loess {modreg} | R Documentation

## Local Polynomial Regression Fitting

### Description

Fit a polynomial surface determined by one or more numerical predictors, using local fitting.

### Usage

    loess(formula, data, weights, subset, na.action, model = FALSE,
          span = 0.75, enp.target, degree = 2,
          parametric = FALSE, drop.square = FALSE, normalize = TRUE,
          family = c("gaussian", "symmetric"),
          method = c("loess", "model.frame"),
          control = loess.control(...), ...)

### Arguments

`formula` | a formula specifying the response and one or more numeric predictors (best specified via an interaction, but can also be specified additively).
`data` | an optional data frame within which to look first for the response, predictors and weights.
`weights` | optional weights for each case.
`subset` | an optional specification of a subset of the data to be used.
`na.action` | the action to be taken with missing values in the response or predictors. The default is to stop.
`model` | should the model frame be returned?
`span` | the parameter α which controls the degree of smoothing.
`enp.target` | an alternative way to specify `span`, as the approximate equivalent number of parameters to be used.
`degree` | the degree of the polynomials to be used, up to 2.
`parametric` | should any terms be fitted globally rather than locally? Terms can be specified by name, number or as a logical vector of the same length as the number of predictors.
`drop.square` | for fits with more than one predictor and `degree=2`, should the quadratic term (and cross-terms) be dropped for particular predictors? Terms are specified in the same way as for `parametric`.
`normalize` | should the predictors be normalized to a common scale if there is more than one? The normalization used is to set the 10% trimmed standard deviation to one. Set to false for spatial coordinate predictors and others know to be a common scale.
`family` | if `"gaussian"` fitting is by least-squares, and if `"symmetric"` a re-descending M estimator is used with Tukey's biweight function.
`method` | fit the model or just extract the model frame.
`control` | control parameters: see `loess.control`.
`...` | control parameters can also be supplied directly.

### Details

Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their distance from x (with differences in `parametric' variables being ignored when computing the distance). The size of the neighbourhood is controlled by α (set by `span` or `enp.target`). For α < 1, the neighbourhood includes proportion α of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3. For α > 1, all points are used, with the `maximum distance' assumed to be α times the actual maximum distance.

For the default family, fitting is by (weighted) least squares. For `family="symmetric"` a few iterations of an M-estimation procedure with Tukey's biweight are used. Be aware that as the initial value is the least-squares fit, this need not be a very resistant fit.

It can be important to tune the control list to achieve acceptable speed. See loess.control for details.

### Value

An object of class `"loess"`.

### Note

As this is based on the `cloess` package available at `netlib`, it is similar to but not identical to the `loess` function of S. In particular, conditioning is not implemented.

The memory usage of this implementation of `loess` is roughly quadratic in the number of points, with 1000 points taking about 10Mb.

### Author(s)

B.D. Ripley, based on the `cloess` package of Cleveland, Grosse and Shyu.

### References

W.S. Cleveland, E. Grosse and W.M. Shyu (1992) Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole.

### See Also

`loess.control`, `predict.loess`

### Examples

    data(cars)
    cars.lo <- loess(dist ~ speed, cars)
    predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE)
    # to get extrapolation
    cars.lo2 <- loess(dist ~ speed, cars,
      control=loess.control(surface="direct"))
    predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)

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