mcmcr is an R package to manipulate Monte Carlo Markov
Chain (MCMC) samples.
For the purposes of this discussion, an MCMC sample represents the value of a term from a single iteration of a single chain. While a simple parameter such as an intercept corresponds to a single term, more complex parameters such as an interaction between two factors consists of multiple terms with their own inherent dimensionality - in this case a matrix. A set of MCMC samples can be stored in different ways.
The three most common S3 classes store MCMC samples as follows:
coda::mcmc stores the MCMC samples from a single chain
as a matrix where each each row represents an iteration and each column
represents a variablecoda::mcmc.list stores multiple mcmc
objects (with identical dimensions) as a list where each object
represents a parallel chainrjags::mcarray stores the samples from a single
parameter where the initial dimensions are the parameter dimensions, the
second to last dimension is iterations and the last dimension is
chains.In the first two cases the terms/parameters are represented by a
single dimension which means that the dimensionality inherent in the
parameters is stored in the labelling of the variables, ie,
"bIntercept", "bInteraction[1,2]", "bInteraction[2,1]", ....
The structure of the mcmc and mcmc.list
objects emphasizes the time-series nature of MCMC samples and is
optimized for thining. In contrast mcarray objects preserve
the dimensionality of the parameters.
The mcmcr package defines three related S3 classes which
also preserve the dimensionality of the parameters:
mcmcr::mcmcarray is very similar to
rjags::mcarray except that the first dimension is the
chains, the second dimension is iterations and the subsequent dimensions
represent the dimensionality of the parameter (it is called
mcmcarray to emphasize that the MCMC dimensions ie the
chains and iterations come first);mcmcr::mcmcr stores multiple uniquely named
mcmcarray objects with the same number of chains and
iterations.mcmcr::mcmcrs stores multiple mcmcr
objects with the same parameters, chains and iterations.All five classes (mcmc, mcmc.list,
mcarray, mcmcarray, mcmcr and
mcmcrs) are collectively referred to as MCMC objects.
mcmcarray objects were developed to facilitate
manipulation of the MCMC samples. mcmcr objects were
developed to allow a set of dimensionality preserving parameters from a
single analysis to be manipulated as a whole. mcmcrs
objects were developed to allow the results of multiple analyses using
the same model to be manipulated together.
The mcmcr package (together with the term and nlist packages)
introduces a variety of (often) generic functions to manipulate and
query mcmcarray, mcmcr and mcmcrs
objects (and term and nlist and
nlists objects).
In particular it provides functions to
mcarray, mcmc and
mcmc.list objects;coef table (as a tibble);nchains, niters,
term::npars, term::nterms,
nlist::nsims and nlist::nsams as well as it’s
parameter dimensions (term::pdims) and term indices
(term::tindex);subset objects by chains, iterations and/or
parameters;bind_xx a pair of objects by their
xx_chains, xx_iterations,
xx_parameters or (parameter)
xx_dimensions;combine_samples (or combine_samples_n) or
combine the samples of a single MCMC object by reducing its dimensions
using combine_dimensions;collapse_chains or split_chains an
object’s chains;mcmc_map over an objects values;mcmc_aperm;converged using
rhat and esr (effectively sampling rate);thin, rhat, ess
(effective sample size), print, plot etc said
objects.The code is opinionated which has the advantage of providing a small
set of stream-lined functions. For example the only ‘convergence’ metric
is the uncorrected, untransformed, univariate split R-hat (potential
scale reduction factor). If you can convince me that additional features
are important I will add them or accept a pull request (see below).
Alternatively you might want to use the mcmcr package to
manipulate your samples before coercing them to an
mcmc.list to take advantage of all the summary functions in
packages such as coda.
library(mcmcr)
#> Registered S3 method overwritten by 'mcmcr':
#> method from
#> as.mcmc.nlists nlist
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
coef(mcmcr_example, simplify = TRUE)
#> term estimate lower upper svalue
#> 1 alpha[1] 3.7180250 2.2120540 5.232403 9.645658
#> 2 alpha[2] 4.7180250 3.2120540 6.232403 9.645658
#> 3 beta[1,1] 0.9716535 0.2514796 1.713996 5.397731
#> 4 beta[2,1] 1.9716535 1.2514796 2.713996 7.323730
#> 5 beta[1,2] 1.9716535 1.2514796 2.713996 7.323730
#> 6 beta[2,2] 2.9716535 2.2514796 3.713996 9.645658
#> 7 sigma 0.7911975 0.4249618 2.559520 9.645658
rhat(mcmcr_example, by = "term")
#> $alpha
#> [1] 2.002 2.002
#>
#> $beta
#> [,1] [,2]
#> [1,] 1.147 1.147
#> [2,] 1.147 1.147
#>
#> $sigma
#> [1] 1
plot(mcmcr_example[["alpha"]])
To install the release version from CRAN.
install.packages("mcmcr")The website for the release version is at https://poissonconsulting.github.io/mcmcr/.
To install the development version from GitHub
# install.packages("remotes")
remotes::install_github("poissonconsulting/mcmcr")or from r-universe.
install.packages("mcmcr", repos = c("https://poissonconsulting.r-universe.dev", "https://cloud.r-project.org"))Please report any issues.
Pull requests are always welcome.
Please note that the mcmcr 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. ISBN: 978-1-4200-7941-8.