cost.dea: Linear Programming for Cost Minimization

In nonparaeff: Nonparametric Methods for Measuring Efficiency and Productivity

Linear Programming for Cost Minimization

Description

Solve the Cost Minimization Probem with Given Input Prices

Usage

cost.dea(base = NULL, frontier = NULL, noutput = 1, input.price = NULL)

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 (M).
input.price	A vector for market prices of input factors.

Details

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

Value

A data frame with J1*(M+6), which has optimal M input factors, minimized cost when overally efficient, minimized cost when technically-efficient, revealed cost, overall efficiency, allocative efficiency, and technical efficiency.

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

revenue.dea

Examples

dat.io <- data.frame(y = c(1, 1, 1, 1, 1, 1, 1),
                          x1 = c(2, 3, 5, 9, 6, 3, 8),
                          x2 = c(8, 6, 3, 2, 7, 9, 4))
dat.wm<- c(w1 = 1, w2 = 2)      ## market prices of input factors
(re <- cost.dea(base = dat.io, noutput = 1, input.price = dat.wm))

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