cv.coxplsDR: Cross-validating a plsDR-Model

In plsRcox: Partial Least Squares Regression for Cox Models and Related Techniques

Cross-validating a plsDR-Model

Description

This function cross-validates coxplsDR models.

Usage

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

Arguments

data	A list of three items: x the explanatory variables passed to coxplsDR's Xplan argument, time passed to coxplsDR's time argument, status coxplsDR's status argument.
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 cross-validation process.
nt	The number of components to include in the model. It this is not supplied, 10 components are fitted.
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 ?
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 coxplsDR.

Details

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

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.

Author(s)

Frédéric Bertrand
[e-mail]
http://www-irma.u-strasbg.fr/~fbertran/

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.

See Also

See Also coxplsDR

Examples

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)
(cv.coxplsDR.res=cv.coxplsDR(list(x=X_train_micro,time=Y_train_micro,status=C_train_micro),nt=3))

plsRcox documentation built on Dec. 1, 2022, 1:31 a.m.