R in a Nutshell, 2nd Edition

Smoothing

This section describes a number of functions for fitting piecewise smooth curves to data. Functions in this section are particularly useful for plotting charts; there are even convenience functions for using these functions to show fitted values in some graphics packages.

Splines

One method for fitting a function to source data is with splines. With a linear model, a single line is fitted to all the data. With spline methods, a set of different polynomials is fitted to different sections of the data.

You can compute simple cubic splines with the spline function in the stats package:

spline(x, y = NULL, n = 3 * length(x), method = "fmm", xmin = min(x),
    xmax = max(x), xout, ties = mean)

Here is a description of the arguments to smooth.spline.

Argument	Description	Default
x	A vector specifying the predictor variable, or a two-column matrix specifying both the predictor and the response variables.
y	If x is a vector, then y is a vector containing the response variable.	NULL
n	If xout is not specified, then interpolation is done at n equally spaced points between xmin and xmax.	3*length(x)
method	Specifies the type of spline. Allowed values include "fmm", "natural", "periodic", and "monoH.FC".	"fmm"
xmin	Lowest x value for interpolations.	min(x)
xmax	Highest x value for interpolations.	max(x)
xout	An optional vector of values specifying where interpolation should be done.
ties	A method for handling ties. Either the string "ordered" or a function that returns a single numeric value.	mean

To return a function ...