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CCA: An R Package to Extend Canonical Correlation Analysis

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  • González, Ignacio
  • Déjean, Sébastien
  • Martin, Pascal G. P.
  • Baccini, Alain

Abstract

Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.

Suggested Citation

  • González, Ignacio & Déjean, Sébastien & Martin, Pascal G. P. & Baccini, Alain, 2008. "CCA: An R Package to Extend Canonical Correlation Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i12).
    • Handle: RePEc:jss:jstsof:v:023:i12
    DOI: http://hdl.handle.net/10.18637/jss.v023.i12
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    Cited by:

    1. Hongxing Li & Alasdair Cohen & Zheng Li & Mengjie Zhang, 2018. "The Impacts of Socioeconomic Development on Rural Drinking Water Safety in China: A Provincial-Level Comparative Analysis," Sustainability, MDPI, vol. 11(1), pages 1-12, December.
    2. Florian Rohart & Benoît Gautier & Amrit Singh & Kim-Anh Lê Cao, 2017. "mixOmics: An R package for ‘omics feature selection and multiple data integration," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-19, November.
    3. Cruz-Cano, Raul & Lee, Mei-Ling Ting, 2014. "Fast regularized canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 88-100.
    4. Langworthy, Benjamin W. & Stephens, Rebecca L. & Gilmore, John H. & Fine, Jason P., 2021. "Canonical correlation analysis for elliptical copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    5. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    6. Alicja Grzeskowiak, 2016. "Satisfaction with chosen aspects of life in Poland - evaluation by canonical correlation methods," International Journal of Social Sciences, International Institute of Social and Economic Sciences, vol. 5(1), pages 60-71, February.
    7. Corona Francisco & Horrillo Juan de Dios Tena & Wiper Michael Peter, 2017. "On the importance of the probabilistic model in identifying the most decisive games in a tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(1), pages 11-23, March.
    8. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    9. Lykou, Anastasia & Whittaker, Joe, 2010. "Sparse CCA using a Lasso with positivity constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3144-3157, December.
    10. Jimmy Martin-Delgado & Aurora Mula & Rafael Manzanera & Jose Joaquin Mira, 2022. "Measuring the Impact of Future Outbreaks? A Secondary Analysis of Routinely Available Data in Spain," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    11. Dmitry Kobak & Yves Bernaerts & Marissa A. Weis & Federico Scala & Andreas S. Tolias & Philipp Berens, 2021. "Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 980-1000, August.

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