smc.acfTest

This function peforms a 1st order test of the Residual Significant Multivariate Correlation Matrix in order to help determine if the <code>smc</code> should be performed correcting for 1st order autocorrelation.

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

Nelson Afanador

mvdalab

Multivariate Data Analysis Laboratory

smc.acfTest function

<dl><dt>object</dt>
<dd>an object of class <code>mvdareg</code>, i.e. <code>plsFit</code>.</dd><dt>ncomp</dt>
<dd>the number of components to include in the acf assessment</dd></dl>

Arguments

Nelson Lee Afanador (<a href="/link/nelson.afanador%40mvdalab.com?package=mvdalab&version=1.7" data-mini-rdoc="mvdalab::nelson.afanador@mvdalab.com">nelson.afanador@mvdalab.com</a>)

Author

Test of the Residual Significant Multivariate Correlation Matrix for the presence of Autocorrelation — smc.acfTest

<dl>

<dt>object</dt>
<dd>an object of class <code>mvdareg</code>, i.e. <code>plsFit</code>.</dd>

<dt>ncomp</dt>
<dd>the number of components to include in the acf assessment</dd>

</dl>

Nelson Lee Afanador (<a href='mailto:nelson.afanador@mvdalab.com'>nelson.afanador@mvdalab.com</a>)

smc.acfTest: Test of the Residual Significant Multivariate Correlation Matrix for the presence of Autocorrelation

Description

Usage

Value

Arguments

Author

Details

References

Examples