| 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.; 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.