locfit.robust {locfit} | R Documentation |

## Robust Local Regression

### Description

`locfit.robust`

implements a robust local regression where
outliers are iteratively identified and downweighted, similarly
to the lowess method (Cleveland, 1979). The iterations and scale
estimation are performed on a global basis.

The scale estimate is 6 times the median absolute residual, while the robust downweighting uses the bisquare function. These are performed in the S code so easily changed.

This can be interpreted as an extension of M estimation to local
regression. An alternative extension (implemented in locfit via
`family="qrgauss"`

) performs the iteration and scale estimation
on a local basis.

### Usage

```
locfit.robust(x, y, weights, ..., iter=3)
```

### Arguments

`x` |
Either a |

`y` |
If |

`weights` |
weights to use in the fitting. |

`...` |
Other arguments to |

`iter` |
Number of iterations to perform |

### Value

`"locfit"`

object.

### References

Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assn. 74, 829-836.

### See Also

*locfit*version 1.5-9.10 Index]