`R/point_interval.R`

`point_interval.Rd`

Translates draws from distributions in a (possibly grouped) data frame into point and interval summaries (or set of point and interval summaries, if there are multiple groups in a grouped data frame).

```
point_interval(
.data,
...,
.width = 0.95,
.point = median,
.interval = qi,
.simple_names = TRUE,
na.rm = FALSE,
.exclude = c(".chain", ".iteration", ".draw", ".row"),
.prob
)
# S3 method for default
point_interval(
.data,
...,
.width = 0.95,
.point = median,
.interval = qi,
.simple_names = TRUE,
na.rm = FALSE,
.exclude = c(".chain", ".iteration", ".draw", ".row"),
.prob
)
# S3 method for numeric
point_interval(
.data,
...,
.width = 0.95,
.point = median,
.interval = qi,
.simple_names = FALSE,
na.rm = FALSE,
.exclude = c(".chain", ".iteration", ".draw", ".row"),
.prob
)
# S3 method for rvar
point_interval(
.data,
...,
.width = 0.95,
.point = median,
.interval = qi,
.simple_names = TRUE,
na.rm = FALSE
)
# S3 method for distribution
point_interval(
.data,
...,
.width = 0.95,
.point = median,
.interval = qi,
.simple_names = TRUE,
na.rm = FALSE
)
qi(x, .width = 0.95, .prob, na.rm = FALSE)
ll(x, .width = 0.95, na.rm = FALSE)
ul(x, .width = 0.95, na.rm = FALSE)
hdi(
x,
.width = 0.95,
na.rm = FALSE,
...,
density = density_bounded(trim = TRUE),
n = 4096,
.prob
)
Mode(x, na.rm = FALSE, ...)
# S3 method for default
Mode(x, na.rm = FALSE, ..., density = density_bounded(trim = TRUE), n = 2001)
# S3 method for rvar
Mode(x, na.rm = FALSE, ...)
# S3 method for distribution
Mode(x, na.rm = FALSE, ...)
hdci(x, .width = 0.95, na.rm = FALSE)
mean_qi(.data, ..., .width = 0.95)
median_qi(.data, ..., .width = 0.95)
mode_qi(.data, ..., .width = 0.95)
mean_ll(.data, ..., .width = 0.95)
median_ll(.data, ..., .width = 0.95)
mode_ll(.data, ..., .width = 0.95)
mean_ul(.data, ..., .width = 0.95)
median_ul(.data, ..., .width = 0.95)
mode_ul(.data, ..., .width = 0.95)
mean_hdi(.data, ..., .width = 0.95)
median_hdi(.data, ..., .width = 0.95)
mode_hdi(.data, ..., .width = 0.95)
mean_hdci(.data, ..., .width = 0.95)
median_hdci(.data, ..., .width = 0.95)
mode_hdci(.data, ..., .width = 0.95)
```

- .data
Data frame (or grouped data frame as returned by

`group_by()`

) that contains draws to summarize.- ...
Bare column names or expressions that, when evaluated in the context of

`.data`

, represent draws to summarize. If this is empty, then by default all columns that are not group columns and which are not in`.exclude`

(by default`".chain"`

,`".iteration"`

,`".draw"`

, and`".row"`

) will be summarized. These columns can be numeric, distributional objects,`posterior::rvar`

s, or list columns of numeric values to summarise.- .width
vector of probabilities to use that determine the widths of the resulting intervals. If multiple probabilities are provided, multiple rows per group are generated, each with a different probability interval (and value of the corresponding

`.width`

column).- .point
Point summary function, which takes a vector and returns a single value, e.g.

`mean()`

,`median()`

, or`Mode()`

.- .interval
Interval function, which takes a vector and a probability (

`.width`

) and returns a two-element vector representing the lower and upper bound of an interval; e.g.`qi()`

,`hdi()`

- .simple_names
When

`TRUE`

and only a single column / vector is to be summarized, use the name`.lower`

for the lower end of the interval and`.upper`

for the upper end. If`.data`

is a vector and this is`TRUE`

, this will also set the column name of the point summary to`.value`

. When`FALSE`

and`.data`

is a data frame, names the lower and upper intervals for each column`x`

`x.lower`

and`x.upper`

. When`FALSE`

and`.data`

is a vector, uses the naming scheme`y`

,`ymin`

and`ymax`

(for use with ggplot).- na.rm
logical value indicating whether

`NA`

values should be stripped before the computation proceeds. If`FALSE`

(the default), any vectors to be summarized that contain`NA`

will result in point and interval summaries equal to`NA`

.- .exclude
A character vector of names of columns to be excluded from summarization if no column names are specified to be summarized. Default ignores several meta-data column names used in ggdist and tidybayes.

- .prob
Deprecated. Use

`.width`

instead.- x
vector to summarize (for interval functions:

`qi`

and`hdi`

)- density
For

`hdi()`

and`Mode()`

, the kernel density estimator to use, either as a function (e.g.`density_bounded`

,`density_unbounded`

) or as a string giving the suffix to a function that starts with`density_`

(e.g.`"bounded"`

or`"unbounded"`

). The default,`"bounded"`

, uses the bounded density estimator of`density_bounded()`

, which itself estimates the bounds of the distribution, and tends to work well on both bounded and unbounded data.- n
For

`hdi()`

and`Mode()`

, the number of points to use to estimate highest-density intervals or modes.

