adea_load_leverage {adea}

Search for leverage units (DMU's) with a greater impact on load levels in DEA analysis

Description

Search for leverage units (DMU's) with a greater impact on load levels in DEA analysis.

Usage

adea_load_leverage(
  input,
  output,
  orientation = c("input", "output"),
  load.orientation = c("inoutput", "input", "output"),
  load.diff = 0.05,
  ndel = 1,
  nmax = 0,
  solver = "auto"
)

Arguments

input

A matrix or a data frame containing the inputs of the units to be evaluated, with one row for each DMU and one column for each input.

output

A matrix or a data frame containing the outputs of the units to be evaluated, with one row for each DMU and one column for each output.

orientation

Use "input" for input orientation or "output" for output orientation in DEA model. The default is "input".

load.orientation

This parameter allows the selection of variables to be included in load analysis. The default is "inoutput" which means that all input and output variables will be included. Use "input" or "output" to include only input or output variables in load analysis.

load.diff

Minimum difference in load to consider a subset of DMUs as a leverage one

ndel

Maximum number of units to drop out in each try.

nmax

Maximum number of DMU sets to include in results. 0 for no limit.

solver

The solver used by ROI to solve the DEA optimization problem. The default is "auto." The solver must be installed and capable of solving linear programming problems. Use ROI_installed_solvers() to list them.

Details

A leverage unit is a DMU that significantly alters the results of the current procedure, in this case, a DMU that produce a large change in variable loads.

Value

The function returns a list with the following named members:

• loads: Load of each model after removing the corresponding DMUs

• loads.diff: For each model the difference between its load and the original one

• dmu.indexs: Index of DMUs removed in each model

Note

This function has to solve a large number of large linear programs that grows with DMUs. So computation time required may be very large, be patient.

Examples

data('cardealers4')
input <- cardealers4[, c('Employees', 'Depreciation')]
output <- cardealers4[, c('CarsSold', 'WorkOrders')]
adea_load_leverage(input, output, ndel = 2)
#         load  load.diff DMUs
# 1  1.0000000 0.33333333 1, 6
# 2  1.0000000 0.33333333 3, 4
# 3  1.0000000 0.33333333 2, 3
# 4  1.0000000 0.33333333 2, 5
# 5  1.0000000 0.33333333 4, 6
# 6  1.0000000 0.33333333    2
# 7  1.0000000 0.33333333 1, 4
# 8  1.0000000 0.33333333 2, 6
# 9  1.0000000 0.33333333 1, 2
# 10 0.9635628 0.29689609 2, 4
# 11 0.8743243 0.20765766 5, 6
# 12 0.8479940 0.18132736 1, 3
# 13 0.8420551 0.17538843 3, 6
# 14 0.8243243 0.15765766 1, 5
# 15 0.8000000 0.13333333    6
# 16 0.8000000 0.13333333    4
# 17 0.8000000 0.13333333    1
# 18 0.8000000 0.13333333    3
# 19 0.7461771 0.07951041 3, 5
# 20 0.7358231 0.06915643    5