ksmooth {modreg} | 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(100, length(x)), x.points)

### Arguments

`x` |
input x values |

`y` |
input y values |

`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` . |

### 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 is implemented purely for compatibility with S,
although it is nowhere near as slow as the S function. Better kernel
smoothers are available in other packages.

### Examples

data(cars)
with(cars, {
plot(speed, dist)
lines(ksmooth(speed, dist, "normal", bandwidth=2), col=2)
lines(ksmooth(speed, dist, "normal", bandwidth=5), col=3)
})