honest_split

Creates honest sample split by randomly selecting prespecified share of observations
to belong to honest sample and to training 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

honest_split function

<dl><dt>data</dt>
<dd>dataframe or matrix of features and outcomes to be split honestly</dd>
<dt>honesty.fraction</dt>
<dd>share of sample to belong to honesty sample</dd>
<dt>orf</dt>
<dd>logical, if honest split should be done for orf or not</dd></dl>

Arguments

honest sample split — honest_split

<dl>

<dt>data</dt>
<dd>dataframe or matrix of features and outcomes to be split honestly</dd>


<dt>honesty.fraction</dt>
<dd>share of sample to belong to honesty sample</dd>


<dt>orf</dt>
<dd>logical, if honest split should be done for orf or not</dd>

</dl>

honest_split: honest sample split

Description

Usage

Value

Arguments