hdrscatterplot: Scatterplot showing bivariate highest density regions

In hdrcde: Highest Density Regions and Conditional Density Estimation

Description

Produces a scatterplot where the points are coloured according to the bivariate HDRs in which they fall.

Usage

hdrscatterplot(
  x,
  y,
  levels = c(1, 50, 99),
  kde.package = c("ash", "ks"),
  noutliers = NULL,
  label = NULL
)

Arguments

x	Numeric vector or matrix with 2 columns.
y	Numeric vector of same length as x.
levels	Percentage coverage for HDRs
kde.package	Package to be used in calculating the kernel density estimate when den=NULL.
noutliers	Number of outliers to be labelled. By default, all points outside the largest HDR are labelled.
label	Label of outliers of same length as x and y. By default, all outliers are labelled as the row index of the point (x, y).

Details

The bivariate density is estimated using kernel density estimation. Either ash2 or kde is used to do the calculations. Then Hyndman's (1996) density quantile algorithm is used to compute the HDRs. The scatterplot of (x,y) is created where the points are coloured according to which HDR they fall. A ggplot object is returned.

Author(s)

Rob J Hyndman

See Also

hdr.boxplot.2d

Examples

x <- c(rnorm(200, 0, 1), rnorm(200, 4, 1))
y <- c(rnorm(200, 0, 1), rnorm(200, 4, 1))
hdrscatterplot(x, y)
hdrscatterplot(x, y, label = paste0("p", 1:length(x)))

hdrcde documentation built on Jan. 18, 2021, 9:05 a.m.