Visually exploring missing values in multivariable data using a graphical user interface

Xiaoyue Cheng, Dianne Helen Cook, Heike Hofmann

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1 - 23
Number of pages23
JournalJournal of Statistical Software
Issue number6
Publication statusPublished - 2015

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