Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many scientific fields such as chemometrics, pattern recognition, genomic sequence analysis, and so on. The so-called seedCCA is a newly developed R package that implements not only the standard and seeded CCA but also partial least squares. The package enables us to fit CCA to large-p and small-n data. The paper provides a complete guide. Also, the seeded CCA application results are compared with the regularized CCA in the existing R package. It is believed that the package, along with the paper, will contribute to high-dimensional data analysis in various science field practitioners and that the statistical methodologies in multivariate analysis become more fruitful.
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For attribution, please cite this work as
Researcher, et al., "The R Journal: SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares", The R Journal, 2021
BibTeX citation
@article{RJ-2021-026,
author = {Researcher, Bo-Young Kim, and Associate, Yunju Im, Postdoctoral and Professor, Jae Keun Yoo,},
title = {The R Journal: SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-026},
doi = {10.32614/RJ-2021-026},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {7-20}
}