Variable clustering in high-dimensional linear regression: The R package clere

Yengo, Loic, Jacques, Julien, Biernacki, Christophe and Canouil, Mickael (2016) Variable clustering in high-dimensional linear regression: The R package clere. The R Journal, 8 1: 92-106.

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Author Yengo, Loic
Jacques, Julien
Biernacki, Christophe
Canouil, Mickael
Title Variable clustering in high-dimensional linear regression: The R package clere
Journal name The R Journal   Check publisher's open access policy
ISSN 2073-4859
Publication date 2016-08-01
Year available 2016
Sub-type Article (original research)
Open Access Status File (Publisher version)
Volume 8
Issue 1
Start page 92
End page 106
Total pages 15
Place of publication Vienna, 1040 Austria
Publisher Technische Universitaet Wien * Institut fuer Statistik und Wahrscheinlichkeitstheorie
Collection year 2017
Language eng
Formatted abstract
Dimension reduction is one of the biggest challenges in high-dimensional regression models. We recently introduced a new methodology based on variable clustering as a means to reduce dimensionality. We present here the R package clere that implements some refinements of this methodology. An overview of the package functionalities as well as examples to run an analysis are described. Numerical experiments on real data were performed to illustrate the good predictive performance of our parsimonious method compared to standard dimension reduction approaches. 
Keyword Em Algorithm
Selection
Model
Regularization
Predictors
Likelihood
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes https://journal.r-project.org/archive/2016-1/jacques-yengo-biernacki-etal.pdf

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Institute for Molecular Bioscience - Publications
 
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