monitor {rstan} | R Documentation |
Similar to the print
method for stanfit
objects, but monitor
takes an array of simulations as its argument rather than a stanfit
object. For a 3-D array (iterations * chains * parameters) of MCMC draws,
monitor
computes means, standard deviations, quantiles, Monte Carlo
standard errors, split Rhats, and effective sample sizes. By default, half of
the iterations are considered warmup and are excluded.
monitor(sims, warmup = floor(dim(sims)[1]/2), probs = c(0.025, 0.25, 0.5, 0.75, 0.975), digits_summary = 1, print = TRUE, ...)
sims |
A 3-D array (iterations * chains * parameters) of MCMC simulations from any MCMC algorithm. |
warmup |
The number of warmup iterations to be excluded
when computing the summaries. The default is half of the total number
of iterations. If |
probs |
A numeric vector specifying quantiles of interest. The
defaults is |
digits_summary |
The number of significant digits to use when printing the summary, defaulting to 1. Applies to the quantities other than the effective sample size, which is always rounded to the nearest integer. |
print |
Logical, indicating whether to print the summary after the computations are performed. |
... |
Additional arguments passed to the underlying |
A 2-D array with rows corresponding to parameters and columns to the summary statistics.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org.
S4 class stanfit
and particularly its
print
method.
csvfiles <- dir(system.file('misc', package = 'rstan'), pattern = 'rstan_doc_ex_[0-9].csv', full.names = TRUE) fit <- read_stan_csv(csvfiles) # The following is just for the purpose of giving an example # since print can be used for a stanfit object. monitor(extract(fit, permuted = FALSE, inc_warmup = TRUE))