print.plsRcoxmodel.R

#' Print method for plsRcox models
#' 
#' This function provides a print method for the class \code{"plsRcoxmodel"}
#' 
#' 
#' @param x an object of the class \code{"plsRcoxmodel"}
#' @param \dots not used
#' @return \code{NULL}
#' @author Frédéric Bertrand\cr
#' \email{frederic.bertrand@@utt.fr}\cr
#' \url{http://www-irma.u-strasbg.fr/~fbertran/}
#' @seealso \code{\link{print}}
#' @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.\cr
#' 
#' 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.
#' @keywords methods print
#' @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)
#' print(modpls)
#' 
#' @export 
print.plsRcoxmodel <- function(x,...)
{
  cat("Number of required components:\n")
  print(x$nt)
  cat("Number of successfully computed components:\n")
  print(x$computed_nt)
  cat("Coefficients:\n")
  print(x$Coeffs)
  cat("Information criteria and Fit statistics:\n")
  print(x$InfCrit)
  if (!is.null(x$family))
  {
    cat("Model with all the required components:\n")
    print(x$FinalModel)
  }
}