sk.decompose

make SVD as well as orthogonal complements

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.decompose function

<dl><dt>A</dt>
<dd>the input matrix</dd>
<dt>D</dt>
<dd>the linear transform</dd></dl>

Arguments

make SVD as well as orthogonal complements — sk.decompose

<dl>

<dt>A</dt>
<dd>the input matrix</dd>


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

</dl>

sk.decompose: make SVD as well as orthogonal complements

Description

Usage

Value

Arguments

Examples