Generics and methods for extracting quantities needed for plotting from various types of model objects. Currently methods are only provided for stanfit (rstan) and stanreg (rstanarm) objects, but adding new methods should be relatively straightforward.

log_posterior(object, ...)

nuts_params(object, ...)

rhat(object, ...)

neff_ratio(object, ...)

# S3 method for stanfit
log_posterior(object, inc_warmup = FALSE, ...)

# S3 method for stanreg
log_posterior(object, inc_warmup = FALSE, ...)

# S3 method for stanfit
nuts_params(object, pars = NULL, inc_warmup = FALSE, ...)

# S3 method for stanreg
nuts_params(object, pars = NULL, inc_warmup = FALSE, ...)

# S3 method for list
nuts_params(object, pars = NULL, ...)

# S3 method for stanfit
rhat(object, pars = NULL, ...)

# S3 method for stanreg
rhat(object, pars = NULL, regex_pars = NULL, ...)

# S3 method for stanfit
neff_ratio(object, pars = NULL, ...)

# S3 method for stanreg
neff_ratio(object, pars = NULL, regex_pars = NULL, ...)

Arguments

object

The object to use.

...

Arguments passed to individual methods.

inc_warmup

A logical scalar (defaulting to FALSE) indicating whether to include warmup draws, if applicable.

pars

An optional character vector of parameter names. For nuts_params these will be NUTS sampler parameter names rather than model parameters. If pars is omitted all parameters are included.

regex_pars

An optional regular expression to use for parameter selection. Can be specified instead of pars or in addition to pars.

Value

log_posterior

log_posterior methods return a molten data frame (see melt). The data frame should have columns "Iteration" (integer), "Chain" (integer), and "Value" (numeric). See Examples, below.

nuts_params

nuts_params methods return a molten data frame (see melt). The data frame should have columns "Parameter" (factor), "Iteration" (integer), "Chain" (integer), and "Value" (numeric). See Examples, below.

rhat, neff_ratio

Methods return (named) vectors.

See also

MCMC-nuts, MCMC-diagnostics

Examples

# NOT RUN {
library(rstanarm)
fit <- stan_glm(mpg ~ wt, data = mtcars)

np <- nuts_params(fit)
head(np)
tail(np)

lp <- log_posterior(fit)
head(lp)
tail(lp)
# }