robustpca

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework.
The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta.
This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
with their loadings. The number of groups and factors can be estimated if they are unknown.

Ewoud Heyndels

RCTS

Clustering Time Series While Resisting Outliers

robustpca function

<dl><dt>object</dt>
<dd>input</dd>
<dt>number_eigenvectors</dt>
<dd>number of eigenvectors to extract</dd>
<dt>KMAX</dt>
<dd>The maximal number of principal components to compute. This is a parameter in cellWise::MacroPCA()</dd>
<dt>verbose_robustpca</dt>
<dd>when TRUE, it prints messages: used for testing (requires Matrix-package when set to TRUE)</dd></dl>

Arguments

Function that uses robust PCA and estimates robust factors and loadings. — robustpca

<dl>

<dt>object</dt>
<dd>input</dd>


<dt>number_eigenvectors</dt>
<dd>number of eigenvectors to extract</dd>


<dt>KMAX</dt>
<dd>The maximal number of principal components to compute. This is a parameter in cellWise::MacroPCA()</dd>


<dt>verbose_robustpca</dt>
<dd>when TRUE, it prints messages: used for testing (requires Matrix-package when set to TRUE)</dd>

</dl>

robustpca: Function that uses robust PCA and estimates robust factors and loadings.

Description

Usage

Value

Arguments

Details