ks (version 1.4.12)

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, origin=c(0,0,0), endpts,
    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. 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.

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.diag(x, binned=TRUE, pilot="samse")
fhat <- kde(x, H=H.pi, binned=TRUE)  

plot(fhat, origin=c(-3,-3,-3))