ss16pusa_sample_nozeros

<p>Example dataframe for input categorical data without structural zeros (without missing values). It contains 1000 observations and 3 variables.</p><table><tr><td>
    [,1] </td>
<td> MAR </td>
<td> marital status </td>
<td> 5 levels </td>
</tr><tr><td>
    [,2] </td>
<td> SEX    </td>
<td> sex </td>
<td> 2 levels </td>
</tr><tr><td>
    [,3] </td>
<td> WKL   </td>
<td> When last worked </td>
<td> 3 levels </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_sample_nozeros: Example dataframe for input categorical data without structural zeros (without missing values).

Description

Arguments

[,1]	MAR	marital status	5 levels
[,2]	SEX	sex	2 levels
[,3]	WKL	When last worked	3 levels