poststrat_30

A dataset used for poststratification modeling based on the 2014–2018 American Community Survey (ACS), 
including 12,000 combinations of demographic and geographic strata. The table includes more combinations than the number of observed units, so some strata are not represented in the CCES sample. This dataset includes only the 30 states that are present in the <code>cces_30_df</code>.A data frame with 7200 rows and 6 variables:<dl>
 <dt>state</dt>
<dd>A factor variable representing the 30 selected U.S. states. The same states as those included in cces_30_df.</dd> <dt>eth</dt>
<dd>An ethnicity variable stored as a factor (White, Black, Hispanic, Other).</dd> <dt>gender</dt>
<dd>A factor variable representing gender (Female, Male).</dd> <dt>age</dt>
<dd>A factor variable dividing respondents into age groups (18–29, 30–39, 40–49, 50–59, 60–69, 70+).</dd> <dt>educ</dt>
<dd>A factor variable representing educational attainment (No HS, HS, Some college, 4-Year College, Post-grad).</dd> <dt>n</dt>
<dd>Number of individuals in each demographic-geographic combination</dd>
</dl>

datasets

Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' <doi:10.18637/jss.v097.i01>, 'bartMachine' <doi:10.18637/jss.v070.i04>, and 'dbarts' <https://CRAN.R-project.org/package=dbarts>. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> is also considered. The BARP model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480> was implemented with reference to <https://github.com/jbisbee1/BARP> and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods.

Dong-eun Lee

bartXViz

Visualization of BART and BARP using SHAP

Eun-Kyung Lee

poststrat_30 function

A dataset used for poststratification modeling based on the 2014–2018 American Community Survey (ACS), 
including 12,000 combinations of demographic and geographic strata. The table includes more combinations than the number of observed units, so some strata are not represented in the CCES sample. This dataset includes only the 30 states that are present in the <code>cces_30_df</code>.

A data frame with 7200 rows and 6 variables:<dl>
 <dt>state</dt>
<dd>A factor variable representing the 30 selected U.S. states. The same states as those included in cces_30_df.</dd>

 <dt>eth</dt>
<dd>An ethnicity variable stored as a factor (White, Black, Hispanic, Other).</dd>

 <dt>gender</dt>
<dd>A factor variable representing gender (Female, Male).</dd>

 <dt>age</dt>
<dd>A factor variable dividing respondents into age groups (18–29, 30–39, 40–49, 50–59, 60–69, 70+).</dd>

 <dt>educ</dt>
<dd>A factor variable representing educational attainment (No HS, HS, Some college, 4-Year College, Post-grad).</dd>

 <dt>n</dt>
<dd>Number of individuals in each demographic-geographic combination</dd>


</dl>

poststrat_30: Post-Stratification Table of 2014-2018 American Community Survey (ACS)

Description

Arguments

References