Plot Performance Measures for Various Classifications

Draws a graphical summary of a particular performance measure for a list of classifications

Usage

# S4 method for ClassifyResult
performancePlot(results, ...)

# S4 method for list
performancePlot(
  results,
  metric = "auto",
  characteristicsList = list(x = "auto"),
  aggregate = character(),
  coloursList = list(),
  orderingList = list(),
  densityStyle = c("box", "violin"),
  yLimits = NULL,
  fontSizes = c(24, 16, 12, 12),
  title = NULL,
  margin = grid::unit(c(1, 1, 1, 1), "lines"),
  rotate90 = FALSE,
  showLegend = TRUE
)

Arguments

results: A list of ClassifyResult objects.
...: Not used by end user.
metric: Default: "auto". The name of the performance measure or "auto". If the results are classification then balanced accuracy will be displayed. Otherwise, the results would be survival risk predictions and then C-index will be displayed. This is one of the names printed in the Performance Measures field when a ClassifyResult object is printed, or if none are stored, the performance metric will be calculated automatically.
characteristicsList: A named list of characteristics. Each element's name must be one of "x", "row", "column", "fillColour", or "fillLine". The value of each element must be a characteristic name, as stored in the "characteristic" column of the results' characteristics table. Only "x" is mandatory. It is "auto" by default, which will identify a characteristic that has a unique value for each element of results.
aggregate: A character vector of the levels of characteristicsList['x'] to aggregate to a single number by taking the mean. This is particularly meaningful when the cross-validation is leave-k-out, when k is small.
coloursList: A named list of plot aspects and colours for the aspects. No elements are mandatory. If specified, each list element's name must be either "fillColours" or "lineColours". If a characteristic is associated to fill or line by characteristicsList but this list is empty, a palette of colours will be automatically chosen.
orderingList: An optional named list. Any of the variables specified to characteristicsList can be the name of an element of this list and the value of the element is the order in which the factors should be presented in, in case alphabetical sorting is undesirable. Special values "performanceAscending" and "performanceDescending" indicate that the order of levels will be computed based on the median performance value of the characteristic being sorted into ascending or descending order.
densityStyle: Default: "box". Either "violin" for violin plot or "box" for box plot. If cross-validation is not repeated, then a bar chart.
yLimits: The minimum and maximum value of the performance metric to plot.
fontSizes: A vector of length 4. 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. The fourth number is the font size of the titles of grouped plots, if any are produced. In other words, when rowVariable or columnVariable are not NULL.
title: An overall title for the plot.
margin: The margin to have around the plot.
rotate90: Logical. IF TRUE, the plot is horizontal.
showLegend: If TRUE, a legend is plotted next to the plot. If FALSE, it is hidden.

Value

An object of class ggplot and a plot on the current graphics device, if plot is TRUE.

Details

If there are multiple values for a performance measure in a single result object, it is plotted as a violin plot, unless aggregate is TRUE, in which case the all predictions in a single result object are considered simultaneously, so that only one performance number is calculated, and a barchart is plotted.

Author

Dario Strbenac

Examples


  predicted <- DataFrame(sample = sample(LETTERS[1:10], 80, replace = TRUE),
                         permutation = rep(1:2, each = 40),
                         class = factor(rep(c("Healthy", "Cancer"), 40)))
  actual <- factor(rep(c("Healthy", "Cancer"), each = 5))
  result1 <- ClassifyResult(DataFrame(characteristic = c("Data Set", "Selection Name", "Classifier Name",
                                                         "Cross-validation"),
                            value = c("Example", "t-test", "Differential Expression", "2 Permutations, 2 Folds")),
                            LETTERS[1:10], paste("Gene", 1:100), list(paste("Gene", 1:100), paste("Gene", c(10:1, 11:100)), paste("Gene", 1:100), paste("Gene", 1:100)),
                            list(paste("Gene", 1:3), paste("Gene", c(2, 5, 6)), paste("Gene", 1:4), paste("Gene", 5:8)),
                            list(function(oracle){}), NULL, predicted, actual)
  result1 <- calcCVperformance(result1, "Macro F1")

  predicted <- DataFrame(sample = sample(LETTERS[1:10], 80, replace = TRUE),
                          permutation = rep(1:2, each = 40),
                          class = factor(rep(c("Healthy", "Cancer"), 40)))
                               
  result2 <- ClassifyResult(DataFrame(characteristic = c("Data Set", "Selection Name", "Classifier Name",
                                                         "Cross-validation"),
                            value = c("Example", "Bartlett Test", "Differential Variability", "2 Permutations, 2 Folds")),
                            LETTERS[1:10], paste("Gene", 1:100), list(paste("Gene", 1:100), paste("Gene", c(10:1, 11:100)), paste("Gene", 1:100), paste("Gene", 1:100)),
                            list(c(1:3), c(4:6), c(1, 6, 7, 9), c(5:8)),
                            list(function(oracle){}), NULL, predicted, actual)
  result2 <- calcCVperformance(result2, "Macro F1")
  
  performancePlot(list(result1, result2), metric = "Macro F1",
                  title = "Comparison")