circular (version 0.1)

points.circular: Add Points to a Circular Plot

Description

Add points to a plot of circular data points on the current graphics device.

Usage

points.circular(x, pch = 16, cex = 1, stack = FALSE, sep = 0.025, shrink = 1, 
    bins, col, next.points, plot.info, zero, rotation, ...)

Arguments

x

a vector, matrix or data.frame. The object is coerced to class circular.

pch

point character to use. See help on par.

cex

point character size. See help on par.

stack

logical: if TRUE, points are stacked on the perimeter of the circle. Otherwise, all points are plotted on the perimeter of the circle. Default is FALSE.

sep

constant used to specify the distance between stacked points, if stack==TRUE or in the case of more than one dataset. Default is 0.025; smaller values will create smaller spaces.

shrink

parameter that controls the size of the plotted circle. Default is 1. Larger values shrink the circle, while smaller values enlarge the circle.

bins

if stack==TRUE, bins is the number of arcs to partition the circle with.

col

color of the points. The values are recycled if needed.

next.points

if stack=FALSE, the distance from the circle the next dataset is plotted. This overrides the information in plot.info.

plot.info

an object from plot.circular that contains information on the zero, the rotation and next.points.

zero

the zero of the plot. This overrides the information in plot.info.

rotation

the rotation of the plot. This overrides the information in plot.info.

...

futher parameters passed to plot.default.

Details

When there are many closely distributed observations, stacking is recommended. When stacking the points, if there are many points in a particular bin, it may be necessary to shrink the plot of the circle so that all points fit. This is controlled with the parameter shrink. Generally the parameter sep does not need adjustment, however, when shrinking the plot, or for a very large number of observations, it may be helpful.

See Also

plot.circular.

Examples

data.1 <- rvonmises(n=100, mu=0, kappa=3)
data.2 <- rvonmises(n=100, mu=pi/3, kappa=3) 
res <- plot(data.1, stack=FALSE, bins=150, shrink=1.5, col=1) 
points(data.2, plot.info=res, col=2)