Data analysis, common to all empirical sciences, often requires complete data sets. Unfortu nately, real world data collection will usually result in data values not being observed. We present a package for robust multiple imputation (the ImputeRobust package) that allows the use of generalized additive models for location, scale, and shape in the context of chained equations. The paper describes the basics of the imputation technique which builds on a semi-parametric regression model (GAMLSS) and the algorithms and functions provided with the corresponding package. Furthermore, some illustrative examples are provided.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-014.zip
Econometrics, Multivariate, OfficialStatistics, SocialSciences
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For attribution, please cite this work as
Salfran & Spiess, "Generalized Additive Model Multiple Imputation by Chained Equations With Package ImputeRobust", The R Journal, 2018
BibTeX citation
@article{RJ-2018-014,
author = {Salfran, Daniel and Spiess, Martin},
title = {Generalized Additive Model Multiple Imputation by Chained Equations With Package ImputeRobust},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-014},
doi = {10.32614/RJ-2018-014},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {61-72}
}