Partial Least Squares Components for Cox Models (fast backend)

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
)

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.

Value

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

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).