ks (version 1.4.2)

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

Description

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

Usage

## 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, ptcol="blue", lcol="black", ...)
## trivariate
## S3 method for class 'kde':
plot(fhat, display="rgl", cont=c(25,50,75), colors, alphavec,
    size=3, ptcol="blue", add=FALSE, origin=c(0,0,0), endpts,
    xlabs="x", ylabs="y", zlabs="z", ...)

Value

• Plot of 2-d kernel density estimate is sent to graphics window. Plot for 3-d is generated by the misc3d and rgl libraries and is sent to RGL window.

synopsis

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

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. The default contours are cont=c(25,50,75), the default colors are heat.colors and the default opacity alphavec ranges from 0.1 to 0.5. origin is the point where the three axes meet. endpts is the vector of the maximum axis values to be plotted. Default endpts is the maxima for the plotting grid from x (automatically generated by kde).

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)

## trivariate example
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, origin=c(-3,-3,-3))