predict.ibr: Predicted values using iterative bias reduction smoothers

Description Usage Arguments Value Author(s) References See Also Examples

Description

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

Usage

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## 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.

See Also

ibr, summary.ibr

Examples

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## 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)

ibr documentation built on May 2, 2019, 8:22 a.m.