plsRcox-package {plsRcox}

plsRcox-package: Partial Least Squares Regression for Cox Models and Related Techniques

Description

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.

References

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. <doi:10.1093/bioinformatics/btu660>. 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.

Examples

# The original allelotyping dataset

library(plsRcox)
data(micro.censure)
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
Y_test_micro <- micro.censure$survyear[81:117]
C_test_micro <- micro.censure$DC[81:117]

data(Xmicro.censure_compl_imp)
X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),
FUN="as.numeric",MARGIN=2)[1:80,]
X_train_micro_df <- data.frame(X_train_micro)

# coxsplsDR
cox_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,
ncomp=6,eta=.5)
cox_splsDR_fit
cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,
ncomp=6,eta=.5,trace=TRUE)
cox_splsDR_fit2
cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5)
cox_splsDR_fit3
rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)