broom (version 0.4.1)

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

Description

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

Usage

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

## Not run: 
# 
# # 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)
# }
# 
# ## End(Not run)

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))
}