monitor.Rd
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 3D 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, ...) # S3 method for simsummary print(x, digits = 3, se = FALSE, ...) # S3 method for simsummary [(x, i, j, drop = if (missing(i)) TRUE else length(j) == 1)
sims  A 3D 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. 
Logical, indicating whether to print the summary after the computations are performed. 

...  Additional arguments passed to the underlying 
x  An object of class 
digits  An integer scalar defaulting to 3 for the number of decimal places to print 
se  A logical scalar defaulting to 
i  A vector indicating which rows of the object created by 
j  A vector indicating which columns of the object crated by 
drop  A logical scalar indicating whether the resulting object should return a vector where possible 
A 2D array with rows corresponding to parameters and columns to the summary statistics that can be printed and subset.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mcstan.org.
csvfiles < dir(system.file('misc', package = 'rstan'), pattern = 'rstan_doc_ex_[09].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))#> Inference for the input samples (4 chains: each with iter = 200; warmup = 100): #> #> Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS #> mu 0.3 0.1 0.4 0.1 0.2 1.01 348 229 #> sigma 0.9 1.1 1.5 1.2 0.2 1.01 227 178 #> z[1,1] 1.6 0.0 1.4 0.0 0.9 1.01 308 301 #> z[2,1] 1.4 0.1 1.7 0.1 1.0 1.01 329 246 #> z[3,1] 1.9 0.1 1.7 0.1 1.1 1.00 455 294 #> z[1,2] 1.7 0.0 1.5 0.0 1.0 1.01 278 138 #> z[2,2] 1.5 0.1 1.5 0.0 0.9 1.01 438 251 #> z[3,2] 1.6 0.1 1.9 0.1 1.0 1.02 344 124 #> alpha 0.0 0.4 1.6 0.5 0.5 1.01 354 116 #> lp__ 21.5 17.2 14.6 17.5 2.3 1.03 117 208 #> #> For each parameter, Bulk_ESS and Tail_ESS are crude measures of #> effective sample size for bulk and tail quantities respectively (an ESS > 100 #> per chain is considered good), and Rhat is the potential scale reduction #> factor on rank normalized split chains (at convergence, Rhat <= 1.05).