plsRcox (version 1.8.2)

coxpls3DR: Fitting a PLSR model on the (Deviance) Residuals

Description

This function computes the PLSR 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. It uses the package plsRglm.

Usage

coxpls3DR(Xplan, ...)
# S3 method for default
coxpls3DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  ...
)
# S3 method for formula
coxpls3DR(
  Xplan,
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleX = TRUE,
  scaleY = TRUE,
  nt = min(7, ncol(Xplan)),
  typeVC = "none",
  plot = FALSE,
  allres = FALSE,
  dataXplan = NULL,
  subset,
  weights,
  model_frame = FALSE,
  sparse = FALSE,
  sparseStop = TRUE,
  model_matrix = FALSE,
  contrasts.arg = NULL,
  ...
)

Value

If allres=FALSE :

cox_pls3DR

Final Cox-model.

If allres=TRUE :

tt_pls3DR

PLSR components.

cox_pls3DR

Final Cox-model.

pls3DR_mod

The PLSR model.

Details

If allres=FALSE returns only the final Cox-model. If allres=TRUE returns a list with the PLS components, the final Cox-model and the PLSR 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, PLS_lm

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_pls3DR_fit <- coxpls3DR(X_train_micro,Y_train_micro,C_train_micro,nt=7))
(cox_pls3DR_fit2 <- coxpls3DR(~X_train_micro,Y_train_micro,C_train_micro,nt=7))
(cox_pls3DR_fit3 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,dataXplan=X_train_micro_df))
(cox_pls3DR_fit4 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE))
(cox_pls3DR_fit5 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE,sparseStop=FALSE))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3DR_fit,cox_pls3DR_fit2,
cox_pls3DR_fit3,cox_pls3DR_fit4,cox_pls3DR_fit5)