RegularizedSCA: Regularized Simultaneous Component Analysis of multiblock data in R

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.
Original languageEnglish
JournalBehavior Research Methods
Publication statusAccepted/In press - 2019

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@article{a326d0ed5b1b473cbffd331cfee42837,
title = "RegularizedSCA: Regularized Simultaneous Component Analysis of multiblock data in R",
abstract = "This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.",
author = "Zhengguo Gu and {Van Deun}, Katrijn",
year = "2019",
language = "English",
journal = "Behavior Research Methods",
issn = "1554-351X",
publisher = "Springer",

}

RegularizedSCA : Regularized Simultaneous Component Analysis of multiblock data in R. / Gu, Zhengguo; Van Deun, Katrijn.

In: Behavior Research Methods, 2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - RegularizedSCA

T2 - Regularized Simultaneous Component Analysis of multiblock data in R

AU - Gu, Zhengguo

AU - Van Deun, Katrijn

PY - 2019

Y1 - 2019

N2 - This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.

AB - This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.

M3 - Article

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-351X

ER -