predict_forest_preds_for_ME

Fast ORF Predictions for estimation of marginal effects at mean

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

predict_forest_preds_for_ME function

<dl><dt>forest</dt>
<dd>list of ranger forest objects</dd>
<dt>data</dt>
<dd>list of n x n matrices of indicator data for orf</dd>
<dt>pred_data</dt>
<dd>list of prediction data (X_mean_up/down)</dd></dl>

Arguments

ORF Predictions for Marginal Effects — predict_forest_preds_for_ME

<dl>

<dt>forest</dt>
<dd>list of ranger forest objects</dd>


<dt>data</dt>
<dd>list of n x n matrices of indicator data for orf</dd>


<dt>pred_data</dt>
<dd>list of prediction data (X_mean_up/down)</dd>

</dl>

predict_forest_preds_for_ME: ORF Predictions for Marginal Effects

Description

Usage

Value

Arguments