This function crossvalidates 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, ... )
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. 
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 
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 
The number of components requested
Vector with the mean values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components.
Vector with the mean values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_CD for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_hc for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_sh for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_Uno for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_hz.train for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_hz.test for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
Vector with the mean values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
Vector with the mean values, across folds, of iBrierScore unw for models with 0 to nt components.
Vector with the mean values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
Vector with the mean values, across folds, of iBrierScore w for models with 0 to nt components.
Vector with the mean values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
Vector with the standard error values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components.
Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_CD for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_hc for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_sh for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_Uno for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_hz.train for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_hz.test for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_survivalROC.train for models with 0 to nt components.
Vector with the standard error values, across folds, of iAUC_survivalROC.test for models with 0 to nt components.
Vector with the standard error values, across folds, of iBrierScore unw for models with 0 to nt components.
Vector with the standard error values, across folds, of iSchmidScore (robust BS) unw for models with 0 to nt components.
Vector with the standard error values, across folds, of iBrierScore w for models with 0 to nt components.
Vector with the standard error values, across folds, of iSchmidScore (robust BS) w for models with 0 to nt components.
Explicit list of the values that were omited values in each fold.
Vector with the standard error values, across folds, of, per fold unit, Crossvalidated logpartiallikelihood for models with 0 to nt components.
Vector with the standard error values, across folds, of, per fold unit, van Houwelingen Crossvalidated logpartiallikelihood for models with 0 to nt components.
Optimal Nbr of components, min Crossvalidated logpartiallikelihood criterion.
Optimal Nbr of components, min+1se Crossvalidated logpartiallikelihood criterion.
Optimal Nbr of components, min van Houwelingen Crossvalidated logpartiallikelihood.
Optimal Nbr of components, min+1se van Houwelingen Crossvalidated logpartiallikelihood.
Optimal Nbr of components, max iAUC_CD criterion.
Optimal Nbr of components, max+1se iAUC_CD criterion.
Optimal Nbr of components, max iAUC_hc criterion.
Optimal Nbr of components, max+1se iAUC_hc criterion.
Optimal Nbr of components, max iAUC_sh criterion.
Optimal Nbr of components, max+1se iAUC_sh criterion.
Optimal Nbr of components, max iAUC_Uno criterion.
Optimal Nbr of components, max+1se iAUC_Uno criterion.
Optimal Nbr of components, max iAUC_hz.train criterion.
Optimal Nbr of components, max+1se iAUC_hz.train criterion.
Optimal Nbr of components, max iAUC_hz.test criterion.
Optimal Nbr of components, max+1se iAUC_hz.test criterion.
Optimal Nbr of components, max iAUC_survivalROC.train criterion.
Optimal Nbr of components, max+1se iAUC_survivalROC.train criterion.
Optimal Nbr of components, max iAUC_survivalROC.test criterion.
Optimal Nbr of components, max+1se iAUC_survivalROC.test criterion.
Optimal Nbr of components, min iBrierScore unw criterion.
Optimal Nbr of components, min+1se iBrierScore unw criterion.
Optimal Nbr of components, min iSchmidScore unw criterion.
Optimal Nbr of components, min+1se iSchmidScore unw criterion.
Optimal Nbr of components, min iBrierScore w criterion.
Optimal Nbr of components, min+1se iBrierScore w criterion.
Optimal Nbr of components, min iSchmidScore w criterion.
Optimal Nbr of components, min+1se iSchmidScore w criterion.
If
details=TRUE
, matrices with the error values for every folds across
each of the components and each of the criteria
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.
Lars model fitted on the residuals.
All results of the functions that perform error computation, for each fold, each component and error criterion.
It only computes the recommended van Houwelingen CV partial likelihood
criterion criterion. Set allCVcrit=TRUE
to retrieve the 13 other
ones.
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.
See Also larsDR_coxph
Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://wwwirma.ustrasbg.fr/~fbertran/
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) #> Rsquared: 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 #>