kde2d
From MASS v7.3-47
by Brian Ripley
Two-dimensional kernel density estimation with an axis-aligned 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 length-2 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
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