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

Computes the covariance inflation criterion (CIC) of Tibshirani and Knight (1999) for submodels of a full linear model.

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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. |

`nperms` |
number of permuted data sets to generate. |

`covests` |
sum of the null-hypothesis covariances between the outcomes and the fitted values for the best linear model of each size. If |

`nullcic` |
CIC for the intercept-only model. |

A list with components

`leaps` |
all-subsets regression object (for the unpermuted data) returned by function |

`covests` |
sum of the (estimated) null-hypothesis covariances between the outcomes and the fitted values for the best linear model of each size. |

`enp` |
effective number of parameters for models of each size, as defined by Tibshirani and Knight (1999). |

`cic` |
CIC for each of the models given in the |

`nullcic` |
CIC for the intercept-only model. |

`best` |
vector of logicals indicating which predictors are included in the minimum-CIC model. |

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

Tibshirani, R., and Knight, K. (1999). The covariance inflation criterion for adaptive model selection. *Journal of the Royal Statistical Society, Series B*, 61, 529–546.

`leaps`

(in the package of the same name)

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