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

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Authors Xiaoyue Cheng, Dianne Cook, Heike Hofmann
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper 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.
Date of publication 2015
Code Programming Language R
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