adapt_X_estimating_less_variables

When running the algorithm with a different number of observable variables then the number that is available, reformat X. (Mainly used for testing)

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

adapt_X_estimating_less_variables function

<dl><dt>X</dt>
<dd>dataframe with the observed variables</dd>
<dt>vars_est</dt>
<dd>number of available observed variables for which a coefficient will be estimated</dd></dl>

Arguments

When running the algorithm with a different number of observable variables then the number that is available, reformat X. (Mainly used for testing) — adapt_X_estimating_less_variables

<dl>

<dt>X</dt>
<dd>dataframe with the observed variables</dd>


<dt>vars_est</dt>
<dd>number of available observed variables for which a coefficient will be estimated</dd>

</dl>

adapt_X_estimating_less_variables: When running the algorithm with a different number of observable variables then the number that is available, reformat X. (Mainly used for testing)

Description

Usage

Value

Arguments