threshold

compute the threshold for variable selection

Split Knockoff is a data adaptive variable selection framework for controlling the
(directional) false discovery rate (FDR) in structural sparsity, where variable
selection on linear transformation of parameters is of concern. This proposed scheme
relaxes the linear subspace constraint to its neighborhood, often known as variable
splitting in optimization.
Simulation experiments can be reproduced following the Vignette.
'Split Knockoffs' is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.

Yuxuan Chen

SplitKnockoff

Split Knockoffs for Structural Sparsity

Haoxue Wang

Yang Cao

Xinwei Sun

Yuan Yao

threshold function

<dl><dt>W</dt>
<dd>statistics W_j for testing null hypothesis beta_j = 0</dd>
<dt>q</dt>
<dd>target FDR</dd>
<dt>method</dt>
<dd>option$method can be 'knockoff' or 'knockoff+'</dd></dl>

Arguments

compute the threshold for variable selection — threshold

<dl>

<dt>W</dt>
<dd>statistics W_j for testing null hypothesis beta_j = 0</dd>


<dt>q</dt>
<dd>target FDR</dd>


<dt>method</dt>
<dd>option$method can be 'knockoff' or 'knockoff+'</dd>

</dl>

threshold: compute the threshold for variable selection

Description

Usage

Value

Arguments