popmis

Hox pupil popularity data with some missing popularity scores

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

Lisa Doove

Shahab Jolani

Margarita Moreno-Betancur

Ian White

Philipp Gaffert

Florian Meinfelder

Bernie Gray

popmis function

A data frame with 2000 rows and 7 columns:<dl class="dl-horizontal">
<dt>pupil</dt><dd>Pupil number within school</dd> 
<dt>school</dt><dd>School number</dd> 
<dt>popular</dt><dd>Pupil popularity with 848 missing entries</dd>
<dt>sex</dt><dd>Pupil gender</dd> 
<dt>texp</dt><dd>Teacher experience (years)</dd> 
<dt>const</dt><dd>Constant intercept term</dd>
<dt>teachpop</dt><dd>Teacher popularity</dd> </dl>

Format

Hox pupil popularity data with missing popularity scores — popmis

A data frame with 2000 rows and 7 columns:<dl class='dl-horizontal'>
<dt>pupil</dt><dd>Pupil number within school</dd> 
<dt>school</dt><dd>School number</dd> 
<dt>popular</dt><dd>Pupil popularity with 848 missing entries</dd>
<dt>sex</dt><dd>Pupil gender</dd> 
<dt>texp</dt><dd>Teacher experience (years)</dd> 
<dt>const</dt><dd>Constant intercept term</dd>
<dt>teachpop</dt><dd>Teacher popularity</dd> </dl>

Hox pupil popularity data with missing popularity scores

popmis: Hox pupil popularity data with missing popularity scores

Description

Arguments

Format

Details

Examples