ar.dual.dea: Assurance Region Data Envelopment Aanlysis (AR-DEA)

In nonparaeff: Nonparametric Methods for Measuring Efficiency and Productivity

Assurance Region Data Envelopment Aanlysis (AR-DEA)

Description

Solve the AR-DEA

Usage

ar.dual.dea(base = NULL, frontier = NULL,
           noutput = 1, orientation=1, rts = 1, ar.l = NULL,
           ar.r = NULL, ar.dir = NULL, dual = FALSE)

Arguments

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.

Details

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

Value

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

Author(s)

Dong-hyun Oh, [e-mail]

References

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).

See Also

dea, dual.dea

Examples

## 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("<=", "<=")))

nonparaeff documentation built on June 21, 2022, 9:05 a.m.