post_summ: Obtain posterior summaries and diagnostics of specific nodes
In postpack: Utilities for Processing Posterior Samples Stored in 'mcmc.lists'

post_summ

R Documentation

Obtain posterior summaries and diagnostics of specific nodes

Description

Allows rapid calculation of summaries and diagnostics from specific nodes stored in mcmc.list objects.

Usage

post_summ(
  post,
  params,
  digits = NULL,
  probs = c(0.5, 0.025, 0.975),
  Rhat = FALSE,
  neff = FALSE,
  mcse = FALSE,
  by_chain = FALSE,
  auto_escape = TRUE
)

Arguments

`post`	A `mcmc.list` object.
`params`	A vector of regular expressions specifying the nodes to match for summarization. Accepts multi-element vectors to match more than one node at a time. See `match_params()` and `vignette("pattern-matching")` for more details.
`digits`	Control rounding of summaries. Passed to `base::round()` and defaults to `NULL`, which produces no rounding.
`probs`	Posterior quantiles to calculate. Passed to `stats::quantile()`. Defaults to `probs = c(0.5, 0.025, 0.975)` (i.e., median and equal-tailed 95 percent credible interval).
`Rhat`	Calculate the Rhat convergence diagnostic using `coda::gelman.diag()`? Fair warning: this can take a bit of time to run on many nodes/samples.
`neff`	Calculate the number of effective MCMC samples using `coda::effectiveSize()`? Fair warning: this can take a bit of time to run on many nodes/samples.
`mcse`	Calculate the Monte Carlo standard error for the posterior mean and reported quantiles using the `mcmcse::mcse()` and `mcmcse::mcse.q()` functions (batch means method with batch size automatically calculated)? Fair warning: this can take a bit of time to run on many nodes/samples.
`by_chain`	Calculate posterior summaries for each chain rather than for the aggregate across chains? Defaults to `FALSE`. The arguments `Rhat`, `neff`, and `mcse` are ignored if `by_chain = TRUE` and a warning will be returned.
`auto_escape`	Automatically escape `"["` and `"]"` characters for pattern matching? See `match_params()` for details.

Value

A matrix object with summary statistics as rows and nodes as columns. If by_chain = TRUE, an array with chain-specific summaries as the third dimension is returned instead.

Examples

# load example mcmc.list
data(cjs)

# calculate posterior summaries for the "p" nodes
# ("p[1]" doesn't exist in model)
post_summ(cjs, "p")

# do this by chain
post_summ(cjs, "p", by_chain = TRUE)

# calculate Rhat and Neff diagnostic summaries as well
# multiple node names too
post_summ(cjs, c("b0", "p"), Rhat = TRUE, neff = TRUE)

# calculate Monte Carlo SE for mean and quantiles, with rounding
post_summ(cjs, "p", mcse = TRUE, digits = 3)

# summarize different quantiles: median and central 80%
post_summ(cjs, "p", probs = c(0.5, 0.1, 0.9))

postpack documentation built on Dec. 28, 2022, 1:23 a.m.