Description Usage Arguments Details Value Author(s) References See Also Examples

Solve the AR-DEA

1 2 3 |

`base` |
A data set for DMUs to be evaluated. A data frame with J1*(M+N) dimention, where J1 is the number of DMUs, M for the number of inputs, and N for the number of outputs. |

`frontier` |
A data set for DMUs to be used in constructing a production possibility set (PPS). A data frame with J2*(M+N) dimention, where J2 is the number of DMUs, M for the number of inputs, and N for the number of outputs. |

`noutput` |
The number of outputs (N). |

`orientation` |
Orientation of measurement. 1 for the input-oriented measure, and 2 for the output-oriented measure. |

`rts` |
Returns to scale. 1 for the CRS assumption, and 2 for the VRS assumption. |

`ar.l` |
A data frame for the assurance region of which is the left-hand. |

`ar.r` |
A vector for the assurance region of which is the right-hand. |

`ar.dir` |
A vector for the assurance region of which is the direction. |

`dual` |
Logical. |

The AR model under the CRS assumption is calculated. For model specification, take a look at Cooper et al. (2007).

A data frame with J1*(M+N), which has efficiency scores, optimal virtual prices. Take a look at the example below.

Dong-hyun Oh, [email protected]

Cooper, W., Seiford, L. and Tone, K. (2007). *Data envelopment
analysis: a comprehensive text with models, applications, references
and DEA-solver software* (2nd ed.). Springer Verlag, New York.

Lee, J. and Oh, D. (forthcoming). *Efficiency Analysis: Data
Envelopment Analysis*. Press (in Korean).

1 2 3 4 5 6 7 8 9 10 | ```
## AR constraint of 0.25 <= v2/v1 <= 1.
library(Hmisc)
library(lpSolve)
ar.dat <- data.frame(y = c(1, 1, 1, 1, 1, 1),
x1 = c(2, 3, 6, 3, 6, 6),
x2 = c(5, 3, 1, 8, 4, 2))
(re <-
ar.dual.dea(ar.dat, noutput = 1, orientation = 1, rts = 1, ar.l =
matrix(c(0, 0, 0.25, -1, -1, 1), nrow = 2, ncol = 3), ar.r = c(0, 0),
ar.dir = c("<=", "<=")))
``` |

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