Derive MCMC Parameters.

mcmcderive

Why mcmcderive?

mcmcderive is an R package to generate derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code.

This is useful because it means Bayesian models can be fitted without the inclusion of derived parameters which add unnecessary clutter and slows model fitting. For more information on MCMC samples see Brooks et al. (2011).

Installation

To install the latest release version from CRAN

install.packages("mcmcderive")

To install the latest development version from GitHub

remotes::install_github("poissonconsulting/mcmcderive")

Demonstration

library(mcmcderive)

mcmcr::mcmcr_example
#> $alpha
#> [1] 3.718025 4.718025
#> 
#> nchains:  2 
#> niters:  400 
#> 
#> $beta
#>           [,1]     [,2]
#> [1,] 0.9716535 1.971654
#> [2,] 1.9716535 2.971654
#> 
#> nchains:  2 
#> niters:  400 
#> 
#> $sigma
#> [1] 0.7911975
#> 
#> nchains:  2 
#> niters:  400

expr <- "
  log(alpha2) <- alpha
  gamma <- sum(alpha) * sigma
"

mcmc_derive(mcmcr::mcmcr_example, expr, silent = TRUE)
#> $alpha2
#> [1]  41.18352 111.94841
#> 
#> nchains:  2 
#> niters:  400 
#> 
#> $gamma
#> [1] 6.60742
#> 
#> nchains:  2 
#> niters:  400

Parallel Chains

If the MCMC object has multiple chains the run time can be substantially reduced by generating the derived parameters for each chain in parallel. In order for this to work it is necessary to:

1. Ensure plyr and doParallel are installed using install.packages(c("plyr", "doParallel")).
2. Register a parallel backend using doParallel::registerDoParallel(4).
3. Set parallel = TRUE in the call to mcmc_derive().

Extras

To facilitate the translation of model code into R code the extras package provides the R equivalent to common model functions such as pow(), phi() and log() <-.

Contribution

Please report any issues.

Pull requests are always welcome.

Code of Conduct

Please note that the mcmcderive project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.