hdrcde (version 3.5.0)

hdr.boxplot: Highest Density Region Boxplots

Description

Calculates and plots a univariate highest density regions boxplot.

Usage

hdr.boxplot(
  x,
  prob = c(0.99, 0.5),
  h = hdrbw(BoxCox(x, lambda), mean(prob)),
  lambda = 1,
  boxlabels = "",
  col = rev(gray(seq(9)/10)),
  main = "",
  xlab = "",
  ylab = "",
  pch = 1,
  border = 1,
  outline = TRUE,
  space = 0.25,
  ...
)

Value

nothing.

Arguments

x

Numeric vector containing data or a list containing several vectors.

prob

Probability coverage required for HDRs stats::density().

h

Optional bandwidth for calculation of density.

lambda

Box-Cox transformation parameter where \(0 \le \lambda \le 1\).

boxlabels

Label for each box plotted.

col

Colours for regions of each box.

main

Overall title for the plot.

xlab

Label for x-axis.

ylab

Label for y-axis.

pch

Plotting character.

border

Width of border of box.

outline

If not TRUE, the outliers are not drawn.

space

The space between each box, between 0 and 0.5.

...

Other arguments passed to plot.

Author

Rob J Hyndman

Details

The density is estimated using kernel density estimation. A Box-Cox transformation is used if lambda!=1, as described in Wand, Marron and Ruppert (1991). This allows the density estimate to be non-zero only on the positive real line. The default kernel bandwidth h is selected using the algorithm of Samworth and Wand (2010).

Hyndman's (1996) density quantile algorithm is used for calculation.

References

Hyndman, R.J. (1996) Computing and graphing highest density regions. American Statistician, 50, 120-126.

Samworth, R.J. and Wand, M.P. (2010). Asymptotics and optimal bandwidth selection for highest density region estimation. The Annals of Statistics, 38, 1767-1792.

Wand, M.P., Marron, J S., Ruppert, D. (1991) Transformations in density estimation. Journal of the American Statistical Association, 86, 343-353.

See Also

hdr.boxplot.2d(), hdr(), hdr.den()

Examples

# Old faithful eruption duration times
hdr.boxplot(faithful$eruptions)

# Simple bimodal example
x <- c(rnorm(100, 0, 1), rnorm(100, 5, 1))
par(mfrow = c(1, 2))
boxplot(x)
hdr.boxplot(x)

# Highly skewed example
x <- exp(rnorm(100, 0, 1))
par(mfrow = c(1, 2))
boxplot(x)
hdr.boxplot(x, lambda = 0)