Description Usage Arguments Value Author(s) References Examples

Given a design matrix and a response vector, the function selects a threshold for the sample correlation matrix, computes an adaptive measure for the contribution of each variable to the response variable based on the thus-thresholded sample correlation matrix, and chooses a variable at each iteration. Once variables are selected in the "active" set, the extended BIC is used for the final model selection.

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`X` |
design matrix. |

`y` |
response vector. |

`thr.step` |
a step size used for threshold selection. When thr.step==NULL, it is chosen automatically. |

`thr.rep` |
the number of times for which the threshold selection procedure is repeated. |

`max.size` |
the maximum number of the variables conditional on which the contribution of each variable to the response is measured (when max.size==NULL, it is set to be half the number of observations). |

`max.count` |
the maximum number of iterations. |

`op` |
when op==1, rescaling 1 is used to compute the tilted correlation. If op==2, rescaling 2 is used. |

`bic.gamma` |
a parameter used to compute the extended BIC. |

`eps` |
an effective zero. |

`active` |
active set containing the variables selected over the iterations. |

`thr.seq` |
a sequence of thresholds selected over the iterations. |

`bic.seq` |
extended BIC computed over the iterations. |

`active.hat` |
finally chosen variables using the extended BIC. |

Haeran Cho

H. Cho and P. Fryzlewicz (2012) High-dimensional variable selection via tilting, Journal of the Royal Statistical Society Series B, 74: 593-622.

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