treesForRCV

This function generates a deep EAT and all pruning for each train set.

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

treesForRCV function

<dl><dt>notLv</dt>
<dd>Train set.</dd>
<dt>x</dt>
<dd>Column input indexes in data.</dd>
<dt>y</dt>
<dd>Column output indexes in data.</dd>
<dt>fold</dt>
<dd>Parts in which the original set is divided to perform Cross-Validation.</dd>
<dt>numStop</dt>
<dd>Minimum number of observations in a node to be split.</dd></dl>

Arguments

Trees for RCV — treesForRCV

<dl>

<dt>notLv</dt>
<dd>Train set.</dd>


<dt>x</dt>
<dd>Column input indexes in data.</dd>


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


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


<dt>numStop</dt>
<dd>Minimum number of observations in a node to be split.</dd>

</dl>

treesForRCV: Trees for RCV

Description

Usage

Value

Arguments