Functions are provided to determine production frontiers and technical
efficiency measures through non-parametric techniques based upon regression trees.
The package includes code for estimating radial input, output, directional and
additive measures, plotting graphical representations of the scores and the production
frontiers by means of trees, and determining rankings of importance of input variables
in the analysis. Additionally, an adaptation of Random Forest by a set of individual
Efficiency Analysis Trees for estimating technical efficiency is also included. More
details in: <doi:10.1016/j.eswa.2020.113783>.

Miriam Esteve

Efficiency Analysis Trees

Víctor España

Juan Aparicio

Xavier Barber

RCV function

<dl><dt>N</dt>
<dd>Number of rows in data.</dd>
<dt>Lv</dt>
<dd>Test set.</dd>
<dt>y</dt>
<dd>Column output indexes in data.</dd>
<dt>alphaIprim</dt>
<dd>Alpha obtained as the square root of the product of two consecutive alpha values in tree_alpha list. It is used to find the best pruning tree.</dd>
<dt>fold</dt>
<dd>Parts in which the original data is divided into to perform Cross-Validation.</dd>
<dt>TAiv</dt>
<dd>List with each possible pruning for the deep tree generated with the train set and its associated alpha values.</dd></dl>

Arguments

RCV — RCV

<dl>

<dt>N</dt>
<dd>Number of rows in data.</dd>


<dt>Lv</dt>
<dd>Test set.</dd>


<dt>y</dt>
<dd>Column output indexes in data.</dd>


<dt>alphaIprim</dt>
<dd>Alpha obtained as the square root of the product of two consecutive alpha values in tree_alpha list. It is used to find the best pruning tree.</dd>


<dt>fold</dt>
<dd>Parts in which the original data is divided into to perform Cross-Validation.</dd>


<dt>TAiv</dt>
<dd>List with each possible pruning for the deep tree generated with the train set and its associated alpha values.</dd>

</dl>

RCV: RCV

Description

Usage

Value

Arguments