The miWQS package in the Comprehensive R Archive Network (CRAN) utilizes weighted quantile sum regression (WQS) in the multiple imputation (MI) framework. The data analyzed is a set/mixture of continuous and correlated components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, which may differ among the components. This type of data is found in areas such as chemical epidemiological studies, sociology, and genomics. The miWQS package can be run using complete or incomplete data, which may be placed in the first quantile, or imputed using bootstrap or Bayesian approach. This article provides a stepwise and hands-on approach to handle uncertainty due to values below the detection limit in correlated component mixture problems.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-014.zip
miWQS, wqs, gWQS, mice, norm, mi, coda, Rsolnp, glm2, rlist, Hmisc, tidyr, ggplot2, survival, invgamma, truncnorm, purrr, GGally, rticles
MissingData, SocialSciences, OfficialStatistics, Bayesian, ClinicalTrials, Econometrics, Multivariate, Distributions, gR, Graphics, Optimization, Phylogenetics, ReproducibleResearch, Survival, TeachingStatistics
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For attribution, please cite this work as
Hargarten & Wheeler, "The R Journal: miWQS: Multiple Imputation Using Weighted Quantile Sum Regression", The R Journal, 2021
BibTeX citation
@article{RJ-2021-014,
author = {Hargarten, Paul M. and Wheeler, David C.},
title = {The R Journal: miWQS: Multiple Imputation Using Weighted Quantile Sum Regression},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-014},
doi = {10.32614/RJ-2021-014},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {226-250}
}