get_orf_variance

get variance of ordered random forest predictions based on honest sample
splitting as described in Lechner (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

get_orf_variance function

<dl><dt>honest_pred</dt>
<dd>list of vectors of honest forest predictions</dd>
<dt>honest_weights</dt>
<dd>list of n x n matrices of honest forest weights</dd>
<dt>train_pred</dt>
<dd>list of vectors of honest forests predictions from train sample</dd>
<dt>train_weights</dt>
<dd>list of vectors of honest forests predictions from train sample</dd>
<dt>Y_ind_honest</dt>
<dd>list of vectors of 0-1 outcomes for the honest sample</dd></dl>

Arguments

Get ORF Variance — get_orf_variance

<dl>

<dt>honest_pred</dt>
<dd>list of vectors of honest forest predictions</dd>


<dt>honest_weights</dt>
<dd>list of n x n matrices of honest forest weights</dd>


<dt>train_pred</dt>
<dd>list of vectors of honest forests predictions from train sample</dd>


<dt>train_weights</dt>
<dd>list of vectors of honest forests predictions from train sample</dd>


<dt>Y_ind_honest</dt>
<dd>list of vectors of 0-1 outcomes for the honest sample</dd>

</dl>

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get_orf_variance: Get ORF Variance

Description

Usage

Value

Arguments