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

```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 2.1.0 Index]