The R Journal: accepted article

This article will be copy edited and may be changed before publication.

FHDI: An R Package for Fractional Hot Deck Imputation PDF download
Jongho Im, In Ho Cho and Jae Kwang Kim

Abstract 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. The FHDI substitutes missing items with a few observed values jointly obtained from a set of donors whereas the FEFI used 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 output.

Received: 2017-06-02; online 2018-05-21, supplementary material, (984 bytes)
CRAN packages: mice, mi, Amelia, VIM, FHDI
CRAN Task Views implied by cited CRAN packages: OfficialStatistics, SocialSciences, Multivariate

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Jongho Im and In Ho Cho and Jae Kwang Kim},
  title = {{FHDI: An R Package for Fractional Hot Deck Imputation}},
  year = {2018},
  journal = {{The R Journal}},
  url = {}