Description Usage Arguments Value Acknowledgement Author(s) References Examples

This function generates realizations of random variables as described in the simple example of Battey, H. S. & Cox, D. R. (2018).

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`s` |
Number of signal variables. |

`a` |
Number of noise variables correlated with signal variables. |

`sigStrength` |
Signal strength. |

`rho` |
Correlation among signal variables and noise variables correlated with signal variables. |

`n` |
Sample size. |

`noise` |
Variance of the observations around the true regression line. |

`var` |
Variance of the potential explanatory variables. |

`d` |
Number of potential explanatory variables. |

`intercept` |
Expected value of the response variable when all potential explanatory variables are at zero. It is only considered when type.response="N". |

`type.response` |
Generates gaussian ("N") or survival ("S") data from a proportional hazards model with Weibull baseline hazard. |

`DGP.seed` |
Seed for the random number generator. |

`scale` |
scale parameter of the proportional hazards model with Weibull baseline hazard. |

`shape` |
shape parameter of the proportional hazards model with Weibull baseline hazard. |

`rate` |
rate parameter of the exponential distribution of censoring times. If not provided, uncensored data are generated. |

`X` |
The simulated design matrix. |

`Y` |
The simulated response variable. |

`TRUE.idx` |
Indices of the variables in the true model. |

`status` |
If type.response="S", provides the status from survival data. |

The work was supported by the UK Engineering and Physical Sciences Research Council under grant number EP/P002757/1.

Hoeltgebaum, H. H.

Cox, D. R. and Battey, H. S. (2017). Large numbers of explanatory variables, a semi-descriptive analysis. *Proceedings of the National Academy of Sciences*, 114(32), 8592-8595.

Battey, H. S. and Cox, D. R. (2018). Large numbers of explanatory variables: a probabilistic assessment. *Proceedings of the Royal Society of London, A.*, 474(2215), 20170631.

Hoeltgebaum, H., & Battey, H. S. (2019). HCmodelSets: An R Package for Specifying Sets of Well-fitting Models in High Dimensions. *The R Journal*, 11(2), 370-379.

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