Computes the mean ranked probability score (RPS) for evaluating the accuracy of
ordered probability predictions

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

rps function

<dl><dt>predictions</dt>
<dd>matrix of predictions (n x categories)</dd>
<dt>observed</dt>
<dd>vector of observed ordered categorical outcomes (n x 1)</dd></dl>

Arguments

Ranked Probability Score — rps

<dl>

<dt>predictions</dt>
<dd>matrix of predictions (n x categories)</dd>


<dt>observed</dt>
<dd>vector of observed ordered categorical outcomes (n x 1)</dd>

</dl>

rps: Ranked Probability Score

Description

Usage

Value

Arguments