Scatter Plot Smoothing
DESCRIPTION:
Gives a robust, local smooth of scatterplot data. Among other options is the fraction of data smoothed at each point.
USAGE: 
lowess(x, y, f = 2/3, iter = 3, delta = .01*range(x))
REQUIRED ARGUMENTS:
x,y:
vectors of data for a scatter plot.
OPTIONAL ARGUMENTS:
f:
fraction of the data used for smoothing at each x point. The larger the f value, the smoother the fit.
iter:
number of iterations used in computing robust estimates.
delta:
interval size (in units corresponding to x). If lowess estimates at two x values within delta of one another, it fits any points between them by linear interpolation. The default is 1% of the range of x. If delta=0 all but identical x values are estimated independently.
VALUE:
list containing components named x and y which are the x,y points of the smoothed scatter plot. Note that x is a sorted version of the input x vector, with duplicate points removed.
NOTE:
This function may be slow for large numbers of points; execution time is proportional to (iter*f*n^2). Increasing delta should speed things up, as will decreasing f.
DETAILS:
This is a scatterplot smoother - it does not make any assumptions about the x values being evenly spaced. Lowess uses robust locally linear fits. A window, dependent on f, is placed about each x value; points that are inside the window are weighted so that nearby points get the most weight.
REFERENCES:
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74, 829-836.

Chambers, J. M., Cleveland, W. S., Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis. Wadsworth, Belmont, California.

SEE ALSO:
approx , lines , loess , plot , smooth , supsmu .
EXAMPLES: 
x <- car.miles
y <- car.gals


plot(x,y)
lines(lowess(x,y))  # scatter plot with smooth


fit <- lowess(x,y)
resid <- y-approx(fit,x)$y # residual from smooth