cv.coxDKsplsDR: Cross-validating a DKsplsDR-Model

Description

This function cross-validates coxDKsplsDR models.

Usage

cv.coxDKsplsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  nt = 10,
  eta = 0.5,
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  scaleY = FALSE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Value

nt: The number of components requested
cv.error1: Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2: Vector with the mean values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood 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, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2: Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood 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, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2: Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min1: Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1: Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2: Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2: Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
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.
errormat1-14: If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14: If details=TRUE, matrices with logical values for every folds across each of the components and each of the criteria: TRUE if the computation was completed and FALSE it is failed.
All_indics: All results of the functions that perform error computation, for each fold, each component and error criterion.

Details

It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

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.

Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), https://arxiv.org/abs/1810.01005.

Examples

Run this code


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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