predict.smooth.spline {stats} | R Documentation |
Predict from Smoothing Spline Fit
Description
Predict a smoothing spline fit at new points, return the derivative if
desired. The predicted fit is linear beyond the original data.
Usage
## S3 method for class 'smooth.spline':
predict(object, x, deriv = 0, ...)
Arguments
object |
a fit from smooth.spline . |
x |
the new values of x. |
deriv |
integer; the order of the derivative required. |
... |
further arguments passed to or from other methods. |
Value
A list with components
x |
The input x . |
y |
The fitted values or derivatives at x . |
See Also
smooth.spline
Examples
attach(cars)
cars.spl <- smooth.spline(speed, dist, df=6.4)
## "Proof" that the derivatives are okay, by comparing with approximation
diff.quot <- function(x,y) {
## Difference quotient (central differences where available)
n <- length(x); i1 <- 1:2; i2 <- (n-1):n
c(diff(y[i1]) / diff(x[i1]), (y[-i1] - y[-i2]) / (x[-i1] - x[-i2]),
diff(y[i2]) / diff(x[i2]))
}
xx <- unique(sort(c(seq(0,30, by = .2), kn <- unique(speed))))
i.kn <- match(kn, xx)# indices of knots within xx
op <- par(mfrow = c(2,2))
plot(speed, dist, xlim = range(xx), main = "Smooth.spline & derivatives")
lines(pp <- predict(cars.spl, xx), col = "red")
points(kn, pp$y[i.kn], pch = 3, col="dark red")
mtext("s(x)", col = "red")
for(d in 1:3){
n <- length(pp$x)
plot(pp$x, diff.quot(pp$x,pp$y), type = 'l', xlab="x", ylab="",
col = "blue", col.main = "red",
main= paste("s",paste(rep("'",d), collapse=""),"(x)", sep=""))
mtext("Difference quotient approx.(last)", col = "blue")
lines(pp <- predict(cars.spl, xx, deriv = d), col = "red")
points(kn, pp$y[i.kn], pch = 3, col="dark red")
abline(h=0, lty = 3, col = "gray")
}
detach(); par(op)
[Package
stats version 2.2.1
Index]