iterchoiceS1e {ibr}R Documentation

Number of iterations selection for iterative bias reduction model

Description

Evaluate at each iteration proposed in the grid the value of different criteria: GCV, AIC, corrected AIC, BIC and gMDL (along with the ddl and sigma squared). The minimum of these criteria gives an estimate of the optimal number of iterations. This function is not intended to be used directly.

Usage

iterchoiceS1e(y, K, tUy, eigenvaluesS1, ddlmini, ddlmaxi)

Arguments

y The response variable
K A numeric vector which give the search grid for iterations
eigenvaluesS1 Vector of the eigenvalues of the symmetric smoothing matrix S.
tUy The transpose of the matrix of eigen vectors of the symmetric smoothing matrix S times the vector of observation y.
ddlmini The number of eigen values of S equal to 1.
ddlmaxi The maximum df. No criteria are calculated beyond the number of iterations that leads to df bigger than this bound.

Value

Returns the values of GCV, AIC, corrected AIC, BIC, gMDL, df and sigma squared for each value of the grid K. Inf are returned if the iteration leads to a smoother with a df bigger than ddlmaxi.

Author(s)

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

References

Cornillon, P. A., Hengartner, N. and Matzner-Lober, E. (2009) Recursive Bias Estimation for high dimensional regression smoothers. submitted.

See Also

ibr, iterchoiceS1


[Package ibr version 1.2 Index]