BRMS

Bayesian Modelling

The brms R package provides an interface to Stan for Bayesian generalized non-linear multivariate multilevel models. It supports a wide range of response distributions and modeling options, making it a flexible choice for complex Bayesian modeling tasks². You can use it to fit Bayesian regression models, including linear, robust linear, count data, survival, response times, ordinal, zero-inflated, and even self-defined mixture models, all in a multilevel context. If you’re interested in Bayesian modeling, brms is a powerful tool to explore.

Installation:

To install and use the brms R package, follow these steps:

  • Open your R environment (such as RStudio).
  • Install the brms package using the following command: 
    
install.packages("brms")
  • Alternatively, you can install it directly from the R console using the menu: Tools → Install Packages.

Loading the Package:

After installation, load the brms package into your R session: library(brms)

Modeling Example:

Let’s say you want to model seizure counts in epileptic patients using a Poisson regression. You have variables like zAge, zBase, and Trt (treatment). Fit the model with the following code: fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient), data = epilepsy, family = poisson()) Replace count, zAge, zBase, Trt, and epilepsy with your actual data and variables. The results (posterior draws) can be explored using summary(fit1). Remember to adapt the example to your specific data and research question. If you need further assistance, feel free to ask! 😊Certainly! To install and use the brms R package, follow these steps:

  1. Installation:

    • Open your R environment (such as RStudio).

    • Install the brms package using the following command:

install.packages("brms")

  1. Loading the Package:

    • After installation, load the brms package into your R session:
     library(brms)

  1. Modeling Example:

    • Let’s say you want to model seizure counts in epileptic patients using a Poisson regression.

    • You have variables like zAge, zBase, and Trt (treatment).

    • Fit the model with the following code:

     fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient), data = epilepsy, family = poisson())

Remember to adapt the example to your specific data and research question.

Source: Conversation with Copilot, 25/07/2024 (1) The Ultimate Guide to the brms Package in R. https://rbasics.org/packages/brms-package-in-r/. (2) CRAN: Package brms - The Comprehensive R Archive Network. https://cran.r-project.org/web/packages/brms/index.html. (3) Advanced Bayesian Multilevel Modeling with the R Package brms. https://journal.r-project.org/articles/RJ-2018-017/. (4) brms package - RDocumentation. https://www.rdocumentation.org/packages/brms/versions/2.21.0. (5) undefined. https://github.com/paul-buerkner/brms/issues. (6) undefined. https://github.com/paul-buerkner/brms. (7) undefined. https://discourse.mc-stan.org/. (8) undefined. https://paul-buerkner.github.io/brms/. (9) undefined. https://mc-stan.org/r-packages/. (10) undefined. https://mc-stan.org/%29.

References

  1. R packages: installing, loading, using and updating R packages. https://www.youtube.com/watch?v=2nfWaMsD1fw.

  2. How to Install Packages in R | R Tutorial 1.13 | MarinStatsLectures. https://www.youtube.com/watch?v=3RWb5U3X-T8.

  3. How to Install Packages in R Studio and Handling Installation Errors. https://www.youtube.com/watch?v=-wTcDqJWSzA.

  4. brms package - RDocumentation. https://www.rdocumentation.org/packages/brms/versions/2.21.0.

  5. The Ultimate Guide to the brms Package in R. https://rbasics.org/packages/brms-package-in-r/.

  6. GitHub - paul-buerkner/brms: brms R package for Bayesian generalized …. https://github.com/paul-buerkner/brms.

  7. undefined. https://mc-stan.org/.

Edit Notes

  • Citrix Build on 25th July 2024
  • UX improvements in 22nd August 2024 (Citrix)