knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6, fig.height = 4 ) library('adea')
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 [@Fernandez2018] and [@Villanueva2021].
Let's load and have a look at the
tokyo_libraries dataset with
adea_load_leverage function looks for units that produce higher change in loads.
The following call
input <- tokyo_libraries[, 1:4] output <- tokyo_libraries[, 5:6] adea_load_leverage(input, output)
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)
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)
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.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.