The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The pls Package: Principal Component and Partial Least Squares Regression in R
Open accessAbstract
The pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905 · cited 7×
“Other PLSR algorithms give identical results to SIMPLS in the case of one Y variable, but deviate slightly for the multivariate Y case; the differences are not likely to be important in practice.”
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Cited by
Building Predictive Models in R Using the caret Package
Kuhn, M. (2008) · Journal of Statistical Software · doi:10.18637/jss.v028.i05 · Cited by 8,863
Predicting effective microRNA target sites in mammalian mRNAs
Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015) · eLife · doi:10.7554/elife.05005 · Cited by 7,318
Zhalnina, K., Louie, K. B., Hao, Z., Mansoori, N., da Rocha, U. N., Shi, S., Cho, H., Karaoz, U., Loqué, D., Bowen, B. P., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0129-3 · Cited by 2,368
Fumagalli, M., Sironi, M., Pozzoli, U., Ferrer-Admettla, A., Pattini, L., & Nielsen, R. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002355 · Cited by 600
Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
Abdi, H., & Williams, L. J. (2013) · Methods in Molecular Biology · doi:10.1007/978-1-62703-059-5_23 · Cited by 448
Metadata
- Authors
- Mevik, B.-H., & Wehrens, R.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2007
- Volume
- 18
- Issue
- 2
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2140959043
- Cited by
- 1,782
- References
- 3
- FWCI
- 30.09
- Type
- journal article
- Language
- en
- Open access
- diamond
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