compute p-values from penalized zero-inflated Poisson, negative binomial and geometric model with multi-split data

1 2 3 |

`formula` |
symbolic description of the model, see details. |

`data` |
argument controlling formula processing
via |

`weights` |
optional numeric vector of weights. If |

`subset` |
subset of data |

`na.action` |
how to deal with missing data |

`offset` |
Not implemented yet |

`standardize` |
logical value, should variables be standardized? |

`family` |
family to fit |

`penalty` |
penalty considered as one of |

`gamma.count` |
The tuning parameter of the |

`gamma.zero` |
The tuning parameter of the |

`prop` |
proportion of data split, default is 50/50 split |

`trace` |
logical value, if TRUE, print detailed calculation results |

`B` |
number of repeated multi-split replications |

`...` |
Other arguments passing to |

compute p-values from penalized zero-inflated Poisson, negative binomial and geometric model with multi-split data

`count.pval` |
raw p-values in the count component |

`zero.pval` |
raw p-values in the zero component |

`count.pval.q` |
Q value for the count component |

`zero.pval.q` |
Q value for the zero component |

Zhu Wang <zwang@connecticutchildrens.org>

Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2013)
*p-Values for High-Dimensional Regression*, *Journal of the American Statistical Association*, **104(488)**, 1671–1681

Zhu Wang, Shuangge Ma, Ching-Yun Wang, Michael Zappitelli, Prasad Devarajan and Chirag R. Parikh (2014)
*EM for Regularized Zero Inflated Regression Models with Applications to Postoperative Morbidity after Cardiac Surgery in Children*, *Statistics in Medicine*. 33(29):5192-208.

Zhu Wang, Shuangge Ma and Ching-Yun Wang (2015) *Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany*, *Biometrical Journal*. 57(5):867-84.

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