njtierney / Mmcc
Programming Languages
mmcc
Tidying up MCMC output can be a real pain. There are plenty of packages that help with summarising MCMC and providing their own summaries, but sometimes you just want a tidy data structure so you can do your own thing. And quickly.
mmcc
provides tidying functions that return tidy data structure from
mcmc.list objects. It uses data.table
as the backend for speediness,
it also provides broom
tidiers to assist in some quick summaries.
Installation
Install from github using:
# install.packages("remotes")
remotes::install_github("njtierney/mmcc")
Using mmcc
mcmc_to_dt
takes an mcmc.list
object and turns it into a
data.table
of the format:
library(coda)
data(line)
head(data.frame(line$line1))
#> alpha beta sigma
#> 1 7.17313 -1.566200 11.233100
#> 2 2.95253 1.503370 4.886490
#> 3 3.66989 0.628157 1.397340
#> 4 3.31522 1.182720 0.662879
#> 5 3.70544 0.490437 1.362130
#> 6 3.57910 0.206970 1.043500
library(mmcc)
mcmc_dt <- mcmc_to_dt(line)
mcmc_dt
#> iteration chain parameter value
#> 1: 1 1 alpha 7.173130
#> 2: 2 1 alpha 2.952530
#> 3: 3 1 alpha 3.669890
#> 4: 4 1 alpha 3.315220
#> 5: 5 1 alpha 3.705440
#> ---
#> 1196: 196 2 sigma 1.306930
#> 1197: 197 2 sigma 0.846828
#> 1198: 198 2 sigma 0.465129
#> 1199: 199 2 sigma 0.672417
#> 1200: 200 2 sigma 0.639787
tidy.mcmc.list
takes an mcmc.list
, turns it into a data.table
and
summarises it in terms of each parameter’s mean, median, standard
deviation and credible interval with level given by conf.level
:
tidy(line)
#> parameter mean sd 2.5% median 97.5%
#> 1: alpha 2.9875644 0.4983950 1.9650403 3.0188300 3.876589
#> 2: beta 0.7991864 0.3366834 0.1430713 0.7962500 1.469723
#> 3: sigma 0.9680519 0.7413014 0.4249618 0.7911975 2.559520
We can also optionally ask for a subset of the parameters with a vector
of colnames
and summarise for each chain:
tidy(line,
chain = TRUE,
colnames=c("alpha"))
#> parameter chain mean sd 2.5% median 97.5%
#> 1: alpha 1 2.982615 0.5313900 2.085719 2.973115 3.838839
#> 2: alpha 2 2.992514 0.4643476 1.965040 3.063630 3.890256
This may be useful if we want to make a plot that shows how a given parameter varies from chain to chain.
library(ggplot2)
line_tidy <- tidy(line, chain = TRUE)
ggplot(data = line_tidy,
aes(x = factor(chain),
y = mean)) +
geom_pointrange(aes(ymin = `2.5%`,
ymax = `97.5%`)) +
facet_wrap(~parameter,
nrow = 1,
scales = "free_y") +
theme_bw() +
xlab("Chain") +
ylab("Value")
Why mmcc?
Full credit does to Sam Clifford for the name.
To quote Sam:
…it’s all about reshaping and manipulating mcmc chains…
…therefore, mmcc
Future work
- Create summaries for each parameter
- Perform diagnostic summaries for convergence
- provide a suite of plotting in plotly, for speed, and interactivity.
Code of Conduct
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.