big_pls_cox_fast: Partial Least Squares Components for Cox Models (fast backend)

Description

Compute PLS components for Cox models, using a fast C++ backend for both in-memory matrices and bigmemory::big.matrix objects.

Usage

big_pls_cox_fast(
  X,
  time,
  status,
  ncomp = 2L,
  control = survival::coxph.control(),
  keepX = NULL
)

Value

A list with the computed scores, loadings, weights, scaling information and the fitted Cox model returned by survival::coxph.fit.

Arguments

X: A numeric matrix or a bigmemory::big.matrix object containing the predictors.
time: Numeric vector of survival times.
status: Integer (0/1) vector of event indicators.
ncomp: Number of latent components to compute.
control: Optional list passed to survival::coxph.control.
keepX: Optional integer vector specifying the number of variables to retain (naive sparsity) in each component. A value of zero keeps all predictors. If a single integer is supplied it is recycled across components.

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., & Maumy-Bertrand, M. (2015). Deviance residuals-based sparse PLS and sparse kernel PLS for censored data. Bioinformatics, 31(3), 397–404. doi:10.1093/bioinformatics/btu660

Bertrand, F., Bastien, P., Meyer, N., & Maumy-Bertrand, M. (2014). PLS models for censored data. In Proceedings of UseR! 2014 (p. 152).