startAuc: AUC of a single attribute

In RatingScaleReduction: Rating Scale Reduction Procedure

Description

Compute AUC of every single attribute

Usage

startAuc(attribute, D)

Arguments

attribute	a matrix or data.frame containing attributes
D	the decision vector

Value

auc	AUC of a single attribute
item	attribute labels
summary	a summary table

Author(s)

Waldemar W. Koczkodaj, Alicja Wolny-Dominiak

References

1. W.W. Koczkodaj, T. Kakiashvili, A. Szymanska, J. Montero-Marin, R. Araya, J. Garcia-Campayo, K. Rutkowski, D. Strzalka, How to reduce the number of rating scale items without predictability loss? Scientometrics, 909(2):581-593(open access), 2017
https://link.springer.com/article/10.1007/s11192-017-2283-4

2. X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J.-C. Sanchez, and M. Muller. proc: an opensource package for r and s+ to analyze and compare roc curves. BMC Bioinformatics, 2011
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-77

Examples

#creating the matrix of attributes and the decision vector
#must be as.numeric()
data(aSAH)
attach(aSAH)
is.numeric(aSAH)

attribute <-data.frame(as.numeric(gender), 
as.numeric(age), as.numeric(wfns), as.numeric(s100b), as.numeric(ndka))
colnames(attribute) <-c("a1", "a2", "a3", "a4", "a5")
decision <-as.numeric(outcome)

#compute AUC of all attributes
start <-startAuc(attribute, decision)
start$summary

Example output

Loading required package: pROC
Type 'citation("pROC")' for a citation.

Attaching package: 'pROC'

The following objects are masked from 'package:stats':

    cov, smooth, var

Loading required package: ggplot2
[1] FALSE
  item       auc
1   a1 0.4088753
2   a2 0.6150068
3   a3 0.8236789
4   a4 0.7313686
5   a5 0.6119580

RatingScaleReduction documentation built on Jan. 21, 2021, 5:06 p.m.