fit-method-summary: Compute a summary table of estimates and diagnostics

In stan-dev/cmdstanr: R Interface to 'CmdStan'

Compute a summary table of estimates and diagnostics

Description

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.

Usage

summary(variables = NULL, ...)

Arguments

variables	(character vector) The variables to include.
...	Optional arguments to pass to posterior::summarise_draws().

Value

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.

See Also

CmdStanMCMC, CmdStanMLE, CmdStanLaplace, CmdStanVB, CmdStanGQ

Examples

## Not run: 
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)))


## End(Not run)

stan-dev/cmdstanr documentation built on April 26, 2024, 2:02 a.m.