distribution {ClassifyR} | R Documentation

## Get Frequencies of Feature Selection and Sample Errors

### Description

There are two modes. For aggregating feature selection results, the function counts the number of times each feature was selected in all cross-validations. For aggregating classification results, the error rate for each sample is calculated. This is useful in identifying outlier samples that are difficult to classify.

### Usage

      ## S4 method for signature 'ClassifyResult'
    distribution(result, dataType = c("features", "samples"),
                     plotType = c("density", "histogram"), summaryType = c("percentage", "count"),
                     plot = TRUE, xMax = NULL, xLabel = "Percentage of Cross-validations",
                   yLabel = "Density", title = "Distribution of Feature Selections",
                 fontSizes = c(24, 16, 12), ...)

### Arguments

`result` | An object of class `ClassifyResult`.
`dataType` | Whether to calculate sample-wise error rate or the number of times a feature was selected.
`plotType` | Whether to draw a probability density curve or a histogram.
`summaryType` | Whether to summarise the feature selections as a percentage or count.
`plot` | Whether to draw a plot of the frequency of selection or error rate.
`xMax` | Maximum data value to show in plot.
`xLabel` | The label for the x-axis of the plot.
`yLabel` | The label for the y-axis of the plot.
`title` | An overall title for the plot.
`fontSizes` | A vector of length 3. The first number is the size of the title. The second number is the size of the axes titles. The third number is the size of the axes values.
`...` | Further parameters, such as `colour` and `fill`, passed to `geom_histogram` or `stat_density`, depending on the value of `plotType`.

### Value

If `type` is "features", a vector as long as the number of features that were chosen at least once containing the number of times the feature was chosen in cross validations or the percentage of times chosen. If `type` is "samples", a vector as long as the number of samples, containing the cross-validation error rate of the sample. If `plot` is `TRUE`, then a plot is also made on the current graphics device.

### Author(s)

Dario Strbenac

### Examples

      #if(require(sparsediscrim))
      #{
        data(asthma)
        resubstituteParams <- ResubstituteParams(nFeatures = seq(5, 25, 5),
                                             performanceType = "balanced error",
                                             better = "lower")

        result <- runTests(measurements, classes, datasetName = "Asthma",
                           classificationName = "Different Means",
                           permutations = 5,
                           params = list(SelectParams(limmaSelection, "Moderated t Statistic",
                                                      resubstituteParams = resubstituteParams),
                                         TrainParams(DLDAtrainInterface),
                                         PredictParams(DLDApredictInterface)
                                        )
                          )
        featureDistribution <- distribution(result, "features", summaryType = "count",
                                            plotType = "histogram",
                                            xLabel = "Number of Cross-validations", yLabel = "Count",
                                            binwidth = 1)
        print(head(featureDistribution))
      #}

[Package ClassifyR version 2.12.0 Index]