predict.npregress: Predicted values using using local polynomials

In ibr: Iterative Bias Reduction

Predicted values using using local polynomials

Description

Predicted values from a local polynomials of degree less than 2. See locpoly for fast binned implementation over an equally-spaced grid of local polynomial (gaussian kernel only)
Missing values are not allowed.

Usage

## S3 method for class 'npregress'
predict(object, newdata, interval=
 c("none", "confidence", "prediction"), deriv=FALSE, ...)

Arguments

object	Object of class npregress.
newdata	An optional vector of values to be predicted. If omitted, the fitted values are used.
interval	Type of interval calculation. Only none is currently avalaible.
deriv	Bolean. If TRUE it returns the first derivative of the local polynomial (of degree1).
...	Further arguments passed to or from other methods.

Value

Produces a vector of predictions. If deriv is TRUE the value is a named list with components: yhat which contains predictions and (if relevant) deriv the first derivative of the local polynomial of degree 1.

Author(s)

Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.

References

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

See Also

npregress, summary.npregress, locpoly

Examples

f <- function(x){sin(5*pi*x)}
n <- 100
x <- runif(n)
z <- f(x)
sigma2 <- 0.05*var(z)
erreur<-rnorm(n,0,sqrt(sigma2))
y<-z+erreur
grid <- seq(min(x),max(x),length=500)
res <- npregress(x,y,bandwidth=0.02,control.par=list(degree=1))
plot(x,y)
lines(grid,predict(res,grid))

ibr documentation built on Sept. 13, 2023, 5:08 p.m.