forest_weights_fitted

Computes forest in-sample honest weights 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

forest_weights_fitted function

<dl><dt>forest</dt>
<dd>An <code>ocf.forest</code> object.</dd>
<dt>honest_sample</dt>
<dd>Honest sample.</dd>
<dt>train_sample</dt>
<dd>Training sample.</dd></dl>

Arguments

Forest In-Sample Honest Weights — forest_weights_fitted

<dl>

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


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


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

</dl>

forest_weights_fitted: Forest In-Sample Honest Weights

Description

Usage

Value

Arguments

Details