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).
To install the latest release version from CRAN
install.packages("mcmcderive")
To install the latest development version from GitHub
remotes::install_github("poissonconsulting/mcmcderive")
library(mcmcderive)
::mcmcr_example
mcmcr#> $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
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:
install.packages(c("plyr", "doParallel"))
.doParallel::registerDoParallel(4)
.parallel = TRUE
in the call to
mcmc_derive()
.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() <-
.
Please report any issues.
Pull requests are always welcome.
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.
Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.