The $summary() method runs
summarise_draws() from the posterior
package and returns the output. For MCMC, only post-warmup draws are
included in the summary.
There is also a $print() method that prints the same summary stats but
removes the extra formatting used for printing tibbles and returns the
fitted model object itself. The $print() method may also be faster than
$summary() because it is designed to only compute the summary statistics
for the variables that will actually fit in the printed output whereas
$summary() will compute them for all of the specified variables in order
to be able to return them to the user. See Examples.
summary(variables = NULL, ...)(character vector) The variables to include.
Optional arguments to pass to posterior::summarise_draws().
The $summary() method returns the tibble data frame created by
posterior::summarise_draws().
The $print() method returns the fitted model object itself (invisibly),
which is the standard behavior for print methods in R.
if (FALSE) { # \dontrun{
fit <- cmdstanr_example("logistic")
fit$summary()
fit$print()
fit$print(max_rows = 2) # same as print(fit, max_rows = 2)
# include only certain variables
fit$summary("beta")
fit$print(c("alpha", "beta[2]"))
# include all variables but only certain summaries
fit$summary(NULL, c("mean", "sd"))
# can use functions created from formulas
# for example, calculate Pr(beta > 0)
fit$summary("beta", prob_gt_0 = ~ mean(. > 0))
# can combine user-specified functions with
# the default summary functions
fit$summary(variables = c("alpha", "beta"),
posterior::default_summary_measures()[1:4],
quantiles = ~ quantile2(., probs = c(0.025, 0.975)),
posterior::default_convergence_measures()
)
# the functions need to calculate the appropriate
# value for a matrix input
fit$summary(variables = "alpha", dim)
# the usual [stats::var()] is therefore not directly suitable as it
# will produce a covariance matrix unless the data is converted to a vector
fit$print(c("alpha", "beta"), var2 = ~var(as.vector(.x)))
} # }