find_independent_regression_vectors.glmnet_multivariate

<code>find_independent_regression_vectors</code> tries to find a situation where K variables
are selected, so that the selected columns of matrix Xp are independent.

internal

Implementation of the automatic shift detection method for
Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
phylogenies. Some tools to handle equivalent shifts configurations are also
available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.

Paul Bastide

PhylogeneticEM

Automatic Shift Detection using a Phylogenetic EM

Mahendra Mariadassou

find_independent_regression_vectors.glmnet_multivariate function

<dl><dt>Xp</dt>
<dd>(transformed) matrix of regression</dd>
<dt>K</dt>
<dd>number of non-zero components allowed</dd>
<dt>root</dt>
<dd>integer, position of the root column (intercept) excluded from the fit. null 
if no root column.</dd></dl>

Arguments

Given a regularization path, find K selected independent variables. — find_independent_regression_vectors.glmnet_multivariate

<dl>

<dt>Xp</dt>
<dd>(transformed) matrix of regression</dd>


<dt>K</dt>
<dd>number of non-zero components allowed</dd>


<dt>root</dt>
<dd>integer, position of the root column (intercept) excluded from the fit. null 
if no root column.</dd>

</dl>

find_independent_regression_vectors.glmnet_multivariate: Given a regularization path, find K selected independent variables.

Description

Usage

Value

Arguments

Details