splinefun (stats)

Interpolating Splines

Description

Perform cubic spline interpolation of given data points, returning either a list of points obtained by the interpolation or a function performing the interpolation.

Usage

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

Arguments

x,y

interpolated. Alternatively a single plotting structure can be specified: see xy.coords. |

method

values are “fmm”, “natural” and “periodic”. |

n

spanning the interval [xmin, xmax]. |

xmin left-hand endpoint of the interpolation interval.
xmax right-hand endpoint of the interpolation interval.

Details

The inputs can contain missing values which are deleted, so at least one complete (x, y) pair is required. 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”.

These interpolation splines can also be used for extrapolation, that is prediction at points outside the range of x. Extrapolation makes little sense for method = “fmm”; for natural splines it is linear using the slope of the interpolating curve at the nearest data point.

Value

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 with formal arguments x and deriv, the latter defaulting to zero. This function can be used to evaluate the interpolating cubic spline (deriv=0), or its derivatives (deriv=1,2,3) at the points x, where the spline function interpolates the data points originally specified. This is often more useful than spline.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth \& Brooks/Cole.

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

See Also

approx and approxfun for constant and linear interpolation.

Package [rp>splines], especially interpSpline and periodicSpline for interpolation splines. That package also generates spline bases that can be used for regression splines.

smooth.spline for smoothing splines.

Examples

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)
 
y <- sin((x-0.5)*pi)
f <- splinefun(x, y)
ls(envir = environment(f))
splinecoef <- get("z", envir = environment(f))
curve(f(x), 1, 10, col = "green", lwd = 1.5)
points(splinecoef, col = "purple", cex = 2)
curve(f(x, deriv=1), 1, 10, col = 2, lwd = 1.5)
curve(f(x, deriv=2), 1, 10, col = 2, lwd = 1.5, n = 401)
curve(f(x, deriv=3), 1, 10, col = 2, lwd = 1.5, n = 401)
par(op)

Wiki discussion

 
rdoc/stats/splinefun.txt · Last modified: 2006/06/12
 
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