R: An Interface for sparsediscrim Package's dlda Function

DLDAinterface {ClassifyR}

R Documentation

An Interface for sparsediscrim Package's dlda Function

Description

DLDAtrainInterface generates a trained diagonal LDA classifier and DLDApredictInterface uses it to make predictions on a test data set.

Usage

  ## S4 method for signature 'matrix'
DLDAtrainInterface(measurements, classes, ...)
  ## S4 method for signature 'DataFrame'
DLDAtrainInterface(measurements, classes, verbose = 3)
  ## S4 method for signature 'MultiAssayExperiment'
DLDAtrainInterface(measurements, targets = names(measurements), ...)
  ## S4 method for signature 'dlda,matrix'
DLDApredictInterface(model, test, ...)
  ## S4 method for signature 'dlda,DataFrame'
DLDApredictInterface(model, test, verbose = 3)
  ## S4 method for signature 'dlda,MultiAssayExperiment'
DLDApredictInterface(model, test, targets = names(test), ...)

Arguments

`measurements`	Either a `matrix`, `DataFrame` or `MultiAssayExperiment` containing the training data. For a `matrix`, the rows are features, and the columns are samples. If of type `DataFrame`, the data set is subset to only those features of type `integer`.
`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.
`model`	A fitted model as returned by `DLDAtrainInterface`.
`test`	An object of the same class as `measurements` with no samples in common with `measurements` and the same number of features as it. Also, if a `DataFrame`, the `class` column must be absent.
`targets`	If `measurements` is a `MultiAssayExperiment`, the names of the data tables to be used. `"clinical"` is also a valid value and specifies that integer 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 (e.g. `verbose`).
`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

If measurements is an object of class MultiAssayExperiment, the factor of sample classes must be stored in the DataFrame accessible by the colData function with column name "class".

Value

For DLDAtrainInterface, a trained DLDA classifier. For DLDApredictInterface, a result of type list with elements class, scores and posterior, as created by sparsediscrim's predict method.

Author(s)

Dario Strbenac

Examples

  # if(require(sparsediscrim)) Package currently removed from CRAN.
  #{
    # 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))))
    classes <- factor(rep(c("Poor", "Good"), each = 25))
    colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix))
    rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix))
    selected <- rownames(genesMatrix)[91:100]
    trainingSamples <- c(1:20, 26:45)
    testingSamples <- c(21:25, 46:50)
    
    classifier <- DLDAtrainInterface(genesMatrix[selected, trainingSamples],
                                     classes[trainingSamples])
    DLDApredictInterface(classifier, genesMatrix[selected, testingSamples])[["class"]]
  #}

[Package ClassifyR version 2.0.10 Index]