samplesMetricMap: Plot a Grid of Sample Error Rates or Accuracies

Description Usage Arguments Details Value Author(s) Examples

Description

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

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
  ## S4 method for signature 'list'
samplesMetricMap(results,
                     comparison = c("classificationName", "datasetName", "selectionName",
                                "validation"),
                 metric = c("error", "accuracy"), featureValues = NULL, featureName = NULL,
                     metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),
                                        c("#880015", "#A53F4F", "#C37F8A", "#E1BFC4", "#FFFFFF")),
                     classColours = c("#3F48CC", "#880015"), groupColours = c("darkgreen", "yellow2"),
                     fontSizes = c(24, 16, 12, 12, 12),
                  mapHeight = 4, title = "Error Comparison", showLegends = TRUE,
                 xAxisLabel = "Sample Name", showXtickLabels = TRUE,
                 yAxisLabel = "Analysis", showYtickLabels = TRUE,
                 legendSize = grid::unit(1, "lines"), plot = TRUE)
  ## S4 method for signature 'matrix'
samplesMetricMap(results, classes,
                     metric = c("error", "accuracy"),
                     featureValues = NULL, featureName = NULL,
                     metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),
                                        c("#880015", "#A53F4F", "#C37F8A", "#E1BFC4", "#FFFFFF")),
                     classColours = c("#3F48CC", "#880015"), groupColours = c("darkgreen", "yellow2"),
                     fontSizes = c(24, 16, 12, 12, 12),
                  mapHeight = 4, title = "Error Comparison", showLegends = TRUE,
                 xAxisLabel = "Sample Name", showXtickLabels = TRUE,
                 yAxisLabel = "Analysis", showYtickLabels = TRUE,
                 legendSize = grid::unit(1, "lines"), plot = TRUE)                 

Arguments

results

A list of ClassifyResult objects. Could also be a matrix, for backwards compatibility.

classes

If results is a matrix, this is a factor vector of the same length as the number of columns that results has.

comparison

The aspect of the experimental design to compare.

metric

The sample-wise metric to plot.

featureValues

If not NULL, can be a named factor or named numeric vector specifying some variable of interest to plot underneath the class bar.

featureName

A label describing the information in featureValues. It must be specified if featureValues is.

metricColours

A vector of colours for metric 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.

groupColours

A vector of colours for group levels. Only useful if groups is not NULL.

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.

Details

The names of results determine the row names that will be in the plot. The length of metricColours determines how many bins the metric values will be discretised to.

Value

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

Author(s)

Dario Strbenac

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
  predicted <- data.frame(sample = LETTERS[sample(10, 100, replace = TRUE)],
                          class = rep(c("Healthy", "Cancer"), each = 50))
  actual <- factor(rep(c("Healthy", "Cancer"), each = 5), levels = c("Healthy", "Cancer"))
  features <- sapply(1:100, function(index) paste(sample(LETTERS, 3), collapse = ''))
  result1 <- ClassifyResult("Example", "Differential Expression", "t-test",
                            LETTERS[1:10], features, 100, list(1:100), list(sample(10, 10)),
                            list(function(oracle){}), list(predicted), actual,
                            list("permuteFold", 100, 5))
  predicted[, "class"] <- sample(predicted[, "class"])
  result2 <- ClassifyResult("Example", "Differential Variability", "Bartlett Test",
                            LETTERS[1:10], features, 100, list(1:100), list(sample(10, 10)),
                            list(function(oracle){}), list(predicted), actual,
                            validation = list("leave", 2))
  result1 <- calcCVperformance(result1, "sample error")
  result2 <- calcCVperformance(result2, "sample error")
  groups <- factor(rep(c("Male", "Female"), length.out = 10))
  names(groups) <- LETTERS[1:10]
  cholesterol <- c(4.0, 5.5, 3.9, 4.9, 5.7, 7.1, 7.9, 8.0, 8.5, 7.2)
  names(cholesterol) <- LETTERS[1:10]
  
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2))
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2),
                                featureValues = groups, featureName = "Gender")
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2),
                                featureValues = cholesterol, featureName = "Cholesterol")                                

ClassifyR documentation built on Nov. 1, 2018, 2:18 a.m.