Description Usage Arguments Value Author(s) References Examples

Model selection by an extended information criterion (EIC), based on nonparametric bootstrapping, was introduced by Ishiguro et al. (1997). This function implements the extension by Reiss et al. (2012) to adaptive linear model selection.

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`y` |
outcome vector |

`X` |
model matrix. This should not include an intercept column; such a column is added by the function. |

`nboot` |
number of bootstrap samples. |

`pvec` |
vector of possible dimensions of the model to consider: by default, ranges from 1 (intercept only) to |

`say.which` |
logical: should the predictors selected for each bootstrap sample be reported? |

`reuse` |
logical: should the best full-data model of each size be reused in calculating the overoptimism estimate, as opposed to reselecting the best model of each size for each training set? |

A list with components

`nlogsig2hat` |
value of the first (non-penalty) term of the criterion, i.e., sample size times log of MLE of the variance, for best model of each dimension in |

`penalty` |
the second (penalty) term of the criterion. |

`eic` |
the EIC, i.e., the sum of the previous two components. |

`best` |
a vector of logicals indicating which columns of the model matrix are included in the EIC-minimizing model. |

Philip Reiss phil.reiss@nyumc.org and Lei Huang huangracer@gmail.com

Ishiguro, M., Sakamoto, Y., and Kitagawa, G. (1997). Bootstrapping log likelihood and EIC, an extension of AIC. *Annals of the Institute of Statistical Mathematics*, 49, 411–434.

Reiss, P. T., Huang, L., Cavanaugh, J. E., and Roy, A. K. (2012). Resampling-based information criteria for adaptive linear model selection.
*Annals of the Institute of Statistical Mathematics*, to appear. Available at http://works.bepress.com/phil_reiss/17

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