get_weights_C

Computes honest weights from the random forest as in Wager &amp; Athey (2019)
for the train and honest sample based on the honest 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

get_weights_C function

<dl><dt>x</dt>
<dd>leaf_IDs_train - list of leaf IDs in train data</dd>
<dt>y</dt>
<dd>leaf_IDs - list of leaf IDs in honest data</dd>
<dt>z</dt>
<dd>leaf_size - list of leaf sizes in honest data</dd></dl>

Arguments

Get honest weights (C++) — get_weights_C

<dl>

<dt>x</dt>
<dd>leaf_IDs_train - list of leaf IDs in train data</dd>


<dt>y</dt>
<dd>leaf_IDs - list of leaf IDs in honest data</dd>


<dt>z</dt>
<dd>leaf_size - list of leaf sizes in honest data</dd>

</dl>

get_weights_C: Get honest weights (C++)

Description

Usage

Arguments