Description Usage Arguments Value Note

2 major differences being that the knockoff variables are to be passed directly to the function, not made inside of it; and secondly you can pass the amount of cores wanted directly to the statistic function chosen. Be careful as this may need to be 'NULL'-ed if the statistic function you choose does not allow for multi-cores.

1 2 |

`X` |
The data frame to be the explanatory variables |

`y` |
The response variable, a single vector the same length as X |

`Xk` |
The knockoff variables created seperately (see the 'knockoff' package for more details) |

`statistic` |
The statistic to be used in the variable selection process. Defaults to the difference in coeffiecents for a basic linear model. |

`fdr` |
The False Discovery Rate bounded between (0,1). The default is .1 |

`offset` |
Allows for more or less conservative selections. 1 for more (default) , 0 for less. |

`cores` |
The number of cores you would like to use. The default is 2. If more is stated than is possible an error will be returned. |

Same as with the standard knockoff package function 'knockoff.filter', see that for more.

This funtion can only be used *after* creating the knockoff variables, the goal was to split those two functions inorder to get a a faster speed up on the loop for response variables with the same explanatory variables.

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