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README.md

drake R package Stan model example

The goal of this workflow is to validate a small Bayesian hierarchical model.

y_i ~ iid Normal(alpha + x_i * beta, sigma^2)
alpha ~ Normal(0, 1)
beta ~ Normal(0, 1)
sigma ~ Uniform(0, 1)

We simulate multiple datasets from the model and fit the model on each dataset. For each model fit, we determine if the 50% credible interval of the regression coefficient beta contains the true value of beta used to generate the data. If we implemented the model correctly, roughly 50% of the models should recapture the true beta in 50% credible intervals.

The drake pipeline

The drake R package manages the workflow. It automatically skips steps of the pipeline when the results are already up to date, which is critical for Bayesian data analysis because it usually takes a long time to run Markov chain Monte Carlo. It also helps users understand and communicate this work dependency graphs (see r_vis_drake_graph()).

File structure

The files in this example are organized as follows.

├── run.sh
├── run.R
├── _drake.R
├── sge.tmpl
├── R/
├──── packages.R
├──── functions.R
├──── plan.R
├── stan/
├──── model.stan
└── report.Rmd
File Purpose
run.sh Shell script to run run.R in a persistent background process. Works on Unix-like systems. Helpful for long computations on servers.
run.R R script to run r_make().
_drake.R The special R script that powers functions r_make() and friends (details here).
sge.tmpl A clustermq template file to deploy targets in parallel to a Sun Grid Engine cluster.
R/packages.R A custom R script loading the packages we need.
R/functions.R A custom R script with user-defined functions.
R/plan.R A custom R script that defines the drake plan.
stan/model.stan The specification of our Stan model.
report.Rmd An R Markdown report summarizing the results of the analysis.

How to run

  1. Install the packages mentioned in R/packages.R.
  2. Run the drake pipeline by either running run.R or run.sh. (The latter is for Unix-like systems only). This computation could take a while.
  3. View the validation results in the output report.html file.
  4. Make changes to the R code or Stan model, rerun the pipeline, and watch drake skip steps that are already up to date.

Scale out

This computation is currently downsized for pedagogical purposes. To scale it up, open the R/plan.R script and increase the number of simulations (the number inside seq_len() in the index target).

High-performance computing

You can run this project locally on your laptop or remotely on a cluster. The comments in the _drake.R file have specific directions. Details on high-performance computing are available in this chapter of the manual.