MCMCsummary | R Documentation |
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,
exact = TRUE,
probs = c(0.025, 0.5, 0.975),
hpd_prob = 0.95,
HPD = FALSE,
pg0 = FALSE,
digits = NULL,
round = NULL,
Rhat = TRUE,
n.eff = TRUE,
func = NULL,
func_name = NULL
)
object |
Object containing MCMC output. See DETAILS below. |
params |
Character string (or vector of character strings) denoting parameters to be returned in summary output. Default |
excl |
Character string (or vector of character strings) denoting parameters to exclude. Used in conjunction with |
ISB |
Ignore Square Brackets (ISB). Logical specifying whether square brackets should be ignored in the |
exact |
Logical specifying whether input from |
probs |
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). |
hpd_prob |
Scalar in (0,1) representing probability used to calculate highest posterior density intervals for the selected parameters. Default is 0.95. |
HPD |
Logical specifying whether to calculate equal-tailed credible intervals ( |
pg0 |
Logical specifying whether to calculate the proportion of the posterior that is greater than 0, rounded to 2 digits. |
digits |
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. |
round |
Number of decimal places to round to for posterior summary. Cannot be used in conjunction with |
Rhat |
Logical specifying whether to calculate and display the potential scale reduction statistic (Rhat). Values near 1 suggest convergence (Brooks and Gelman 1998). |
n.eff |
Logical specifying whether to calculate and display the number of effective samples for each parameter. |
func |
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). |
func_name |
Character string (or vector of character strings) specifying labels for output from |
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.
object
argument can be a stanfit
object (rstan
package), a CmdStanMCMC
object (cmdstanr
package), a stanreg
object (rstanarm
package), a brmsfit
object (brms
package), an mcmc.list
object (coda
and rjags
packages), mcmc
object (coda
and nimble
packages), list
object (nimble
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.
For mcmc.list
, mcmc
, and 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
(as well as CmdStanMCMC
, stanreg
, and brmsfit
objects) and jagsUI
objects, Rhat is calculated using a 'split chain' Rhat (in their respective packages), which is thought to be a more conservative diagnostic (Stan Development Team 2018).
For mcmc.list
, mcmc
, and list
objects, the number of effective samples is calculated using the effectiveSize
function in the coda
package. For stanfit
(as well as CmdStanMCMC
, stanreg
, and brmsfit
objects) and jagsUI
objects, n.eff is calculated using a slightly different method of computation for the number of effective samples (Stan Development Team 2018). For CmdStanMCMC
objects, both bulk and tail n.eff is calculated.
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. https://mc-stan.org
#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]'
MCMCsummary(MCMC_data, params = c('beta[1]', 'beta[4]', 'alpha[3]'), ISB = FALSE, exact = TRUE)
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