Generalized Additive Model Multiple Imputation by Chained Equations With Package ImputeRobust

Abstract:

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.

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Authors

Affiliations

Daniel Salfran

 

Martin Spiess

 

Published

May 15, 2018

Received

May 2, 2017

DOI

10.32614/RJ-2018-014

Volume

Pages

10/1

61 - 72

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-014.zip

CRAN packages used

ImputeRobust, mice, gamlss

CRAN Task Views implied by cited packages

Econometrics, Multivariate, OfficialStatistics, SocialSciences

Footnotes

    Reuse

    Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

    Citation

    For attribution, please cite this work as

    Salfran & Spiess, "The R Journal: 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 = {The R Journal: 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}
    }