ggdensity (version 1.0.0)

method_histogram: Bivariate histogram HDR estimator

Description

Function used to specify bivariate histogram density estimator for get_hdr() and layer functions (e.g. geom_hdr()).

Usage

method_histogram(bins = NULL, smooth = FALSE, nudgex = "none", nudgey = "none")

Arguments

bins

Number of bins along each axis. Either a vector of length 2 or a scalar value which is recycled for both dimensions. Defaults to normal reference rule (Scott, pg 87).

smooth

If TRUE, HDRs are smoothed by the marching squares algorithm.

nudgex, nudgey

Horizontal and vertical rules for choosing witness points when smooth == TRUE. Accepts character vector: "left", "none", "right" (nudgex) or "down", "none", "up" (nudgey).

Details

For more details on the use and implementation of the method_*() functions, see vignette("method", "ggdensity").

References

Scott, David W. Multivariate Density Estimation (2e), Wiley.

Examples

if (FALSE) {

# Histogram estimators can be useful when data has boundary constraints
set.seed(1)
df <- data.frame(x = rexp(1e3), y = rexp(1e3))

ggplot(df, aes(x, y)) +
  geom_hdr(method = method_histogram()) +
  geom_point(size = 1)

# The resolution of the histogram estimator can be set via `bins`
ggplot(df, aes(x, y)) +
  geom_hdr(method = method_histogram(bins = c(8, 25))) +
  geom_point(size = 1)

# By setting `smooth = TRUE`, we can graphically smooth the "blocky" HDRs
ggplot(df, aes(x, y)) +
  geom_hdr(method = method_histogram(smooth = TRUE)) +
  geom_point(size = 1)

# However, we need to set `nudgex` and `nudgey` to align the HDRs correctly
ggplot(df, aes(x, y)) +
  geom_hdr(method = method_histogram(smooth = TRUE, nudgex = "left", nudgey = "down")) +
  geom_point(size = 1)

# Can also be used with `get_hdr()` for numerical summary of HDRs
res <- get_hdr(df, method = method_histogram())
str(res)
}