get_honest

get honest prediction, i.e. fitted values for in sample data
(train and honest sample) 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

get_honest function

<dl><dt>forest</dt>
<dd>estimated forest object of type ranger</dd>
<dt>honest_data</dt>
<dd>honest dataframe</dd>
<dt>train_data</dt>
<dd>train dataframe</dd></dl>

Arguments

Get Honest Predictions — get_honest

<dl>

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


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


<dt>train_data</dt>
<dd>train dataframe</dd>

</dl>

get_honest: Get Honest Predictions

Description

Usage

Value

Arguments