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)
point_intervalh(...)
qi(x, .width = 0.95, .prob, na.rm = FALSE)
hdi(x, .width = 0.95, .prob, na.rm = FALSE)
Mode(x, na.rm = FALSE)
hdci(x, .width = 0.95, na.rm = FALSE)
mean_qi(.data, ..., .width = 0.95)
mean_qih(...)
median_qi(.data, ..., .width = 0.95)
median_qih(...)
mode_qi(.data, ..., .width = 0.95)
mode_qih(...)
mean_hdi(.data, ..., .width = 0.95)
mean_hdih(...)
median_hdi(.data, ..., .width = 0.95)
median_hdih(...)
mode_hdi(.data, ..., .width = 0.95)
mode_hdih(...)
mean_hdci(.data, ..., .width = 0.95)
mean_hdcih(...)
median_hdci(.data, ..., .width = 0.95)
median_hdcih(...)
mode_hdci(.data, ..., .width = 0.95)
mode_hdcih(...)
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.
This can be list columns.
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).
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).
logical value indicating whether NA values should be stripped before the computation proceeds.
If FALSE (the default), any vectors to be summarised that contain NA will result in
point and interval summaries equal to NA.
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 tidybayes.
Deprecated. Use .width instead.
vector to summarize (for interval functions: qi and hdi)
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 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. 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 using methods like
geom_eye, geom_eyeh, or stat_summary.
The functions ending in h (e.g., point_intervalh, median_qih)
behave identically to the function without the h, except that when passed a vector,
they return a data frame with x/xmin/xmax instead of
y/ymin/ymax. This allows them to be used as values of the
fun.data = argument of stat_summaryh. Note: these
functions are not necessary if you use the point_interval
argument of stats and geoms in the tidybayes package (e.g.
stat_pointintervalh, geom_halfeyeh, etc), as
these automatically adjust the function output to match their required aesthetics.
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. Internally hdi uses
hdi with allowSplit = TRUE (when multimodal) and with
allowSplit = FALSE (when not multimodal).
hdci yields the highest-density continuous interval. Note: If the distribution
is multimodal, this may not actually be the highest-density interval (there may be a higher-density
discontinuous interval). Internally hdci uses
hdi with allowSplit = FALSE; see that function for more
information on multimodality and continuous versus discontinuous intervals.
# NOT RUN {
library(dplyr)
library(ggplot2)
set.seed(123)
rnorm(1000) %>%
median_qi()
data.frame(x = rnorm(1000)) %>%
median_qi(x, .width = c(.50, .80, .95))
data.frame(
x = rnorm(1000),
y = rnorm(1000, mean = 2, sd = 2)
) %>%
median_qi(x, y)
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))
multimodal_draws = data.frame(
x = c(rnorm(5000, 0, 1), rnorm(2500, 4, 1))
)
multimodal_draws %>%
mode_hdi(.width = c(.66, .95))
multimodal_draws %>%
ggplot(aes(x = x, y = 0)) +
geom_halfeyeh(fun.data = mode_hdih, .width = c(.66, .95))
# }
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