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

被引：25
|
作者：
Cheng, Xiaoyue [1 ]
Cook, Dianne [2 ]
Hofmann, Heike [3 ,4 ]
机构：
[1] Univ Nebraska, Dept Math, Durham Sci Ctr 225, Omaha, NE 68182 USA
[2] Monash Univ, Dept Econometr & Business Stat, Clayton, Vic 3800, Australia
[3] Iowa State Univ, Dept Stat, Ames, IA 50011 USA
[4] Iowa State Univ, Stat Lab, Ames, IA 50011 USA
来源：
JOURNAL OF STATISTICAL SOFTWARE | 2015年 / 68卷 / 06期
关键词：
missing values; imputation; exploratory data analysis; statistical graphics; data visualization; graphical user interface; MULTIPLE IMPUTATION; XGOBI;
D O I：
10.18637/jss.v068.i06
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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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收藏
页码：1 / 23
页数：23
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