splinefun(x, y, method = "fmm")
spline(x, y, n = 3*length(x), method = "fmm",
xmin = min(x), xmax = max(x))
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. |
spline performs cubic spline interpolation of the given data
points. It 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.
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".
approx and approxfun for constant and
linear interpolation.
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)