tidybayes (version 3.0.7)

summarise_draws.grouped_df: Summaries of draws in grouped_df objects

Description

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.

Usage

# S3 method for grouped_df
summarise_draws(.x, ...)

Value

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.

Arguments

.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.

Author

Matthew Kay

Details

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).

See Also

posterior::summarise_draws(), point_interval()

Examples

library(posterior)
library(dplyr)

d = posterior::example_draws()

# The default posterior::summarise_draws() summarises all variables without
# splitting out indices:
summarise_draws(d)

# 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()

# Summary functions can also be provided, as in posterior::summarise_draws():
d %>%
  spread_draws(theta[i]) %>%
  summarise_draws(median, mad, rhat, ess_tail)