# predict.npregress: Predicted values using using local polynomials In ibr: Iterative Bias Reduction

## 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

 ```1 2 3``` ```## 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.

`npregress`, `summary.npregress`, `locpoly`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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)) ```

### Example output ```Loading required package: mgcv