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Redundancy Analysis an Alternative for Canonical Correlation Analysis
Abstract
A component method is presented maximizing Stewart and Love's redundancy index. Relationships with multiple correlation and principal component analysis are pointed out and a rotational procedure for obtaining bi-orthogonal variates is given. An elaborate example comparing canonical correlation analysis and redundancy analysis on artificial data is presented.
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Kootte, R. S., Levin, E., Salojärvi, J., Smits, L. P., Hartstra, A. V., Udayappan, S. D., Hermes, G., Bouter, K. E., Koopen, A. M., Holst, J. J., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.09.008 · Cited by 1,039
Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
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Chibwe, L., Parrott, J. L., Shires, K., Khan, H., Clarence, S., Lavalle, C., Sullivan, C., O'Brien, A. M., De Silva, A. O., Muir, D. C. G., et al. (2022) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5140 · Cited by 159
Garali, I., Adanyeguh, I. M., Ichou, F., Perlbarg, V., Seyer, A., Colsch, B., Moszer, I., Guillemot, V., Durr, A., Mochel, F., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx060 · Cited by 68
Genomic selection using random regressions on known and latent environmental covariates
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Multiset sparse partial least squares path modeling for high dimensional omics data analysis
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“Abbreviations CCA: Canonical correlation analysis; CCL: Chronic lymphocytic leukaemia; CI: Confidence interval; ENeT: Elastic net; LVs: Latent variables; LASSO: Least absolute shrinkage and selection operator; MVs: Manifest variables; MOFA: Multi-omics factor analysis; msPLS: Multiset sparse partial least squares path…”
MethodInfluential
Metadata
- Authors
- van den Wollenberg, A. L.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1977-06
- Volume
- 42
- Issue
- 2
- Pages
- 207-219
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2124634097
- Cited by
- 831
- References
- 0
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
