ClassifyR (version 1.2.4)

errorMap: Plot a Grid of Sample Error Rates

Description

A grid of coloured tiles is drawn. There is one column for each sample and one row for each classification result.

Usage

"errorMap"(results, errorColours = list(c("#0000FF", "#3F3FFF", "#7F7FFF", "#BFBFFF", "#FFFFFF"), c("#FF0000", "#FF3F3F", "#FF7F7F", "#FFBFBF", "#FFFFFF")), classColours = c("blue", "red"), fontSizes = c(24, 16, 12, 12, 12), mapHeight = 4, title = "Error Comparison", showLegends = TRUE, xAxisLabel = "Sample Name",  showXtickLabels = TRUE, showYtickLabels = TRUE, yAxisLabel = "Analysis", legendSize = grid::unit(1, "lines"), plot = TRUE)

Arguments

results

A list of ClassifyResult objects.

errorColours

A vector of colours for error levels.

classColours

Either a vector of colours for class levels if both classes should have same colour, or a list of length 2, with each component being a vector of the same length. The vector has the colour gradient for each class.

fontSizes

A vector of length 5. 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 size of the legends' titles. The fifth number is the font size of the legend labels.

mapHeight

Height of the map, relative to the height of the class colour bar.

title

The title to place above the plot.

showLegends

Logical. IF FALSE, the legend is not drawn.

xAxisLabel

The name plotted for the x-axis. NULL suppresses label.

showXtickLabels

Logical. IF FALSE, the x-axis labels are hidden.

showYtickLabels

Logical. IF FALSE, the y-axis labels are hidden.

yAxisLabel

The name plotted for the y-axis. NULL suppresses label.

legendSize

The size of the boxes in the legends.

plot

Logical. IF TRUE, a plot is produced on the current graphics device.

Value

A plot is produced and a grob is returned that can be saved to a graphics device.

Details

The names of results determine the row names that will be in the plot. The length of errorColours determines how many bins the error rates will be discretised to.

Examples

  predicted <- data.frame(sample = sample(10, 100, replace = TRUE),
                          label = rep(c("Healthy", "Cancer"), each = 50))
  actual <- factor(rep(c("Healthy", "Cancer"), each = 5))
  result1 <- ClassifyResult("Example", "Differential Expression", LETTERS[1:10], LETTERS[10:1], list(1:100), list(sample(10, 10)),
                            list(predicted), actual, list("fold", 100, 5))
  predicted[, "label"] <- sample(predicted[, "label"])
  result2 <- ClassifyResult("Example", "Differential Deviation", LETTERS[1:10], LETTERS[10:1], list(1:100), list(sample(10, 10)),
                            list(predicted), actual, validation = list("leave", 1))
  # wholePlot <- errorMap(list(Gene = result1, Protein = result2)) # Wait for namespace problems to be fixed.
  # ggsave("wholePlot.png", wholePlot)