ROCscore | R Documentation |

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.

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

`xobs` |
a matrix of size |

`yobs` |
a matrix of size |

`type` |
a direction to choose among "output", "input" and "hyper" |

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).

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 |

Abdelaati Daouia and Thibault Laurent

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

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

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