FHDI: An R package for fractional hot deck imputation

Jongho Im, In Ho Cho, Jae Kwang Kim

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)140-154
Number of pages15
JournalR Journal
Issue number1
Publication statusPublished - 2018 Jul 1

Bibliographical note

Publisher Copyright:
© The R Foundation.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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