splinefun {stats} | R Documentation |

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

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

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

`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. |

`ties` |
Handling of tied `x` values. Either a function
with a single vector argument returning a single number result or
the string `"ordered"` . |

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.

`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`

.

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*.

`approx`

and `approxfun`

for constant and
linear interpolation.

Package **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.

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) ## An example with ties (non-unique x values): set.seed(1); x <- round(rnorm(30), 1); y <- sin(pi * x) + rnorm(30)/10 plot(x,y, main="spline(x,y) when x has ties") lines(spline(x,y, n= 201), col = 2) ## visualizes the non-unique ones: tx <- table(x); mx <- as.numeric(names(tx[tx > 1])) ry <- matrix(unlist(tapply(y, match(x,mx), range, simplify=FALSE)), ncol=2, byrow=TRUE) segments(mx, ry[,1], mx, ry[,2], col = "blue", lwd = 2)

[Package *stats* version 2.5.0 Index]