ss16pusa_ds_MCZ

<p>Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset. It contains 8 structural zero cases with 5 variables.</p><table><tr><td>
    [,1] </td>
<td> AGEP = 16 and SCHL = Bachelor's degree</td>
</tr><tr><td>
    [,2] </td>
<td> AGEP = 16 and SCHL = Doctorate degree</td>
</tr><tr><td>
    [,3] </td>
<td> AGEP = 16 and SCHL = Master's degree</td>
</tr><tr><td>
    [,4] </td>
<td> AGEP = 16 and SCHL = Professional degree </td>
</tr><tr><td>
    [,5] </td>
<td> AGEP = 17 and SCHL = Bachelor's degree</td>
</tr><tr><td>
    [,6] </td>
<td> AGEP = 17 and SCHL = Doctorate degree</td>
</tr><tr><td>
    [,7] </td>
<td> AGEP = 17 and SCHL = Master's degree </td>
</tr><tr><td>
    [,8] </td>
<td> AGEP = 17 and SCHL = Professional degree </td>
</tr></table>

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

ss16pusa_ds_MCZ: Example dataframe for structrual zeros based on the ss16pusa_sample_zeros dataset.

Description

Arguments

[,1]	AGEP = 16 and SCHL = Bachelor's degree
[,2]	AGEP = 16 and SCHL = Doctorate degree
[,3]	AGEP = 16 and SCHL = Master's degree
[,4]	AGEP = 16 and SCHL = Professional degree
[,5]	AGEP = 17 and SCHL = Bachelor's degree
[,6]	AGEP = 17 and SCHL = Doctorate degree
[,7]	AGEP = 17 and SCHL = Master's degree
[,8]	AGEP = 17 and SCHL = Professional degree