| rsr {RatingScaleReduction} | R Documentation |
Rating scale reduction
Description
This package implements a rather sophisticated method published in (Koczkodaj et al., 2017) In essence, it is a stepwise method fro maximizing the area under the area (AUC) of receiver operating characteristic (ROC). In this description, data mining terminology will be used:
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examples (observations in statistics),
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variables in statistics,
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class or decision attribute (decision variable may be used statistics).
The implemented algorithm (when reduced to its minimum) comes to using a loop for all attributes (with the class excluded) to compute AUC. Subsequently, attributes are sorted in the descending order by AUC. The attribute with the largest AUC is added to a subset of all attributes (evidently, it cannot be empty since it is supposed to be the minimum subset S of all attributes with the maximum AUC). We keep adding the next in line (according to AUC) attribute to the subset S checking AUC. If it decreases, we stop the procedure. The above procedure can be described by the following algorithm.
Algorithm:
compute AUC of all attributes excluding class
sort attributes by their AUC in the ascending order
select the attribute with the largest AUC to subset S
select the next attribute A with the largest AUC to subset S
if the AUC of the subset S is larger that AUC of the former AUC then go to 3
There are a lot of checking (e.g., if the dataset is not empty or full of replications) involved.
Usage
rsr(attribute, D, plotRSR = FALSE, method=c('Stop1Max', 'StopGlobalMax'))
Arguments
attribute |
a matrix or data.frame containing attributes |
D |
the decision vector |
plotRSR |
If TRUE the ROC curve is ploted |
method |
the Stop reduction criteria: First Max of AUC or Global Max of AUC, default: 'Stop1Max' |
Value
rsr.auc |
total AUC of atrtibutes |
rsr.label |
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)
#rating scale reduction procedure
rsred <-rsr(attribute, decision, plotRSR=TRUE)
rsred