mean_squared_error

<code>mean_squared_error()</code> estimates the mean squared error for the desired
loss unit.

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

mean_squared_error function

<dl><dt>pred</dt>
<dd>Predictions of outcome. A numeric vector of outcome predictions.</dd>
<dt>data.valid</dt>
<dd>Test data set. A tibble of data that was not used for
prediction.</dd>
<dt>y</dt>
<dd>Outcome variable. A character vector containing the column names of
the outcome variable.</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></dl>

Arguments

Estimates the mean squared prediction error. — mean_squared_error

<dl>

<dt>pred</dt>
<dd>Predictions of outcome. A numeric vector of outcome predictions.</dd>


<dt>data.valid</dt>
<dd>Test data set. A tibble of data that was not used for
prediction.</dd>


<dt>y</dt>
<dd>Outcome variable. A character vector containing the column names of
the outcome variable.</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>

</dl>

mean_squared_error: Estimates the mean squared prediction error.

Description

Usage

Value

Arguments