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An influential or leverage units is a one that produce a big changes in results. In this case is a unit that produce a big change in model load.
For more information about loads help of the package about adea
or see (Fernandez-Palacin, Lopez-Sanchez, and Munoz-Marquez 2018) and (Villanueva-Cantillo and Munoz-Marquez 2021).
Let’s load and have a look at the tokyo_libraries
dataset with
data(tokyo_libraries)
head(tokyo_libraries)
#> Area.I1 Books.I2 Staff.I3 Populations.I4 Regist.O1 Borrow.O2
#> 1 2.249 163.523 26 49.196 5.561 105.321
#> 2 4.617 338.671 30 78.599 18.106 314.682
#> 3 3.873 281.655 51 176.381 16.498 542.349
#> 4 5.541 400.993 78 189.397 30.810 847.872
#> 5 11.381 363.116 69 192.235 57.279 758.704
#> 6 10.086 541.658 114 194.091 66.137 1438.746
adea_load_leverage
function looks for units that produce higher change in loads. The following call
tokyo_libraries[, 1:4]
input <- tokyo_libraries[, 5:6]
output <-adea_load_leverage(input, output)
#> load load.diff DMUs
#> 1 0.6028718 0.14740482 23
#> 2 0.4004102 0.05505682 6
shows that units 23 and 6 produce changes greater than the default value for load.diff
which is 0.05.
The output is sorted in decreasing order of load.diff
which is the change in load model.
Those call only considers changes taking units one by one, but using ndel
parameter remove of more than one unit at the same time can be tested. The following call tests all groups of two units
adea_load_leverage(input, output, load.diff = 0.1, ndel = 2)
#> load load.diff DMUs
#> 1 0.8333337 0.3778667 9, 23
#> 2 0.6315800 0.1761130 20, 23
#> 3 0.6315800 0.1761130 15, 23
#> 4 0.6315800 0.1761130 4, 23
#> 5 0.6315800 0.1761130 12, 23
#> 6 0.6315800 0.1761130 14, 23
#> 7 0.6315800 0.1761130 16, 23
#> 8 0.6315800 0.1761130 18, 23
#> 9 0.6315800 0.1761130 22, 23
#> 10 0.6315800 0.1761130 11, 23
#> 11 0.6315800 0.1761130 10, 23
#> 12 0.6315800 0.1761130 3, 23
#> 13 0.6225027 0.1670357 2, 23
#> 14 0.6107273 0.1552603 7, 23
#> 15 0.6028718 0.1474048 23
#> 16 0.6020337 0.1465667 13, 23
#> 17 0.6010336 0.1455666 1, 23
#> 18 0.5980232 0.1425562 8, 23
#> 19 0.5879663 0.1324993 21, 23
#> 20 0.3334068 0.1220602 6, 9
#> 21 0.3430363 0.1124307 5, 6
#> 22 0.5599886 0.1045216 17, 23
This results in a very long list, so the number or groups in output can be limited, for example, to 10, as in the following call
adea_load_leverage(input, output, load.diff = 0.1, ndel = 2, nmax = 10)
#> load load.diff DMUs
#> 1 0.8333337 0.3778667 9, 23
#> 2 0.6315800 0.1761130 20, 23
#> 3 0.6315800 0.1761130 15, 23
#> 4 0.6315800 0.1761130 4, 23
#> 5 0.6315800 0.1761130 12, 23
#> 6 0.6315800 0.1761130 14, 23
#> 7 0.6315800 0.1761130 16, 23
#> 8 0.6315800 0.1761130 18, 23
#> 9 0.6315800 0.1761130 22, 23
#> 10 0.6315800 0.1761130 11, 23
This shows that the best option to remove two units is not the same as remove the two firsts in the one by one analysis. This is because that there are interactions between the units effects.
From this point, decision maker or researcher have to handle this units properly, to avoid biases in DEA results.
Each call to adea_load_leverage
requires to solve a big set of a large linear program, so is a very demanding of computation resource, and can require a very large time, so be patient.
Fernandez-Palacin, Fernando, Marı́a Auxiliadora Lopez-Sanchez, and Manuel Munoz-Marquez. 2018. “Stepwise selection of variables in DEA using contribution loads.” Pesquisa Operacional 38 (1): 31–52. http://dx.doi.org/10.1590/0101-7438.2018.038.01.0031.
Villanueva-Cantillo, Jeyms, and Manuel Munoz-Marquez. 2021. “Methodology for Calculating Critical Values of Relevance Measures in Variable Selection Methods in Data Envelopment Analysis.” European Journal of Operational Research 290 (2): 657–70. https://doi.org/10.1016/j.ejor.2020.08.021.
Universidad de Cádiz, fernando.fernandez@uca.es↩︎
Universidad de Cádiz, manuel.munoz@uca.es↩︎
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.