Description Usage Arguments Value Examples

Perform variable selection for high dimensional data

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`x` |
the predictor matrix |

`y` |
the time and status object for survival |

`B` |
times of bootstrap |

`ngrp` |
the number of blocks to separate variables into. Default is 15*p/N, where p is the number of predictors and N is the sample size. |

`parallel` |
Logical TRUE or FALSE. Whether to use multithread computing, which can save consideratable amount of time for high dimensional data. Default is TRUE. |

`family` |
what family of data types. Default is 'competing'. Quantile regression for competing risks will be available through the developmental version on github |

`ncore` |
Number of cores used for parallel computing, if parallel=TRUE |

`object` |
the RAEN object containing the variable selection results |

`newdata` |
the predictor matrix for prediction |

`...` |
other parameters to pass |

a dataframe with the variable names and the regression coefficients

the linear predictor of the outcome risk

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