\Header{loess}{Local Polynomial Regression Fitting}
\keyword{smooth}{loess}
\keyword{loess}{loess}
\begin{Description}\relax
Fit a polynomial surface determined by one or more numerical
predictors, using local fitting.
\end{Description}
\begin{Usage}
\begin{verbatim}
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(...), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{formula}] a formula specifying the numeric response and one to four
numeric predictors (best specified via an interaction, but can also
be specified additively).
\item[\code{data}] an optional data frame within which to look first for the
response, predictors and weights.
\item[\code{weights}] optional weights for each case.
\item[\code{subset}] an optional specification of a subset of the data to be
used.
\item[\code{na.action}] the action to be taken with missing values in the
response or predictors.  The default is given by
\code{getOption("na.action")}.
\item[\code{model}] should the model frame be returned?
\item[\code{span}] the parameter \eqn{\alpha}{} which controls the degree of
smoothing.
\item[\code{enp.target}] an alternative way to specify \code{span}, as the
approximate equivalent number of parameters to be used.
\item[\code{degree}] the degree of the polynomials to be used, up to 2.
\item[\code{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.
\item[\code{drop.square}] for fits with more than one predictor and
\code{degree=2}, should the quadratic term (and cross-terms) be
dropped for particular predictors?  Terms are specified in the same
way as for \code{parametric}.
\item[\code{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.
\item[\code{family}] if \code{"gaussian"} fitting is by least-squares, and if
\code{"symmetric"} a re-descending M estimator is used with Tukey's
biweight function.
\item[\code{method}] fit the model or just extract the model frame.
\item[\code{control}] control parameters: see \code{\Link{loess.control}}.
\item[\code{...}] control parameters can also be supplied directly.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Fitting is done locally.  That is, for the fit at point \eqn{x}{}, the
fit is made using points in a neighbourhood of \eqn{x}{}, weighted by
their distance from \eqn{x}{} (with differences in \sQuote{parametric}
variables being ignored when computing the distance). The size of the
neighbourhood is controlled by \eqn{\alpha}{} (set by \code{span} or
\code{enp.target}).  For \eqn{\alpha < 1}{}, the neighbourhood includes
proportion \eqn{\alpha}{} of the points, and these have tricubic weighting
(proportional to \eqn{(1 - \mathrm{(dist/maxdist)}^3)^3}{(1 -
(dist/maxdist)^3)^3}. For \eqn{\alpha > 1}{}, all points are used, with
the \sQuote{maximum distance} assumed to be
\eqn{\alpha^{1/p}}{alpha^1/p} times the actual maximum distance for
\eqn{p}{} explanatory variables.

For the default family, fitting is by (weighted) least squares. For
\code{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 \code{\Link{loess.control}} for details.
\end{Details}
\begin{Value}
An object of class \code{"loess"}.
\end{Value}
\begin{Note}\relax
As this is based on the \code{cloess} package available at
\code{netlib}, it is similar to but not identical to the \code{loess}
function of S. In particular, conditioning is not implemented.

The memory usage of this implementation of \code{loess} is roughly
quadratic in the number of points, with 1000 points taking about 10Mb.
\end{Note}
\begin{Author}\relax
B.D. Ripley, based on the \code{cloess} package of Cleveland,
Grosse and Shyu avaliable at \url{http://www.netlib.org/a/}.
\end{Author}
\begin{References}\relax
W.S. Cleveland, E. Grosse and W.M. Shyu (1992) Local regression
models. Chapter 8 of \emph{Statistical Models in S} eds J.M. Chambers
and T.J. Hastie, Wadsworth \& Brooks/Cole.
\end{References}
\begin{SeeAlso}\relax
\code{\Link{loess.control}},
\code{\Link{predict.loess}}.

\code{\Link{lowess}}, the ancestor of \code{loess} (with
different defaults!).
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(cars)
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)
\end{ExampleCode}
\end{Examples}

