The statistical analysis of compositional data
The statistical analysis of compositional data
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Computational Statistics & Data Analysis - Data visualization
Constructing and reading mosaicplots
Computational Statistics & Data Analysis - Data visualization
Visual Statistics: Seeing Data with Dynamic Interactive Graphics (Wiley Series in Probability and Statistics)
Research Article: Robust data imputation
Computational Biology and Chemistry
Escaping RGBland: Selecting colors for statistical graphics
Computational Statistics & Data Analysis
Interactive and Dynamic Graphics for Data Analysis With R and GGobi
Interactive and Dynamic Graphics for Data Analysis With R and GGobi
Imputation of missing values for compositional data using classical and robust methods
Computational Statistics & Data Analysis
Detection of multivariate outliers in business survey data with incomplete information
Advances in Data Analysis and Classification
Iterative stepwise regression imputation using standard and robust methods
Computational Statistics & Data Analysis
Multiple imputation in principal component analysis
Advances in Data Analysis and Classification
Visualizing missing data: graph interpretation user study
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
| Hi-index | 0.00 |
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 $${{\sf R}}$$ 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 $${{\sf R}}$$ without the need of additional software.