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Graphical Tools of Discrete Longitudinal Data Presentation in R

Author

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  • Genge Ewa

    (University of Economics in Katowice, Katowice, Poland)

Abstract

Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results. The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures. However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories. The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes. We will focus on the most important features of the categorical longitudinal data – category sequences and their graphical presentation. We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.

Suggested Citation

  • Genge Ewa, 2019. "Graphical Tools of Discrete Longitudinal Data Presentation in R," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 26-39, September.
    • Handle: RePEc:vrs:eaiada:v:23:y:2019:i:3:p:26-39:n:3
    DOI: 10.15611/eada.2019.3.03
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    References listed on IDEAS

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    1. Christian Brzinsky-Fay & Ulrich Kohler & Magdalena Luniak, 2006. "Sequence analysis with Stata," Stata Journal, StataCorp LP, vol. 6(4), pages 435-460, December.
    2. Cees H. Elzinga & Aart C. Liefbroer, 2007. "De-standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis," European Journal of Population, Springer;European Association for Population Studies, vol. 23(3), pages 225-250, October.
    3. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    4. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    longitudinal data; categorical sequences; sequence visualization;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • G40 - Financial Economics - - Behavioral Finance - - - General

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