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#' Print method for summaries of plsRcox models
#'
#' This function provides a print method for the class
#' \code{"summary.plsRcoxmodel"}
#'
#'
#' @param x an object of the class \code{"summary.plsRcoxmodel"}
#' @param \dots not used
#' @return \item{language}{call of the model}
#' @author Frédéric Bertrand\cr
#' \email{frederic.bertrand@@utt.fr}\cr
#' \url{http://www-irma.u-strasbg.fr/~fbertran/}
#' @seealso \code{\link{print}} and \code{\link{summary}}
#' @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(summary(modpls))
#'
#' @export
print.summary.plsRcoxmodel <- function(x, ...)
{
cat("Call:\n")
print(x$call)
}
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