Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Xiaoyue Cheng, Dianne Cook, Heike Hofmann
Title: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface
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.

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Paper: Visually Exploring Missing Values in Multivariable Data Using a Graphical User Interface     Download PDF (Downloads: 5852)
Supplements:
MissingDataGUI_0.2-4.tar.gz: R source package Download (Downloads: 130; 118KB)
v68i06-replication.zip: Replication materials Download (Downloads: 95; 88KB)
DOI: 10.18637/jss.v068.i06

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.