Description Usage Arguments Value References See Also

Creates fixed-design knockoff variables for the original variables.

1 2 | ```
MFKnockoffs.create.fixed(X, method = c("sdp", "equi"), sigma = NULL,
y = NULL, randomize = F)
``` |

`X` |
normalized n-by-p design matrix (n >= 2p) |

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

`sigma` |
noise level, used to augment the data with extra rows if necessary (default: NULL) |

`y` |
vector of observed responses, used to estimate the noise level if 'sigma' is not provided (default: NULL) |

`randomize` |
whether the knockoffs are deterministic or randomized (default:False) |

An object of class "MFKnockoffs.variables". This object is a list containing at least the following components:

`X` |
The n-by-p matrix of original variables (possibly augmented or transformed) |

`X_k` |
The n-by-p matrix of knockoff variables |

`y` |
The vector of observed responses (possibly augmented) |

Barber and Candes, Controlling the false discovery rate via knockoffs. Ann. Statist. 43 (2015), no. 5, 2055–2085. https://projecteuclid.org/euclid.aos/1438606853

Fixed-design knockoff assume a linear regression model for Y|X. Moreover, they only guarantee FDR control with statistics satisfying the "sufficiency" property. In particular, the default statistics with cross-validated lasso does not satisfy this property and should not be used with fixed-design knockoffs.

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

,
`MFKnockoffs.create.gaussian`

MFKnockoffs documentation built on May 2, 2019, 6:33 a.m.

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