predict.MARSAdapted

This function predicts the expected output by a <code>MARS</code>
object.

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

predict.MARSAdapted function

<dl><dt>object</dt>
<dd>A <code>MARSAdapted</code> object.</dd>
<dt>newdata</dt>
<dd><code>data.frame</code>. Set of input variables to predict on.</dd>
<dt>x</dt>
<dd>Inputs index.</dd>
<dt>class</dt>
<dd>Model for prediction. <code>1</code> MARS Boost without smoothing
procedure.<code>2</code> MARS Boost with smoothing procedure..</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Model Prediction for Adapted Multivariate Adaptive Frontier Splines. — predict.MARSAdapted

<dl>

<dt>object</dt>
<dd>A <code>MARSAdapted</code> object.</dd>


<dt>newdata</dt>
<dd><code>data.frame</code>. Set of input variables to predict on.</dd>


<dt>x</dt>
<dd>Inputs index.</dd>


<dt>class</dt>
<dd>Model for prediction. <code>1</code> MARS Boost without smoothing
procedure.<code>2</code> MARS Boost with smoothing procedure..</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

predict.MARSAdapted: Model Prediction for Adapted Multivariate Adaptive Frontier Splines.

Description

Usage

Value

Arguments