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<. K2_set, K2_set) # on sauvegarde le jeu de données pour le "training set, individus) rosen3 <- c(list(a=t(K2_train)),list(b=scale(staudt.tim.bis)),list(c=staudt.status.bi s))

#. Hp, . Cox_kplsdr-<-cox, and . Zph, COX_KPLSDR, global=TRUE) print(hp.COX_KPLSDR) par(mfrow=c(2,5)) plot(hp.COX_PLS, var=1) plot(hp.COX_PLS, var=2) plot(hp.COX_PLS, var=3) plot(hp.COX_PLS, var=4) plot(hp, COX_PLS, var=5) plot(hp.COX_PLS, var=6) plot(hp.COX_PLS, var=7) plot(hp.COX_PLS, var=8) plot(hp.COX_PLS, var=9) plot(hp.COX_PLS, var=10) fct_survie_rosen_KPLSDR <-survfit(COX_KPLSDR) plot(log(fct_survie_rosen_KPLSDR$time), pp.log(- log

#. ###-kplsdr-auc-mat_kplsdr, <. Kplsdrb, and <. , scale(deviance_residuals_rosen)~mat_kplsdr,ncomp=10) mat_kplsdr <-KPLSDRb$scores COX_KPLSDRb <-coxph(survie_rosen ~ mat_kplsdr) mat_kplsdr <-rosen4$d pred_kplsdr <-predict(KPLSDRb,mat_kplsdr) pred_kplsdr <-matrix(pred_kplsdr,80,10) mat_kplsdr <-pred_kplsdr colnames(mat_kplsdr) <-colnames(KPLSDRb$scores) row.names(mat_kplsdr) <-row