retrieve {shinystan}

Get summary statistics from shinystan object

Description

From a shinystan object get rhat, effective sample size, posterior quantiles, means, standard deviations, sampler diagnostics, etc.

Usage

retrieve(sso, what, ...)

Arguments

sso

A shinystan object.

what

What do you want to get? See Details, below.

...

Optional arguments, in particular pars to specify parameter names (by default all parameters will be used). For NUTS sampler parameters only (e.g. stepsize, treedepth) inc_warmup can also be specified to include/exclude warmup iterations (the default is FALSE). See Details, below.

Details

The argument what can take on the values below. 'Args: arg' means that arg can be specified in ... for this value of what.

"rhat", "Rhat", "r_hat", or "R_hat"

returns: Rhat statistics. Args: pars

"N_eff", "n_eff", "neff", "Neff", "ess", or "ESS"

returns: Effective sample sizes. Args: pars

"mean"

returns: Posterior means. Args: pars

"sd"

returns: Posterior standard deviations. Args: pars

"se_mean" or "mcse"

returns: Monte Carlo standard error. Args: pars

"median"

returns: Posterior medians. Args: pars.

"quantiles" or any string with "quant" in it (not case sensitive)

returns: 2.5%, 25%, 50%, 75%, 97.5% posterior quantiles. Args: pars.

"avg_accept_stat" or any string with "accept" in it (not case sensitive)

returns: Average value of "accept_stat" (which itself is the average acceptance probability over the NUTS subtree). Args: inc_warmup

"prop_divergent" or any string with "diverg" in it (not case sensitive)

returns: Proportion of divergent iterations for each chain. Args: inc_warmup

"max_treedepth" or any string with "tree" or "depth" in it (not case sensitive)

returns: Maximum treedepth for each chain. Args: inc_warmup

"avg_stepsize" or any string with "step" in it (not case sensitive)

returns: Average stepsize for each chain. Args: inc_warmup

Note

Sampler diagnostics (e.g. "avg_accept_stat") only available for models originally fit using Stan.

Examples

# Using example shinystan object 'eight_schools'
sso <- eight_schools
retrieve(sso, "rhat")
retrieve(sso, "mean", pars = c('theta[1]', 'mu'))
retrieve(sso, "quantiles")
retrieve(sso, "max_treedepth")  # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc.
retrieve(sso, "prop_divergent")
retrieve(sso, "prop_divergent", inc_warmup = TRUE)