plsRcox (version 1.8.2)

predict.plsRcoxmodel: Print method for plsRcox models

Description

This function provides a predict method for the class "plsRcoxmodel"

Usage

# S3 method for plsRcoxmodel
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("lp", "risk", "expected", "terms", "scores"),
  se.fit = FALSE,
  reference = c("strata", "sample", "zero"),
  y = NULL,
  weights,
  methodNA = "adaptative",
  verbose = TRUE,
  ...
)

Value

When type is "response", a matrix of predicted response values is returned.
When type is "scores", a score matrix is returned.

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

predict.coxph

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,]
Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

modpls <- plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=3)

predict(modpls)    
#Identical to predict(modpls,type="lp")    

predict(modpls,type="risk")    
predict(modpls,type="expected")    
predict(modpls,type="terms")    
predict(modpls,type="scores")    

predict(modpls,se.fit=TRUE)    
#Identical to predict(modpls,type="lp")    
predict(modpls,type="risk",se.fit=TRUE)    
predict(modpls,type="expected",se.fit=TRUE)    
predict(modpls,type="terms",se.fit=TRUE)    
predict(modpls,type="scores",se.fit=TRUE)    


#Identical to predict(modpls,type="lp")    
predict(modpls,newdata=X_train_micro[1:5,],type="risk")    
predict(modpls,newdata=X_train_micro[1:5,],type="expected")    
predict(modpls,newdata=X_train_micro[1:5,],type="terms")    
predict(modpls,newdata=X_train_micro[1:5,],type="scores")    

#Identical to predict(modpls,type="lp")    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",se.fit=TRUE)    
predict(modpls,newdata=X_train_micro[1:5,],type="expected",se.fit=TRUE)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",se.fit=TRUE)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores")    

newY_micro <- survival::Surv(Y_train_micro[1:5], C_train_micro[1:5])
predict(modpls,newdata=unname(X_train_micro[1:5,]),type="expected",y=newY_micro)    

predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="risk",comps=4))

predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="terms",comps=4))

predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=1)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=2)    
predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=3)    
try(predict(modpls,newdata=X_train_micro[1:5,],type="scores",comps=4))