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README

plsRcox plsRcox, Cox-Models in a High Dimensional Setting in R FrÃ©dÃ©ric Bertrand and Myriam Maumy-Bertrand

The goal of plsRcox is provide Cox models in a high dimensional setting in R.

plsRcox implements partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings https://doi.org/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.

The package was presented at the User2014! conference. FrÃ©dÃ©ric Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Bertrand (2014). âplsRcox, Cox-Models in a high dimensional setting in Râ, book of abstracts, User2014!, Los Angeles, page 177, http://user2014.r-project.org/abstracts/posters/177_Bertrand.pdf.

The plsRcox package contains an original allelotyping dataset from âAllelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatmentâ, BenoÃ®t Romain, AgnÃ¨s Neuville, Nicolas Meyer, CÃ©cile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub and Dominique Guenot (2010), BMC Cancer, 10:561, https://doi.org/10.1186/1471-2407-10-561.

Support for parallel computation and GPU is being developped.

The package provides several modelling techniques related to penalized Cox models or extensions of partial least squares to Cox models. The first two were new algorithms.

coxsplsDR and cv.coxsplsDR (Philippe Bastien, Frederic Bertrand, Nicolas Meyer, and Myriam Maumy-Bertrand (2015), âDeviance residuals-based sparse PLS and sparse kernel PLS regression for censored dataâ, Bioinformatics, 31(3):397-404, https://doi.org/10.1093/bioinformatics/btu660),
coxDKsplsDR and cv.coxDKsplsDR (Philippe Bastien, Frederic Bertrand, Nicolas Meyer, and Myriam Maumy-Bertrand (2015), âDeviance residuals-based sparse PLS and sparse kernel PLS regression for censored dataâ, Bioinformatics, 31(3):397-404, https://doi.org/10.1093/bioinformatics/btu660),
coxDKplsDR and cv.coxDKplsDR (Philippe Bastien (2008), âDeviance residuals based PLS regression for censored data in high dimensional settingâ, Chemometrics and Intelligent Laboratory Systems, 91:78â86, https://doi.org/10.1016/j.chemolab.2007.09.009),
coxpls and cv.coxpls (Nguyen, D.V., Rocke, D.M. (2002), âPartial least squares proportional hazard regression for application to DNA microarray survival dataâ, Bioinformatics, 18(12):1625â1632),
coxplsDR and cv.coxplsDR (Philippe Bastien (2008), âDeviance residuals based PLS regression for censored data in high dimensional settingâ, Chemometrics and Intelligent Laboratory Systems, 91:78â86, https://doi.org/10.1016/j.chemolab.2007.09.009),
DKplsRcox,
larsDR and cv.larsDR (Segal, M.R. (2006), âMicroarray Gene Expression Data with Linked Survival Phenotypes: Diffuse large-B- Cell Lymphoma Revisitedâ, Biostatistics, 7:268-285, https://doi.org/10.1093/biostatistics/kxj006),
plsRcox and cv.plsRcox (Philippe Bastien, Vincenzo Esposito Vinzi, and Michel Tenenhaus (2005), âPLS generalised linear regressionâ, Computational Statistics & Data Analysis, 48(1):17â46, https://doi.org/10.1016/j.csda.2004.02.005),
autoplsRcox and cv.autoplsRcox (Philippe Bastien, Vincenzo Esposito Vinzi, and Michel Tenenhaus (2005), âPLS generalised linear regressionâ, Computational Statistics & Data Analysis, 48(1):17â46, https://doi.org/10.1016/j.csda.2004.02.005),

Performance comparisons. Example of biplot of data.

This website and these examples were created by F. Bertrand and M. Maumy-Bertrand.

Installation

You can install the released version of plsRcox from CRAN with:

install.packages("plsRcox")

You can install the development version of plsRcox from github with:

devtools::install_github("fbertran/plsRcox")

Example 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)

Compute deviance residuals with some options.

