CRAN/E | influenceAUC

influenceAUC

Identify Influential Observations in Binary Classification

Installation

About

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) doi:10.1080/10543406.2017.1377728 provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

Bug report File report

Key Metrics

Version 0.1.2
Published 2020-05-30 1399 days ago
Needs compilation? no
License GPL-3
CRAN checks influenceAUC results

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Maintainer

Maintainer

Bo-Shiang Ke

naivete0907@gmail.com

Authors

Bo-Shiang Ke

cre / aut / cph

Yuan-chin Ivan Chang

aut

Wen-Ting Wang

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

influenceAUC archive

Imports

dplyr
geigen
ggplot2
ggrepel
methods
ROCR