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 $print() method returns the fitted model object itself (invisibly),
which is the standard behavior for print methods in R.
# \dontrun{
fit <- cmdstanr_example("logistic")
fit$summary()
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ -66.0 -65.6 1.45 1.26 -68.7 -64.3 1.00 1922.
#> 2 alpha 0.378 0.373 0.221 0.218 0.0220 0.750 1.00 4329.
#> 3 beta[1] -0.669 -0.660 0.252 0.257 -1.09 -0.264 1.00 3807.
#> 4 beta[2] -0.279 -0.273 0.223 0.223 -0.649 0.0786 1.00 3937.
#> 5 beta[3] 0.677 0.669 0.267 0.257 0.248 1.13 1.00 3914.
#> 6 log_lik[1] -0.516 -0.510 0.0985 0.0955 -0.690 -0.361 1.00 4145.
#> 7 log_lik[2] -0.404 -0.386 0.148 0.137 -0.677 -0.198 1.00 4343.
#> 8 log_lik[3] -0.502 -0.466 0.215 0.206 -0.908 -0.213 1.00 4197.
#> 9 log_lik[4] -0.447 -0.432 0.150 0.147 -0.720 -0.233 1.00 3438.
#> 10 log_lik[5] -1.18 -1.16 0.280 0.281 -1.68 -0.753 1.00 4471.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
fit$print()
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> lp__ -65.96 -65.63 1.45 1.26 -68.70 -64.28 1.00 1922 2690
#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617
#> beta[1] -0.67 -0.66 0.25 0.26 -1.09 -0.26 1.00 3806 3246
#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.08 1.00 3937 2974
#> beta[3] 0.68 0.67 0.27 0.26 0.25 1.13 1.00 3913 3133
#> log_lik[1] -0.52 -0.51 0.10 0.10 -0.69 -0.36 1.00 4145 3089
#> log_lik[2] -0.40 -0.39 0.15 0.14 -0.68 -0.20 1.00 4342 3107
#> log_lik[3] -0.50 -0.47 0.22 0.21 -0.91 -0.21 1.00 4196 3253
#> log_lik[4] -0.45 -0.43 0.15 0.15 -0.72 -0.23 1.00 3437 2969
#> log_lik[5] -1.18 -1.16 0.28 0.28 -1.68 -0.75 1.00 4471 3204
#>
#> # showing 10 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
fit$print(max_rows = 2) # same as print(fit, max_rows = 2)
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> lp__ -65.96 -65.63 1.45 1.26 -68.70 -64.28 1.00 1922 2690
#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617
#>
#> # showing 2 of 105 rows (change via 'max_rows' argument or 'cmdstanr_max_rows' option)
# include only certain variables
fit$summary("beta")
#> # A tibble: 3 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 beta[1] -0.669 -0.660 0.252 0.257 -1.09 -0.264 1.00 3807. 3246.
#> 2 beta[2] -0.279 -0.273 0.223 0.223 -0.649 0.0786 1.00 3937. 2974.
#> 3 beta[3] 0.677 0.669 0.267 0.257 0.248 1.13 1.00 3914. 3133.
fit$print(c("alpha", "beta[2]"))
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> alpha 0.38 0.37 0.22 0.22 0.02 0.75 1.00 4329 2617
#> beta[2] -0.28 -0.27 0.22 0.22 -0.65 0.08 1.00 3937 2974
# include all variables but only certain summaries
fit$summary(NULL, c("mean", "sd"))
#> # A tibble: 105 × 3
#> variable mean sd
#> <chr> <dbl> <dbl>
#> 1 lp__ -66.0 1.45
#> 2 alpha 0.378 0.221
#> 3 beta[1] -0.669 0.252
#> 4 beta[2] -0.279 0.223
#> 5 beta[3] 0.677 0.267
#> 6 log_lik[1] -0.516 0.0985
#> 7 log_lik[2] -0.404 0.148
#> 8 log_lik[3] -0.502 0.215
#> 9 log_lik[4] -0.447 0.150
#> 10 log_lik[5] -1.18 0.280
#> # ℹ 95 more rows
# can use functions created from formulas
# for example, calculate Pr(beta > 0)
fit$summary("beta", prob_gt_0 = ~ mean(. > 0))
#> # A tibble: 3 × 2
#> variable prob_gt_0
#> <chr> <dbl>
#> 1 beta[1] 0.0015
#> 2 beta[2] 0.102
#> 3 beta[3] 0.993
# can combine user-specified functions with
# the default summary functions
fit$summary(variables = c("alpha", "beta"),
posterior::default_summary_measures()[1:4],
quantiles = ~ quantile2(., probs = c(0.025, 0.975)),
posterior::default_convergence_measures()
)
#> # A tibble: 4 × 10
#> variable mean median sd mad q2.5 q97.5 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 alpha 0.378 0.373 0.221 0.218 -0.0434 0.812 1.00 4329. 2618.
#> 2 beta[1] -0.669 -0.660 0.252 0.257 -1.17 -0.191 1.00 3807. 3246.
#> 3 beta[2] -0.279 -0.273 0.223 0.223 -0.728 0.143 1.00 3937. 2974.
#> 4 beta[3] 0.677 0.669 0.267 0.257 0.164 1.22 1.00 3914. 3133.
# the functions need to calculate the appropriate
# value for a matrix input
fit$summary(variables = "alpha", dim)
#> # A tibble: 1 × 3
#> variable dim.1 dim.2
#> <chr> <int> <int>
#> 1 alpha 1000 4
# 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)))
#> variable var2
#> alpha 0.05
#> beta[1] 0.06
#> beta[2] 0.05
#> beta[3] 0.07
# }