Description Usage Arguments Details Value See Also Examples
Plots a heatmap, that shows the performance of cascades of a Groupwise object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |
x |
A Groupwise object as it is returned by |
class.sort |
The classes can be sorted either according to the first cascade in the Groupwise object (”), or based on the class frequency ('max'). |
row.sort |
The cascade can be sorted either based on the maximal-minimal class-wise sensitivity ('sens') or according the class frequency. |
main |
See |
xlab |
A title for the x axis (see |
ylab |
A title for the y axis (see |
digits |
Integer indicating the number of decimal places to be used (see |
ignore |
A numeric value between 0 and 1. All confusion and purity values below this number are not written as string into the corresponding element. |
casc.colors |
A 2-element vector of the color for the minimal and maximal class-wise sensitivity. The color palette is calcuated by an interpolation between the 2 given colors. |
na.color |
Color, which is used for indicating the empty elements (if the given class is not part of the cascade). |
color.key |
Specifies whether a color key is drawn (TRUE) or not (FALSE). |
las |
See |
cex |
See |
cex.lab |
See |
srt |
Angle used to rotate the strings of the x-axis and y-axis labels (see |
... |
Further arguments passed from other methods. |
This function plots a heatmap with the cascades of the Groupwise object in the rows and all classes present in any of the cascades in the columns. The colors indicate whether a given class is present in the corresponding cascade and with which sensitivity. Internally converts the Groupwise object to a Subcascades object and plots the corresponding heatmap.
No return value, called to generate the heatmap plot of the Groupwise Object.
groupwise
, subcascades
, plot.Subcascades
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library(TunePareto)
data(esl)
data <- esl$data
labels <- esl$labels
foldList <- generateCVRuns(labels = labels,
ntimes = 2,
nfold = 2,
leaveOneOut = FALSE,
stratified = TRUE)
predMap <- predictionMap(data, labels, foldList = foldList,
classifier = tunePareto.svm(), kernel='linear')
# generate Subcascades object
subc <- subcascades(predMap,thresh=0.7,size=c(3,4))
groupwise <- groupwise(subc)
plot(groupwise,row.sort='max')
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