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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