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Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface

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  • Cheng, Xiaoyue
  • Cook, Dianne
  • Hofmann, Heike

Abstract

Missing values are common in data, and usually require attention in order to conduct the statistical analysis. One of the first steps is to explore the structure of the missing values, and how missingness relates to the other collected variables. This article describes an R package, that provides a graphical user interface (GUI) designed to help explore the missing data structure and to examine the results of different imputation methods. The GUI provides numerical and graphical summaries conditional on missingness, and includes imputations using fixed values, multiple imputations and nearest neighbors.

Suggested Citation

  • Cheng, Xiaoyue & Cook, Dianne & Hofmann, Heike, 2015. "Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i06).
    • Handle: RePEc:jss:jstsof:v:068:i06
    DOI: http://hdl.handle.net/10.18637/jss.v068.i06
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Lawrence, Michael & Temple Lang, Duncan, 2010. "RGtk2: A Graphical User Interface Toolkit for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i08).
    3. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    4. Swayne, Deborah F. & Lang, Duncan Temple & Buja, Andreas & Cook, Dianne, 2003. "GGobi: evolving from XGobi into an extensible framework for interactive data visualization," Computational Statistics & Data Analysis, Elsevier, vol. 43(4), pages 423-444, August.
    5. Su, Yu-Sung & Gelman, Andrew & Hill, Jennifer & Yajima, Masanao, 2011. "Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i02).
    6. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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    Cited by:

    1. Nicholas Tierney & Dianne Cook, 2018. "Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations," Monash Econometrics and Business Statistics Working Papers 14/18, Monash University, Department of Econometrics and Business Statistics.

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