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

In frontiles: Partial Frontier Efficiency Analysis

The ROC curve to help choosing alpha and m parameters

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

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

Arguments

xobs	a matrix of size n_1 \times p, input of sample points
yobs	a matrix of size n_1 \times 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

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

frontiles documentation built on April 3, 2023, 5:15 p.m.