# ksmooth

##### Kernel Regression Smoother

The Nadaraya--Watson 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
$\pm$
`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`

. 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

##### 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)
})
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*

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