# predict.smooth.spline

##### 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
x The input `x`

.y The fitted values or derivatives at `x`

.

##### See Also

##### Examples

`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)
```

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*

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