Description Usage Arguments Value References See Also

Samples multivariate Gaussian fixed-design knockoff variables for the original variables.

1 2 | ```
MFKnockoffs.create.gaussian(X, mu, Sigma, method = c("asdp", "sdp", "equi"),
diag_s = NULL)
``` |

`X` |
normalized n-by-p realization of the design matrix |

`mu` |
mean vector of length p for X |

`Sigma` |
p-by-p covariance matrix for X |

`method` |
either 'equi', 'sdp' or 'asdp' (default:'asdp') |

`diag_s` |
pre-computed vector of covariances between the original variables and the knockoffs. This will be computed according to 'method', if not supplied |

n-by-p matrix of knockoff variables

Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html

Other methods for creating knockoffs: `MFKnockoffs.create.approximate_gaussian`

,
`MFKnockoffs.create.fixed`

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