Bivariate histogram HDR estimator — method

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)
}