Fractional hot deck imputation (FHDI), proposed by Kalton and Kish (1984) and investigated by Kim and Fuller (2004), is a tool for handling item nonresponse in survey sampling. In FHDI, each missing item is filled with multiple observed values yielding a single completed data set for subsequent analyses. An R package FHDI is developed to perform FHDI and also the fully efficient fractional imputation (FEFI) method of (Fuller and Kim, 2005) to impute multivariate missing data with arbitrary missing patterns. FHDI substitutes missing items with a few observed values jointly obtained from a set of donors whereas the FEFI uses all the possible donors. This paper introduces FHDI as a tool for implementing the multivariate version of fractional hot deck imputation discussed in Im et al. (2015) as well as FEFI. For variance estimation of FHDI and FEFI, the Jackknife method is implemented, and replicated weights are provided as a part of the output.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-020.zip
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For attribution, please cite this work as
Im, et al., "FHDI: An R Package for Fractional Hot Deck Imputation", The R Journal, 2018
BibTeX citation
@article{RJ-2018-020,
author = {Im, Jongho and Cho, In Ho and Kim, Jae Kwang},
title = {FHDI: An R Package for Fractional Hot Deck Imputation},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-020},
doi = {10.32614/RJ-2018-020},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {140-154}
}