R: Construction of a Confusion Table

confusionTable {ORION}

R Documentation

Construction of a Confusion Table

Description

Confusion table and class assignments of one cascade.

Usage

confusionTable(
  predictionMap = NULL,
  cascade = NULL,
  other.classes = NULL,
  sort = TRUE
)

Arguments

`predictionMap`	A PredictionMap object as it is returned by `predictionMap`-function. It is made up of a list of two matrices(pred and meta). Both matrices provide information on individual samples column-wise. The rownames of the pred-matrix (e.g. [0vs1]) show the classes of the binary base classifier. The elements are the prediction result of a specific training. The rows that correspond to base classifiers that would separate the same class consists of -1. Those rows are not used within the analysis. The meta information connects the values in the pred-matrix to a specific fold, run and contains the original label.
`cascade`	A numeric vector of classes or a character string of type '1>2>3' of at least two class labels reflected in 'predictionMap'.
`other.classes`	This parameter can be either NULL, 'all' or a numeric vector of classes that are not part of the cascade parameter. If other.classes is: - NULL, only the cascade classes are evaluated. - 'all', all remaining classes are evaluated. - a vector of classes, those classes are evaluated.
`sort`	If TRUE (default) the classes that are not part of cascade are sorted based on their confusion.

Value

An object of type ConfusionTable including the sensitivities, with the label of the predicted classes in the rows and the labels of the original class in the columns.

Examples

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')

# Calculation of the confusion matrix for '0>2>3>4'.
confTable = confusionTable(predMap, cascade = '0>2>3>4')
# Calculation of the confusion matrix for '0>2>3>4' 
# and the assignment of all samples of the other classes.
confTable = confusionTable(predMap, cascade = '0>2>3>4', 
                other.classes='all', sort = TRUE)