# 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

values at which the smoothed fit is evaluated. Guaranteed to be in increasing order.

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 KernSmooth.

##### Examples

`library(stats)`

```
# NOT RUN {
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 3.5.0, License: Part of R 3.5.0*