ebma_folding

#' <code>ebma_folding()</code> generates a data fold that will not be used in
classifier tuning. It is data that is needed to determine the optimal
tolerance for EBMA.

A tool that improves the prediction performance of multilevel
regression with post-stratification (MrP) by combining a number of machine
learning methods. For information on the method, please refer to Broniecki,
Wüest, Leemann (2020) ''Improving Multilevel Regression with
Post-Stratification Through Machine Learning (autoMrP)'' in the
'Journal of Politics'. Final pre-print version:
<https://lucasleemann.files.wordpress.com/2020/07/automrp-r2pa.pdf>.

Philipp Broniecki

autoMrP

Improving MrP with Ensemble Learning

Reto Wüest

Lucas Leemann

Florian Schaffner

Hadley Wickham

ebma_folding function

<dl><dt>data</dt>
<dd>The full survey data. A tibble.</dd>
<dt>L2.unit</dt>
<dd>Geographic unit. A character scalar containing the column name
of the geographic unit in <code>survey</code> and <code>census</code> at which outcomes
should be aggregated.</dd>
<dt>ebma.size</dt>
<dd>EBMA fold size. A number in the open unit interval
indicating the proportion of respondents to be allocated to the EBMA fold.
Default is \(1/3\).</dd></dl>

Arguments

Generates data fold to be used for EBMA tuning — ebma_folding

<dl>

<dt>data</dt>
<dd>The full survey data. A tibble.</dd>


<dt>L2.unit</dt>
<dd>Geographic unit. A character scalar containing the column name
of the geographic unit in <code>survey</code> and <code>census</code> at which outcomes
should be aggregated.</dd>


<dt>ebma.size</dt>
<dd>EBMA fold size. A number in the open unit interval
indicating the proportion of respondents to be allocated to the EBMA fold.
Default is \(1/3\).</dd>

</dl>

ebma_folding: Generates data fold to be used for EBMA tuning

Description

Usage

Value

Arguments