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http://dx.doi.org/10.29220/CSAM.2021.28.4.369

vlda: An R package for statistical visualization of multidimensional longitudinal data  

Lee, Bo-Hui (Department of Advertising and Public Relations, Silla University)
Ryu, Seongwon (Department of Statistics, Pusan National University)
Choi, Yong-Seok (Department of Statistics, Pusan National University)
Publication Information
Communications for Statistical Applications and Methods / v.28, no.4, 2021 , pp. 369-391 More about this Journal
Abstract
The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.
Keywords
vlda; visualization; longitudinal data; R;
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  • Reference
1 Allison PD (2001). Logistic Regression Using the SAS System, Theory and Application, SAS Institute Inc.
2 Everitt BS and Hothorn T (2006). A Handbook of Statistical Analyses Using R(2nd ed), Chapman & Hall, CRC Press, Florida, USA.
3 Gohel D (2016). ggiraph: Make 'ggplot2' Graphics Interactive, R package version 0.3.2.
4 Greenacre MJ (1984). Theory and Applications of Correspondence Analysis, Academic Press, New York.
5 Greenacre M and Hastie T (1987). The geometric interpretation of correspondence analysis, Journal of the American Statistical Association, 82, 437-447.   DOI
6 Koch GG, Landis JR, Freeman JL, Freeman DH, and Lehnen RC (1977). A general methodology for the analysis of experiments with repeated measurement of categorical data, Biometrics, 33, 133-158.   DOI
7 Lebart L, Morineau A, and Warwick K (1984). Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices, John Wiley & Sons, New York.
8 Lee B (2019). Visualization of Multidimensional Longitudinal Data (Ph.D. thesis), Pusan National University, Pusan.
9 Liang KY and Zeger SL (1986). Longitudinal data analysis using generalized linear models, Biometrika, 73, 13-22.   DOI
10 Singer JD and Willett JB (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, Oxford University Press, Oxford.
11 Wickham H (2011). ggplot2, Wiley Interdisciplinary Reviews: Computational Statistics, 3, 180-185.   DOI
12 Xiao N (2018). ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for 'ggplot2', R package version 2.9.
13 Jeong KM and Choi YS (2009). Introduction to Categorical Data Analysis - Application and Interpretation of the SAS(2nd ed), Free Academy, Seoul.
14 R Development Core team A et al. (2011). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, URL: http://www.R-project.org.