brandsma

Dataset with raw data from Snijders and Bosker (2012) containing
data from 4106 pupils attending 216 schools. This dataset
includes all pupils and schools with missing data.

datasets

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
its own imputation model. Built-in imputation models are provided for
continuous data (predictive mean matching, normal), binary data (logistic
regression), unordered categorical data (polytomous logistic regression)
and ordered categorical data (proportional odds). MICE can also impute
continuous two-level data (normal model, pan, second-level variables).
Passive imputation can be used to maintain consistency between variables.
Various diagnostic plots are available to inspect the quality of the
imputations.

Stef van Buuren

mice

Multivariate Imputation by Chained Equations

Karin Groothuis-Oudshoorn

Gerko Vink

Rianne Schouten

Alexander Robitzsch

Patrick Rockenschaub

Lisa Doove

Shahab Jolani

Margarita Moreno-Betancur

Ian White

Philipp Gaffert

Florian Meinfelder

Bernie Gray

Vincent Arel-Bundock

Mingyang Cai

Thom Volker

Edoardo Costantini

Caspar van Lissa

Hanne Oberman

brandsma function

<code>brandsma</code> is a data frame with 4106 rows and 14 columns:<dl>
<dt><code>sch</code></dt>
<dd>School number</dd><dt><code>pup</code></dt>
<dd>Pupil ID</dd><dt><code>iqv</code></dt>
<dd>IQ verbal</dd><dt><code>iqp</code></dt>
<dd>IQ performal</dd><dt><code>sex</code></dt>
<dd>Sex of pupil</dd><dt><code>ses</code></dt>
<dd>SES score of pupil</dd><dt><code>min</code></dt>
<dd>Minority member 0/1</dd><dt><code>rpg</code></dt>
<dd>Number of repeated groups, 0, 1, 2</dd><dt><code>lpr</code></dt>
<dd>language score PRE</dd><dt><code>lpo</code></dt>
<dd>language score POST</dd><dt><code>apr</code></dt>
<dd>Arithmetic score PRE</dd><dt><code>apo</code></dt>
<dd>Arithmetic score POST</dd><dt><code>den</code></dt>
<dd>Denomination classification 1-4 - at school level</dd><dt><code>ssi</code></dt>
<dd>School SES indicator - at school level</dd>
</dl>

Format

Brandsma school data used Snijders and Bosker (2012) — brandsma

<code>brandsma</code> is a data frame with 4106 rows and 14 columns:<dl>
<dt><code>sch</code></dt>
<dd>School number</dd>

<dt><code>pup</code></dt>
<dd>Pupil ID</dd>

<dt><code>iqv</code></dt>
<dd>IQ verbal</dd>

<dt><code>iqp</code></dt>
<dd>IQ performal</dd>

<dt><code>sex</code></dt>
<dd>Sex of pupil</dd>

<dt><code>ses</code></dt>
<dd>SES score of pupil</dd>

<dt><code>min</code></dt>
<dd>Minority member 0/1</dd>

<dt><code>rpg</code></dt>
<dd>Number of repeated groups, 0, 1, 2</dd>

<dt><code>lpr</code></dt>
<dd>language score PRE</dd>

<dt><code>lpo</code></dt>
<dd>language score POST</dd>

<dt><code>apr</code></dt>
<dd>Arithmetic score PRE</dd>

<dt><code>apo</code></dt>
<dd>Arithmetic score POST</dd>

<dt><code>den</code></dt>
<dd>Denomination classification 1-4 - at school level</dd>

<dt><code>ssi</code></dt>
<dd>School SES indicator - at school level</dd>


</dl>

Brandsma school data used Snijders and Bosker (2012)

brandsma: Brandsma school data used Snijders and Bosker (2012)

Description

Arguments

Format

References