ksmooth
Kernel Regression Smoother
The NadarayaWatson kernel regression estimate.
 Keywords
 smooth
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
$+/$
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 coverrange.x
. Long vectors are supported.
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 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 \href{https://CRAN.Rproject.org/package=#1}{\pkg{#1}}KernSmoothKernSmooth.
Examples
library(stats)
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)
})
Community examples
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