samplesMetricMap | R Documentation |
A grid of coloured tiles is drawn. There is one column for each sample and one row for each cross-validation result.
## S4 method for signature 'ClassifyResult'
samplesMetricMap(results, ...)
## S4 method for signature 'list'
samplesMetricMap(
results,
comparison = "auto",
metric = "auto",
featureValues = NULL,
featureName = NULL,
metricColours = list(c("#FFFFFF", "#CFD1F2", "#9FA3E5", "#6F75D8", "#3F48CC"),
c("#FFFFFF", "#E1BFC4", "#C37F8A", "#A53F4F", "#880015")),
classColours = c("#3F48CC", "#880015"),
groupColours = c("darkgreen", "yellow2"),
fontSizes = c(24, 16, 12, 12, 12),
mapHeight = 4,
title = "auto",
showLegends = TRUE,
xAxisLabel = "Sample Name",
showXtickLabels = TRUE,
yAxisLabel = "Analysis",
showYtickLabels = TRUE,
legendSize = grid::unit(1, "lines")
)
## S4 method for signature 'matrix'
samplesMetricMap(
results,
classes,
metric = c("Sample Error", "Sample 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")
)
results |
A list of |
... |
Parameters not used by the |
comparison |
Default: |
metric |
Default: |
featureValues |
If not NULL, can be a named factor or named numeric vector specifying some variable of interest to plot above the heatmap. |
featureName |
A label describing the information in
|
metricColours |
If the outcome is categorical, a list of vectors of colours for metric levels for each class. If the outcome is numeric, such as a risk score, then a single vector of colours for the metric levels for all samples. |
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
|
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. |
yAxisLabel |
The name plotted for the y-axis. NULL suppresses label. |
showYtickLabels |
Logical. IF FALSE, the y-axis labels are hidden. |
legendSize |
The size of the boxes in the legends. |
classes |
If |
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.
A grob is returned that can be drawn on a graphics device.
Dario Strbenac
predicted <- DataFrame(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(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], features, list(1:100), list(sample(10, 10)),
list(function(oracle){}), NULL, predicted, actual)
predicted[, "class"] <- sample(predicted[, "class"])
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], features, list(1:100), list(sample(10, 10)),
list(function(oracle){}), NULL, predicted, actual)
result1 <- calcCVperformance(result1)
result2 <- calcCVperformance(result2)
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")
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