plsRcox (version 1.8.2)

larsDR_coxph: Fitting a LASSO/LARS model on the (Deviance) Residuals

Description

This function computes the Cox Model based on lars variables computed model with

• as the response: the Residuals of a Cox-Model fitted with no covariate

• as explanatory variables: Xplan.

It uses the package lars to perform PLSR fit.

Usage

larsDR_coxph(Xplan, ...)
# S3 method for default
larsDR_coxph(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = FALSE,
  scaleY = TRUE,
  plot = FALSE,
  typelars = "lasso",
  normalize = TRUE,
  max.steps,
  use.Gram = TRUE,
  allres = FALSE,
  verbose = TRUE,
  ...
)
# S3 method for formula
larsDR_coxph(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = FALSE,
  scaleY = TRUE,
  plot = FALSE,
  typelars = "lasso",
  normalize = TRUE,
  max.steps,
  use.Gram = TRUE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  model_matrix = FALSE,
  verbose = TRUE,
  contrasts.arg = NULL,
  ...
)

Value

If allres=FALSE :

cox_larsDR

Final Cox-model.

If allres=TRUE :

DR_coxph

The (Deviance) Residuals.

larsDR

The LASSO/LARS model fitted to the (Deviance) Residuals.

X_larsDR

The eXplanatory variables.

cox_larsDR

Final Cox-model.

Details

This function computes the LASSO/LARS model with the Residuals of a Cox-Model fitted with an intercept as the only explanatory variable as the response and Xplan as explanatory variables. Default behaviour uses the Deviance residuals.

If allres=FALSE returns only the final Cox-model. If allres=TRUE returns a list with the (Deviance) Residuals, the LASSO/LARS model fitted to the (Deviance) Residuals, the eXplanatory variables and the final Cox-model. allres=TRUE is useful for evluating model prediction accuracy on a test sample.

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

coxph, lars

Examples

data(micro.censure)
data(Xmicro.censure_compl_imp)

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]

(cox_larsDR_fit <- larsDR_coxph(X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
(cox_larsDR_fit <- larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE))
(cox_larsDR_fit <- larsDR_coxph(~.,Y_train_micro,C_train_micro,max.steps=6,
use.Gram=FALSE,scaleX=TRUE,dataXplan=X_train_micro_df))

larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE)
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,scaleX=FALSE)
larsDR_coxph(~X_train_micro,Y_train_micro,C_train_micro,max.steps=6,use.Gram=FALSE,
scaleX=TRUE,allres=TRUE)

rm(X_train_micro,Y_train_micro,C_train_micro,cox_larsDR_fit)