# predict.smooth.spline

0th

Percentile

##### Predict from Smoothing Spline Fit

Predict a smoothing spline fit at new points, return the derivative if desired. The predicted fit is linear beyond the original data.

Keywords
smooth
##### Usage
## S3 method for class 'smooth.spline':
predict(object, x, deriv = 0, \dots)
##### 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
• xThe input x.
• yThe fitted values or derivatives at x.

smooth.spline
library(stats) require(graphics) attach(cars) cars.spl <- smooth.spline(speed, dist, df = 6.4) print.default(cars.spl) ## "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$y), type = "l", xlab = "x", ylab = "", col = "blue", col.main = "red", main = paste0("s" ,paste(rep("'", d), collapse = ""), "(x)")) mtext("Difference quotient approx.(last)", col = "blue") lines(pp <- predict(cars.spl, xx, deriv = d), col = "red") print(pp) points(kn, pp$y[i.kn], pch = 3, col = "dark red") abline(h = 0, lty = 3, col = "gray") } detach(); par(op)