ddf: Linear Programming for the Directional Distance Function

In nonparaeff: Nonparametric Methods for Measuring Efficiency and Productivity

Linear Programming for the Directional Distance Function

Description

Solve the Additive Model under the VRS assumption

Usage

ddf(base = NULL, frontier = NULL, noutput = 1, direction = 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 (N).
direction	A directional vector for inputs and outputs.

Details

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

Value

A data frame with J1*(J1+M+N), of which has efficiency scores, optimal weightes and optimal slacks. 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

direc.dea

Examples

## Simple Example of one input and one output.
my.dat <- data.frame(y = c(1, 2, 4, 6, 7, 9, 9),
                     x = c(3, 2, 6, 4, 8, 8, 10))
(re <- ddf(my.dat, noutput = 1, direction = c(1, 1)))

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