| ksmooth {stats} | R Documentation |
Kernel Regression Smoother
Description
The Nadaraya–Watson kernel regression estimate.
Usage
ksmooth(x, y, kernel = c("box", "normal"), bandwidth = 0.5,
range.x = range(x),
n.points = max(100L, length(x)), x.points)
Arguments
x |
input x values. Long vectors are supported. |
y |
input y values. Long vectors are supported. |
kernel |
the kernel to be used. Can be abbreviated. |
bandwidth |
the bandwidth. The kernels are scaled so that their
quartiles (viewed as probability densities) are at
|
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, |
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 |
Note
This function was implemented for compatibility with S, although it is nowhere near as slow as the S function. Better kernel smoothers are available in other packages such as KernSmooth.
Examples
require(graphics)
with(cars, {
plot(speed, dist)
lines(ksmooth(speed, dist, "normal", bandwidth = 2), col = 2)
lines(ksmooth(speed, dist, "normal", bandwidth = 5), col = 3)
})