DR_coxph(Y_train_micro,C_train_micro,plot=TRUE)

#>           1           2           3           4           5           6 
#> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 
#>           7           8           9          10          11          12 
#> -0.38667660  1.57418914 -0.54695398 -0.15811388  2.10405254 -0.23145502 
#>          13          14          15          16          17          18 
#> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 
#>          19          20          21          22          23          24 
#>  0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 
#>          25          26          27          28          29          30 
#> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 
#>          31          32          33          34          35          36 
#>  2.30072697 -0.49023986 -0.54695398 -0.73444882  1.31082939 -0.97633722 
#>          37          38          39          40          41          42 
#>  1.70134282 -0.54695398 -0.15811388  1.07714870 -0.15811388 -0.49023986 
#>          43          44          45          46          47          48 
#> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 
#>          49          50          51          52          53          54 
#> -0.38667660 -0.09836581 -0.79392956  0.46851068 -0.34003013  1.95366297 
#>          55          56          57          58          59          60 
#>  2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 
#>          61          62          63          64          65          66 
#> -0.28961087  1.44879795  1.82660327 -0.38667660  0.96936094 -0.15811388 
#>          67          68          69          70          71          72 
#> -0.43627414 -0.49023986  1.18850436 -0.97633722 -0.97633722  0.86322194 
#>          73          74          75          76          77          78 
#> -0.43627414 -0.49023986 -0.38667660  0.76231394 -0.97633722 -0.43627414 
#>          79          80 
#> -0.54695398 -0.43627414

DR_coxph(Y_train_micro,C_train_micro,scaleY=FALSE,plot=TRUE)

#>           1           2           3           4           5           6 
#> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 
#>           7           8           9          10          11          12 
#> -0.38667660  1.57418914 -0.54695398 -0.15811388  2.10405254 -0.23145502 
#>          13          14          15          16          17          18 
#> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 
#>          19          20          21          22          23          24 
#>  0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 
#>          25          26          27          28          29          30 
#> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 
#>          31          32          33          34          35          36 
#>  2.30072697 -0.49023986 -0.54695398 -0.73444882  1.31082939 -0.97633722 
#>          37          38          39          40          41          42 
#>  1.70134282 -0.54695398 -0.15811388  1.07714870 -0.15811388 -0.49023986 
#>          43          44          45          46          47          48 
#> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 
#>          49          50          51          52          53          54 
#> -0.38667660 -0.09836581 -0.79392956  0.46851068 -0.34003013  1.95366297 
#>          55          56          57          58          59          60 
#>  2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 
#>          61          62          63          64          65          66 
#> -0.28961087  1.44879795  1.82660327 -0.38667660  0.96936094 -0.15811388 
#>          67          68          69          70          71          72 
#> -0.43627414 -0.49023986  1.18850436 -0.97633722 -0.97633722  0.86322194 
#>          73          74          75          76          77          78 
#> -0.43627414 -0.49023986 -0.38667660  0.76231394 -0.97633722 -0.43627414 
#>          79          80 
#> -0.54695398 -0.43627414

DR_coxph(Y_train_micro,C_train_micro,scaleY=TRUE,plot=TRUE)

#>           1           2           3           4           5           6 
#> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 
#>           7           8           9          10          11          12 
#> -0.38667660  1.57418914 -0.54695398 -0.15811388  2.10405254 -0.23145502 
#>          13          14          15          16          17          18 
#> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 
#>          19          20          21          22          23          24 
#>  0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 
#>          25          26          27          28          29          30 
#> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 
#>          31          32          33          34          35          36 
#>  2.30072697 -0.49023986 -0.54695398 -0.73444882  1.31082939 -0.97633722 
#>          37          38          39          40          41          42 
#>  1.70134282 -0.54695398 -0.15811388  1.07714870 -0.15811388 -0.49023986 
#>          43          44          45          46          47          48 
#> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 
#>          49          50          51          52          53          54 
#> -0.38667660 -0.09836581 -0.79392956  0.46851068 -0.34003013  1.95366297 
#>          55          56          57          58          59          60 
#>  2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 
#>          61          62          63          64          65          66 
#> -0.28961087  1.44879795  1.82660327 -0.38667660  0.96936094 -0.15811388 
#>          67          68          69          70          71          72 
#> -0.43627414 -0.49023986  1.18850436 -0.97633722 -0.97633722  0.86322194 
#>          73          74          75          76          77          78 
#> -0.43627414 -0.49023986 -0.38667660  0.76231394 -0.97633722 -0.43627414 
#>          79          80 
#> -0.54695398 -0.43627414

