plsRcox: 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 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 doi:10.48550/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.

See also

Useful links:

• https://fbertran.github.io/plsRcox/

• https://github.com/fbertran/plsRcox/

• Report bugs at https://github.com/fbertran/plsRcox/issues/

Author

This package has been written by Frédéric Bertrand and Myriam Maumy-Bertrand. Maintainer: Frédéric Bertrand <frederic.bertrand@lecnam.net>

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
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_splsDR)
#> 
#>         coef exp(coef) se(coef)     z        p
#> dim.1 0.8093    2.2462   0.2029 3.989 6.63e-05
#> dim.2 0.9295    2.5333   0.2939 3.163  0.00156
#> dim.3 0.9968    2.7096   0.4190 2.379  0.01736
#> dim.4 0.9705    2.6391   0.3793 2.558  0.01052
#> dim.5 0.2162    1.2413   0.2811 0.769  0.44192
#> dim.6 0.4380    1.5496   0.3608 1.214  0.22473
#> 
#> Likelihood ratio test=55.06  on 6 df, p=4.51e-10
#> n= 80, number of events= 17 
cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,
ncomp=6,eta=.5,trace=TRUE)
#> The variables that join the set of selected variables at each step:
#> - 1th step (K=1):
#> X_train_microD20S107 X_train_microD5S346 X_train_microD1S225 X_train_microD3S1282 X_train_microD15S127 X_train_microD1S207 X_train_microD2S138 X_train_microD10S191 X_train_microD14S65 X_train_microD4S414
#> X_train_microD16S408 X_train_microT X_train_microN X_train_microSTADE
#> - 2th step (K=2):
#> X_train_microD22S928 X_train_microD16S422 X_train_microD3S1283 X_train_microAgediag X_train_microM
#> - 3th step (K=3):
#> X_train_microD1S305 X_train_microD8S283 X_train_microD10S192 X_train_microsexe X_train_microSiege
#> - 4th step (K=4):
#> X_train_microD17S794 X_train_microD13S173 X_train_microTP53 X_train_microD6S264 X_train_microD2S159 X_train_microD6S275
#> - 5th step (K=5):
#> X_train_microD18S61 X_train_microD9S171 X_train_microD8S264 X_train_microD18S53 X_train_microD4S394 X_train_microD11S916
#> - 6th step (K=6):
#> X_train_microD17S790
cox_splsDR_fit2
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_splsDR)
#> 
#>         coef exp(coef) se(coef)     z        p
#> dim.1 0.8093    2.2462   0.2029 3.989 6.63e-05
#> dim.2 0.9295    2.5333   0.2939 3.163  0.00156
#> dim.3 0.9968    2.7096   0.4190 2.379  0.01736
#> dim.4 0.9705    2.6391   0.3793 2.558  0.01052
#> dim.5 0.2162    1.2413   0.2811 0.769  0.44192
#> dim.6 0.4380    1.5496   0.3608 1.214  0.22473
#> 
#> Likelihood ratio test=55.06  on 6 df, p=4.51e-10
#> n= 80, number of events= 17 
cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5)
cox_splsDR_fit3
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_splsDR)
#> 
#>         coef exp(coef) se(coef)     z        p
#> dim.1 0.8093    2.2462   0.2029 3.989 6.63e-05
#> dim.2 0.9295    2.5333   0.2939 3.163  0.00156
#> dim.3 0.9968    2.7096   0.4190 2.379  0.01736
#> dim.4 0.9705    2.6391   0.3793 2.558  0.01052
#> dim.5 0.2162    1.2413   0.2811 0.769  0.44192
#> dim.6 0.4380    1.5496   0.3608 1.214  0.22473
#> 
#> Likelihood ratio test=55.06  on 6 df, p=4.51e-10
#> n= 80, number of events= 17 
rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)