totalAuc: AUC of the running total of attributes

In RatingScaleReduction: Rating Scale Reduction Procedure

Description

AUC values are computed for all individual attributes. We sort them in an ascending order. We beging with the attribute having the largest AUC and add to it the second, third,... attribute until AUC of the total of them decreases.

Usage

totalAuc(attribute, D, plotT = FALSE)

Arguments

attribute	a matrix or data.frame containing attributes
D	the decision vector
plotT	If TRUE the plot is created: x - labels of atrributes, y - total AUC in ascending order

Value

ordered.attribute	ordered attribute matrix
total.auc	total AUC
item	ordered 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. T. Kakiashvili, W. W. Koczkodaj, and M. Woodbury-Smith. Improving the medical scale predictability by the pairwise comparisons method: Evidence from a clinical data study. Computer Methods and Programs in Biomedicine, 105(3), 2012
https://www.sciencedirect.com/science/article/abs/pii/S0169260711002586

3. 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)

#arrange start AUC in an ascending order and compute total AUC according to 
#Rating Scale Reduction procedure

tot <-totalAuc(attribute, decision, plotT=TRUE)
tot$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
   AUC one variable AUC running total
a3        0.8236789         0.8236789
a4        0.7313686         0.8241870
a2        0.6150068         0.6531165
a5        0.6119580         0.6825881
a1        0.4088753         0.6805556

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