Get Frequencies of Feature Selection or Sample-wise Predictive Performance

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 predictive results, the accuracy or C-index for each sample is visualised. This is useful in identifying samples that are difficult to predict well.

Arguments

result: An object of class ClassifyResult.
...: Further parameters, such as colour and fill, passed to geom_histogram or stat_density, depending on the value of plotType.
dataType: Default: "features". Whether to summarise sample-wise error rate ("samples") or the number of times or frequency a feature was selected.
plotType: Whether to draw a probability density curve or a histogram.
summaryType: If feature selection, whether to summarise as a proportion or count.
plot: Whether to draw a plot of the frequency of selection or error rate.
xMax: Maximum data value to show in 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.
ordering: Default: "descending". A character string, either "descending" or "ascending", which specifies the ordering direction for sorting the summary.

Value

If dataType 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 proportion of times chosen. If dataType 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

Dario Strbenac

Examples


  #if(require(sparsediscrim))
  #{
    data(asthma)
    result <- crossValidate(measurements, classes, nRepeats = 5)
    featureDistribution <- distribution(result, "features", summaryType = "count",
                                        plotType = "histogram", binwidth = 1)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).

    print(head(featureDistribution))
#> allFeaturesText
#>    CROCC    SSBP4 C10orf95    CTXN1  TMEM190   ZDHHC1 
#>       24       24       23       22       22       22 
  #}