prediction. See runTests for a driver function that enables a number of different cross-validation schemes to be applied and uses this function to perform each iteration.

Value

If called directly by the user rather than being used internally by runTests, a ClassifyResult object. Otherwise a list of different aspects of the result which is passed back to runTests.

Author(s)

Dario Strbenac

Examples

  #if(require(sparsediscrim))
  #{
    data(asthma)
    tuneList <- list(nFeatures = seq(5, 25, 5), performanceType = "Balanced Error")
    selectParams <- SelectParams("limma", tuneParams = tuneList)
    modellingParams <- ModellingParams(selectParams = selectParams)
    trainIndices <- seq(1, nrow(measurements), 2)
    testIndices <- seq(2, nrow(measurements), 2)
    
    runTest(measurements[trainIndices, ], classes[trainIndices],
            measurements[testIndices, ], classes[testIndices], modellingParams = modellingParams)
  #}

DarioS/ClassifyR documentation built on Nov. 4, 2024, 1:06 p.m.