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,
            ...)

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.
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 (vicente.coll@uv.es). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolós (vicente.bolos@uv.es). Department of Business Mathematics

Rafael Benítez (rafael.suarez@uv.es). 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")

Examples

Run this code

 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)