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This package contains 31 data sets provided by Singer and Willett (2003) with their book, Applied longitudinal data analysis: Modeling change and event occurrence, that are suitable for longitudinal mixed effects modelling, longitudinal structural equation modelling, and survival analysis. All the data sets in this package are real data from real studies; however, most were modified by Singer and Willett (2003) for the illustration of statistical methods, so they may not match the results of the original studies.

There are eleven data sets for longitudinal mixed effects modelling:

There is one data set for longitudinal structural equation modelling:

  • ?alcohol_use_2: Adolescent alcohol consumption and peer pressure (Chapter 8)

There are twenty data sets for survival analysis:

Vignettes and articles

There is one vignette with tips and tricks for working with longitudinal data:

There are fourteen articles on the package documentation website demonstrating how to recreate examples from the textbook in R:

  • Chapter 2: Exploring longitudinal data on change
  • Chapter 3: Introducing the multilevel model for change
  • Chapter 4: Doing data analysis with the multilevel model for change
  • Chapter 5: Treating time more flexibly
  • Chapter 6: Modeling discontinuous and nonlinear change
  • Chapter 7: Examining the multilevel model’s error covariance structure
  • Chapter 8: Modeling change using covariance structure analysis
  • Chapter 9: A framework for investigating event occurrence
  • Chapter 10: Describing discrete-time event occurrence data
  • Chapter 11: Fitting basic discrete-time hazard models
  • Chapter 12: Extending the discrete-time hazard model
  • Chapter 13: Describing continuous-time event occurrence data
  • Chapter 14: Fitting the Cox regression model
  • Chapter 15: Extending the Cox regression model

Documentation

See at https://mccarthy-m-g.github.io/alda/ and also in the installed package: help(package = "alda").

References

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press, USA. https://doi.org/10.1093/acprof:oso/9780195152968.001.0001