deaR (version 1.2.5)

model_sbmeff: Slack based measure (SBM) of efficiency model.

Description

Calculate the SBM model proposed by Tone (2001).

Usage

model_sbmeff(datadea,
             dmu_eval = NULL,
             dmu_ref = NULL,
             weight_input = 1,
             weight_output = 1,
             orientation = c("no", "io", "oo"),
             rts = c("crs", "vrs", "nirs", "ndrs", "grs"),
             L = 1,
             U = 1,
             kaizen = FALSE,
             maxfr = NULL,
             tol = 1e-6,
             silent = FALSE,
             compute_target = TRUE,
             returnlp = FALSE,
             ...)

Arguments

datadea

The data, 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_input

A value, vector of length m, or matrix m x ne (where ne is the length of dmu_eval) with weights to inputs corresponding to the relative importance of items.

weight_output

A value, vector of length m, or matrix m x ne (where ne is the length of dmu_eval) with weights to outputs corresponding to the relative importance of items.

orientation

A string, equal to "no" (non-oriented), "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).

kaizen

Logical. If TRUE, the kaizen version of SBM (Tone 2010), also known as SBM-Max, is computed.

maxfr

A list with the maximal friends sets, as it is returned by function maximal_friends. If NULL (default) this list is computed internally.

tol

Numeric, a tolerance margin for checking efficiency (only for the kaizen version).

silent

Logical. If FALSE (default) it prints all the messages from function maximal_friends.

compute_target

Logical. If it is TRUE, it computes targets.

returnlp

Logical. If it is TRUE, it returns the linear problems (objective function and constraints). If kaizen is TRUE it is ignored.

...

Other options (currently not implemented)

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

Tone, K. (2001). "A Slacks-Based Measure of Efficiency in Data Envelopment Analysis", European Journal of Operational Research, 130, 498-509. tools:::Rd_expr_doi("10.1016/S0377-2217(99)00407-5")

Tone, K. (2010). "Variations on the theme of slacks-based measure of efficiency in DEA", European Journal of Operational Research, 200, 901-907. tools:::Rd_expr_doi("10.1016/j.ejor.2009.01.027")

Cooper, W.W.; Seiford, L.M.; Tone, K. (2007). Data Envelopment Analysis. A Comprehensive Text with Models, Applications, References and DEA-Solver Software. 2nd Edition. Springer, New York. tools:::Rd_expr_doi("10.1007/978-0-387-45283-8")

See Also

model_nonradial, model_deaps, model_profit, model_sbmsupereff

Examples

# Replication of results in Tone (2001, p.505)
data("Tone2001")
data_example <- read_data(Tone2001, 
                          ni = 2, 
                          no = 2)
result_SBM <- model_sbmeff(data_example, 
                           orientation = "no", 
                           rts = "crs")
result_CCR <- model_basic(data_example, 
                          orientation = "io", 
                          rts = "crs")
efficiencies(result_SBM)
efficiencies(result_CCR)
slacks(result_SBM)
slacks(result_CCR)
 
# Example. Replication of results in Tone (2003), pp 10-11 case 1:1.
data("Tone2003")
data_example <- read_data(Tone2003,
                          ni = 1,
                          no = 2,
                          ud_outputs = 2)
result <- model_sbmeff(data_example,
                       rts = "vrs")
efficiencies(result)
targets(result)