deaR (version 1.5.2)

model_deaps: Preference Structure DEA model.

Description

With this non-radial DEA model (Zhu, 1996), the user can specify the preference input (or output) weigths that reflect the relative degree of desirability of the adjustments of the current input (or output) levels.

Usage

model_deaps(datadea,
            dmu_eval = NULL,
            dmu_ref = NULL,
            weight_eff = 1,
            orientation = c("io", "oo"),
            rts = c("crs", "vrs", "nirs", "ndrs", "grs"),
            L = 1,
            U = 1,
            restricted_eff = TRUE,
            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.

weight_eff

Preference weights. If input-oriented, it is a value, vector of length m, or matrix m x ne (where ne is the lenght of dmu_eval) with the weights applied to the input efficiencies. If output-oriented, it is a value, vector of length s, or matrix s x ne with the weights applied to the output efficiencies.

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).

restricted_eff

Logical. If it is TRUE, the efficiencies are restricted to be <=1 (input-oriented) or >=1 (output-oriented).

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 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

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")

Zhu, J. (2014). Quantitative Models for Performance Evaluation and Benchmarking. Data Envelopment Analysis with Spreadsheets. 3rd Edition Springer, New York. tools:::Rd_expr_doi("10.1007/978-3-319-06647-9")

See Also

model_nonradial, model_profit, model_sbmeff

Examples

 data("Fortune500")
 data_deaps <- make_deadata(datadea = Fortune500,
                            ni = 3, 
                            no = 2)
 result <- model_deaps(data_deaps, 
                       weight_eff = c(1, 2, 3), 
                       orientation = "io", 
                       rts = "vrs")
 efficiencies(result)