kde2d {MASS} | R Documentation

## Two-Dimensional Kernel Density Estimation

### Description

Two-dimensional kernel density estimation with an axis-aligned bivariate normal kernel, evaluated on a square grid.

### 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`).
`n` | Number of grid points in each direction.
`lims` | The limits of the rectangle covered by the grid as `c(xl, xu, yl, yu)`.

### Value

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.

### References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### Examples

    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")

[Package MASS version 7.2-49 Index]