Description Usage Arguments Details Value Author(s) References See Also Examples
This function crossvalidates coxDKsplsDR models.
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data 
A list of three items:

method 
A character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. The Efron approximation is used as the default here, it is more accurate when dealing with tied death times, and is as efficient computationally. 
nfold 
The number of folds to use to perform the crossvalidation process. 
nt 
The number of components to include in the model. It this is not supplied, 10 components are fitted. 
eta 
Thresholding parameter. 
plot.it 
Shall the results be displayed on a plot ? 
se 
Should standard errors be plotted ? 
givefold 
Explicit list of omited values in each fold can be provided using this argument. 
scaleX 
Shall the predictors be standardized ? 
scaleY 
Should the 
folddetails 
Should values and completion status for each folds be returned ? 
allCVcrit 
Should the other 13 CV criteria be evaled and returned ? 
details 
Should all results of the functions that perform error computations be returned ? 
namedataset 
Name to use to craft temporary results names 
save 
Should temporary results be saved ? 
verbose 
Should some CV details be displayed ? 
... 
Other arguments to pass to 
It only computes the recommended iAUCSurvROC criterion. Set
allCVcrit=TRUE
to retrieve the 13 other ones.
nt 
The number of components requested 
cv.error1 
Vector with the mean values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components. 
cv.error2 
Vector with the mean values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components. 
cv.error3 
Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components. 
cv.error4 
Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components. 
cv.error5 
Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components. 
cv.error6 
Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components. 
cv.error7 
Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components. 
cv.error8 
Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components. 
cv.error9 
Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components. 
cv.error10 
Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components. 
cv.error11 
Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components. 
cv.error12 
Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components. 
cv.error13 
Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components. 
cv.error14 
Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components. 
cv.se1 
Vector with the standard error values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components. 
cv.se2 
Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components. 
cv.se3 
Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components. 
cv.se4 
Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components. 
cv.se5 
Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components. 
cv.se6 
Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components. 
cv.se7 
Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components. 
cv.se8 
Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components. 
cv.se9 
Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components. 
cv.se10 
Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components. 
cv.se11 
Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components. 
cv.se12 
Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components. 
cv.se13 
Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components. 
cv.se14 
Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components. 
folds 
Explicit list of the values that were omited values in each fold. 
lambda.min1 
Vector with the standard error values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components. 
lambda.min2 
Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components. 
lambda.min1 
Optimal Nbr of components, min Crossvalidated logpartiallikelihood criterion. 
lambda.se1 
Optimal Nbr of components, min+1se Crossvalidated logpartiallikelihood criterion. 
lambda.min2 
Optimal Nbr of components, min van Houwelingen Crossvalidated logpartiallikelihood. 
lambda.se2 
Optimal Nbr of components, min+1se van Houwelingen Crossvalidated logpartiallikelihood. 
lambda.min3 
Optimal Nbr of components, max iAUC_CD criterion. 
lambda.se3 
Optimal Nbr of components, max+1se iAUC_CD criterion. 
lambda.min4 
Optimal Nbr of components, max iAUC_hc criterion. 
lambda.se4 
Optimal Nbr of components, max+1se iAUC_hc criterion. 
lambda.min5 
Optimal Nbr of components, max iAUC_sh criterion. 
lambda.se5 
Optimal Nbr of components, max+1se iAUC_sh criterion. 
lambda.min6 
Optimal Nbr of components, max iAUC_Uno criterion. 
lambda.se6 
Optimal Nbr of components, max+1se iAUC_Uno criterion. 
lambda.min7 
Optimal Nbr of components, max iAUC_hz.train criterion. 
lambda.se7 
Optimal Nbr of components, max+1se iAUC_hz.train criterion. 
lambda.min8 
Optimal Nbr of components, max iAUC_hz.test criterion. 
lambda.se8 
Optimal Nbr of components, max+1se iAUC_hz.test criterion. 
lambda.min9 
Optimal Nbr of components, max iAUC_survivalROC.train criterion. 
lambda.se9 
Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion. 
lambda.min10 
Optimal Nbr of components, max iAUC_survivalROC.test criterion. 
lambda.se10 
Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion. 
lambda.min11 
Optimal Nbr of components, min iBrierScore unw criterion. 
lambda.se11 
Optimal Nbr of components, min+1se iBrierScore unw criterion. 
lambda.min12 
Optimal Nbr of components, min iSchmidScore unw criterion. 
lambda.se12 
Optimal Nbr of components, min+1se iSchmidScore unw criterion. 
lambda.min13 
Optimal Nbr of components, min iBrierScore w criterion. 
lambda.se13 
Optimal Nbr of components, min+1se iBrierScore w criterion. 
lambda.min14 
Optimal Nbr of components, min iSchmidScore w criterion. 
lambda.se14 
Optimal Nbr of components, min+1se iSchmidScore w criterion. 
errormat114 
If

completed.cv114 
If

All_indics 
All results of the functions that perform error computation, for each fold, each component and error criterion. 
Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://wwwirma.ustrasbg.fr/~fbertran/
plsRcox, CoxModels in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam MaumyBertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residualsbased sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam MaumyBertrand (2015), Bioinformatics, 31(3):397404, doi:10.1093/bioinformatics/btu660.
Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and MaumyBertrand, M. (2018), https://arxiv.org/abs/1810.01005.
See Also coxDKsplsDR
1 2 3 4 5 6 7 8 9 10 11  data(micro.censure)
data(Xmicro.censure_compl_imp)
set.seed(123456)
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)
Y_train_micro < micro.censure$survyear[1:80]
C_train_micro < micro.censure$DC[1:80]
#Should be run with a higher value of nt (at least 10) and a grid of eta
(cv.coxDKsplsDR.res=cv.coxDKsplsDR(list(x=X_train_micro,time=Y_train_micro,
status=C_train_micro),nt=3,eta=.1))

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