modelddsPLSCpp_Rcpp

Build a ddsPLS model once the bootstrap operations has allowed to find a correct lambda.

A sparse Partial Least Squares implementation which uses soft-threshold
estimation of the covariance matrices and therein introduces
sparsity. Number of components and regularization coefficients are automatically set.

Hadrien Lorenzo

ddsPLS

Data-Driven Sparse Partial Least Squares

modelddsPLSCpp_Rcpp function

<dl><dt>U</dt>
<dd>The weights for X part.</dd>
<dt>V</dt>
<dd>The weights for Y part.</dd>
<dt>X</dt>
<dd>The matrix of X part.</dd>
<dt>Y</dt>
<dd>The matrix of X part.</dd>
<dt>lambdas</dt>
<dd>The to be tested values for lambda.</dd>
<dt>R</dt>
<dd>The number of components to build.</dd>
<dt>n</dt>
<dd>The number of observations.</dd>
<dt>p</dt>
<dd>The number of variables of X part.</dd>
<dt>q</dt>
<dd>The number of variables of Y part.</dd>
<dt>lambda0</dt>
<dd>The vector of regulation parameters.</dd></dl>

Arguments

C++ code to build models, internal function — modelddsPLSCpp_Rcpp

<dl>

<dt>U</dt>
<dd>The weights for X part.</dd>


<dt>V</dt>
<dd>The weights for Y part.</dd>


<dt>X</dt>
<dd>The matrix of X part.</dd>


<dt>Y</dt>
<dd>The matrix of X part.</dd>


<dt>lambdas</dt>
<dd>The to be tested values for lambda.</dd>


<dt>R</dt>
<dd>The number of components to build.</dd>


<dt>n</dt>
<dd>The number of observations.</dd>


<dt>p</dt>
<dd>The number of variables of X part.</dd>


<dt>q</dt>
<dd>The number of variables of Y part.</dd>


<dt>lambda0</dt>
<dd>The vector of regulation parameters.</dd>

</dl>

modelddsPLSCpp_Rcpp: C++ code to build models, internal function

Description

Usage

Arguments