A data frame containing point summaries and intervals, with at least one column corresponding
to the point summary, one to the lower end of the interval, one to the upper end of the interval, the
width of the interval (`.width`

), the type of point summary (`.point`

), and the type of interval (`.interval`

).

If `.data`

is a data frame, then `...`

is a list of bare names of
columns (or expressions derived from columns) of `.data`

, on which
the point and interval summaries are derived. Column expressions are processed
using the tidy evaluation framework (see `rlang::eval_tidy()`

).

For a column named `x`

, the resulting data frame will have a column
named `x`

containing its point summary. If there is a single
column to be summarized and `.simple_names`

is `TRUE`

, the output will
also contain columns `.lower`

(the lower end of the interval),
`.upper`

(the upper end of the interval).
Otherwise, for every summarized column `x`

, the output will contain
`x.lower`

(the lower end of the interval) and `x.upper`

(the upper
end of the interval). Finally, the output will have a `.width`

column
containing the' probability for the interval on each output row.

If `.data`

includes groups (see e.g. `dplyr::group_by()`

),
the points and intervals are calculated within the groups.

If `.data`

is a vector, `...`

is ignored and the result is a
data frame with one row per value of `.width`

and three columns:
`y`

(the point summary), `ymin`

(the lower end of the interval),
`ymax`

(the upper end of the interval), and `.width`

, the probability
corresponding to the interval. This behavior allows `point_interval`

and its derived functions (like `median_qi`

, `mean_qi`

, `mode_hdi`

, etc)
to be easily used to plot intervals in ggplot stats using methods like
`stat_eye()`

, `stat_halfeye()`

, or `stat_summary()`

.

`median_qi`

, `mode_hdi`

, etc are short forms for
`point_interval(..., .point = median, .interval = qi)`

, etc.

`qi`

yields the quantile interval (also known as the percentile interval or
equi-tailed interval) as a 1x2 matrix.

`hdi`

yields the highest-density interval(s) (also known as the highest posterior
density interval). **Note:** If the distribution is multimodal, `hdi`

may return multiple
intervals for each probability level (these will be spread over rows). You may wish to use
`hdci`

(below) instead if you want a single highest-density interval, with the caveat that when
the distribution is multimodal `hdci`

is not a highest-density interval.

`hdci`

yields the highest-density *continuous* interval, also known as the shortest
probability interval. **Note:** If the distribution is multimodal, this may not actually
be the highest-density interval (there may be a higher-density
discontinuous interval, which can be found using `hdi`

).

`ll`

and `ul`

yield lower limits and upper limits, respectively (where the opposite
limit is set to either `Inf`

or `-Inf`

).

```
library(dplyr)
library(ggplot2)
set.seed(123)
rnorm(1000) %>%
median_qi()
#> y ymin ymax .width .point .interval
#> 1 0.009209639 -1.941554 2.037887 0.95 median qi
data.frame(x = rnorm(1000)) %>%
median_qi(x, .width = c(.50, .80, .95))
#> # A tibble: 3 × 6
#> x .lower .upper .width .point .interval
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 0.0549 -0.653 0.753 0.5 median qi
#> 2 0.0549 -1.24 1.34 0.8 median qi
#> 3 0.0549 -1.99 1.91 0.95 median qi
data.frame(
x = rnorm(1000),
y = rnorm(1000, mean = 2, sd = 2)
) %>%
median_qi(x, y)
#> x x.lower x.upper y y.lower y.upper .width .point
#> 1 -0.05057431 -2.012529 1.934141 1.983618 -1.946229 5.947635 0.95 median
#> .interval
#> 1 qi
data.frame(
x = rnorm(1000),
group = "a"
) %>%
rbind(data.frame(
x = rnorm(1000, mean = 2, sd = 2),
group = "b")
) %>%
group_by(group) %>%
median_qi(.width = c(.50, .80, .95))
#> # A tibble: 6 × 7
#> group x .lower .upper .width .point .interval
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 a -0.0328 -0.707 0.636 0.5 median qi
#> 2 b 2.06 0.759 3.44 0.5 median qi
#> 3 a -0.0328 -1.27 1.23 0.8 median qi
#> 4 b 2.06 -0.559 4.48 0.8 median qi
#> 5 a -0.0328 -2.00 1.84 0.95 median qi
#> 6 b 2.06 -1.75 5.91 0.95 median qi
multimodal_draws = data.frame(
x = c(rnorm(5000, 0, 1), rnorm(2500, 4, 1))
)
multimodal_draws %>%
mode_hdi(.width = c(.66, .95))
#> # A tibble: 3 × 6
#> x .lower .upper .width .point .interval
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -0.0938 -1.30 1.30 0.66 mode hdi
#> 2 -0.0938 3.50 4.44 0.66 mode hdi
#> 3 -0.0938 -1.72 5.50 0.95 mode hdi
multimodal_draws %>%
ggplot(aes(x = x, y = 0)) +
stat_halfeye(point_interval = mode_hdi, .width = c(.66, .95))
```