fct_select_lambda

Internal Penalty Parameter Selection Function.

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_select_lambda function

<dl><dt>x</dt>
<dd>A matrix of predictors.</dd>
<dt>y</dt>
<dd>A matrix of responses.</dd>
<dt>k</dt>
<dd>The number of groups.</dd>
<dt>clust_assign</dt>
<dd>The current clustering assignment.</dd>
<dt>initial</dt>
<dd>An initial penalty parameter.</dd>
<dt>type</dt>
<dd>A type.</dd>
<dt>verbose</dt>
<dd>A boolean indicating whether to print to screen.</dd></dl>

Arguments

Internal Penalty Parameter Selection Function. — fct_select_lambda

<dl>

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


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


<dt>k</dt>
<dd>The number of groups.</dd>


<dt>clust_assign</dt>
<dd>The current clustering assignment.</dd>


<dt>initial</dt>
<dd>An initial penalty parameter.</dd>


<dt>type</dt>
<dd>A type.</dd>


<dt>verbose</dt>
<dd>A boolean indicating whether to print to screen.</dd>

</dl>

fct_select_lambda: Internal Penalty Parameter Selection Function.

Description

Usage

Value

Arguments