stantargets R package validation example
The goal of this workflow is to demonstrate the stantargets R package using a practical example. In this scenario, we validate a small Bayesian model using an interval-based method similar to simulation-based calibration (SBC; Cook, Gelman, and Rubin 2006; Talts et al. 2020). 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.
y_i ~ iid Normal(alpha + x_i * beta, sigma^2) alpha ~ Normal(0, 1) beta ~ Normal(0, 1) sigma ~ HalfCauchy(0, 1)
The stantargets R package manages the workflow. stantargets is an extension to pacakges targets and cmdstanr for Bayesian data analysis, and it makes both the latter packages easier to use, especially multi-rep simulation studies like this one. stantargets makes decisions about how the pipeline is constructed, cmdstanr runs and postprocesses the models, and targets orchestrates the computation while skipping expensive computations if the results are already up to date.
stantargets and not just targets?
stantargets makes the validation study much simpler than with targets alone. To demonstrate, https://github.com/wlandau/targets-stan is a implementation of the same workflow without stantargets, and the _targets.R file is much longer and more complicated. With stantargets, the pipeline in the _targets.R file becomes much simpler and easier to define.
You can try out this example project as long as you have a browser and an internet connection. Click here to navigate your browser to an RStudio Cloud instance. Alternatively, you can clone or download this code repository and install the R packages listed here.
In the R console, call the tar_make() function to run the pipeline. Then, call tar_read(cover_continuous) to retrieve observed coverage, and open report.html in a web browser to view the full results of the validation studyz. Experiment with other functions such as tar_visnetwork() to learn how they work.
The files in this example are organized as follows.
├── run.sh ├── run.R ├── _targets.R ├── _targets/ ├── sge.tmpl ├── R │ └── functions.R ├── stan │ └── model.stan └── report.RmdFile 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 tar_make() or tar_make_clustermq() (uncomment the function of your choice.) _targets.R The special R script that declares the targets pipeline. See tar_script() for details. sge.tmpl A clustermq template file to deploy targets in parallel to a Sun Grid Engine cluster. The comments in this file explain some of the choices behind the pipeline construction and arguments to tar_target(). R/functions.R A custom R script with functions to create Stan datasets. stan/model.stan The specification of our Stan model. report.Rmd An R Markdown report summarizing the results of the analysis. For more information on how to include R Markdown reports as reproducible components of the pipeline, see the tar_render() function from the tarchetypes package and the literate programming chapter of the manual.
This computation is currently downsized for pedagogical purposes. To scale it up, open the _targets.R script and increase the number of simulations (the number inside seq_len() in the index target).
You can run this project locally on your laptop or remotely on a cluster. You have several choices, and they each require modifications to run.R and _targets.R.
run.R Instructions for _targets.R Sequential Low-spec local machine or Windows. Uncomment tar_make() No action required. Local multicore Local machine with a Unix-like OS. Uncomment tar_make_clustermq() Uncomment options(clustermq.scheduler = "multicore") Sun Grid Engine Sun Grid Engine cluster. Uncomment tar_make_clustermq() Uncomment options(clustermq.scheduler = "sge", clustermq.template = "sge.tmpl")
Cook, Samantha R., Andrew Gelman, and Donald B. Rubin. 2006. “Validation of Software for Bayesian Models Using Posterior Quantiles.” Journal of Computational and Graphical Statistics 15 (3): 675–92. http://www.jstor.org/stable/27594203.
Talts, Sean, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman. 2020. “Validating Bayesian Inference Algorithms with Simulation-Based Calibration.” http://arxiv.org/abs/1804.06788.
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