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(), .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()
| .x, object | (draws) A |
|---|---|
| ... | 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. |
| .args | (named list) Optional arguments passed to the summary functions. |
| .cores | (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 |
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.
#> [1] "draws_array" "draws" "array"#> '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 x 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 x 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 4.64summarise_draws(x, mean, mcse = mcse_mean)#> # A tibble: 10 x 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#> # A tibble: 10 x 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 x 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 12.2summarise_draws(x, default_convergence_measures())#> # A tibble: 10 x 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 x 6 #> variable mcse_mean mcse_median mcse_sd mcse_q5 mcse_q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mu 0.150 0.319 0.149 0.551 0.415 #> 2 tau 0.213 0.250 0.156 0.114 0.964 #> 3 theta[1] 0.319 0.262 0.233 0.820 1.36 #> 4 theta[2] 0.202 0.213 0.166 0.676 0.848 #> 5 theta[3] 0.447 0.346 0.329 2.18 0.623 #> 6 theta[4] 0.189 0.287 0.154 0.956 0.449 #> 7 theta[5] 0.232 0.139 0.213 1.62 0.736 #> 8 theta[6] 0.222 0.421 0.195 1.16 0.432 #> 9 theta[7] 0.250 0.270 0.178 0.458 0.622 #> 10 theta[8] 0.273 0.372 0.241 0.997 1.29#> # A tibble: 10 x 17 #> variable var.1 var.2 var.3 var.4 var.5 var.6 var.7 var.8 var.9 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mu 12.1 0.652 -0.288 -0.153 0.652 17.5 -0.157 -1.46 -0.288 #> 2 tau 11.9 -2.40 -0.0611 -0.799 -2.40 13.1 -1.27 -0.304 -0.0611 #> 3 theta[1] 46.4 0.805 -3.43 0.782 0.805 42.6 -9.72 -1.65 -3.43 #> 4 theta[2] 27.0 -0.132 0.00865 -1.52 -0.132 26.6 -5.31 -2.10 0.00865 #> 5 theta[3] 44.9 -8.23 -3.24 -3.66 -8.23 36.2 -2.01 0.194 -3.24 #> 6 theta[4] 22.8 0.266 -1.42 -1.74 0.266 27.6 -2.15 0.593 -1.42 #> 7 theta[5] 30.9 0.879 0.840 -4.10 0.879 29.8 2.34 -0.505 0.840 #> 8 theta[6] 34.8 0.754 -3.73 -3.03 0.754 24.9 0.0602 0.939 -3.73 #> 9 theta[7] 30.5 2.41 -0.968 -1.16 2.41 34.8 -2.53 -3.50 -0.968 #> 10 theta[8] 25.7 1.01 -3.63 -1.32 1.01 33.1 0.314 -4.44 -3.63 #> # ... with 7 more variables: var.10 <dbl>, var.11 <dbl>, var.12 <dbl>, #> # var.13 <dbl>, var.14 <dbl>, var.15 <dbl>, var.16 <dbl># illustrate use of '.args' ws <- rexp(ndraws(x)) summarise_draws(x, weighted.mean, .args = list(w = ws))#> # A tibble: 10 x 2 #> variable weighted.mean #> <chr> <dbl> #> 1 mu 4.14 #> 2 tau 4.22 #> 3 theta[1] 7.41 #> 4 theta[2] 5.03 #> 5 theta[3] 3.35 #> 6 theta[4] 4.77 #> 7 theta[5] 2.87 #> 8 theta[6] 3.33 #> 9 theta[7] 6.67 #> 10 theta[8] 4.73