classifyInterface {ClassifyR} | R Documentation

## An Interface for PoiClaClu Package's Classify Function

### Description

More details of Poisson LDA are available in the documentation of `Classify`.

### Usage

      ## S4 method for signature 'matrix'
    classifyInterface(measurements, classes, test, ...)
      ## S4 method for signature 'DataFrame'
    classifyInterface(measurements, classes, test, ..., returnType = c("class", "score", "both"), verbose = 3)
      ## S4 method for signature 'MultiAssayExperiment'
    classifyInterface(measurements, test, targets = names(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. 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.
`test` | An object of the same class as `measurements` with no samples in common with `measurements` and the same number of features as it.
`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 or parameters that `Classify` can accept.
`returnType` | Default: `"class"`. Either `"class"`, `"score"` or `"both"`. Sets the return value from the prediction to either a vector of class labels, matrix of scores for each class, or both labels and scores in a `data.frame`.
`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

Data tables which consist entirely of non-integer data cannot be analysed. 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

Either a factor vector of predicted classes, a matrix of scores for each class, or a table of both the class labels and class scores, depending on the setting of `returnType`.

### Author(s)

Dario Strbenac

### Examples

      if(require(PoiClaClu))
      {
        readCounts <- CountDataSet(n = 100, p = 1000, 2, 5, 0.1)
        # Rows are for features, columns are for samples.
        trainData <- t(readCounts[['x']])
        classes <- factor(paste("Class", readCounts[['y']]))
        testData <- t(readCounts[['xte']])
        storage.mode(trainData) <- storage.mode(testData) <- "integer"
        classified <- classifyInterface(trainData, classes, testData)

        setNames(table(paste("Class", readCounts[["yte"]]) == classified), c("Incorrect", "Correct"))
      }

[Package ClassifyR version 2.8.0 Index]