This function provides a print method for the class "plsRcoxmodel"
# S3 method for plsRcoxmodel
print(x, ...)an object of the class "plsRcoxmodel"
not used
NULL
plsRcox, Cox-Models in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.
data(micro.censure)
data(Xmicro.censure_compl_imp)
X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),FUN="as.numeric",MARGIN=2)[1:80,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]
modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
print(modpls)
#> Number of required components:
#> [1] 3
#> Number of successfully computed components:
#> [1] 3
#> Coefficients:
#> [,1]
#> D18S61 0.657688859
#> D17S794 -0.265485544
#> D13S173 0.532071747
#> D20S107 2.764628048
#> TP53 0.635427658
#> D9S171 0.008139129
#> D8S264 -0.346586438
#> D5S346 -1.628707075
#> D22S928 -1.199432030
#> D18S53 0.550835752
#> D1S225 -1.098480981
#> D3S1282 -1.784482327
#> D15S127 1.905056253
#> D1S305 -1.028283057
#> D1S207 1.202494887
#> D2S138 -1.610961966
#> D16S422 -0.970535096
#> D9S179 -0.209672191
#> D10S191 -1.143815474
#> D4S394 0.239525569
#> D1S197 0.087674404
#> D6S264 0.289838007
#> D14S65 -1.281410428
#> D17S790 -0.335500453
#> D5S430 0.789195774
#> D3S1283 0.453349027
#> D4S414 1.313974219
#> D8S283 -0.179467540
#> D11S916 0.457823141
#> D2S159 0.719452513
#> D16S408 -1.343339387
#> D6S275 -0.568676682
#> D10S192 -0.011708963
#> sexe -0.080266201
#> Agediag 0.051845736
#> Siege -0.157190141
#> T 0.865566178
#> N 0.903857312
#> M -0.883429770
#> STADE -0.079069085
#> 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