ks (version 1.5.0)

plot.kde: Kernel density estimate plot for 1- to 3-dimensional data

Description

Kernel density estimate plot for 1- to 3-dimensional data.

Usage

## univariate
## S3 method for class 'kde':
plot(fhat, xlab, ylab="Density function", add=FALSE,
  ptcol="blue", lcol="black", drawpoints=TRUE, ...)
## bivariate
## S3 method for class 'kde':
plot(fhat, display="slice", cont=c(25,50,75), ncont=NULL,
    cex=0.7, xlab, ylab, zlab="Density function", theta=-30,
    phi=40, d=4, add=FALSE, drawpoints=FALSE, drawlabels=TRUE, pch,
    ptcol="blue", lcol="black", ...)
## trivariate
## S3 method for class 'kde':
plot(fhat, cont=c(25,50,75), colors, alphavec,
    size=3, ptcol="blue", add=FALSE, xlab, ylab, zlab, drawpoints=FALSE,
    ...)

Value

• Plot of 1-d and 2-d kernel density estimates are sent to graphics window. Plot for 3-d is generated by the misc3d and rgl libraries and is sent to RGL window (TEMPORARILY DISABLED UNTIL rgl COMPILES AGAIN).

synopsis

## S3 method for class 'kde': plot(x, drawpoints=FALSE, ...)

Details

-- The 1-d plot is a standard plot of a 1-d curve. If drawpoints=TRUE then a rug plot is added. -- 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. Default colours 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), the default colors are heat.colors and the default opacity alphavec ranges from 0.1 to 0.5.

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

## univariate example
x <- rnorm.mixt(n=100, mus=1, sigmas=1, props=1)
fhat <- kde(x, h=sqrt(0.09))  
plot(fhat)


## bivariate example
data(unicef)
H.scv <- Hscv(unicef)
fhat <- kde(unicef, H=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=8, drawpoints=FALSE, drawlabels=FALSE)
plot(fhat, display="persp")
plot(fhat, display="image", col=rev(heat.colors(100)))
layout(1)

plot(fhat, display="filled")
  ## filled contour plot not compatible with layout()


## large sample - 10000 sample from bivariate standard normal 
x <- rmvnorm.mixt(10000, c(0,0), diag(2))    
H.pi <- Hpi.diag(x, binned=TRUE)
fhat <- kde(x, H=H.pi, binned=TRUE) 
plot(fhat, drawpoints=FALSE, cont=seq(10,90, by=20))## 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=1000, mus=mus, Sigmas=Sigmas, props=props)
H.pi <- Hpi(x, pilot="samse")
fhat <- kde(x, H=H.pi)  
plot(fhat)