predict_forest_weights

predict forest weights, i.e. out-of-sample weights based on honest or train
sample produced by the random forest algorithm as defined in Wager &amp; Athey (2018)

internal

An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered categorical
outcomes (so-called ordered choice models). Additionally to common machine
learning algorithms the 'orf' package provides functions for estimating
marginal effects as well as statistical inference thereof and thus provides
similar output as in standard econometric models for ordered choice. The
core forest algorithm relies on the fast C++ forest implementation from
the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.

Gabriel Okasa

Ordered Random Forests

predict_forest_weights function

<dl><dt>forest</dt>
<dd>estimated forest object of type ranger</dd>
<dt>data</dt>
<dd>train (honest) dataframe</dd>
<dt>pred_data</dt>
<dd>prediction dataframe</dd></dl>

Arguments

Predict Forest Weights — predict_forest_weights

<dl>

<dt>forest</dt>
<dd>estimated forest object of type ranger</dd>


<dt>data</dt>
<dd>train (honest) dataframe</dd>


<dt>pred_data</dt>
<dd>prediction dataframe</dd>

</dl>

predict_forest_weights: Predict Forest Weights

Description

Usage

Value

Arguments