Perform cubic spline monotonic interpolation of given data points, returning either a list of points obtained by the interpolation or a function performing the interpolation. The splines are constrained to be monotonically increasing (i.e., the slope is never negative).
Usage
cm.spline(x, y = NULL, n = 3 * length(x), xmin = min(x), xmax = max(x), ...)
cm.splinefun(x, y = NULL, ...)
Value
cm.spline
returns a list containing components x and y which give the ordinates where interpolation took place and the interpolated values.
cm.splinefun
returns a function which will perform cubic spline interpolation of the given data points. This is often more useful than spline.
Arguments
x, y
vectors giving the coordinates of the points to be interpolated. Alternatively a single plotting structure can be specified: see xy.coords.
n
interpolation takes place at n equally spaced points spanning the interval [xmin, xmax].
xmin
left-hand endpoint of the interpolation interval.
xmax
right-hand endpoint of the interpolation interval.
...
Other arguments are ignored.
Author
Rob J Hyndman
Details
These are simply wrappers to the splinefun function family from the stats package.
References
Forsythe, G. E., Malcolm, M. A. and Moler, C. B. (1977) Computer Methods for Mathematical Computations.
Hyman (1983) SIAM J. Sci. Stat. Comput.4(4):645-654.
Dougherty, Edelman and Hyman 1989 Mathematics of Computation, 52: 471-494.
x <- seq(0,4,l=20)
y <- sort(rnorm(20))
plot(x,y)
lines(spline(x, y, n = 201), col = 2) # Not necessarily monotoniclines(cm.spline(x, y, n = 201), col = 3) # Monotonic