sk.W_path

generates the split knockoff statistics W based on the beta(lambda) from
a split LASSO path in the intercepetion assignment step.

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

sk.W_path function

<dl><dt>X</dt>
<dd>the design matrix</dd>
<dt>D</dt>
<dd>the linear transform</dd>
<dt>y</dt>
<dd>the response vector</dd>
<dt>nu</dt>
<dd>the parameter for variable splitting</dd>
<dt>option</dt>
<dd>options for creating the Knockoff statistics
option$lambda: the choice of lambda for the path</dd></dl>

Arguments

W statistics generator based on the beta(lambda) from a split LASSO path — sk.W_path

<dl>

<dt>X</dt>
<dd>the design matrix</dd>


<dt>D</dt>
<dd>the linear transform</dd>


<dt>y</dt>
<dd>the response vector</dd>


<dt>nu</dt>
<dd>the parameter for variable splitting</dd>


<dt>option</dt>
<dd>options for creating the Knockoff statistics
option$lambda: the choice of lambda for the path</dd>

</dl>

sk.W_path: W statistics generator based on the beta(lambda) from a split LASSO path

Description

Usage

Value

Arguments