fill_rcj

Fills in the optimized number of groups and group specific factors for each C.

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

fill_rcj function

<dl><dt>df</dt>
<dd>input</dd>
<dt>all_best_values</dt>
<dd>data frame with the optimal number of groups, common factors and group specific factors</dd>
<dt>subset</dt>
<dd>index of the subsample</dd>
<dt>S_cand</dt>
<dd>vector with candidate values for the number of estimated groups</dd>
<dt>kg_cand</dt>
<dd>vector with candidate values for the number of estimated group specific factors</dd></dl>

Arguments

Fills in the optimized number of groups and group specific factors for each C. — fill_rcj

<dl>

<dt>df</dt>
<dd>input</dd>


<dt>all_best_values</dt>
<dd>data frame with the optimal number of groups, common factors and group specific factors</dd>


<dt>subset</dt>
<dd>index of the subsample</dd>


<dt>S_cand</dt>
<dd>vector with candidate values for the number of estimated groups</dd>


<dt>kg_cand</dt>
<dd>vector with candidate values for the number of estimated group specific factors</dd>

</dl>

fill_rcj: Fills in the optimized number of groups and group specific factors for each C.

Description

Usage

Value

Arguments

Examples