This paper introduces new tools for the visualization of missing values. The tools can be used for exploring the data and the structure of the missing values. Depending on this structure, the tools can be helpful for identifying the mechanism generating the missings. This knowledge is important for selecting an appropriate imputation method to reliably estimate the missing values. The visualization tools are implemented in the R-package VIM (visualization and imputation of missing values). A graphical user interface allows an easy handling of the plot methods. The developed tool can be used for data from official statistics, but also for data from various other fields. Special attention is given to data with spatial coordinates, and in that case the missing data information can be displayed with maps.
[1]
E. Wegman.
Hyperdimensional Data Analysis Using Parallel Coordinates
,
1990
.
[2]
Nicole A. Lazar,et al.
Statistical Analysis With Missing Data
,
2003,
Technometrics.
[3]
David E. Booth,et al.
Analysis of Incomplete Multivariate Data
,
2000,
Technometrics.
[4]
D. Cox,et al.
An Analysis of Transformations
,
1964
.
[5]
Deborah F. Swayne,et al.
Interactive and Dynamic Graphics for Data Analysis - With R and GGobi
,
2007,
Use R.
[6]
Catherine Plaisant,et al.
Visualizing Missing Data: Graph Interpretation User Study
,
2005,
INTERACT.
[7]
D. Rubin.
INFERENCE AND MISSING DATA
,
1975
.