get_honest_C

Computes honest predictions (fitted values) from the random forest
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_honest_C function

<dl><dt>x</dt>
<dd>unique_leaves (List)[ntree]</dd>
<dt>y</dt>
<dd>honest_y (NumericVector)[nrow]</dd>
<dt>z</dt>
<dd>honest_leaves (NumericMatrix)[nrow, ntree]</dd>
<dt>w</dt>
<dd>train_leaves (NumericMatrix)[nrow, ntree]</dd></dl>

Arguments

Get honest predictions (C++) — get_honest_C

<dl>

<dt>x</dt>
<dd>unique_leaves (List)[ntree]</dd>


<dt>y</dt>
<dd>honest_y (NumericVector)[nrow]</dd>


<dt>z</dt>
<dd>honest_leaves (NumericMatrix)[nrow, ntree]</dd>


<dt>w</dt>
<dd>train_leaves (NumericMatrix)[nrow, ntree]</dd>

</dl>

get_honest_C: Get honest predictions (C++)

Description

Usage

Arguments