locfit.robust {locfit}R Documentation

Robust Local Regression


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.


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


x Either a locfit model formula or a numeric vector of the predictor variable.
y If x is numeric, y gives the response variable.
weights Prior weights (or sample sizes) for individual observations. This is typically used where observations have unequal variance.
... Other arguments to locfit.raw
iter Number of iterations to perform


"locfit" object.


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

See Also

locfit, locfit.raw

[Package locfit version 1.1-9 Index]