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).
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 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, 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 alpha^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 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 avaliable at http://www.netlib.org/a/.

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 2.1.0 Index]