

   spline[fun] {base}                           R Documentation

   IInntteerrppoollaattiinngg SSpplliinneess

   DDeessccrriippttiioonn::

        `spline' performs cubic spline interpolation of the
        given data points.  It returns a list containing compo-
        nents `x' and `y' which give the ordinates where inter-
        polation took place and the interpolated values.

        `splinefun' returns a function which will perform cubic
        spline interpolation of the given data points.  This is
        often more useful than `spline'.

   UUssaaggee::

        splinefun(x, y, method = "fmm")
        spline(x, y, n = 3*length(x), method = "fmm",
               xmin = min(x), xmax = max(x))

   AArrgguummeennttss::

        x,y: vectors giving the coordinates of the points to be
             interpolated.  Alternatively a single plotting
             structure can be specified.

     method: specifies the type of spline to be used.  Possible
             values are `"fmm"', `"natural"' and `"periodic"'.

          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.

   DDeettaaiillss::

        If `method = "fmm"', the spline used is that of
        Forsythe, Malcolm and Moler (an exact cubic is fitted
        through the four points at each end of the data, and
        this is used to determine the end conditions).  Natural
        splines are used when `method="natural"', and periodic
        splines when `method="periodic"'.

   RReeffeerreenncceess::

        Forsythe, G. E., M. A. Malcolm and C. B. Moler (1977).
        Computer Methods for Mathematical Computations.

   SSeeee AAllssoo::

        `approx' and `approxfun' for constant and linear inter-
        polation.

   EExxaammpplleess::

        op <- par(mfrow = c(2,1), mgp = c(2,.8,0), mar = .1+c(3,3,3,1))
        n <- 9
        x <- 1:n
        y <- rnorm(n)
        plot(x, y, main = paste("spline[fun](.) through",n,"points"))
        lines(spline(x, y))
        lines(spline(x, y, n = 201), col = 2)

        y <- (x-6)^2
        plot(x, y, main = "spline(.) -- 3 methods")
        lines(spline(x, y, n = 201), col = 2)
        lines(spline(x, y, n = 201, method = "natural"), col = 3)
        lines(spline(x, y, n = 201, method = "periodic"), col = 4)
        legend(6,25, c("fmm","natural","periodic"), col=2:4, lty=1)

        f <- splinefun(x, y)
        ls(envir = environment(f))
        splinecoef <- eval(expression(z), envir = environment(f))
        curve(f(x), 1, 10, col = "green", lwd = 1.5)
        points(splinecoef, col = "purple", cex = 2)
        par(op)

