previousSelection {ClassifyR} | R Documentation

## Automated Selection of Previously Selected Features

### Description

Uses the feature selection of the same cross-validation iteration of a previous classification for the current classification task.

### Usage

      ## S4 method for signature 'matrix'
    previousSelection(measurements, ...)
      ## S4 method for signature 'DataFrame'
    previousSelection(measurements, classes, datasetName,
                      classifyResult, minimumOverlapPercent = 80,
                            selectionName = "Previous Selection", .iteration, verbose = 3)
      ## S4 method for signature 'MultiAssayExperiment'
    previousSelection(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` | Do not specify this variable. It is ignored and only used to create consistency of formal parameters with other feature selection methods.
`...` | Variables not used by the `matrix` nor the `MultiAssayExperiment` method which are passed into and used by the `DataFrame` method.
`datasetName` | A name for the data set used. Stored in the result.
`classifyResult` | An existing classification result from which to take the feature selections from.
`minimumOverlapPercent` | If at least this many selected features can't be identified in the current data set, then the selection stops with an error.
`selectionName` | A name to identify this selection method by. Stored in the result.
`.iteration` | Do not specify this variable. It is set by `runTests` if this function is being repeatedly called by `runTests`.
`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.

### Value

An object of class `SelectResult`.

### Author(s)

Dario Strbenac

### Examples

      #if(require(sparsediscrim))
      #{
        # Genes 76 to 100 have differential expression.
        genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
        genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample)
                                     c(rnorm(75, 9, 2), rnorm(25, 14, 2))))
        colnames(genesMatrix) <- paste("Sample", 1:50)
        rownames(genesMatrix) <- paste("Gene", 1:100)
        classes <- factor(rep(c("Poor", "Good"), each = 25))
        resubstitute <- ResubstituteParams(nFeatures = seq(10, 100, 10),
                            performanceType = "error", better = "lower")
        result <- runTests(genesMatrix, classes, "Example", "Differential Expression",
                           permutations = 2, fold = 2,
                           params = list(SelectParams(), TrainParams(), PredictParams()))

        # Genes 50 to 74 have differential expression in new data set.
        newDataset <- sapply(1:25, function(sample) c(rnorm(100, 9, 2)))
        newDataset <- cbind(newDataset, rbind(sapply(1:25, function(sample) rnorm(49, 9, 2)),
                                              sapply(1:25, function(sample) rnorm(25, 14, 2)),
                                              sapply(1:25, function(sample) rnorm(26, 9, 2))))

        rownames(newDataset) <- rownames(genesMatrix)
        colnames(newDataset) <- colnames(genesMatrix)

        newerResult <- runTests(newDataset, classes, "Latest Data Set",
                                "Differential Expression", permutations = 2, fold = 2,
                           params = list(SelectParams(previousSelection,
                                         intermediate = ".iteration",
                                         classifyResult = result),
                           TrainParams(), PredictParams()))

        # However, only genes 76 to 100 are chosen, because the feature selections are
        # carried over from the first cross-validated classification.
        features(newerResult)
      #}

[Package ClassifyR version 2.0.10 Index]