splinefun                package:base                R Documentation

_I_n_t_e_r_p_o_l_a_t_i_n_g _S_p_l_i_n_e_s

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_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_e_t_a_i_l_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"'.

_V_a_l_u_e:

     `spline' returns a list containing components `x' and `y' which
     give the ordinates where interpolation 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'.

_R_e_f_e_r_e_n_c_e_s:

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

_S_e_e _A_l_s_o:

     `approx' and `approxfun' for constant and linear interpolation.

_E_x_a_m_p_l_e_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)

