<p>Example dataframe for structrual zeros based on the NYMockexample dataset. It contains 8 structural zero cases with 10 variables.</p><table class="table">
<tr><td>[,1]</td><td>AGE = 15 and EDUC = 8</td></tr>
<tr><td>[,2]</td><td>AGE = 16 and VESTAT = 2</td></tr>
<tr><td>[,3]</td><td>OWNERSHIP = 0 and MORTGAGE = 4</td></tr>
<tr><td>[,4]</td><td>AGE = 17 and EDUC = 11</td></tr>
<tr><td>[,5]</td><td>AGE = [36, 50] and EMPSTAT = 0</td></tr>
<tr><td>[,6]</td><td>AGE &gt; 70 and DISABWRK = 0</td></tr>
<tr><td>[,7]</td><td>AGE &lt; 15 and EDUC = 10</td></tr>
<tr><td>[,8]</td><td>OWNERSHIP = 2 and MORTGAGE = 1</td></tr>
</table><p></p><p></p>

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

Jingchen Hu

NPBayesImputeCat

Non-Parametric Bayesian Multiple Imputation for Categorical Data

MCZ function

<p>Example dataframe for structrual zeros based on the NYMockexample dataset. It contains 8 structural zero cases with 10 variables.</p><table class='table'>
<tr><td>[,1]</td><td>AGE = 15 and EDUC = 8</td></tr>
<tr><td>[,2]</td><td>AGE = 16 and VESTAT = 2</td></tr>
<tr><td>[,3]</td><td>OWNERSHIP = 0 and MORTGAGE = 4</td></tr>
<tr><td>[,4]</td><td>AGE = 17 and EDUC = 11</td></tr>
<tr><td>[,5]</td><td>AGE = [36, 50] and EMPSTAT = 0</td></tr>
<tr><td>[,6]</td><td>AGE &gt; 70 and DISABWRK = 0</td></tr>
<tr><td>[,7]</td><td>AGE &lt; 15 and EDUC = 10</td></tr>
<tr><td>[,8]</td><td>OWNERSHIP = 2 and MORTGAGE = 1</td></tr>
</table>



MCZ: Example dataframe for structrual zeros based on the NYMockexample dataset.

Description

Arguments

[,1]	AGE = 15 and EDUC = 8
[,2]	AGE = 16 and VESTAT = 2
[,3]	OWNERSHIP = 0 and MORTGAGE = 4
[,4]	AGE = 17 and EDUC = 11
[,5]	AGE = [36, 50] and EMPSTAT = 0
[,6]	AGE > 70 and DISABWRK = 0
[,7]	AGE < 15 and EDUC = 10
[,8]	OWNERSHIP = 2 and MORTGAGE = 1