| totalAuc {RatingScaleReduction} | R Documentation |
AUC of the running total of attributes
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