rstan (version 2.9.0-3)

monitor: Compute the summary for MCMC simulation samples and monitor the convergence

Description

For a 3-d array (the number of iterations * the number of chains * the number of parameters) of MCMC simulation samples, this function computes the summaries such as mean, standard deviation, standard error of the mean, and quantiles. And for monitoring the convergence, split Rhat and the effective sample size are also computed. By default, half of the iterations are considered to be warmup samples and thus excluded.

Usage

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, ...)

Arguments

sims

A 3-dimension array of samples simulated from any MCMC algorithm. The first dimension is for the number of iterations; the second for the number of chains; the third for the parameters.

warmup

The number of warmup iterations that would be excluded for computing the summaries; default to half of the total number of iterations.

probs

Quantiles of interest; defaults to "c(0.025,0.25,0.5,0.75,0.975)"

digits_summary

The number of significant digits for printing out the summary; defaults to 1. The effective sample size is always rounded to integers.

...

Additional arguments for the underlying print method.

print

Logical; indicating whether to print the summary.

Value

• A summary given as a 2 dimension array for the input samples: each row is for one parameter; the columns are the mean, standard deviation, quantiles, split Rhat, the effective sample size, etc.

Details

Similar to the print function for stanfit object, the function prints out a summary for the simulated samples. In particular, for monitoring the convergence, the printout includes the split Rhat and the effective sample size.

References

The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org.

See Also

S4 class stanfit and particularly its method print.

Examples

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))