R: Kernel Regression Smoother

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.
`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`. 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 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)
    })

[Package stats version 3.1.2 Index]