deaR (version 1.4.1)

model_supereff: Radial super-efficiency basic DEA model

Description

Solve Andersen and Petersen radial Super-efficiency DEA model.

Usage

model_supereff(datadea,
               dmu_eval = NULL,
               dmu_ref = NULL,
               supereff_modelname = c("basic"),
               ...)

Arguments

datadea

An object of class deadata.

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.

supereff_modelname

A string containing the name of the radial model to apply super-efficiency.

...

orientation, rts and other model parameters.

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

Andersen, P.; Petersen, N.C. (1993). "A procedure for ranking efficient units in data envelopment analysis", Management Science, 39, 1261-1264.

Tone, K. (2002). "A slacks-based measure of super-efficiency in data envelopment analysis", European Journal of Operational Research, 143, 32-41.

See Also

model_basic, model_sbmsupereff, model_addsupereff

Examples

# Example 1.
# Replication of results in Tone (2002, p.38)
data("Power_plants")
data_example <- make_deadata(Power_plants, 
                             ni = 4, 
                             no = 2)
result <- model_supereff(data_example, 
                         orientation = "io", 
                         rts = "crs") 
eff <- efficiencies(result)
 
# Example 2. 
# Results of Super-efficiency with vrs returns to scale show infeasibility solutions 
# for DMUs D4 and D6 (these DMUs are not shown in deaR results).
data("Power_plants")
data_example2 <- make_deadata(Power_plants, 
                              ni = 4, 
                              no = 2) 
result2 <- model_supereff(data_example2, 
                          orientation = "io", 
                          rts = "vrs") 
eff2 <- efficiencies(result2)