<p>Example dataframe for input categorical data with missing values based on the NYMockexample dataset. It contains 2000 observations and 10 variables.</p><table class="table">
<tr><td>[,1]</td><td>OWNERSHIP</td><td>ownership of dwelling</td><td>3 levels: N/A; Owned or being bought (loan);</td></tr>
<tr><td></td><td></td><td></td><td>Rented.</td></tr>
<tr><td>[,2]</td><td>MORTGAGE</td><td>mortgate status</td><td>4 levels: N/A; No, owned free and clear;</td></tr>
<tr><td></td><td></td><td></td><td>Yes, mortgaged / deed of trust or similar debt;</td></tr>
<tr><td></td><td></td><td></td><td>Yes, contract to purchase.</td></tr>
<tr><td>[,3]</td><td>AGE</td><td>age</td><td>9 levels: [0, 14]; 15; 16; 17; [18, 24]; [25, 35]; [36, 50];</td></tr>
<tr><td></td><td></td><td></td><td>9  [51, 70]; [71, ).</td></tr>
<tr><td>[,4]</td><td>SEX</td><td>sex</td><td>2 levels: Male; Female.</td></tr>
<tr><td>[,5]</td><td>MARST</td><td>martial status</td><td>6 levels: Married, spouse present; Married, spouse absent;</td></tr>
<tr><td></td><td></td><td></td><td>Separated; Divorced; Widowed; Never married / single.</td></tr>
<tr><td>[,6]</td><td>RACESING</td><td>single race identification</td><td>5 levels: White; Black; American Indian / Alaska Native;</td></tr>
<tr><td></td><td></td><td></td><td>Asian and / or Pacific Islander; Other race, non-Hispanic.</td></tr>
<tr><td>[,7]</td><td>EDUC</td><td>educational attainment</td><td>11 levels: N/A or no schooling; Nursery school to grade 4;</td></tr>
<tr><td></td><td></td><td></td><td>Grade 5, 6, 7, or 8; Grade 9; Grade 10; Grade 11;</td></tr>
<tr><td></td><td></td><td></td><td>Grade 12; 1 year of college; 2 years of college;</td></tr>
<tr><td></td><td></td><td></td><td>4 years of college; 5+ years of college.</td></tr>
<tr><td>[,8]</td><td>EMPSTAT</td><td>employment status</td><td>4 levels: N/A; Employed; Unemployed; Not in labor force.</td></tr>
<tr><td>[,9]</td><td>DISABWRK</td><td>work disability status</td><td>3 levels: N/A; No disability that affects work;</td></tr>
<tr><td></td><td></td><td></td><td>Disability causes difficulty working.</td></tr>
<tr><td>[,10]</td><td>VESTAT</td><td>veteran status</td><td>3 levels: N/A; Not a veteran; Veteran.</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

X function

<p>Example dataframe for input categorical data with missing values based on the NYMockexample dataset. It contains 2000 observations and 10 variables.</p><table class='table'>
<tr><td>[,1]</td><td>OWNERSHIP</td><td>ownership of dwelling</td><td>3 levels: N/A; Owned or being bought (loan);</td></tr>
<tr><td></td><td></td><td></td><td>Rented.</td></tr>
<tr><td>[,2]</td><td>MORTGAGE</td><td>mortgate status</td><td>4 levels: N/A; No, owned free and clear;</td></tr>
<tr><td></td><td></td><td></td><td>Yes, mortgaged / deed of trust or similar debt;</td></tr>
<tr><td></td><td></td><td></td><td>Yes, contract to purchase.</td></tr>
<tr><td>[,3]</td><td>AGE</td><td>age</td><td>9 levels: [0, 14]; 15; 16; 17; [18, 24]; [25, 35]; [36, 50];</td></tr>
<tr><td></td><td></td><td></td><td>9  [51, 70]; [71, ).</td></tr>
<tr><td>[,4]</td><td>SEX</td><td>sex</td><td>2 levels: Male; Female.</td></tr>
<tr><td>[,5]</td><td>MARST</td><td>martial status</td><td>6 levels: Married, spouse present; Married, spouse absent;</td></tr>
<tr><td></td><td></td><td></td><td>Separated; Divorced; Widowed; Never married / single.</td></tr>
<tr><td>[,6]</td><td>RACESING</td><td>single race identification</td><td>5 levels: White; Black; American Indian / Alaska Native;</td></tr>
<tr><td></td><td></td><td></td><td>Asian and / or Pacific Islander; Other race, non-Hispanic.</td></tr>
<tr><td>[,7]</td><td>EDUC</td><td>educational attainment</td><td>11 levels: N/A or no schooling; Nursery school to grade 4;</td></tr>
<tr><td></td><td></td><td></td><td>Grade 5, 6, 7, or 8; Grade 9; Grade 10; Grade 11;</td></tr>
<tr><td></td><td></td><td></td><td>Grade 12; 1 year of college; 2 years of college;</td></tr>
<tr><td></td><td></td><td></td><td>4 years of college; 5+ years of college.</td></tr>
<tr><td>[,8]</td><td>EMPSTAT</td><td>employment status</td><td>4 levels: N/A; Employed; Unemployed; Not in labor force.</td></tr>
<tr><td>[,9]</td><td>DISABWRK</td><td>work disability status</td><td>3 levels: N/A; No disability that affects work;</td></tr>
<tr><td></td><td></td><td></td><td>Disability causes difficulty working.</td></tr>
<tr><td>[,10]</td><td>VESTAT</td><td>veteran status</td><td>3 levels: N/A; Not a veteran; Veteran.</td></tr>
</table>



X: Example dataframe for input categorical data with missing values based on the NYMockexample dataset.

Description

Arguments

[,1]	OWNERSHIP	ownership of dwelling	3 levels: N/A; Owned or being bought (loan);
			Rented.
[,2]	MORTGAGE	mortgate status	4 levels: N/A; No, owned free and clear;
			Yes, mortgaged / deed of trust or similar debt;
			Yes, contract to purchase.
[,3]	AGE	age	9 levels: [0, 14]; 15; 16; 17; [18, 24]; [25, 35]; [36, 50];
			9 [51, 70]; [71, ).
[,4]	SEX	sex	2 levels: Male; Female.
[,5]	MARST	martial status	6 levels: Married, spouse present; Married, spouse absent;
			Separated; Divorced; Widowed; Never married / single.
[,6]	RACESING	single race identification	5 levels: White; Black; American Indian / Alaska Native;
			Asian and / or Pacific Islander; Other race, non-Hispanic.
[,7]	EDUC	educational attainment	11 levels: N/A or no schooling; Nursery school to grade 4;
			Grade 5, 6, 7, or 8; Grade 9; Grade 10; Grade 11;
			Grade 12; 1 year of college; 2 years of college;
			4 years of college; 5+ years of college.
[,8]	EMPSTAT	employment status	4 levels: N/A; Employed; Unemployed; Not in labor force.
[,9]	DISABWRK	work disability status	3 levels: N/A; No disability that affects work;
			Disability causes difficulty working.
[,10]	VESTAT	veteran status	3 levels: N/A; Not a veteran; Veteran.