pred_honest_C

Computes honest predictions from the random forest for a test 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

pred_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>test_leaves (NumericMatrix)[nrow, ntree]</dd></dl>

Arguments

Predict honest predictions (C++) — pred_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>test_leaves (NumericMatrix)[nrow, ntree]</dd>

</dl>

pred_honest_C: Predict honest predictions (C++)

Description

Usage

Arguments