bigPLScox (version 0.8.1)

bigPLScox-package: bigPLScox-package

Description

Provides Partial least squares Regression for regular, generalized linear and Cox models 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.

Author

Maintainer: Frederic Bertrand (ORCID)

Authors:

• Myriam Maumy-Bertrand (ORCID)

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

Bastien, P., Bertrand, F., Meyer, N., and Maumy-Bertrand, M. (2015). Deviance residuals-based sparse PLS and sparse kernel PLS for binary classification and survival analysis. BMC Bioinformatics, 16, 211.

See Also

big_pls_cox() and big_pls_cox_gd()

Examples

set.seed(314)
library(bigPLScox)
data(sim_data)
head(sim_data)