fct_j_lik

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_j_lik 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>A vector of cluster labels.</dd>
<dt>lambda</dt>
<dd>A vector of penalization parameters.</dd>
<dt>alpha</dt>
<dd>A positive constant DPP parameter.</dd>
<dt>beta</dt>
<dd>A positive constant DPP parameter.</dd>
<dt>y_sparse</dt>
<dd>Should Y coefficients be treated as sparse?</dd>
<dt>max_rank</dt>
<dd>The maximum allowed rank.</dd>
<dt>rank</dt>
<dd>The rank, if known.</dd></dl>

Arguments

Internal Likelihood Function — fct_j_lik

<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>A vector of cluster labels.</dd>


<dt>lambda</dt>
<dd>A vector of penalization parameters.</dd>


<dt>alpha</dt>
<dd>A positive constant DPP parameter.</dd>


<dt>beta</dt>
<dd>A positive constant DPP parameter.</dd>


<dt>y_sparse</dt>
<dd>Should Y coefficients be treated as sparse?</dd>


<dt>max_rank</dt>
<dd>The maximum allowed rank.</dd>


<dt>rank</dt>
<dd>The rank, if known.</dd>

</dl>

Internal Likelihood Function

fct_j_lik: Internal Likelihood Function

Description

Usage

Value

Arguments