| hdr.boxplot | R Documentation | 
Calculates and plots a univariate highest density regions boxplot.
hdr.boxplot(
  x,
  prob = c(99, 50),
  h = hdrbw(BoxCox(x, lambda), mean(prob)),
  lambda = 1,
  boxlabels = "",
  col = gray((9:1)/10),
  main = "",
  xlab = "",
  ylab = "",
  pch = 1,
  border = 1,
  outline = TRUE,
  space = 0.25,
  ...
)
| x | Numeric vector containing data or a list containing several vectors. | 
| prob | Probability coverage required for HDRs
 | 
| h | Optional bandwidth for calculation of density. | 
| lambda | Box-Cox transformation parameter where  | 
| 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 <code>TRUE</code>, the outliers are not drawn. | 
| space | The space between each box, between 0 and 0.5. | 
| ... | Other arguments passed to plot. | 
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.
nothing.
Rob J Hyndman
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.
hdr.boxplot.2d, hdr, hdr.den
# 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)
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