coxsplsDR

(cox_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,eta=.5))
#> 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))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

(cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
dataXplan=X_train_micro_df,eta=.5))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)
#> Warning in rm(cox_splsDR_fit, cox_splsDR_fit2, cox_splsDR_fit3): objet
#> 'cox_splsDR_fit2' introuvable
#> Warning in rm(cox_splsDR_fit, cox_splsDR_fit2, cox_splsDR_fit3): objet
#> 'cox_splsDR_fit3' introuvable

cv.coxsplsDR

set.seed(123456)

(cv.coxsplsDR.res=cv.coxsplsDR(list(x=X_train_micro,time=Y_train_micro, status=C_train_micro),nt=10,eta=.1))
#> CV Fold 1 
#> CV Fold 2 
#> CV Fold 3 
#> CV Fold 4 
#> CV Fold 5

#> $nt
#> [1] 10
#> 
#> $cv.error10
#>  [1] 0.5000000 0.6786893 0.6913293 0.6485690 0.6656184 0.6591497 0.6733976
#>  [8] 0.6252317 0.6388320 0.6592004 0.6589521
#> 
#> $cv.se10
#>  [1] 0.00000000 0.04017423 0.02726346 0.03897730 0.03874068 0.04042522 0.03952962
#>  [8] 0.04645295 0.04782038 0.05168926 0.05259748
#> 
#> $folds
#> $folds$`1`
#>  [1] 60  3  2 14 77  6 50  4 72 32 22  1 41 21 63 25
#> 
#> $folds$`2`
#>  [1] 42 67 65 15 73 48 57 26  7 13 31 53  5 27 37 64
#> 
#> $folds$`3`
#>  [1] 71 23 56 35 75 29 30 18 62 44 12 33 68 49 43 55
#> 
#> $folds$`4`
#>  [1] 54 76 24 16 34 66  9 11 69 40 70 36 39  8 19 20
#> 
#> $folds$`5`
#>  [1] 74 38 46 80 47 78 10 45 51 28 61 79 58 17 52 59
#> 
#> 
#> $lambda.min10
#> [1] 2
#> 
#> $lambda.1se10
#> [1] 0
#> 
#> $nzb
#>  [1]  0 34 40 40 40 40 40 40 40 40 40

coxDKsplsDR

(cox_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6, validation="CV",eta=.5))
#> Kernel :  rbfdot 
#> Estimated_sigma  0.0122308
#> Call:
#> coxph(formula = YCsurv ~ ., data = tt_DKsplsDR)
#> 
#>            coef exp(coef)  se(coef)     z       p
#> dim.1 3.633e+00 3.783e+01 1.245e+00 2.918 0.00352
#> dim.2 9.905e+00 2.003e+04 3.297e+00 3.004 0.00266
#> dim.3 6.491e+00 6.589e+02 2.575e+00 2.521 0.01170
#> dim.4 1.465e+01 2.308e+06 4.848e+00 3.022 0.00251
#> dim.5 6.103e+00 4.473e+02 2.757e+00 2.213 0.02687
#> dim.6 1.249e+01 2.664e+05 4.980e+00 2.508 0.01212
#> 
#> Likelihood ratio test=69.55  on 6 df, p=5.067e-13
#> n= 80, number of events= 17

(cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6, validation="CV",eta=.5))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

(cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
validation="CV",dataXplan=data.frame(X_train_micro),eta=.5))
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

rm(cox_DKsplsDR_fit)

cv.coxsplsDR

set.seed(123456)

(cv.coxDKsplsDR.res=cv.coxDKsplsDR(list(x=X_train_micro,time=Y_train_micro, status=C_train_micro),nt=10,eta=.1))
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01257168 
#> CV Fold 1 
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01198263 
#> CV Fold 2 
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01156809 
#> CV Fold 3 
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01287851 
#> CV Fold 4 
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01127231 
#> CV Fold 5

#> $nt
#> [1] 10
#> 
#> $cv.error10
#>  [1] 0.5000000 0.6381540 0.6963262 0.6537039 0.6204813 0.6886401 0.6632860
#>  [8] 0.6349883 0.6762113 0.6261072 0.6087014
#> 
#> $cv.se10
#>  [1] 0.00000000 0.03036225 0.02912723 0.04020941 0.03577022 0.03542745 0.03283778
#>  [8] 0.04532447 0.03390654 0.02968504 0.03306444
#> 
#> $folds
#> $folds$`1`
#>  [1] 60  3  2 14 77  6 50  4 72 32 22  1 41 21 63 25
#> 
#> $folds$`2`
#>  [1] 42 67 65 15 73 48 57 26  7 13 31 53  5 27 37 64
#> 
#> $folds$`3`
#>  [1] 71 23 56 35 75 29 30 18 62 44 12 33 68 49 43 55
#> 
#> $folds$`4`
#>  [1] 54 76 24 16 34 66  9 11 69 40 70 36 39  8 19 20
#> 
#> $folds$`5`
#>  [1] 74 38 46 80 47 78 10 45 51 28 61 79 58 17 52 59
#> 
#> 
#> $lambda.min10
#> [1] 2
#> 
#> $lambda.1se10
#> [1] 0
#> 
#> $nzb
#>  [1]  0 52 61 64 64 64 64 64 64 64 64

plsRcox

plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> Number of required components:
#> [1] 5
#> Number of successfully computed components:
#> [1] 5
#> Coefficients:
#>                [,1]
#> D18S61   0.68964919
#> D17S794 -1.14362392
#> D13S173  1.37632457
#> D20S107  4.96128745
#> TP53     1.68453950
#> D9S171  -1.46691252
#> D8S264   0.66710776
#> D5S346  -4.61338196
#> D22S928 -1.82005524
#> D18S53   0.79853646
#> D1S225  -1.46234986
#> D3S1282 -1.67925042
#> D15S127  3.92225537
#> D1S305  -2.29680161
#> D1S207   2.02539691
#> D2S138  -3.48975878
#> D16S422 -2.92189625
#> D9S179  -0.59484679
#> D10S191 -1.30136747
#> D4S394   1.34265359
#> D1S197  -0.75014044
#> D6S264   1.32746604
#> D14S65  -3.20882866
#> D17S790  0.55427680
#> D5S430   3.40654627
#> D3S1283  2.12510239
#> D4S414   2.73619967
#> D8S283   0.71955323
#> D11S916  1.45026508
#> D2S159   0.90293134
#> D16S408 -0.59719901
#> D6S275  -1.02204186
#> D10S192  1.14220367
#> sexe     0.67314561
#> Agediag  0.04908478
#> Siege   -0.41985924
#> T        2.70581463
#> N        2.47039973
#> M       -4.53213922
#> STADE    0.48221697
#> Information criteria and Fit statistics:
#>                 AIC       BIC
#> Nb_Comp_0 112.87990 112.87990
#> Nb_Comp_1  85.11075  87.49278
#> Nb_Comp_2  75.49537  80.25942
#> Nb_Comp_3  68.45852  75.60460
#> Nb_Comp_4  63.09284  72.62094
#> Nb_Comp_5  55.30567  67.21581

plsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#> Error in model.matrix(mt0, mf0, contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

plsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> Warning : 25 < 10^{-12}
#> Warning only 3 components could thus be extracted
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> Number of required components:
#> [1] 5
#> Number of successfully computed components:
#> [1] 3
#> Coefficients:
#>                [,1]
#> D18S61   0.00000000
#> D17S794  0.00000000
#> D13S173  0.00000000
#> D20S107  2.22871454
#> TP53     0.00000000
#> D9S171   0.00000000
#> D8S264   0.00000000
#> D5S346  -1.20298526
#> D22S928  0.00000000
#> D18S53   0.00000000
#> D1S225  -1.29459798
#> D3S1282 -1.99426291
#> D15S127  1.39645601
#> D1S305   0.00000000
#> D1S207   1.25164327
#> D2S138  -1.65740160
#> D16S422  0.00000000
#> D9S179   0.00000000
#> D10S191 -1.25360805
#> D4S394   0.00000000
#> D1S197   0.00000000
#> D6S264   0.00000000
#> D14S65  -1.33587373
#> D17S790  0.00000000
#> D5S430   1.72799213
#> D3S1283  0.00000000
#> D4S414   1.03558702
#> D8S283   0.00000000
#> D11S916  0.00000000
#> D2S159   0.00000000
#> D16S408 -1.75748257
#> D6S275   0.00000000
#> D10S192  0.00000000
#> sexe     0.00000000
#> Agediag  0.05075304
#> Siege    0.00000000
#> T        1.36569407
#> N        1.27485618
#> M       -1.17682617
#> STADE   -0.65106093
#> Information criteria and Fit statistics:
#>                 AIC       BIC
#> Nb_Comp_0 112.87990 112.87990
#> Nb_Comp_1  85.54313  87.92516
#> Nb_Comp_2  75.16125  79.92530
#> Nb_Comp_3  73.63097  80.77705

plsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
#> Error in model.matrix(mt0, mf0, contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

cv.plsRcox

set.seed(123456)

(cv.plsRcox.res=cv.plsRcox(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=10,verbose = FALSE))

#> $nt
#> [1] 10
#> 
#> $cv.error5
#>  [1] 0.5000000 0.9674493 0.8840340 0.8881565 0.9611293 0.9694122 0.7785264
#>  [8] 0.7794468 0.7833874 0.7917907 0.7917344
#> 
#> $cv.se5
#>  [1] 0.0000000000 0.0004328242 0.0371488864 0.0389160733 0.0007452107
#>  [6] 0.0040593349 0.0814540651 0.0815717378 0.0820171451 0.0829659086
#> [11] 0.0829564223
#> 
#> $folds
#> $folds$`1`
#>  [1] 60  3  2 14 77  6 50  4 72 32 22  1 41 21 63 25
#> 
#> $folds$`2`
#>  [1] 42 67 65 15 73 48 57 26  7 13 31 53  5 27 37 64
#> 
#> $folds$`3`
#>  [1] 71 23 56 35 75 29 30 18 62 44 12 33 68 49 43 55
#> 
#> $folds$`4`
#>  [1] 54 76 24 16 34 66  9 11 69 40 70 36 39  8 19 20
#> 
#> $folds$`5`
#>  [1] 74 38 46 80 47 78 10 45 51 28 61 79 58 17 52 59
#> 
#> 
#> $lambda.min5
#> [1] 5
#> 
#> $lambda.1se5
#> [1] 0

DKplsRcox

DKplsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#> Kernel :  rbfdot 
#> Estimated_sigma  0.0122308
#> Error in model.matrix(mt0, mf0, contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

DKplsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

DKplsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
#> Kernel :  rbfdot 
#> Estimated_sigma  0.01203267
#> Error in model.matrix(mt0, mf0, contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

DKplsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE, alpha.pvals.expli=.15)
#> Error in model.matrix(mt0, mf0, , contrasts.arg = contrasts.arg): l'argument "contrasts.arg" est manquant, avec aucune valeur par dÃ©faut

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