The summarise_draws() (and summarize_draws()) methods provide a quick way
to get a table of summary statistics and diagnostics. These methods will
convert an object to a draws object if it isn't already. For convenience, a
summary() method for draws and rvar objects are also
provided as an alias for summarise_draws() if the input object is a draws
or rvar object.
summarise_draws(.x, ...)
summarize_draws(.x, ...)
# S3 method for draws
summarise_draws(
.x,
...,
.args = list(),
.num_args = getOption("posterior.num_args", list()),
.cores = 1
)
# S3 method for draws
summary(object, ...)
# S3 method for rvar
summarise_draws(.x, ...)
# S3 method for rvar
summary(object, ...)
default_summary_measures()
default_convergence_measures()
default_mcse_measures()(draws) A draws object or one coercible to a draws object.
Name-value pairs of summary or diagnostic functions. The provided names will be used as the names of the columns in the result unless the function returns a named vector, in which case the latter names are used. The functions can be specified in any format supported by as_function(). See Examples.
(named list) Optional arguments passed to the summary functions.
(named list) Optional arguments passed to
num() for pretty printing of summaries. Can be controlled
globally via the posterior.num_args option.
(positive integer) The number of cores to use for computing
summaries for different variables in parallel. Coerced to integer if
possible, otherwise errors. The default is .cores = 1, in which case no
parallelization is implemented. By default, a socket cluster is used on
Windows and forks otherwise.
The summarise_draws() methods return a tibble data frame.
The first column ("variable") contains the variable names and the remaining
columns contain summary statistics and diagnostics.
The functions default_summary_measures(), default_convergence_measures(),
and default_mcse_measures() return character vectors of names of the
default measures.
The default summary functions used are the ones specified by
default_summary_measures() and default_convergence_measures():
default_summary_measures()
default_convergence_measures()
The var() function should not be used to compute variances due
to its inconsistent behavior with matrices. Instead, please use
distributional::variance().
diagnostics for a list of available diagnostics and links to
their individual help pages.
x <- example_draws("eight_schools")
class(x)
#> [1] "draws_array" "draws" "array"
str(x)
#> 'draws_array' num [1:100, 1:4, 1:10] 2.01 1.46 5.81 6.85 1.81 ...
#> - attr(*, "dimnames")=List of 3
#> ..$ iteration: chr [1:100] "1" "2" "3" "4" ...
#> ..$ chain : chr [1:4] "1" "2" "3" "4"
#> ..$ variable : chr [1:10] "mu" "tau" "theta[1]" "theta[2]" ...
summarise_draws(x)
#> # A tibble: 10 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 4.18 4.16 3.40 3.57 -0.854 9.39 1.02 558. 322.
#> 2 tau 4.16 3.07 3.58 2.89 0.309 11.0 1.01 246. 202.
#> 3 theta[1] 6.75 5.97 6.30 4.87 -1.23 18.9 1.01 400. 254.
#> 4 theta[2] 5.25 5.13 4.63 4.25 -1.97 12.5 1.02 564. 372.
#> 5 theta[3] 3.04 3.99 6.80 4.94 -10.3 11.9 1.01 312. 205.
#> 6 theta[4] 4.86 4.99 4.92 4.51 -3.57 12.2 1.02 695. 252.
#> 7 theta[5] 3.22 3.72 5.08 4.38 -5.93 10.8 1.01 523. 306.
#> 8 theta[6] 3.99 4.14 5.16 4.81 -4.32 11.5 1.02 548. 205.
#> 9 theta[7] 6.50 5.90 5.26 4.54 -1.19 15.4 1.00 434. 308.
#> 10 theta[8] 4.57 4.64 5.25 4.89 -3.79 12.2 1.02 355. 146.
summarise_draws(x, "mean", "median")
#> # A tibble: 10 × 3
#> variable mean median
#> <chr> <dbl> <dbl>
#> 1 mu 4.18 4.16
#> 2 tau 4.16 3.07
#> 3 theta[1] 6.75 5.97
#> 4 theta[2] 5.25 5.13
#> 5 theta[3] 3.04 3.99
#> 6 theta[4] 4.86 4.99
#> 7 theta[5] 3.22 3.72
#> 8 theta[6] 3.99 4.14
#> 9 theta[7] 6.50 5.90
#> 10 theta[8] 4.57 4.64
summarise_draws(x, mean, mcse = mcse_mean)
#> # A tibble: 10 × 3
#> variable mean mcse
#> <chr> <dbl> <dbl>
#> 1 mu 4.18 0.150
#> 2 tau 4.16 0.213
#> 3 theta[1] 6.75 0.319
#> 4 theta[2] 5.25 0.202
#> 5 theta[3] 3.04 0.447
#> 6 theta[4] 4.86 0.189
#> 7 theta[5] 3.22 0.232
#> 8 theta[6] 3.99 0.222
#> 9 theta[7] 6.50 0.250
#> 10 theta[8] 4.57 0.273
summarise_draws(x, ~quantile(.x, probs = c(0.4, 0.6)))
#> # A tibble: 10 × 3
#> variable `40%` `60%`
#> <chr> <dbl> <dbl>
#> 1 mu 3.41 5.35
#> 2 tau 2.47 3.96
#> 3 theta[1] 4.95 7.01
#> 4 theta[2] 4.32 6.13
#> 5 theta[3] 2.54 5.33
#> 6 theta[4] 3.78 6.11
#> 7 theta[5] 2.69 4.69
#> 8 theta[6] 2.92 5.47
#> 9 theta[7] 4.81 7.33
#> 10 theta[8] 3.50 5.92
# using default_*_meaures()
summarise_draws(x, default_summary_measures())
#> # A tibble: 10 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 4.18 4.16 3.40 3.57 -0.854 9.39
#> 2 tau 4.16 3.07 3.58 2.89 0.309 11.0
#> 3 theta[1] 6.75 5.97 6.30 4.87 -1.23 18.9
#> 4 theta[2] 5.25 5.13 4.63 4.25 -1.97 12.5
#> 5 theta[3] 3.04 3.99 6.80 4.94 -10.3 11.9
#> 6 theta[4] 4.86 4.99 4.92 4.51 -3.57 12.2
#> 7 theta[5] 3.22 3.72 5.08 4.38 -5.93 10.8
#> 8 theta[6] 3.99 4.14 5.16 4.81 -4.32 11.5
#> 9 theta[7] 6.50 5.90 5.26 4.54 -1.19 15.4
#> 10 theta[8] 4.57 4.64 5.25 4.89 -3.79 12.2
summarise_draws(x, default_convergence_measures())
#> # A tibble: 10 × 4
#> variable rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl>
#> 1 mu 1.02 558. 322.
