SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares
- 주제(기타) Computer Science, Interdisciplinary Applications; Statistics & Probability
- 설명문(일반) [Kim, Bo-Young] Celltrion, Incheon 22014, South Korea; [Im, Yunju] Yale Univ, Dept Biostat, New Haven, CT 06520 USA; [Yoo, Jae Keun] Ewha Womans Univ, Dept Stat, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 R FOUNDATION STATISTICAL COMPUTING
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000183279
- 본문언어 영어
초록/요약
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.
more