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Analyzing and Visualizing State Sequences in R with TraMineR

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Author Info

  • Alexis Gabadinho
  • Gilbert Ritschard
  • Nicolas S Müller
  • Matthias Studer

Abstract

This article describes the many capabilities offered by the TraMineR toolbox for categorical sequence data. It focuses more specifically on the analysis and rendering of state sequences. Addressed features include the description of sets of sequences by means of transversal aggregated views, the computation of longitudinal characteristics of individual sequences and the measure of pairwise dissimilarities. Special emphasis is put on the multiple ways of visualizing sequences. The core element of the package is the state se- quence object in which we store the set of sequences together with attributes such as the alphabet, state labels and the color palette. The functions can then easily retrieve this information to ensure presentation homogeneity across all printed and graphical displays. The article also demonstrates how TraMineR’s outcomes give access to advanced analyses such as clustering and statistical modeling of sequence data.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 40 ()
Issue (Month): i04 ()
Pages:

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Handle: RePEc:jss:jstsof:40:i04

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  1. Duncan McVicar & Michael Anyadike-Danes, 2002. "Predicting successful and unsuccessful transitions from school to work by using sequence methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 317-334.
  2. Matthias Studer & Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Müller, 2011. "Discrepancy Analysis of State Sequences," Sociological Methods & Research, , vol. 40(3), pages 471-510, August.
  3. Deville, J. -C. & Saporta, G., 1983. "Correspondence analysis, with an extension towards nominal time series," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 169-189.
  4. Francesco C. Billari & Johannes Fürnkranz & Alexia Prskawetz, 2000. "Timing, sequencing and quantum of life course events: a machine learning approach," MPIDR Working Papers WP-2000-010, Max Planck Institute for Demographic Research, Rostock, Germany.
  5. Christian Brzinsky-Fay & Ulrich Kohler & Magdalena Luniak, 2006. "Sequence analysis with Stata," Stata Journal, StataCorp LP, vol. 6(4), pages 435-460, December.
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Cited by:
  1. Zhelyazkova, Nevena, 2013. "Parental leave within the broader work-family trajectory: What can we learn from sequence analysis?," MERIT Working Papers 049, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

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