[Back]

M. Templ, A. Alfons, P. Filzmoser:
"Exploring incomplete data using visualization techniques";
Advances in Data Analysis and Classification, 6 (2012), 29 - 47.

Visualization of incomplete data allows to simultaneously explore the data
and the structure of missing values. This is helpful for learning about the distribution
of the incomplete information in the data, and to identify possible structures of the
missing values and their relation to the available information. The main goal of this
contribution is to stress the importance of exploring missing values using visualization
methods and to present a collection of such visualization techniques for incomplete
data, all of which are implemented in the R package VIM. Providing such functionality
for this widely used statistical environment, visualization of missing values, imputation
and data analysis can all be done from within R without the need of additional software.

visualization · missing values · exploring incomplete data · R