predict.ocf.forest

Prediction method for class <code>ocf.forest</code>.

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

predict.ocf.forest function

<dl><dt>object</dt>
<dd>An <code>ocf.forest</code> object.</dd>
<dt>data</dt>
<dd>Data set of class <code>data.frame</code>. It must contain at least the same covariates used to train the forests.</dd>
<dt>type</dt>
<dd>Type of prediction. Either <code>"response"</code> or <code>"terminalNodes"</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Author

Prediction Method for ocf.forest Objects — predict.ocf.forest

<dl>

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


<dt>data</dt>
<dd>Data set of class <code>data.frame</code>. It must contain at least the same covariates used to train the forests.</dd>


<dt>type</dt>
<dd>Type of prediction. Either <code>"response"</code> or <code>"terminalNodes"</code>.</dd>


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

</dl>

predict.ocf.forest: Prediction Method for ocf.forest Objects

Description

Usage

Value

Arguments

Author

Details

References

See Also