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

Evaluates 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.

1 2 | ```
iterchoiceAe(Y, K, eigenvaluesA, tPADmdemiY, DdemiPA, ddlmini,
ddlmaxi)
``` |

`Y` |
The response variable. |

`K` |
A numeric vector which give the search grid for iterations. |

`eigenvaluesA` |
Vector of the eigenvalues of the
symmetric matrix |

`tPADmdemiY` |
The transpose of the matrix of eigen vectors of the
symmetric matrix |

`DdemiPA` |
The square root of the diagonal matrix |

`ddlmini` |
The number of eigenvalues (numerically) which are equal to 1. |

`ddlmaxi` |
The maximum df. No criteria are calculated beyond the number of iterations that leads to df bigger than this bound. |

See the reference for detailed explanation of *A* and *D*

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`

.

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

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.

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