plot.kde {ks}R Documentation

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

Description

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

Usage

## bivariate
## S3 method for class 'kde':
plot(fhat, display="slice", cont=c(25,50,75), ncont=NULL,cex=0.7, 
    xlabs="x", ylabs="y", zlabs="Density function", theta=-30, phi=40, d=4,
    add=FALSE, drawlabels=TRUE, points.diff=TRUE, pch, ...)

## trivariate
## S3 method for class 'kde':
plot(fhat, display="rgl", cont=c(25,50,75), colors,
  alphalo=0.2, alphahi=0.6, size=3, col="blue", add=FALSE, ...)

Arguments

fhat an object of class kde i.e. output from kde function
display type of display, "slice" for contour plot, "persp" for perspective plot, "image" for image plot, "rgl" for RGL plot
cont vector of percentages (of maximum height) for contour level curves
ncont number of contour level curves
cex,pch,xlabs,ylabs,zlabs,add usual graphics parameters
theta,phi,d graphics parameters for perspective plots
drawlabels draw contour labels
points.diff not currently implemented
colors vector of colours for each group
alphalo, alphahi minimum and maximum transparency
size,col size and colour for plotting symbol
... other graphics parameters

Details

There are three types of plotting displays for 2-d data 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 interactive plot is a series of nested 3-d contours, generated by the misc3d and rgl libraries. The default contours are cont=c(75,50,25), the default colours are colors=rev(heat.colors(length(cont))) and the default opacity ranges between alphalo=0.2 and alphahi=0.4.

Value

Plot of 2-d kernel density estimate is sent to graphics window. Plot for 3-d is sent to RGL window.

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
## Not run: 
mus <- rbind(c(0,0,0), c(-1,1,1))
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)  
plot(fhat)
## End(Not run)

[Package ks version 1.3.4 Index]