model_rdm: Range directional model.

In deaR: Conventional and Fuzzy Data Envelopment Analysis

Range directional model.

Description

Range directional model from Portela et al. (2004).

Usage

model_rdm(datadea,
            dmu_eval = NULL,
            dmu_ref = NULL,
            orientation = c("no", "io", "oo"),
            irdm = FALSE,
            maxslack = TRUE,
            weight_slack_i = 1,
            weight_slack_o = 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.
orientation	A string, equal to "no" (non-oriented), "io" (input oriented), or "oo" (output oriented).
irdm	Logical. If it is TRUE, it applies the IRDM (inverse range directional model).
maxslack	Logical. If it is TRUE, it computes the max slack solution.
weight_slack_i	A value, vector of length m, or matrix m x ne (where ne is the length of dmu_eval) with the weights of the input slacks for the max slack solution.
weight_slack_o	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.
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.

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.

Note

Undesirable inputs/outputs are treated as negative inputs/outputs in this model.

Author(s)

Vicente Coll-Serrano ([e-mail]). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolós ([e-mail]). Department of Business Mathematics

Rafael Benítez ([e-mail]). Department of Business Mathematics

University of Valencia (Spain)

References

Portela, M.; Thanassoulis, E.; Simpson, G. (2004). "Negative data in DEA: a directional distance approach applied to bank branches", Journal of the Operational Research Society, 55 1111-1121.

deaR documentation built on Nov. 5, 2025, 6:35 p.m.