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.
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.
Author
Maintainer: Frederic Bertrand frederic.bertrand@lecnam.net (ORCID)
Authors:
Myriam Maumy-Bertrand myriam.maumy@ehesp.fr (ORCID)
Examples
set.seed(314)
library(bigPLScox)
data(sim_data)
head(sim_data)
#> status X1 X2 X3 X4 X5
#> 0.0013236229370777 1 0.5448667 -0.9205711 1.1017160 1.3558567 1.4346174
#> 0.193665925040523 1 -0.5641483 0.2733279 0.9731780 1.1232252 0.2652977
#> 0.0167866701431944 1 1.4921118 0.2598002 -1.5436997 0.1165158 1.2208183
#> 0.0584127055299712 1 -0.6430141 -0.9807448 -1.2294945 0.8006227 1.5492078
#> 0.732960708716205 1 0.1876928 -1.2571263 0.9016827 1.3562191 -1.6809553
#> 0.508483386474255 0 -0.6141516 -0.8162560 0.2633415 0.4188066 0.2791399
#> X6 X7 X8 X9 X10
#> 0.0013236229370777 -0.8727406 1.5161252 0.7801527 -0.53617252 -0.6990319
#> 0.193665925040523 1.5046047 0.9096495 -1.2200395 -1.57280359 0.8347194
#> 0.0167866701431944 -0.6451659 1.2515692 0.5867273 -0.20080821 0.7492891
#> 0.0584127055299712 1.2557210 0.6188920 0.7123894 -0.67379538 -1.2377412
#> 0.732960708716205 0.7304366 -1.1223302 0.9633307 0.14016470 -0.9996676
#> 0.508483386474255 -0.0538974 -0.1410697 -0.8637916 0.01669784 1.5589135