Tools for analysis of MCMC chains
mcmcHammer is just like all the other packages for analyzing MCMC chains from Bayesian analysis, except that it's not. Like the others, it can create trace plots and density plots. But unlike them, it automates "extracting" variables, especially variables with indices. For example, say your set of MCMC chains have variables named beta0, beta1, and beta2, as well as gamma[1, 1], gamma[1, 2], gamma[2, 1] and gamma[2, 2]. You can easily create trace plots and density plots for each of these with minimal "manual" tweaking of variable names. Perforce:
mh_trace(mcmc, 'beta', i=0:2)
mh_density(mcmc, 'gamma', j=1:2, k=1:2)
...will make trace and density plots for the beta variables and the gamma variables. It can even do so if you can't recall how many variants of a variable there are. For example,
mh_trace(mcmc, 'beta', i=TRUE)
mh_density(mcmc, 'gamma', j=TRUE, k=TRUE)
will make trace or density plots for all variables of the pattern beta* and gamma[*, *].
All plots are in ggplot2 package format, so they can be further manipulated using the grammar of graphics tools in that package.
You can install mcmcHammer from GitHub using:
remotes::install_github('adamlilith/mcmcHammer', dependencies=TRUE)
You may need to install the remotes package first, using:
install.packages('remotes')
Most function require either an object of class mcmc or mcmc.list, which are often the format of output from modeling functions in Bayesian packages. However, they can also be created by the coda package. Functions in mcmcHammer will need to "stack" mcmc objects before they can be used. Here, a "stack" is the product of simply rbind-ing one mcmc matrix on top of the next. Stacking can be done automatically by each function if the argument stacked is FALSE, but if you are using the functions a lot, it's easier and often much faster to stack the mcmc chains first then use this as the input into functions:
library(mcmcHammer)
data(mcmc)
mcmc_stacked <- mh_stack(mcmc)
Now, we can call summary and diagnostic functions with the stacked version of the MCMC chains:
mh_trace_density(mcmc_stacked, param='beta', i=0:5)
mh_summary_by_param(mcmc_stacked, fun=mean, param='gamma', j=1:2, k=1:2)
If we hadn't stacked the chains, we could still use these functions, albeit with some overhead:
mh_trace_density(mcmc, param='beta', i=0:5, stacked=FALSE)
mh_summary_by_param(mcmc, fun=mean, param='gamma', j=1:2, k=1:2, stacked=FALSE)
mh_trace_density: Trace plots and density plotsmh_density: Density plotsmh_trace: Trace plotsmh_summary_by_param: Calculate summary statistics for each variable
mh_stack: "Stack" multiple MCMC chains on one another
mh_paramCreate variable names with or without indices
Can't touch this.
~ Adam ~