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.

smooth.spline

### Examples

require(graphics)

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


[Package stats version 4.3.0 Index]