ks (version 1.3.2)

plot.kde: Kernel density estimate plot for 2- and 3-dimensional data

Description

Kernel density estimate plot for 2- and 3-dimensional data.

Usage

## S3 method for class 'kde':
plot(x, display="slice", ...)

Arguments

x

an object of class kde i.e. output from kde function

display

type of display

...

other graphics parameters - see details below

Value

• Plot of kernel density estimate is sent to graphics window.

Details

There are three types of plotting displays available, controlled by the display parameter.

If display="slice" then a slice/contour plot is generated using contour. The default contours are at 25%, 50%, 75% or cont=c(25,50,75). The user can also set the number of contour level curves by changing the value set to ncont. See examples below. If display="persp" then a perspective/wire-frame plot is generated. The default z-axis limits zlim are determined by the range of the z values i.e. default from the usual persp command. If display="image" then an image plot is generated. The colors are the default from the usual image command.

For 3-dimensional data, the plot is a series of 2-dimensional contour plots. Use layout.mat to control the grid layout from the usual layout command.

References

Bowman, A.W. & Azzalini, A. (1997) Applied Smoothing Techniques for Data Analysis. Clarendon Press. Oxford. Simonoff, J. S., (1996) Smoothing Methods in Statistics. Springer-Verlag. New York.

See Also

kde

Examples

### bivariate example
data(unicef)
H.scv <- Hscv(unicef)
fhat <- kde(unicef, H.scv)

layout(rbind(c(1,2), c(3,4)))
plot(fhat, display="slice", cont=seq(10,90, by=20), cex=0.3)
plot(fhat, display="slice", ncont=5, cex=0.3, drawlabels=FALSE)
plot(fhat, display="persp")
plot(fhat, display="image", col=rev(heat.colors(15)))
layout(1)

### 3-variate example

mus <- rbind(c(0,0,0), c(2,2,2))
Sigma <- matrix(c(1, 0.7, 0.7, 0.7, 1, 0.7, 0.7, 0.7, 1), nr=3, nc=3) 
Sigmas <- rbind(Sigma, Sigma)
props <- c(1/2, 1/2)
x <- rmvnorm.mixt(n=100, mus=mus, Sigmas=Sigmas, props=props)
H.pi <- Hpi(x)
fhat <- kde(x, H.pi, eval.levels=seq(-3,3, length=9))  
plot(fhat, disp="slice", ncont=6, cex=0.3)