What and why

This project is based on Singer and Willett’s classic (2003) text, Applied longitudinal data analysis: Modeling change and event occurrence. You can download the data used in the text at http://www.bristol.ac.uk/cmm/learning/support/singer-willett.html and find a wealth of ideas on how to fit the models in the text at https://stats.idre.ucla.edu/other/examples/alda/. My contributions show how to fit these models and others like them within a Bayesian framework. I make extensive use of Paul Bürkner’s brms package (Bürkner, 2017, 2018, 2022b), which makes it easy to fit Bayesian regression models in R (R Core Team, 2022) using Hamiltonian Monte Carlo (HMC) via the Stan probabilistic programming language (Carpenter et al., 2017). Much of the data wrangling and plotting code is done with packages connected to the tidyverse (Wickham et al., 2019; Wickham, 2022).

Caution: Work in progress

This release contains drafts of Chapters 1 through 7 and 9 through 13. Chapters 1 through 7 provide the motivation and foundational principles for fitting longitudinal multilevel models. Chapters 9 through 13 motivation and foundational principles for fitting discrete-time survival analyses.

In addition to fleshing out more of the chapters, I plan to add more goodies like introductions to multivariate longitudinal models and mixed-effect location and scale models. But there is no time-table for this project. To keep up with the latest changes, check in at the GitHub repository, https://github.com/ASKurz/Applied-Longitudinal-Data-Analysis-with-brms-and-the-tidyverse, or follow my announcements on twitter at https://twitter.com/SolomonKurz.

R setup

To get the full benefit from this ebook, you’ll need some software. Happily, everything will be free (provided you have access to a decent personal computer and an good internet connection).

First, you’ll need to install R, which you can learn about at https://cran.r-project.org/.

Though not necessary, your R experience might be more enjoyable if done through the free RStudio interface, which you can learn about at https://rstudio.com/products/rstudio/.

Once you have installed R, execute the following to install the bulk of the add-on packages. This will probably take a few minutes to finish. Go make yourself a coffee.

packages <- c("bayesplot", "brms", "broom", "devtools", "flextable", "GGally", "ggmcmc", "ggrepel", "gtools", "loo", "patchwork", "psych", "Rcpp", "remotes", "rstan", "StanHeaders", "survival", "tidybayes", "tidyverse")

install.packages(packages, dependencies = T)

A couple of the other packages are not officially available via the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/). You can download them directly from GitHub by executing the following.

devtools::install_github("stan-dev/cmdstanr")
devtools::install_github("rmcelreath/rethinking")

It’s possible you’ll have problems installing some of these packages. Here are some likely suspects and where you can find help:

License and citation

This book is licensed under the Creative Commons Zero v1.0 Universal license. You can learn the details, here. In short, you can use my work. Just please give me the appropriate credit the same way you would for any other scholarly resource. Here’s the citation information:

@book{kurzAppliedLongitudinalDataAnalysis2023,
  title = {Applied longitudinal data analysis in brms and the tidyverse},
  author = {Kurz, A. Solomon},
  year = {2023},
  month = {6},
  edition = {version 0.0.3},
  url = {https://bookdown.org/content/4253/}
}

References

Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. https://doi.org/10.18637/jss.v080.i01
Bürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10(1), 395–411. https://doi.org/10.32614/RJ-2018-017
Bürkner, P.-C. (2022b). brms: Bayesian regression models using ’Stan’. https://CRAN.R-project.org/package=brms
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1). https://doi.org/10.18637/jss.v076.i01
R Core Team. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press, USA. https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195152968.001.0001/acprof-9780195152968
Wickham, H. (2022). tidyverse: Easily install and load the ’tidyverse’. https://CRAN.R-project.org/package=tidyverse
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686