honest_fitted

Computes honest in-sample predictions for an <code>ocf.forest</code> object.

internal

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the
conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal
and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025)
<doi:10.1080/07474938.2024.2429596>.

Riccardo Di Francesco

Ordered Correlation Forest

honest_fitted function

<dl><dt>forest</dt>
<dd>An <code>ocf.forest</code> object.</dd>
<dt>train_sample</dt>
<dd>Training sample.</dd>
<dt>honest_sample</dt>
<dd>Honest sample.</dd>
<dt>y_m_honest</dt>
<dd>Indicator variable, whether the outcome is smaller than or equal to the m-th class.</dd>
<dt>y_m_1_honest</dt>
<dd>Indicator variable, whether the outcome is smaller than or equal to the (m-1)-th class.</dd></dl>

Arguments

Honest In-Sample Predictions — honest_fitted

<dl>

<dt>forest</dt>
<dd>An <code>ocf.forest</code> object.</dd>


<dt>train_sample</dt>
<dd>Training sample.</dd>


<dt>honest_sample</dt>
<dd>Honest sample.</dd>


<dt>y_m_honest</dt>
<dd>Indicator variable, whether the outcome is smaller than or equal to the m-th class.</dd>


<dt>y_m_1_honest</dt>
<dd>Indicator variable, whether the outcome is smaller than or equal to the (m-1)-th class.</dd>

</dl>

honest_fitted: Honest In-Sample Predictions

Description

Usage

Value

Arguments

Details