Density plot with Highest Density Regions
Plots univariate density with highest density regions displayed
hdr.den( x, prob = c(50, 95, 99), den, h = hdrbw(BoxCox(x, lambda), mean(prob)), lambda = 1, xlab = NULL, ylab = "Density", ylim = NULL, plot.lines = TRUE, col = 2:8, bgcol = "gray", legend = FALSE, ... )
x |
Numeric vector containing data. If |
prob |
Probability coverage required for HDRs |
den |
Density of data as list with components |
h |
Optional bandwidth for calculation of density. |
lambda |
Box-Cox transformation parameter where |
xlab |
Label for x-axis. |
ylab |
Label for y-axis. |
ylim |
Limits for y-axis. |
plot.lines |
If |
col |
Colours for regions. |
bgcol |
Colours for the background behind the boxes. Default |
legend |
If |
... |
Other arguments passed to plot. |
Either x or den must be provided. When x is provided,
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.
a list of three components:
hdr |
The endpoints of each interval in each HDR |
mode |
The estimated mode of the density. |
falpha |
The value of the density at the boundaries of each HDR. |
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.
# Old faithful eruption duration times hdr.den(faithful$eruptions) # Simple bimodal example x <- c(rnorm(100,0,1), rnorm(100,5,1)) hdr.den(x)
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