deaR (version 1.2.5)

cross_efficiency_fuzzy: Cross efficiency fuzzy tables

Description

Computes the cross-efficiency fuzzy table from dea data or a Guo-Tanaka dea model solution. The (crisp) relative efficiencies for the case h = 1 are obtained from the CCR model (model_multiplier).

Usage

cross_efficiency_fuzzy(datadea,
                       orientation = c("io", "oo"),
                       h = 1,
                       selfapp = TRUE)

Arguments

datadea

An object of class dea_fuzzy or deadata_fuzzy. If it is of class dea_fuzzy it must have been obtained with modelfuzzy_guotanaka.

orientation

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

h

A numeric vector with the h-levels (in [0,1]).

selfapp

Logical. If it is TRUE, self-appraisal is included in the average scores of A and e.

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

Doyle, J.; Green, R. (1994). “Efficiency and Cross Efficiency in DEA: Derivations, Meanings and the Uses”, Journal of Operational Research Society, 45(5), 567–578. tools:::Rd_expr_doi("10.2307/2584392")

Guo, P.; Tanaka, H. (2001). "Fuzzy DEA: A Perceptual Evaluation Method", Fuzzy Sets and Systems, 119, 149–160. tools:::Rd_expr_doi("10.1016/S0165-0114(99)00106-2")

León, T.; Liern, V.; Ruiz, J.L.; Sirvent, I. (2003). "A Fuzzy Mathematical Programming Approach to the assessment of efficiency with DEA Models", Fuzzy Sets Systems, 139(2), 407–419. tools:::Rd_expr_doi("10.1016/S0165-0114(02)00608-5")

Sexton, T.R., Silkman, R.H.; Hogan, A.J. (1986). Data envelopment analysis: critique and extensions. In: Silkman RH (ed) Measuring efficiency: an assessment of data envelopment analysis, vol 32. Jossey-Bass, San Francisco, pp 73–104. tools:::Rd_expr_doi("10.1002/ev.1441")

Examples

 data("Guo_Tanaka_2001")
 datadea <- read_data_fuzzy(datadea = Guo_Tanaka_2001, 
                            inputs.mL = 2:3, 
                            inputs.dL = 4:5, 
                            outputs.mL = 6:7, 
                            outputs.dL = 8:9)
 result <- cross_efficiency_fuzzy(datadea = datadea, 
                                  h = seq(0, 1, 0.2))