Kernel Regression Smoother

Usage

ksmooth(x, y, kernel=c("box", "normal"), bandwidth=0.5,
        range.x=range(x), n.points=max(100, length(x)), x.points)

Arguments

x input x values
y input y values
kernel The kernel to be used.
bandwidth the bandwidth. The kernels are scaled so that their quartiles (viewed as probability densities) are at +/-0.25*bandwidth.
range.x the range of points to be covered in the output.
n.points the number of points at which to evaluate the fit.
x.points points at which to evaluate the smoothed fit. If missing, n.points are chosen uniformly to cover range.x.

Description

The Nadaraya-Watson kernel regression estimate.

Value

A list with components
x values at which the smoothed fit is evaluated. Guaranteed to be in increasing order.
y fitted values corresponding to x.

Note

This function is implemented purely for compatibility with S, although it is nowhere near as slow as the S function. Better kernel smoothers are available in other packages.

Author(s)

B.D. Ripley

Examples

data(cars)
attach(cars)
plot(speed, dist)
lines(ksmooth(speed, dist, "normal", bandwidth=2), col=2)
lines(ksmooth(speed, dist, "normal", bandwidth=5), col=3)
lines(ksmooth(speed, dist, "normal", bandwidth=10), col=4)

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