prepare_for_robpca

It contains options to use the classical or robust covmatrix or no covariance matrix at all.

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

prepare_for_robpca function

<dl><dt>object</dt>
<dd>this is the object of which we may take the covariance matrix and then to perform robust PCA on</dd>
<dt>NN</dt>
<dd>N</dd>
<dt>TT</dt>
<dd>T</dd>
<dt>option</dt>
<dd>1 (robust covmatrix), 2 (classical covmatrix), 3 (no covmatrix)</dd></dl>

Arguments

Helpfunction: prepares object to perform robust PCA on. — prepare_for_robpca

<dl>

<dt>object</dt>
<dd>this is the object of which we may take the covariance matrix and then to perform robust PCA on</dd>


<dt>NN</dt>
<dd>N</dd>


<dt>TT</dt>
<dd>T</dd>


<dt>option</dt>
<dd>1 (robust covmatrix), 2 (classical covmatrix), 3 (no covmatrix)</dd>

</dl>

prepare_for_robpca: Helpfunction: prepares object to perform robust PCA on.

Description

Usage

Value

Arguments