demography: cm.spline – R documentation

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cm.spline

Monotonic interpolating splines

Description

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, ...)

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.

Details

These are simply wrappers to the splinefun function family from the stats package.

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`.

Author(s)

Rob J Hyndman

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.

Examples

x <- seq(0,4,l=20)
y <- sort(rnorm(20))
plot(x,y)
lines(spline(x, y, n = 201), col = 2) # Not necessarily monotonic
lines(cm.spline(x, y, n = 201), col = 3) # Monotonic

demography

Forecasting Mortality, Fertility, Migration and Population Data

v1.22

GPL (>= 2)

Authors

Rob J Hyndman with contributions from Heather Booth, Leonie Tickle and John Maindonald.

Initial release