popmis


Hox pupil popularity data with some missing popularity scores


datasets

Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. 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

popmis function

A data frame with 2000 rows and 7 columns:
<dl>
<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, J. J. (2002) <em>Multilevel analysis. Techniques and
applications.</em> Mahwah, NJ: Lawrence Erlbaum.

popmis: Hox pupil popularity data with missing popularity scores

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