ibr-package: Iterative Bias Reduction

In ibr: Iterative Bias Reduction

Iterative Bias Reduction

Description

an R package for multivariate smoothing using Iterative Bias Reduction smoother.

Details

• We are interested in smoothing (the values of) a vector of n observations y by d covariates measured at the same n observations (gathered in the matrix X). The iterated Bias Reduction produces a sequence of smoothers

  \hat y=S_k y =(I - (I-S)^k)y,

  where S is the pilot smoother which can be either a kernel or a thin plate spline smoother. In case of a kernel smoother, the kernel is built as a product of univariate kernels.

• The most important parameter of the iterated bias reduction is k the number of iterationsr. Usually this parameter is unknown and is chosen from the search grid K to minimize the criterion (GCV, AIC, AICc, BIC or gMDL).
  The user must choose the pilot smoother (kernel "k", thin plate splines "tps" or Duchon splines "ds") plus the values of bandwidths (kernel) or \lambda thin plate splines). As the choice of these raw values depend on each particular dataset, one can rely on effective degrees of freedom or default values given as degree of freedom, see argument df of the main function ibr.

Index of functions to be used by end user:

ibr:               Iterative bias reduction smoothing
plot.ibr:          Plot diagnostic for an ibr object
predict.ibr:       Predicted values using iterative bias reduction
                   smoothers
forward:           Variable selection for ibr (forward method)
print.summary.ibr: Printing iterative bias reduction summaries
summary.ibr:       Summarizing iterative bias reduction fits

Author(s)

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

Maintainer: Pierre-Andre Cornillon <[e-mail]>

Examples

## Not run: 
data(ozone, package = "ibr")
res.ibr <- ibr(ozone[,-1],ozone[,1],smoother="k",df=1.1)
summary(res.ibr)
predict(res.ibr)
plot(res.ibr)

## End(Not run)

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