# kde2d: Two-Dimensional Kernel Density Estimation In MASS: Support Functions and Datasets for Venables and Ripley's MASS

 kde2d 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`). 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.

 `x, y` The x and y coordinates of the grid points, vectors of length `n`. `z` An `n` by `n` 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

```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")
```

MASS documentation built on Jan. 23, 2023, 5:32 p.m.