| ksmooth {stats} | R Documentation |
The Nadaraya-Watson kernel regression estimate.
ksmooth(x, y, kernel = c("box", "normal"), bandwidth = 0.5,
range.x = range(x), n.points = max(100, length(x)), x.points)
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. |
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. |
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.
with(cars, {
plot(speed, dist)
lines(ksmooth(speed, dist, "normal", bandwidth=2), col=2)
lines(ksmooth(speed, dist, "normal", bandwidth=5), col=3)
})