post_convert: Convert MCMC samples to mcmc.list format

Description

Wrapper around several ways of converting objects to mcmc.list format, automated based on the input object class.

Usage

post_convert(obj)

Value

The same information as passed in the obj argument, but formatted as mcmc.list class.

Arguments

obj: An object storing posterior samples from an MCMC algorithm. Accepted classes are list, matrix, stanfit, bugs, rjags.

Details

Accepted classes are produced by several packages, including but probably not limited to:

stanfit objects are created by rstan::stan(), which also provides rstan::As.mcmc.list(). Rather than requiring users to have 'rstan' installed to use 'postpack', post_convert() will instruct users to use this function if supplied a stanfit object.
bugs objects are created by R2WinBUGS::bugs() and R2OpenBUGS::bugs().
rjags objects are created by R2jags::jags().
list objects are created by nimble::runMCMC(), 'MCMCpack' functions, or custom MCMC algorithms.
matrix objects are created by post_subset() and is often the format of posterior quantities derived from monitored nodes.
mcmc.list objects are created by rjags::coda.samples(), jagsUI::jags.basic(), and jagsUI::jags()$samples. If a mcmc.list object is passed to obj, an error will be returned telling the user this function is not necessary.

If you find that a critical class conversion is missing, please submit an issue requesting its addition with a minimum working example of how it can be created.

Examples

Run this code

## EXAMPLE 1
# load example mcmc.list
data(cjs)

# take a subset from cjs as a matrix, retain chain and iter ids
cjs_sub = post_subset(cjs, "^B", matrix = TRUE, chains = TRUE, iters = TRUE)

# convert back to mcmc.list
class(post_convert(cjs_sub))

## EXAMPLE 2: create mcmc.list from hypothetical MCMC samples; chains are list elements
# create hypothetical samples; can't use postpack on this - not an mcmc.list
samps = lapply(1:3, function(i) {
  m = matrix(rnorm(100), 20, 5)
  colnames(m) = paste0("param", 1:5)
  m
})

# convert
samps_new = post_convert(samps)

# can use postpack now
post_summ(samps_new, "param")

## EXAMPLE 3: create mcmc.list from hypothetical MCMC samples; chains rbind-ed matrices
# create samples
f = function() {
  m = matrix(rnorm(100), 20, 5)
  colnames(m) = paste0("param", 1:5)
  m
}
samps = rbind(f(), f(), f())

# assign chain and iter IDs to each sample
samps = cbind(CHAIN = rep(1:3, each = 20), ITER = rep(1:20, 3), samps)

# convert
samps_new = post_convert(samps)

# can use postpack now
post_summ(samps_new, "param")