Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 유재근 | * |
dc.date.accessioned | 2021-11-10T16:31:10Z | - |
dc.date.available | 2021-11-10T16:31:10Z | - |
dc.date.issued | 2021 | * |
dc.identifier.issn | 2073-4859 | * |
dc.identifier.other | OAK-30075 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/259347 | - |
dc.description.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. | * |
dc.language | English | * |
dc.publisher | R FOUNDATION STATISTICAL COMPUTING | * |
dc.title | SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares | * |
dc.type | Article | * |
dc.relation.issue | 1 | * |
dc.relation.volume | 13 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 7 | * |
dc.relation.lastpage | 20 | * |
dc.relation.journaltitle | R JOURNAL | * |
dc.identifier.wosid | WOS:000684952200002 | * |
dc.author.google | Kim, Bo-Young | * |
dc.author.google | Im, Yunju | * |
dc.author.google | Yoo, Jae Keun | * |
dc.contributor.scopusid | 유재근(23032759600) | * |
dc.date.modifydate | 20240130113500 | * |