Tidying methods for MCMC (Stan, JAGS, etc.) fits

MCMC tidiers will soon be deprecated in broom and there is no ongoing development of these functions at this time. MCMC tidiers are being developed in the broom.mixed package, which is not yet on CRAN.

tidyMCMC(x, pars, estimate.method = "mean", conf.int = FALSE,
  conf.level = 0.95, conf.method = "quantile", droppars = "lp__",
  rhat = FALSE, ess = FALSE, ...)

# S3 method for rjags
tidy(x, pars, estimate.method = "mean",
  conf.int = FALSE, conf.level = 0.95, conf.method = "quantile", ...)

# S3 method for stanfit
tidy(x, pars, estimate.method = "mean",
  conf.int = FALSE, conf.level = 0.95, conf.method = "quantile",
  droppars = "lp__", rhat = FALSE, ess = FALSE, ...)

Arguments

x	an object of class ‘"stanfit"’
pars	(character) specification of which parameters to include
estimate.method	method for computing point estimate ("mean" or median")
conf.int	(logical) include confidence interval?
conf.level	probability level for CI
conf.method	method for computing confidence intervals ("quantile" or "HPDinterval")
droppars	Parameters not to include in the output (such as log-probability information)
rhat, ess	(logical) include Rhat and/or effective sample size estimates?
...	unused

Examples

if (FALSE) {

# Using example from "RStan Getting Started"
# https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

model_file <- system.file("extdata", "8schools.stan", package = "broom")

schools_dat <- list(J = 8,
                    y = c(28,  8, -3,  7, -1,  1, 18, 12),
                    sigma = c(15, 10, 16, 11,  9, 11, 10, 18))

if (requireNamespace("rstan", quietly = TRUE)) {
  set.seed(2015)
  rstan_example <- stan(file = model_file, data = schools_dat,
                        iter = 100, chains = 2)
}

}

if (requireNamespace("rstan", quietly = TRUE)) {
  # the object from the above code was saved as rstan_example.rda
  infile <- system.file("extdata", "rstan_example.rda", package = "broom")
  load(infile)

  tidy(rstan_example)
  tidy(rstan_example, conf.int = TRUE, pars = "theta")

  td_mean <- tidy(rstan_example, conf.int = TRUE)
  td_median <- tidy(rstan_example, conf.int = TRUE, estimate.method = "median")

  library(dplyr)
  library(ggplot2)
  tds <- rbind(mutate(td_mean, method = "mean"),
               mutate(td_median, method = "median"))

  ggplot(tds, aes(estimate, term)) +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    geom_point(aes(color = method))
}