getErrorMeasures-methods: Calculate Error Measures

Description

Computes various error measures for the classification of a data set.

Arguments

Value

A list with components alpha, beta, accuracy,

precision, sensitivity, specificity, ppv and

npv, each a number in the range \([0,1]\).

Methods

signature(object = "RecLinkResult"): Method for S3 result objects of class "RecLinkResult"
signature(object = "RLResult"): Method for S4 objects of class "RLResult", from classification of big data objects (see "RLBigData", "RLBigDataDedup", "RLBigDataLinkage")

A wrapper function errorMeasures(result) exists for compatibility with package version 0.2.

Author

Murat Sariyar, Andreas Borg

Details

Let \(\mathit{TP}\) be the number of correctly classified matches (true positives), \(\mathit{TN}\) the number of correctly classified non-matches (true negatives), \(\mathit{FP}\) and \(\mathit{FN}\) the number of misclassified non-matches and matches (false positives and false negatives). The calculated error measures are:

alpha error: \(\frac{\mathit{FN}}{\mathit{TP}+\mathit{FN}}\)
beta error: \(\frac{\mathit{FP}}{\mathit{TN}+\mathit{FP}}\)
accuracy: \(\frac{\mathit{TP}+\mathit{TN}}{\mathit{TP}+\mathit{TN}+\mathit{FP}+\mathit{FN}}\)
precision: \(\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}}\)
sensitivity: \(\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}}\)
specificity: \(\frac{\mathit{TN}}{\mathit{TN}+\mathit{FP}}\)
ppv: Positive predictive value:\(\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}}\)
npv: Negative predictive value:\(\frac{\mathit{TN}}{\mathit{TN}+\mathit{FN}}\)