plsRcox (version 1.8.1)

coxpls3: Fitting a Cox-Model on PLSR components

Description

This function computes the the Cox-Model with PLSR components as the explanatory variables. It uses the package plsRglm.

Usage

coxpls3(Xplan, ...)
# S3 method for default
coxpls3(
  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
coxpls3(
  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_pls3

Final Cox-model.

If allres=TRUE :

tt_pls3

PLSR components.

cox_pls3

Final Cox-model.

pls3_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_pls3_fit <- coxpls3(X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
(cox_pls3_fit2 <- coxpls3(~X_train_micro,Y_train_micro,C_train_micro,nt=7,typeVC="none"))
(cox_pls3_fit3 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",data=X_train_micro_df))
(cox_pls3_fit4 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=TRUE))
(cox_pls3_fit5 <- coxpls3(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
data=X_train_micro_df,sparse=FALSE,sparseStop=TRUE))

rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3_fit,cox_pls3_fit2,
cox_pls3_fit3,cox_pls3_fit4,cox_pls3_fit5)