ROCscore: The ROC curve to help choosing alpha and m parameters

Description Usage Arguments Details Value Author(s) References Examples

View source: R/ROCscore.r

Description

Computes the percentage of firms super-efficient according to the parameter alpha for alpha-quantile score and m for m-order score in a given direction.

Usage

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ROCscore(xobs, yobs, type="output")

Arguments

xobs

a matrix of size n1 x p, input of sample points

yobs

a matrix of size n1 x q, output of sample points

type

a direction to choose among "output", "input" and "hyper"

Details

A firm is super-efficient if it score is greater than 1. By consulting this graph, we may choose the values of alpha and m which correspond to the desired degree of robustness, i.e. the percentage of high performers of the population we want to exclude in our more realistic benchmarking comparison (see p.78 of Daraio and Simar, 2010).

Value

a data.frame object with:

alpha

different values of alpha

f(alpha)

the percentage of firms super-efficient

m

different values of m

f(m)

the percentage of firms super-efficient

Author(s)

Abdelaati Daouia and Thibault Laurent

References

Daraio and Simar (2007),Advanced Robust and Nonparametric Methods in Efficiency Analysis, Springer.

Examples

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# 1st example
data(spain)
res.roc<-ROCscore(xobs=as.matrix(spain[,c(2,3,4)]),yobs=as.matrix(spain[,1]),
type="output")

Example output

Loading required package: classInt
Loading required package: colorspace
Loading required package: rgl
Loading required package: sp
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl.init' failed, running with 'rgl.useNULL = TRUE'. 

frontiles documentation built on May 1, 2019, 8:45 p.m.