# predict.ibr: Predicted values using iterative bias reduction smoothers In ibr: Iterative Bias Reduction

## Description

Predicted values from iterative bias reduction object.
Missing values are not allowed.

## Usage

 ```1 2 3``` ```## S3 method for class 'ibr' predict(object, newdata, interval= c("none", "confidence", "prediction"), ...) ```

## Arguments

 `object` Object of class `ibr`. `newdata` An optional matrix in which to look for variables with which to predict. If omitted, the fitted values are used. `interval` Type of interval calculation. Only `none` is currently avalaible. `...` Further arguments passed to or from other methods.

## Value

Produces a vector of predictions.

## Author(s)

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

## References

Cornillon, P.-A.; Hengartner, N.; Jegou, N. and Matzner-Lober, E. (2012) Iterative bias reduction: a comparative study. Statistics and Computing, 23, 777-791.

Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2013) Recursive bias estimation for multivariate regression smoothers Recursive bias estimation for multivariate regression smoothers. ESAIM: Probability and Statistics, 18, 483-502.

Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1–26.

`ibr`, `summary.ibr`
 ```1 2 3 4 5``` ```## Not run: data(ozone, package = "ibr") res.ibr <- ibr(ozone[,-1],ozone[,1],df=1.2,K=1:500) summary(res.ibr) predict(res.ibr) ## End(Not run) ```