An implementation of posterior::summarise_draws() for grouped data frames
(dplyr::grouped_df objects) such as returned by dplyr::group_by() and
the various grouped-data-aware functions in tidybayes, such as spread_draws(),
gather_draws(), add_epred_draws(), and add_predicted_draws().
This function provides a quick way to get a variety of summary statistics
and diagnostics on draws.
# S3 method for grouped_df
summarise_draws(.x, ...)A grouped data frame (dplyr::grouped_df object) such as returned by
dplyr::group_by() where the data frame in each group (ignoring grouping columns)
has the structure of a posterior::draws_df() object: ".chain", ".iteration", and
".draw" columns, with the remaining (non-grouping) columns being draws
from variables.
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.
A data frame (actually, a tibble) with all grouping
columns from .x, a "variable" column containing variable names from .x,
and the remaining columns containing summary statistics and diagnostics.
While posterior::summarise_draws() can operate on tidy data frames of
draws in the posterior::draws_df() format, that format does not support
grouping columns. This provides an implementation of summarise_draws()
that does support grouped data tables, essentially applying
posterior::summarise_draws() to every sub-table of .x implied by the
groups defined on the data frame.
See posterior::summarise_draws() for
more details on the summary statistics and diagnostics you can use with this
function. If you just want point summaries and intervals (not diagnostics),
particularly for plotting, see point_interval(), which returns long-format
data tables more suitable for that purpose (especially if you want to plot
multiple uncertainty levels).
library(posterior)
library(dplyr)
d = posterior::example_draws()
# The default posterior::summarise_draws() summarises all variables without
# splitting out indices:
summarise_draws(d)
#> # A tibble: 10 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 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.
# The grouped_df implementation of summarise_draws() in tidybayes can handle
# output from spread_draws(), which is a grouped data table with the indices
# (here, `i`) left as columns:
d %>%
spread_draws(theta[i]) %>%
summarise_draws()
#> # A tibble: 8 × 11
#> # Groups: i [8]
#> i variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 theta 6.75 5.97 6.30 4.87 -1.23 18.9 1.01 400. 254.
#> 2 2 theta 5.25 5.13 4.63 4.25 -1.97 12.5 1.02 564. 372.
#> 3 3 theta 3.04 3.99 6.80 4.94 -10.3 11.9 1.01 312. 205.
#> 4 4 theta 4.86 4.99 4.92 4.51 -3.57 12.2 1.02 695. 252.
#> 5 5 theta 3.22 3.72 5.08 4.38 -5.93 10.8 1.01 523. 306.
#> 6 6 theta 3.99 4.14 5.16 4.81 -4.32 11.5 1.02 548. 205.
#> 7 7 theta 6.50 5.90 5.26 4.54 -1.19 15.4 1.00 434. 308.
#> 8 8 theta 4.57 4.64 5.25 4.89 -3.79 12.2 1.02 355. 146.
# Summary functions can also be provided, as in posterior::summarise_draws():
d %>%
spread_draws(theta[i]) %>%
summarise_draws(median, mad, rhat, ess_tail)
#> # A tibble: 8 × 6
#> # Groups: i [8]
#> i variable median mad rhat ess_tail
#> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 theta 5.97 4.87 1.01 254.
#> 2 2 theta 5.13 4.25 1.02 372.
#> 3 3 theta 3.99 4.94 1.01 205.
#> 4 4 theta 4.99 4.51 1.02 252.
#> 5 5 theta 3.72 4.38 1.01 306.
#> 6 6 theta 4.14 4.81 1.02 205.
#> 7 7 theta 5.90 4.54 1.00 308.
#> 8 8 theta 4.64 4.89 1.02 146.