LMROB

Desgined to make sure the following error does not happen anymore:
Error in if (init$scale == 0) : missing value where TRUE/FALSE needed.
KS2014 is the recommended setting (use "nosetting = FALSE").

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

LMROB function

<dl><dt>parameter_y</dt>
<dd>dependent variable in regression</dd>
<dt>parameter_x</dt>
<dd>independent variables in regression</dd>
<dt>nointercept</dt>
<dd>if TRUE it performs regression without an intercept</dd>
<dt>nosetting</dt>
<dd>option to remove the recommended setting in lmrob(). It is much faster. Defaults to FALSE.</dd></dl>

Arguments

Wrapper around lmrob. — LMROB

<dl>

<dt>parameter_y</dt>
<dd>dependent variable in regression</dd>


<dt>parameter_x</dt>
<dd>independent variables in regression</dd>


<dt>nointercept</dt>
<dd>if TRUE it performs regression without an intercept</dd>


<dt>nosetting</dt>
<dd>option to remove the recommended setting in lmrob(). It is much faster. Defaults to FALSE.</dd>

</dl>

LMROB: Wrapper around lmrob.

Description

Usage

Value

Arguments