RandomEAT

This function builds an individual tree for Random Forest

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

RandomEAT function

<dl><dt>data</dt>
<dd><code>data.frame</code> containing the training set.</dd>
<dt>x</dt>
<dd>Vector. Column input indexes in data.</dd>
<dt>y</dt>
<dd>Vector. Column output indexes in data.</dd>
<dt>numStop</dt>
<dd>Minimum number of observations in a node for a split to be attempted.</dd>
<dt>s_mtry</dt>
<dd>Number of variables randomly sampled as candidates at each split. The available options are: <code>"BRM"</code>, <code>"DEA1"</code>, <code>"DEA2"</code>, <code>"DEA3"</code>, <code>"DEA4"</code> or any integer.</dd></dl>

Arguments

Individual EAT for Random Forest — RandomEAT

<dl>

<dt>data</dt>
<dd><code>data.frame</code> containing the training set.</dd>


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


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


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


<dt>s_mtry</dt>
<dd>Number of variables randomly sampled as candidates at each split. The available options are: <code>"BRM"</code>, <code>"DEA1"</code>, <code>"DEA2"</code>, <code>"DEA3"</code>, <code>"DEA4"</code> or any integer.</dd>

</dl>

RandomEAT: Individual EAT for Random Forest

Description

Usage

Value

Arguments