runTest {ClassifyR} | R Documentation

## 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(measurements, classes, ...)
      ## S4 method for signature 'DataFrame'
    runTest(measurements, classes, featureSets = NULL, metaFeatures = NULL,
                minimumOverlapPercent = 80, datasetName, classificationName,
                training, testing, params = list(SelectParams(), TrainParams(), PredictParams()),
            verbose = 1, .iteration = NULL)
      ## S4 method for signature 'MultiAssayExperiment'
    runTest(measurements, targets = names(measurements), ...)
      ## S4 method for signature 'MultiAssayExperiment'
    runTestEasyHard(measurements, easyDatasetID = "clinical", hardDatasetID = names(measurements)[1],
                       featureSets = NULL, metaFeatures = NULL, minimumOverlapPercent = 80,
                       datasetName = NULL, classificationName = "Easy-Hard Classifier", training, testing, ..., verbose = 1, .iteration = NULL)

### Arguments

`measurements` | Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples. The sample identifiers must be present as column names of the `matrix` or the row names of the `DataFrame`.
`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.
`featureSets` | An object of type `FeatureSetCollection` which defines sets of features or sets of edges.
`metaFeatures` | Either `NULL` or a `DataFrame` which has meta-features of the numeric data of interest.
`minimumOverlapPercent` | If `featureSets` stores sets of features, the minimum overlap of feature IDs with `measurements` for a feature set to be retained in the analysis. If `featureSets` stores sets of network edges, the minimum percentage of edges with both vertex IDs found in `measurements` that a set has to have to be retained in the analysis.
`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.
`...` | For `runTest`, variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method. For `runTestEasyHard`, `easyClassifierParams` and `hardClassifierParams` to be passed to `easyHardClassifierTrain`.
`datasetName` | A name associated with the data set used.
`classificationName` | A name associated with the classification.
`training` | A vector which specifies the training samples.
`testing` | A vector which specifies the test samples.
`params` | A `list` of objects of class of `TransformParams`, `SelectParams`, `TrainParams`, or `PredictParams`. The order they are in the list determines the order in which the stages of classification are done in.
`easyDatasetID` | The name of a data set in `measurements` or "clinical" to indicate the patient information in the column data be used.
`hardDatasetID` | The name of a data set in `measurements` different to the value of `easyDatasetID` to be used for classifying the samples not classified by the easy classifier.
`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`.

### 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. `datasetName` and `classificationName` need to be provided.

### Value

If called directly by the user rather than being used internally by `runTests`, a `SelectResult` object.

### Author(s)

Dario Strbenac

### Examples

      #if(require(sparsediscrim))
      #{
        data(asthma)
        resubstituteParams <- ResubstituteParams(nFeatures = seq(5, 25, 5),
                                             performanceType = "balanced error",
                                             better = "lower")
        runTest(measurements, classes,
                datasetName = "Asthma", classificationName = "Different Means",
                params = list(SelectParams(limmaSelection, "Moderated t Statistic",
                                           resubstituteParams = resubstituteParams),
                              TrainParams(DLDAtrainInterface),
                              PredictParams(DLDApredictInterface)
                              ),
                training = (1:ncol(measurements)) %% 2 == 0,
                testing = (1:ncol(measurements)) %% 2 != 0)
      #}

      genesMatrix <- matrix(c(rnorm(90, 9, 1),
                            9.5, 9.4, 5.2, 5.3, 5.4, 9.4, 9.6, 9.9, 9.1, 9.8),
    		      ncol = 10, byrow = TRUE)

      colnames(genesMatrix) <- paste("Sample", 1:10)
      rownames(genesMatrix) <- paste("Gene", 1:10)
      genders <- factor(c("Male", "Male", "Female", "Female", "Female",
                        "Female", "Female", "Female", "Female", "Female"))

      # Scenario: Male gender can predict the hard-to-classify Sample 1 and Sample 2.
      clinical <- DataFrame(age = c(31, 34, 32, 39, 33, 38, 34, 37, 35, 36),
                            gender = genders,
                            class = factor(rep(c("Poor", "Good"), each = 5)),
    		        row.names = colnames(genesMatrix))
      dataset <- MultiAssayExperiment(ExperimentList(RNA = genesMatrix), clinical)

      selParams <- SelectParams(featureSelection = differentMeansSelection, selectionName = "Difference in Means",
                                resubstituteParams = ResubstituteParams(1:10, "balanced error", "lower"))
      easyHardCV <- runTestEasyHard(dataset, datasetName = "Test Data", classificationName = "Easy-Hard", training = 1:10, testing = 1:10,
                                    easyClassifierParams = list(minCardinality = 2, minPurity = 0.9),
                                    hardClassifierParams = list(selParams, TrainParams(), PredictParams())
                                   )

[Package ClassifyR version 2.8.0 Index]