cv.coxpls: Cross-validating a Cox-Model fitted on PLSR components

Description

This function cross-validates coxpls models. It only computes the recommended iAUCSurvROC criterion. Set allCVcrit=TRUE to retrieve the 13 other ones.

Usage

cv.coxpls(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:

xthe explanatory variables passed tocoxpls'sXplanargument,

time passed to coxpls

Value

ntThe number of components requested
cv.error1Vector with the mean values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.error2Vector 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.error3Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
cv.error4Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
cv.error5Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
cv.error6Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.error7Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.error8Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.error9Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.error10Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.error11Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.error12Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.error13Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
cv.error14Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
cv.se1Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
cv.se2Vector 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.se3Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
cv.se4Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
cv.se5Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
cv.se6Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
cv.se7Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
cv.se8Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
cv.se9Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
cv.se10Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
cv.se11Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
cv.se12Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
cv.se13Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
cv.se14Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
foldsExplicit list of the values that were omited values in each fold.
lambda.min1Vector with the standard error values, across folds, of, per fold unit, Cross-validated log-partial-likelihood for models with 0 to nt components.
lambda.min2Vector 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.min1Optimal Nbr of components, min Cross-validated log-partial-likelihood criterion.
lambda.se1Optimal Nbr of components, min+1se Cross-validated log-partial-likelihood criterion.
lambda.min2Optimal Nbr of components, min van Houwelingen Cross-validated log-partial-likelihood.
lambda.se2Optimal Nbr of components, min+1se van Houwelingen Cross-validated log-partial-likelihood.
lambda.min3Optimal Nbr of components, max iAUC_CD criterion.
lambda.se3Optimal Nbr of components, max+1se iAUC_CD criterion.
lambda.min4Optimal Nbr of components, max iAUC_hc criterion.
lambda.se4Optimal Nbr of components, max+1se iAUC_hc criterion.
lambda.min5Optimal Nbr of components, max iAUC_sh criterion.
lambda.se5Optimal Nbr of components, max+1se iAUC_sh criterion.
lambda.min6Optimal Nbr of components, max iAUC_Uno criterion.
lambda.se6Optimal Nbr of components, max+1se iAUC_Uno criterion.
lambda.min7Optimal Nbr of components, max iAUC_hz.train criterion.
lambda.se7Optimal Nbr of components, max+1se iAUC_hz.train criterion.
lambda.min8Optimal Nbr of components, max iAUC_hz.test criterion.
lambda.se8Optimal Nbr of components, max+1se iAUC_hz.test criterion.
lambda.min9Optimal Nbr of components, max iAUC_survivalROC.train criterion.
lambda.se9Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
lambda.min10Optimal Nbr of components, max iAUC_survivalROC.test criterion.
lambda.se10Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
lambda.min11Optimal Nbr of components, min iBrierScore unw criterion.
lambda.se11Optimal Nbr of components, min+1se iBrierScore unw criterion.
lambda.min12Optimal Nbr of components, min iSchmidScore unw criterion.
lambda.se12Optimal Nbr of components, min+1se iSchmidScore unw criterion.
lambda.min13Optimal Nbr of components, min iBrierScore w criterion.
lambda.se13Optimal Nbr of components, min+1se iBrierScore w criterion.
lambda.min14Optimal Nbr of components, min iSchmidScore w criterion.
lambda.se14Optimal Nbr of components, min+1se iSchmidScore w criterion.
errormat1-14If details=TRUE, matrices with the error values for every folds across each of the components and each of the criteria
completed.cv1-14If 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_indicsAll results of the functions that perform error computation, for each fold, each component and error criterion.

item

method
nfold
nt
plot.it
se
givefold
scaleX
folddetails
allCVcrit
details
namedataset
save
verbose
...

code

coxpls

References

Frederic Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand. plsRcox, Cox-Models in a high dimensional setting in R. UseR 2014. Los Angeles. USA.

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

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