This function computes the mean squared error between two
numeric vectors.

Includes functions to estimate production frontiers
and make ideal output predictions in the Data Envelopment Analysis (DEA)
context using both standard models from DEA and Free Disposal Hull (FDH)
and boosting techniques. In particular, EATBoosting (Guillen et al., 2023
<doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package
includes code for estimating several technical efficiency measures using
different models such as the input and output-oriented radial measures, the
input and output-oriented Russell measures, the Directional Distance
Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based
Measure (SBM).

Maria D. Guillen

boostingDEA

A Boosting Approach to Data Envelopment Analysis

Juan Aparicio

Víctor España

mse function

<dl><dt>y</dt>
<dd>Vector of actual data.</dd>
<dt>yPred</dt>
<dd>Vector of predicted values.</dd></dl>

Arguments

Mean Squared Error — mse

<dl>

<dt>y</dt>
<dd>Vector of actual data.</dd>


<dt>yPred</dt>
<dd>Vector of predicted values.</dd>

</dl>

mse: Mean Squared Error

Description

Usage

Value

Arguments