bigPLSR-package

Provides Partial least squares Regression for big data. It allows for missing data in the explanatory variables. Repeated k-fold cross-validation of such models using various criteria. Bootstrap confidence intervals constructions are also available.

References

Maumy, M., Bertrand, F. (2023). PLS models and their extension for big data. Joint Statistical Meetings (JSM 2023), Toronto, ON, Canada.

Maumy, M., Bertrand, F. (2023). bigPLS: Fitting and cross-validating PLS-based Cox models to censored big data. BioC2023 — The Bioconductor Annual Conference, Dana-Farber Cancer Institute, Boston, MA, USA. Poster. https://doi.org/10.7490/f1000research.1119546.1

See also

Useful links:

• https://fbertran.github.io/bigPLSR/

• https://github.com/fbertran/bigPLSR

• Report bugs at https://github.com/fbertran/bigPLSR/issues

Author

Maintainer: Frederic Bertrand frederic.bertrand@lecnam.net (ORCID)

Authors:

• Myriam Maumy myriam.maumy@ehesp.fr (ORCID)

Examples

set.seed(123)
X <- matrix(rnorm(60), nrow = 20)
y <- X[, 1] - 0.5 * X[, 2] + rnorm(20, sd = 0.1)
fit <- pls_fit(X, y, ncomp = 2, scores = "r", algorithm = "simpls")
head(pls_predict_response(fit, X, ncomp = 2))
#> [1] -0.2557041 -0.3103345  1.8935717  0.1961492  0.2217772  2.3503614