R: Local Polynomial Regression Fitting

loess {stats} | 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 numeric response and one to four numeric predictors (best specified via an interaction, but can also be specified additively). Will be coerced to a formula if necessary.
`data` | an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `loess` is called.
`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 given by `getOption("na.action")`.
`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, normally 1 or 2. (Degree 0 is also allowed, but see the ‘Note’.)
`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 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^1/p times the actual maximum distance for p explanatory variables.

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 `cloess`, 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.

`degree = 0`, local constant fitting, is allowed in this implementation but not documented in the reference. It seems very little tested, so use with caution.

### Author(s)

B. D. Ripley, based on the `cloess` package of Cleveland, Grosse and Shyu (currently available as `dloess` at http://www.netlib.org/a: the R implementation is based on an 1998 version).

### 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`.

`lowess`, the ancestor of `loess` (with different defaults!).

### Examples

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

[Package stats version 2.15.1 Index]