forest_weights_predicted_cpp

Computes forest out-of-sample honest weights for a <code>morf.forest</code> object relative to the m-th 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

forest_weights_predicted_cpp function

<dl><dt>leaf_IDs_test_list</dt>
<dd>List of size <code>n.trees</code>, storing the leaf of each tree where training units fall into.</dd>
<dt>leaf_IDs_honest_list</dt>
<dd>List of size <code>n.trees</code>, storing the leaf of each tree where honest units fall into.</dd>
<dt>leaf_size_honest_list</dt>
<dd>List of size <code>n.trees</code>, storing the size of the leaves of each tree computed with honest units.</dd>
<dt>w</dt>
<dd>1 if marginal effects are being computed, 0 otherwise for normal prediction.</dd></dl>

Arguments

Forest Out-of-Sample Honest Weights — forest_weights_predicted_cpp

<dl>

<dt>leaf_IDs_test_list</dt>
<dd>List of size <code>n.trees</code>, storing the leaf of each tree where training units fall into.</dd>


<dt>leaf_IDs_honest_list</dt>
<dd>List of size <code>n.trees</code>, storing the leaf of each tree where honest units fall into.</dd>


<dt>leaf_size_honest_list</dt>
<dd>List of size <code>n.trees</code>, storing the size of the leaves of each tree computed with honest units.</dd>


<dt>w</dt>
<dd>1 if marginal effects are being computed, 0 otherwise for normal prediction.</dd>

</dl>

forest_weights_predicted_cpp: Forest Out-of-Sample Honest Weights

Description

Usage

Arguments