ss16pusa_sample_zeros_miss

<p>Example dataframe for input categorical data with structural zeros (with missing values). It contains 1000 observations and 5 variables.</p><table class="table">
<tr><td>[,1]</td><td>AGEP</td><td>age</td><td>7 levels: 16; 17; [18, 24]; [25, 35]; [36, 50]; [51, 70]; (70, ).</td></tr>
<tr><td>[,2]</td><td>MAR</td><td>marital status</td><td>5 levels: Married; Widowed; Divorced; Separated;</td></tr>
<tr><td></td><td></td><td></td><td>Never married.</td></tr>
<tr><td>[,3]</td><td>SCHL</td><td>educational attainment</td><td>9 levels: Up to K0; Some K12, no diploma;</td></tr>
<tr><td></td><td></td><td></td><td>High school diploma or GED; Some college, no degree;</td></tr>
<tr><td></td><td></td><td></td><td>Associate's degree; Bachelor's degree; Master's degree;</td></tr>
<tr><td></td><td></td><td></td><td>Professional degree; Doctorate degree.</td></tr>
<tr><td>[,4]</td><td>SEX</td><td>sex</td><td>2 levels: Male; Female.</td></tr>
<tr><td>[,5]</td><td>WKL</td><td>When last worked</td><td>3 levels: Within the last 12 months; 1-5 years ago;</td></tr>
<tr><td></td><td></td><td></td><td>Over 5 years ago or never worked.</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

ss16pusa_sample_zeros_miss function

<p>Example dataframe for input categorical data with structural zeros (with missing values). It contains 1000 observations and 5 variables.</p><table class='table'>
<tr><td>[,1]</td><td>AGEP</td><td>age</td><td>7 levels: 16; 17; [18, 24]; [25, 35]; [36, 50]; [51, 70]; (70, ).</td></tr>
<tr><td>[,2]</td><td>MAR</td><td>marital status</td><td>5 levels: Married; Widowed; Divorced; Separated;</td></tr>
<tr><td></td><td></td><td></td><td>Never married.</td></tr>
<tr><td>[,3]</td><td>SCHL</td><td>educational attainment</td><td>9 levels: Up to K0; Some K12, no diploma;</td></tr>
<tr><td></td><td></td><td></td><td>High school diploma or GED; Some college, no degree;</td></tr>
<tr><td></td><td></td><td></td><td>Associate's degree; Bachelor's degree; Master's degree;</td></tr>
<tr><td></td><td></td><td></td><td>Professional degree; Doctorate degree.</td></tr>
<tr><td>[,4]</td><td>SEX</td><td>sex</td><td>2 levels: Male; Female.</td></tr>
<tr><td>[,5]</td><td>WKL</td><td>When last worked</td><td>3 levels: Within the last 12 months; 1-5 years ago;</td></tr>
<tr><td></td><td></td><td></td><td>Over 5 years ago or never worked.</td></tr>
</table>



ss16pusa_sample_zeros_miss: Example dataframe for input categorical data with structural zeros (with missing values).

Description

Arguments

[,1]	AGEP	age	7 levels: 16; 17; [18, 24]; [25, 35]; [36, 50]; [51, 70]; (70, ).
[,2]	MAR	marital status	5 levels: Married; Widowed; Divorced; Separated;
			Never married.
[,3]	SCHL	educational attainment	9 levels: Up to K0; Some K12, no diploma;
			High school diploma or GED; Some college, no degree;
			Associate's degree; Bachelor's degree; Master's degree;
			Professional degree; Doctorate degree.
[,4]	SEX	sex	2 levels: Male; Female.
[,5]	WKL	When last worked	3 levels: Within the last 12 months; 1-5 years ago;
			Over 5 years ago or never worked.