deaR (version 1.0)

model_sbmsupereff: Slack based measure of superefficiency model

Description

Slack based measure of superefficiency model (Tone 2002) with n DMUs, m inputs, s outputs...

Usage

model_sbmsupereff(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,
            compute_target = TRUE,
            compute_rho = FALSE,
            thr = 1e-6,
            returnlp = FALSE)

Arguments

datadea

The data, including DMUs, inputs and outputs.

dmu_eval

A numeric vector containing which DMUs have to be evaluated.

dmu_ref

A numeric vector containing which DMUs are the evaluation reference set.

weight_input

A value, vector of length m, or matrix m x ne (where ne is the lenght 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 lenght 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).

compute_target

Logical. If it is TRUE, it computes targets, superslacks (t_input and t_output) and slacks.

compute_rho

Logical. If it is TRUE, it computes rho applying model_sbmeff to the DMU (project_input, project_output) if the slacks are not numerically zero (i.e. this DMU is inefficient).

thr

A value. Threshold for numerical zero.

returnlp

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

References

Tone, K. (2002). "A slacks-based measure of super-efficiency in data envelopment analysis", European Journal of Operational Research, 143, 32-41. https://doi.org/10.1016/S0377-2217(01)00324-1

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. https://doi.org/10.1007/978-0-387-45283-8

See Also

model_sbmeff, model_supereff, model_addsupereff

Examples

# NOT RUN {
# Replication of results in Tone(2002, p.39)
data("Power_plants")
data_example <- read_data(Power_plants, ni = 4, no = 2)
result <- model_sbmsupereff(data_example, orientation = "io", rts = "crs") 
efficiencies(result)
slacks(result)$slack_input
references(result)

# }