`mcmcr`

is an R package to manipulate Monte Carlo Markov Chain (MCMC) samples (Brooks et al. 2011).

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 variable`coda::mcmc.list`

stores multiple`mcmc`

objects (with identical dimensions) as a list where each object represents a parallel chain`rjags::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.

`mcmcarray`

objects were developed to facilitate manipulation of the MCMC samples (although they are just one `aperm`

away from `mcarray`

objects they are more intuitive to program with - at least for this programmer!). `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.

In addition the `mcmcr`

package defines the `term`

vector to store and manipulate the term labels, ie, `"bIntercept", "bInteraction[1,2]", "bInteraction[2,1]"`

, when the MCMC samples are summarised in tabular form.

The `mcmcr`

package also introduces a variety of (often) generic functions to manipulate and query `mcmcarray`

, `mcmcr`

and `mcmcrs`

objects. In particular it provides functions to

- coerce from and to
`mcarray`

,`mcmc`

and`mcmc.list`

objects; - extract an objects
`coef`

table (as a tibble); - query an object’s
`nchains`

,`niters`

,`npars`

,`nterms`

,`nsims`

and`nsams`

as well as it’s parameter dimensions (`pdims`

) and term dimensions (`tdims`

); `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 the samples of two (or more) MCMC objects using
`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;- transpose an objects parameter dimensions using
`mcmc_aperm`

; - assess if an object has
`converged`

using`rhat`

and`esr`

(effectively sampling rate); - and of course
`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)
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)
#> term estimate sd zscore lower upper pvalue
#> 1 alpha[1] 3.7180250 0.9007167 4.149545 2.2120540 5.232403 0.0012
#> 2 alpha[2] 4.7180250 0.9007167 5.259772 3.2120540 6.232403 0.0012
#> 3 beta[1,1] 0.9716535 0.3747971 2.572555 0.2514796 1.713996 0.0225
#> 4 beta[2,1] 1.9716535 0.3747971 5.240666 1.2514796 2.713996 0.0050
#> 5 beta[1,2] 1.9716535 0.3747971 5.240666 1.2514796 2.713996 0.0050
#> 6 beta[2,2] 2.9716535 0.3747971 7.908776 2.2514796 3.713996 0.0012
#> 7 sigma 0.7911975 0.7408373 1.306700 0.4249618 2.559520 0.0012
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 latest release version from CRAN

`install.packages("mcmcr")`

To install the latest development version from GitHub

```
if(!"remotes" %in% installed.packages()[,1])
install.packages("remotes")
remotes::install_github("poissonconsulting/mcmcr")
```

To install the latest development version from the Poisson drat repository

```
if(!"drat" %in% installed.packages()[,1])
install.packages("drat")
drat::addRepo("poissonconsulting")
install.packages("mcmcr")
```

Please report any issues.

Pull requests are always welcome.

Please note that this project is released with a Contributor Code of Conduct. By participating in 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.