hdr {hdrcde} | R Documentation |
Calculates and plots highest density regions in one dimension including the HDR boxplot.
hdr(x, prob = c(50, 95, 99), den, h=hdrbw(BoxCox(x,lambda),mean(prob)), lambda=1, nn=5000, all.modes=FALSE) hdr.den(x, prob = c(50, 95, 99), den, h=hdrbw(BoxCox(x,lambda),mean(prob)), lambda=1, xlab=NULL, ylab="Density", ...) 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, ...)
x |
Numeric vector containing data. In hdr and hdr.den ,
if x is missing then den must be provided, and the
HDR is computed from the given density.
For hdr.boxplot , x can be a list containing several vectors. |
prob |
Probability coverage required for HDRs |
den |
Density of data as list with components x and y .
If omitted, the density is estimated from x using density . |
h |
Optional bandwidth for calculation of density. |
lambda |
Box-Cox transformation parameter where 0 <= lambda <= 1 . |
nn |
Number of random numbers used in computing f-alpha quantiles. |
all.modes |
Return all local modes or just the global mode? |
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. |
... |
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 kernel bandwidth is
selected using the algorithm of Samworth and Wand (2009).
Hyndman's (1996) density quantile algorithm is used for calculation. hdr.den
plots the density with the HDRs
superimposed. hdr.boxplot
displays a boxplot based on HDRs.
hdr.boxplot
retuns nothing. hdr
and hdr.den
return 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 Hyndman
Hyndman, R.J. (1996) Computing and graphing highest density regions. American Statistician, 50, 120-126.
Samworth, R.J., and Wand, M.P. (2009). Asymptotics and optimal bandwidth selection for highest density region estimation. Working paper. http://www.uow.edu.au/~mwand/hdrpap.pdf.
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(faithful$eruptions) hdr.boxplot(faithful$eruptions) hdr.den(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) par(mfrow=c(1,1)) hdr.den(x) # Highly skewed example x <- exp(rnorm(100,0,1)) par(mfrow=c(1,2)) boxplot(x) hdr.boxplot(x,lambda=0)