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SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares
- Title
- SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares
- Authors
- Kim, Bo-Young; Im, Yunju; Yoo, Jae Keun
- Ewha Authors
- 유재근
- SCOPUS Author ID
- 유재근
![scopus](/images/layout/icon2.png)
- Issue Date
- 2021
- Journal Title
- R JOURNAL
- ISSN
- 2073-4859
- Citation
- R JOURNAL vol. 13, no. 1, pp. 7 - 20
- Publisher
- R FOUNDATION STATISTICAL COMPUTING
- Indexed
- SCIE; SCOPUS
![WOS](/images/layout/wos.gif)
- Document Type
- Article
- Abstract
- 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.
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
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