TuneExplainedVarianceCov

Computes the difference in absolute value between the desired and observed
proportion of explained variance from the first Principal Component of a
Principal Component Analysis applied on the covariance matrix. The precision
matrix is obtained by adding u to the diagonal of a positive semidefinite
matrix. This function is used to find the value of the constant u that
generates a covariance matrix with desired proportion of explained variance.

internal

This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) <arXiv:2106.02521>).

Barbara Bodinier

fake

Flexible Data Simulation Using the Multivariate Normal
Distribution

TuneExplainedVarianceCov function

<dl><dt>u</dt>
<dd>constant u added to the diagonal of the precision matrix.</dd>
<dt>ev_xx</dt>
<dd>desired proportion of explained variance. If <code>ev_xx=NULL</code>,
the obtained proportion of explained variance is returned.</dd>
<dt>lambda</dt>
<dd>eigenvalues of the positive semidefinite precision matrix.</dd></dl>

Arguments

Tuning function (covariance) — TuneExplainedVarianceCov

<dl>

<dt>u</dt>
<dd>constant u added to the diagonal of the precision matrix.</dd>


<dt>ev_xx</dt>
<dd>desired proportion of explained variance. If <code>ev_xx=NULL</code>,
the obtained proportion of explained variance is returned.</dd>


<dt>lambda</dt>
<dd>eigenvalues of the positive semidefinite precision matrix.</dd>

</dl>

TuneExplainedVarianceCov: Tuning function (covariance)

Description

Usage

Value

Arguments