Density bins and histogram bins as data frames
Generates a data frame of bins representing the kernel density (or
histogram) of a vector, suitable for use in generating predictive
distributions for visualization. These functions were originally
designed for use with the now-deprecated predict_curve(), and
may be deprecated in the future.
density_bins(x, n = 101, ...) histogram_bins(x, n = 30, breaks = n, ...)
These functions are simple wrappers to density() and
hist() that compute density estimates and return their results
in a consistent format: a data frame of bins suitable for use with
the now-deprecated predict_curve().
density_bins computes a kernel density estimate using
density().
histogram_bins computes a density histogram using hist().
A data frame representing bins and their densities with the following columns:
mid |
Bin midpoint |
lower |
Lower endpoint of each bin |
upper |
Upper endpoint of each bin |
density |
Density estimate of the bin |
Matthew Kay
See add_predicted_draws() and stat_lineribbon() for a better approach. These
functions may be deprecated in the future.
library(ggplot2)
library(dplyr)
library(purrr)
library(tidyr)
if (
require("brms", quietly = TRUE) &&
require("modelr", quietly = TRUE)
) {
theme_set(theme_light())
m_mpg = brm(mpg ~ hp * cyl, data = mtcars)
step = 1
mtcars %>%
group_by(cyl) %>%
data_grid(hp = seq_range(hp, by = step)) %>%
add_predicted_draws(m_mpg) %>%
summarise(densities = list(density_bins(.prediction))) %>%
unnest(densities) %>%
ggplot() +
geom_rect(aes(
xmin = hp - step/2, ymin = lower, ymax = upper, xmax = hp + step/2,
fill = ordered(cyl), alpha = density
)) +
geom_point(aes(x = hp, y = mpg, fill = ordered(cyl)), shape = 21, data = mtcars) +
scale_alpha_continuous(range = c(0, 1)) +
scale_fill_brewer(palette = "Set2")
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