#> 2 tau 1.01 246. 202.
#> 3 theta[1] 1.01 400. 254.
#> 4 theta[2] 1.02 564. 372.
#> 5 theta[3] 1.01 312. 205.
#> 6 theta[4] 1.02 695. 252.
#> 7 theta[5] 1.01 523. 306.
#> 8 theta[6] 1.02 548. 205.
#> 9 theta[7] 1.00 434. 308.
#> 10 theta[8] 1.02 355. 146.
summarise_draws(x, default_mcse_measures())
#> # A tibble: 10 × 6
#> variable mcse_mean mcse_median mcse_sd mcse_q5 mcse_q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 0.150 0.319 0.249 0.551 0.415
#> 2 tau 0.213 0.250 0.236 0.114 0.964
#> 3 theta[1] 0.319 0.262 0.327 0.820 1.36
#> 4 theta[2] 0.202 0.213 0.273 0.676 0.848
#> 5 theta[3] 0.447 0.346 0.611 2.18 0.623
#> 6 theta[4] 0.189 0.287 0.251 0.956 0.449
#> 7 theta[5] 0.232 0.139 0.328 1.62 0.736
#> 8 theta[6] 0.222 0.421 0.365 1.16 0.432
#> 9 theta[7] 0.250 0.270 0.260 0.458 0.622
#> 10 theta[8] 0.273 0.372 0.322 0.997 1.29
# compute variance of variables
summarise_draws(x, var = distributional::variance)
#> # A tibble: 10 × 2
#> variable var
#> <chr> <dbl>
#> 1 mu 11.6
#> 2 tau 12.8
#> 3 theta[1] 39.7
#> 4 theta[2] 21.5
#> 5 theta[3] 46.2
#> 6 theta[4] 24.2
#> 7 theta[5] 25.9
#> 8 theta[6] 26.6
#> 9 theta[7] 27.7
#> 10 theta[8] 27.6
# illustrate use of '.args'
ws <- rexp(ndraws(x))
summarise_draws(x, weighted.mean, .args = list(w = ws))
#> # A tibble: 10 × 2
#> variable weighted.mean
#> <chr> <dbl>
#> 1 mu 4.33
#> 2 tau 4.07
#> 3 theta[1] 6.66
#> 4 theta[2] 5.35
#> 5 theta[3] 3.47
#> 6 theta[4] 4.90
#> 7 theta[5] 3.53
#> 8 theta[6] 4.05
#> 9 theta[7] 6.71
#> 10 theta[8] 4.58
# adjust how numerical summaries are printed
summarise_draws(x, .num_args = list(sigfig = 2, notation = "dec"))
#> # A tibble: 10 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 4.2 4.2 3.4 3.6 -0.85 9.4 1.0 558. 322.
#> 2 tau 4.2 3.1 3.6 2.9 0.31 11. 1.0 246. 202.
#> 3 theta[1] 6.7 6.0 6.3 4.9 -1.2 19. 1.0 400. 254.
#> 4 theta[2] 5.3 5.1 4.6 4.3 -2.0 13. 1.0 564. 372.
#> 5 theta[3] 3.0 4.0 6.8 4.9 -10. 12. 1.0 312. 205.
#> 6 theta[4] 4.9 5.0 4.9 4.5 -3.6 12. 1.0 695. 252.
#> 7 theta[5] 3.2 3.7 5.1 4.4 -5.9 11. 1.0 523. 306.
#> 8 theta[6] 4.0 4.1 5.2 4.8 -4.3 12. 1.0 548. 205.
#> 9 theta[7] 6.5 5.9 5.3 4.5 -1.2 15. 1.0 434. 308.
#> 10 theta[8] 4.6 4.6 5.3 4.9 -3.8 12. 1.0 355. 146.