honest_fitted

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

internal

Nonparametric estimator of the ordered choice model using random forests. The estimator modifies a
standard random forest splitting criterion to build a collection of forests, each estimating the conditional
probability of a single class. The package also implements a nonparametric estimator of the covariates’ marginal effects.

Riccardo Di Francesco

morf

Modified Ordered Random Forest

honest_fitted function

<dl><dt>forest</dt>
<dd>An <code>morf.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>morf.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