predict_honest

predict honest prediction for out of sample data
based on the honest sample

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_honest function

<dl><dt>forest</dt>
<dd>estimated forest object of type ranger</dd>
<dt>honest_data</dt>
<dd>honest dataframe</dd>
<dt>test_data</dt>
<dd>test dataframe</dd></dl>

Arguments

Predict Honest Predictions — predict_honest

<dl>

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


<dt>honest_data</dt>
<dd>honest dataframe</dd>


<dt>test_data</dt>
<dd>test dataframe</dd>

</dl>

predict_honest: Predict Honest Predictions

Description

Usage

Value

Arguments