kde2d {MASS}  R Documentation 
Twodimensional kernel density estimation with an axisaligned bivariate normal kernel, evaluated on a square grid.
kde2d(x, y, h, n = 25, lims = c(range(x), range(y)))
x 
x coordinate of data 
y 
y coordinate of data 
h 
vector of bandwidths for x and y directions. Defaults to normal reference bandwidth. 
n 
Number of grid points in each direction. 
lims 
The limits of the rectangle covered by the grid as c(xl, xu, yl, yu) .

A list of three components.
x, y 
The x and y coordinates of the grid points, vectors of length n .

z 
An n x n matrix of the evaluated density.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
attach(geyser) plot(duration, waiting, xlim = c(0.5,6), ylim = c(40,100)) f1 < kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100)) image(f1, zlim = c(0, 0.05)) f2 < kde2d(duration, waiting, n = 50, lims = c(0.5, 6, 40, 100), h = c(width.SJ(duration), width.SJ(waiting)) ) image(f2, zlim = c(0, 0.05)) persp(f2, phi = 30, theta = 20, d = 5) plot(duration[272], duration[1], xlim = c(0.5, 6), ylim = c(1, 6),xlab = "previous duration", ylab = "duration") f1 < kde2d(duration[272], duration[1], h = rep(1.5, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) contour(f1, xlab = "previous duration", ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) f1 < kde2d(duration[272], duration[1], h = rep(0.6, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) contour(f1, xlab = "previous duration", ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) f1 < kde2d(duration[272], duration[1], h = rep(0.4, 2), n = 50, lims = c(0.5, 6, 0.5, 6)) contour(f1, xlab = "previous duration", ylab = "duration", levels = c(0.05, 0.1, 0.2, 0.4) ) detach("geyser")