fct_rank

internal

Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when
the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization
with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically
performing parameter selection to identify low-rank substructures in the coefficient matrix.

Alexander White

mixedLSR

Mixed, Low-Rank, and Sparse Multivariate Regression on
High-Dimensional Data

Sha Cao

Yi Zhao

Chi Zhang

fct_rank function

<dl><dt>x</dt>
<dd>A matrix of predictors.</dd>
<dt>y</dt>
<dd>A matrix of responses.</dd>
<dt>sigma</dt>
<dd>An estimated noise level.</dd>
<dt>eta</dt>
<dd>A rank selection parameter.</dd></dl>

Arguments

Internal Rank Estimation Function — fct_rank

<dl>

<dt>x</dt>
<dd>A matrix of predictors.</dd>


<dt>y</dt>
<dd>A matrix of responses.</dd>


<dt>sigma</dt>
<dd>An estimated noise level.</dd>


<dt>eta</dt>
<dd>A rank selection parameter.</dd>

</dl>

Internal Rank Estimation Function

fct_rank: Internal Rank Estimation Function

Description

Usage

Value

Arguments