Extract summary information from MCMC output (mean, median, quantiles, Gelman-Rubin convergence statistic, number of effective samples, and specified custom metrics) for specific parameters of interest.
MCMCsummary(object, params = "all", excl = NULL, ISB = TRUE,
probs = c(0.025, 0.5, 0.975), hpd_prob = 0.95, HPD = FALSE,
digits = NULL, round = NULL, Rhat = TRUE, n.eff = TRUE,
func = NULL, func_name = NULL)Object containing MCMC output. See DETAILS below.
Character string (or vector of character strings) denoting parameters to be returned in summary output.
Default 'all' returns all parameters in summary output.
Character string (or vector of character strings) denoting parameters to exclude. Used in conjunction with params argument to select parameters of interest.
Ignore Square Brackets (ISB). Logical specifying whether square brackets should be ignored in the params and excl arguments. If TRUE, square brackets are ignored - input from params and excl are otherwise matched exactly. If FALSE, square brackets are not ignored - input from params and excl are matched using grep, which can take arguments in regular expression format. This allows partial names to be used when specifying parameters of interest.
Numeric vector where each element in (0,1) representing probabilities used to calculate posterior sample quantiles for the selected parameters. Default is c(0.025, 0.5, 0.975).
Scalar in (0,1) representing probability used to calculate highest posterior density intervals for the selected parameters. Default is 0.95.
Logical specifying whether to calculate equal-tailed credible intervals (HPD = FALSE) or highest posterior density intervals (HPD = TRUE) for the selected parameters. Default is HPD = FALSE.
Number of significant digits to include for posterior summary. All computed digits will be included by default. Note that Rhat is always rounded to 2 decimal places.
Number of decimal places to round to for posterior summary. Cannot be used in conjunction with digits argument. Note that Rhat is always rounded to 2 decimal places.
Logical specifying whether to calculate and display the potential scale reduction statistic (Rhat). Values near 1 suggest convergence (Brooks and Gelman 1998). Rhat = FALSE will prevent display of this column in summary output. Specifying Rhat = FALSE, may increase function speed for very large mcmc.list objects.
Logical specifying whether to calculate and display the number of effective samples for each parameter. n.eff = FALSE will prevent display of this column in summary output. Specifying n.eff = FALSE, may increase function speed for very large mcmc.list objects. Default is n.eff = TRUE.
Function to be performed on MCMC output. If a function is specified, it will be evaluated on posteriors for each specified parameter and returned as a column in the summary output (or multiple columns if the function returns more than one value).
Character string (or vector of character strings) specifying labels for output from func argument. If func_name is not specified, columns with func argument will be labeled 'func'.
Function returns summary information (including parameter posterior mean, posterior sd, quantiles, potential scale reduction statistic (Rhat), number of effective samples, and other specified metrics) for specified parameters.
For mcmc.list objects, the potential scale reduction statistic statistic (Rhat) is calculated using the gelman.diag function in the coda package (what is typically displayed in the summary output from models fit with JAGS). For stanfit objects (as well as stanreg and brmsfit objects), Rhat is calculated using the rstan package which computes a 'split chain' Rhat, which is thought to be a more conservative diagnostic (Stan Development Team 2018).
For mcmc.list objects, the number of effective samples is calculated using the effectiveSize function in the coda package. For stanfit objects (as well as stanreg and brmsfit objects), n.eff is calculated using the rstan package which (in a similar way to the Rhat computation noted above) employs a slightly different (and more conservative) method of computation for the number of effective samples (Stan Development Team 2018).
object argument can be a stanfit object (rstan package), a stanreg object (rstanarm package), a brmsfit object (brms package), an mcmc.list object (coda package), an R2jags model object (R2jags package), a jagsUI model object (jagsUI package), or a matrix containing MCMC chains (each column representing MCMC output for a single parameter, rows representing iterations in the chain). The function automatically detects the object type and proceeds accordingly.
Brooks, S. P., and A. Gelman. 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7:434.
Stan Development Team. 2018. Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0. http://mc-stan.org
# NOT RUN {
#Load data
data(MCMC_data)
#Summary information for MCMC output - display 2 significant digits
MCMCsummary(MCMC_data, digits = 2)
#Just 'beta' parameters - round to 2 decimal places
MCMCsummary(MCMC_data, params = 'beta', round = 2)
#Just 'beta[1]', 'beta[4]', and 'alpha[3]'
#'params' takes regular expressions when ISB = FALSE, square brackets must be escaped with '\\'
MCMCsummary(MCMC_data, params = c('beta\\[1\\]', 'beta\\[4\\]', 'alpha\\[3\\]'), ISB = FALSE)
# }
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