1d:T7da,# plsRcox-package: Partial Least Squares Regression for Cox Models and Related Techniques Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings , 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 , 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. package ## Examples ```r # 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) ``` ## 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. . Cross validation criteria were studied in , 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.1e:T49a,M7.49933 0.25C3.49635