fct_em

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_em 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>lambda</dt>
<dd>A vector of penalization parameters.</dd>
<dt>clust_assign</dt>
<dd>The current clustering assignment.</dd>
<dt>lik_track</dt>
<dd>A vector storing the log-likelihood by iteration.</dd>
<dt>em_iter</dt>
<dd>The maximum number of EM iterations.</dd>
<dt>verbose</dt>
<dd>A boolean indicating whether to print to screen.</dd></dl>

Arguments

Internal EM Algorithm — fct_em

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


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


<dt>lik_track</dt>
<dd>A vector storing the log-likelihood by iteration.</dd>


<dt>em_iter</dt>
<dd>The maximum number of EM iterations.</dd>


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

</dl>

Internal EM Algorithm

fct_em: Internal EM Algorithm

Description

Usage

Value

Arguments