Description Usage Arguments Value Note References See Also Examples

`RaModel`

generates data from 4 models described in Tian, Y. and Feng, Y., 2021(b) and 8 models described in Tian, Y. and Feng, Y., 2021(a).

1 |

`model.type` |
indicator of the paper covering the model, which can be 'classification' (Tian, Y. and Feng, Y., 2021(b)) or 'screening' (Tian, Y. and Feng, Y., 2021(a)). |

`model.no` |
model number. It can be 1-4 when |

`n` |
sample size |

`p` |
data dimension |

`p0` |
marginal probability of class 0. Default = 0.5. Only used when |

`sparse` |
a logistic object indicating model sparsity. Default = TRUE. Only used when |

`x` |
n * p matrix. n observations and p features. |

`y` |
n responses. |

When `model.type`

= 'classification' and `sparse`

= TRUE, models 1, 2, 4 require *p ≥ 5* and model 3 requires
*p ≥ 50*. When `model.type`

= 'classification' and `sparse`

= FALSE, models 1 and 4 require *p ≥ 50* and
*p ≥ 30*, respectively. When `model.type`

= 'screening', models 1, 4, 5 and 7 require *p ≥ 4*. Models 2 and 8 require *p ≥ 5*. Model 3 requires *p ≥ 22*. Model 5 requires *p ≥ 2*.

Tian, Y. and Feng, Y., 2021(a). RaSE: A variable screening framework via random subspace ensembles. Journal of the American Statistical Association, (just-accepted), pp.1-30.

Tian, Y. and Feng, Y., 2021(b). RaSE: Random subspace ensemble classification. Journal of Machine Learning Research, 22(45), pp.1-93.

1 2 3 4 5 6 7 8 9 10 |

RaSEn documentation built on Oct. 16, 2021, 9:06 a.m.

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