honest_fitted_cpp

Computes honest in-sample predictions for a morf.forest object relative to the desired class.

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_cpp function

<dl><dt>unique_leaves_honest</dt>
<dd>List of size <code>n.trees</code>, storing the unique leaf ids of each tree relative to the honest sample.</dd>
<dt>y_m</dt>
<dd>Indicator variable, equal to 1 if the <code>y</code> is lower or equal than the m-th class and zero otherwise.</dd>
<dt>y_m_1</dt>
<dd>Indicator variable, equal to 1 if the <code>y</code> is lower or equal than the (m-1)-th class and zero otherwise.</dd>
<dt>honest_leaves</dt>
<dd>Matrix of size (<code>n.samples</code> x <code>n.trees</code>). The i-th row stores the id of the leaf where the i-th honest observation falls in each tree.</dd>
<dt>train_leaves</dt>
<dd>Matrix of size (<code>n.samples</code> x <code>n.trees</code>). The i-th row stores the id of the leaf where the i-th training observation falls in each tree.</dd></dl>

Arguments

Honest In-Sample Predictions — honest_fitted_cpp

<dl>

<dt>unique_leaves_honest</dt>
<dd>List of size <code>n.trees</code>, storing the unique leaf ids of each tree relative to the honest sample.</dd>


<dt>y_m</dt>
<dd>Indicator variable, equal to 1 if the <code>y</code> is lower or equal than the m-th class and zero otherwise.</dd>


<dt>y_m_1</dt>
<dd>Indicator variable, equal to 1 if the <code>y</code> is lower or equal than the (m-1)-th class and zero otherwise.</dd>


<dt>honest_leaves</dt>
<dd>Matrix of size (<code>n.samples</code> x <code>n.trees</code>). The i-th row stores the id of the leaf where the i-th honest observation falls in each tree.</dd>


<dt>train_leaves</dt>
<dd>Matrix of size (<code>n.samples</code> x <code>n.trees</code>). The i-th row stores the id of the leaf where the i-th training observation falls in each tree.</dd>

</dl>

honest_fitted_cpp: Honest In-Sample Predictions

Description

Usage

Arguments