This function provides a print method for the class "plsRcoxmodel"

# S3 method for plsRcoxmodel
print(x, ...)

Arguments

x

an object of the class "plsRcoxmodel"

...

not used

Value

NULL

References

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.

See also

Author

Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://www-irma.u-strasbg.fr/~fbertran/

Examples

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