kde2d
From MASS v7.347
by Brian Ripley
Twodimensional kernel density estimation with an axisaligned bivariate normal kernel, evaluated on a square grid.
 Keywords
 dplot
Usage
kde2d(x, y, h, n = 25, lims = c(range(x), range(y)))
Arguments
 x
 x coordinate of data
 y
 y coordinate of data
 h

vector of bandwidths for x and y directions. Defaults to
normal reference bandwidth (see
bandwidth.nrd
). A scalar value will be taken to apply to both directions.  n
 Number of grid points in each direction. Can be scalar or a length2 integer vector.
 lims

The limits of the rectangle covered by the grid as
c(xl, xu, yl, yu)
.
Value
A list of three components.
n
.
n[1]
by n[2]
matrix of the estimated density: rows
correspond to the value of x
, columns to the value of y
.
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Examples
library(MASS)
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")
Community examples
Looks like there are no examples yet.