bpstrip {denstrip} | R Documentation |
Box-percentile strips
Description
Box-percentile strips give a compact illustration of a distribution. The width of the strip is proportional to the probability of a more extreme point. This function adds a box-percentile strip to an existing plot.
Usage
bpstrip(x, prob, at, width, horiz=TRUE, scale=1, limits=c(-Inf, Inf),
col="gray", border=NULL, lwd, lty, ticks=NULL, tlen=1, twd, tty,
lattice=FALSE)
panel.bpstrip(...)
Arguments
x |
Either the vector of points at which the probability is
evaluated (if |
prob |
Probability, or cumulative density, of the distribution
at |
at |
Position of the centre of the strip on the y-axis (if
|
width |
Thickness of the strip at its thickest point, which will be at the median. Defaults to 1/20 of the axis range. |
horiz |
Draw the strip horizontally ( |
scale |
Alternative way of specifying the thickness of the
strip, as a proportion of |
limits |
Vector of minimum and maximum values, respectively, at which to terminate the strip. |
col |
Colour to shade the strip, either as a built-in R
colour name (one of |
border |
Colour of the border, see |
lwd |
Line width of the border (defaults to
|
lty |
Line type of the border (defaults to
|
ticks |
Vector of |
tlen |
Length of the ticks, relative to the thickness of the strip. |
twd |
Line width of these marks (defaults to
|
tty |
Line type of these marks (defaults to
|
lattice |
Set this to |
... |
Other arguments passed to |
Details
The box-percentile strip looks the same as the box-percentile plot
(Esty and Banfield, 2003) which is a generalisation of the boxplot for
summarising data. However, bpstrip
is intended for illustrating
distributions arising from parameter
estimation or prediction. Either the distribution is known
analytically, or an arbitrarily large sample from the distribution is
assumed to be available via a method such as MCMC or bootstrapping.
The function bpplot
in the Hmisc
package can be used to draw vertical box-percentile plots of observed
data.
Author(s)
Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>
References
Jackson, C. H. (2008) Displaying uncertainty with shading. The American Statistician, 62(4):340-347.
Esty, W. W. and Banfield, J. D. (2003) The box-percentile plot. Journal of Statistical Software 8(17).
See Also
Examples
x <- seq(-4, 4, length=1000)
prob <- pnorm(x)
plot(x, xlim=c(-5, 5), ylim=c(-5, 5), xlab="x", ylab="x", type="n")
bpstrip(x, prob, at=1, ticks=qnorm(c(0.25, 0.5, 0.75)))
## Terminate the strip at specific outer quantiles
bpstrip(x, prob, at=2, limits=qnorm(c(0.025, 0.975)))
bpstrip(x, prob, at=3, limits=qnorm(c(0.005, 0.995)))
## Compare with density strip
denstrip(x, dnorm(x), at=0)
## Estimate the density from a large sample
x <- rnorm(10000)
bpstrip(x, at=4)