mcmc_tidiers {broom} | R Documentation

## 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, ...)

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

    ## S3 method for class '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

    ## 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))
    }

[Package broom version 0.4.2 Index]