This function cross-validates larsDR_coxph models.

cv.larsDR(
  data,
  method = c("efron", "breslow"),
  nfold = 5,
  fraction = seq(0, 1, length = 100),
  plot.it = TRUE,
  se = TRUE,
  givefold,
  scaleX = TRUE,
  scaleY = FALSE,
  folddetails = FALSE,
  allCVcrit = FALSE,
  details = FALSE,
  namedataset = "data",
  save = FALSE,
  verbose = TRUE,
  ...
)

Arguments

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 cross-validation process.

fraction

L1 norm fraction.

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 time values 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 larsDR_coxph.

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.

larsmodfull

Lars model fitted on the residuals.

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 van Houwelingen CV partial likelihood criterion 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.

See also

See Also larsDR_coxph

Author

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

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 the default: fraction = seq(0, 1, length = 100) (cv.larsDR.res=cv.larsDR(list(x=X_train_micro,time=Y_train_micro, status=C_train_micro),se=TRUE,fraction=seq(0, 1, length = 4)))
#> CV Fold 1 #> CV Fold 2 #> CV Fold 3 #> CV Fold 4 #> CV Fold 5
#> $fraction #> [1] 0.0000000 0.3333333 0.6666667 1.0000000 #> #> $cv.error2 #> [1] -14.06841 -12.91622 -12.81085 -13.76411 #> #> $cv.se2 #> [1] 1.561171 1.576406 1.510617 1.393355 #> #> $folds #> $folds$`1` #> [1] 60 3 2 14 77 6 50 4 72 32 22 1 41 21 63 25 #> #> $folds$`2` #> [1] 42 67 65 15 73 48 57 26 7 13 31 53 5 27 37 64 #> #> $folds$`3` #> [1] 71 23 56 35 75 29 30 18 62 44 12 33 68 49 43 55 #> #> $folds$`4` #> [1] 54 76 24 16 34 66 9 11 69 40 70 36 39 8 19 20 #> #> $folds$`5` #> [1] 74 38 46 80 47 78 10 45 51 28 61 79 58 17 52 59 #> #> #> $lambda.min2 #> [1] 0 #> #> $lambda.1se2 #> [1] 0 #> #> $nzb #> [1] 0 22 35 40 #> #> $larsmodfull #> #> Call: #> lars(x = x, y = DR_coxph, type = "lasso", use.Gram = FALSE) #> R-squared: 0.689 #> Sequence of LASSO moves: #> N D3S1282 D16S408 D20S107 D15S127 D2S138 Agediag D1S207 D22S928 D10S191 #> Var 38 12 31 4 13 16 35 15 9 19 #> Step 1 2 3 4 5 6 7 8 9 10 #> D5S346 D16S422 T D4S414 D1S225 D17S790 Siege D3S1283 sexe D1S305 M #> Var 8 17 37 27 11 24 36 26 34 14 39 #> Step 11 12 13 14 15 16 17 18 19 20 21 #> D8S283 D14S65 D17S794 TP53 D9S171 D13S173 D2S159 D4S394 D6S264 D16S408 #> Var 28 23 2 5 6 3 30 20 22 -31 #> Step 22 23 24 25 26 27 28 29 30 31 #> D5S430 D8S264 D18S61 D1S197 D11S916 D6S275 D16S408 TP53 D10S192 sexe #> Var 25 7 1 21 29 32 31 -5 33 -34 #> Step 32 33 34 35 36 37 38 39 40 41 #> D9S179 TP53 sexe STADE D8S264 D18S53 D8S264 D17S794 D17S794 #> Var 18 5 34 40 -7 10 7 -2 2 #> Step 42 43 44 45 46 47 48 49 50 #>