Visual Models for Categorical Data in Economic Research
This paper is concerned with the use of visualizing categorical data in qualitative data analysis (Friendly, Visualizing categorical data, SAS Press, 2000. ISBN 1-58025-660-0; Meyer et al., J. Stat. Softw., 2006; Meyer et al., vcd: Visualizing Categorical Data. R package version 1.0.9, 2008). Graphical methods for qualitative data and extension using a variety of R packages will be presented. This paper outlines a general framework for visual models for categorical data. These ideas are illustrated with a variety of graphical methods for categorical data for large, multi-way contingency tables. Graphical methods are available in R software in vcd and vcdExtra library including mosaic plot, association plot, sieve plot, double-decker plot or agreement plot. These R packages include methods for the exploration of categorical data, such as fitting and graphing, plots and tests for independence or visualization techniques for log-linear models. Some graphs, e.g. mosaic display plots are well-suited for detecting patterns of association in the process of model building, others are useful in model diagnosis and graphical presentation and summaries. The use of log-linear analysis, as well as visualizing categorical data in economic research, will be presented in this paper.
- Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrow & F. Czaki (Eds.), Proceedings of the 2nd international symposium on information. Budapest: Akademiai Kiado.Google Scholar
- Bertin, J. (1983). Semiology of graphics. Madison: University of Wisconsin Press.Google Scholar
- Friendly, M. (1992). Mosaic display for log-linear models. In ASA, Proceedings of the statistical graphics section (pp. 61–68), Alexandria, VA.Google Scholar
- Friendly, M. (1995). Conceptual and visual models for categorical data. The Amercian Statistician, 49(2), 153–160.Google Scholar
- Friendly, M. (1999). Extending mosaic displays: Marginal, conditional, and partial views of categorical data. Journal of Computational and Graphical Statistics, 8(3), 373–395.Google Scholar
- Friendly, M. (2000). Visualizing categorical data. Cary, NC: SAS Press. ISBN 1-58025-660-0.Google Scholar
- Hartigan, J. A., & Kleiner, B. (1981). Mosaics for contingency tables. In Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface.Google Scholar
- Hartigan, J. A., & Kleiner, B. (1984). A mosaic of television ratings. The Amercian Statistician, 38(1), 32–35.Google Scholar
- Mayer, D., Hornik, K., & Zeileis, A. (2006). The strucplot framework: Visualizing multi-way contingency tables with vcd. Journal of Statistical Software, 17(3), 1–48.Google Scholar
- Meyer, D., Zeileis, A., & Hornik, K. (2008). VCD: Visualizing categorical data, R package. http://CRAN.R-project.org.
- Theus, M., & Lauer, R. W. (1999). Technical report 12, visualizing loglinear models. Journal of Computational and Graphical Methods, 8(3), 396–412.Google Scholar