deaR (version 1.5.2)

model_nonradial: Non-radial DEA model.

Description

Non-radial DEA model allows for non-proportional reductions in each input or augmentations in each output.

Usage

model_nonradial(datadea,
                dmu_eval = NULL,
                dmu_ref = NULL,
                orientation = c("io", "oo"),
                rts = c("crs", "vrs", "nirs", "ndrs", "grs"),
                L = 1,
                U = 1,
                maxslack = TRUE,
                weight_slack = 1,
                compute_target = TRUE,
                returnlp = FALSE,
                ...)

Value

A list of class dea with the results for the evaluated DMUs (DMU component), along with any other necessary information to replicate the results, such as the name of the model and parameters orientation, rts,

dmu_eval and dmu_ref.

Arguments

datadea

A deadata object, including n DMUs, m inputs and s outputs.

dmu_eval

A numeric vector containing which DMUs have to be evaluated. If NULL (default), all DMUs are considered.

dmu_ref

A numeric vector containing which DMUs are the evaluation reference set. If NULL (default), all DMUs are considered.

orientation

A string, equal to "io" (input-oriented) or "oo" (output-oriented).

rts

A string, determining the type of returns to scale, equal to "crs" (constant), "vrs" (variable), "nirs" (non-increasing), "ndrs" (non-decreasing) or "grs" (generalized).

L

Lower bound for the generalized returns to scale (grs).

U

Upper bound for the generalized returns to scale (grs).

maxslack

Logical. If it is TRUE, it computes the max slack solution.

weight_slack

If input-oriented, it is a value, vector of length s, or matrix s x ne (where ne is the length of dmu_eval) with the weights of the output slacks for the max slack solution. If output-oriented, it is a value, vector of length m, or matrix m x ne with the weights of the input slacks for the max slack solution.

compute_target

Logical. If it is TRUE, it computes targets of the max slack solution. We note that we call "targets" to the "efficient projections" in the strongly efficient frontier.

returnlp

Logical. If it is TRUE, it returns the linear problems (objective function and constraints) of stage 1.

...

Ignored, for compatibility issues.

Author

Vicente Coll-Serrano (). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolós (). Department of Business Mathematics

Rafael Benítez (). Department of Business Mathematics

University of Valencia (Spain)

References

Banker, R.D.; Morey, R.C. (1986). "Efficiency Analysis for Exogenously Fixed Inputs and Outputs", Operations Research, 34, 80-97. tools:::Rd_expr_doi("10.1287/opre.34.4.513")

Färe, R.; Lovell, C.K. (1978). "Measuring the Technical Efficiency of Production", Journal of Economic Theory, 19(1), 150-162. tools:::Rd_expr_doi("10.1016/0022-0531(78)90060-1")

Wu, J.; Tsai, H.; Zhou, Z. (2011). "Improving Efficiency in International Tourist Hotels in Taipei Using a Non-Radial DEA Model", International Journal of Contemporary Hospitatlity Management, 23(1), 66-83. tools:::Rd_expr_doi("10.1108/09596111111101670")

Zhu, J. (1996). “Data Envelopment Analysis with Preference Structure”, The Journal of the Operational Research Society, 47(1), 136. tools:::Rd_expr_doi("10.2307/2584258")

See Also

model_deaps, model_profit, model_sbmeff

Examples

# Replication of results in Wu, Tsai and Zhou (2011)
data("Hotels")
data_hotels <- make_deadata(Hotels, 
                            inputs = 2:5, 
                            outputs = 6:8)
result <- model_nonradial(data_hotels, 
                          orientation = "oo", 
                          rts = "vrs")
efficiencies(result)