plsRcox1.7.7 package

Partial Least Squares Regression for Cox Models and Related Techniques

coxDKpls2DR

Fitting a Direct Kernel PLS model on the (Deviance) Residuals

coxDKplsDR

Fitting a Direct Kernel PLS model on the (Deviance) Residuals

coxDKsplsDR

Fitting a Direct Kernel sPLSR model on the (Deviance) Residuals

coxpls

Fitting a Cox-Model on PLSR components

coxpls2

Fitting a Cox-Model on PLSR components

coxpls2DR

Fitting a PLSR model on the (Deviance) Residuals

coxpls3

Fitting a Cox-Model on PLSR components

coxpls3DR

Fitting a PLSR model on the (Deviance) Residuals

coxplsDR

Fitting a PLSR model on the (Deviance) Residuals

coxsplsDR

Fitting a sPLSR model on the (Deviance) Residuals

cv.autoplsRcox

Cross-validating an autoplsRcox-Model

cv.coxDKplsDR

Cross-validating a DKplsDR-Model

cv.coxDKsplsDR

Cross-validating a DKsplsDR-Model

cv.coxpls

Cross-validating a Cox-Model fitted on PLSR components

cv.coxplsDR

Cross-validating a plsDR-Model

cv.coxsplsDR

Cross-validating a splsDR-Model

cv.larsDR

Cross-validating a larsDR-Model

cv.plsRcox

Cross-validating a plsRcox-Model

DKplsRcox

Partial least squares Regression generalized linear models

DR_coxph

(Deviance) Residuals Computation

internal-plsRcox

Internal plsRcox functions

larsDR_coxph

Fitting a LASSO/LARS model on the (Deviance) Residuals

plsRcox-package

plsRcox-package: Partial Least Squares Regression for Cox Models and R...

plsRcox

Partial least squares Regression generalized linear models

predict.plsRcoxmodel

Print method for plsRcox models

print.plsRcoxmodel

Print method for plsRcox models

print.summary.plsRcoxmodel

Print method for summaries of plsRcox models

summary.plsRcoxmodel

Summary method for plsRcox models

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

  • Maintainer: Frederic Bertrand
  • License: GPL-3
  • Last published: 2022-11-29