R/print.prodlim.R

In prodlim: Product-Limit Estimation for Censored Event History Analysis

#' Print objects in the prodlim library
#' 
#' Pretty printing of objects created with the functionality of the `prodlim'
#' library.
#' 
#' 
#' @aliases print.prodlim print.neighborhood print.Hist
#' @param x Object of class \code{prodlim}, \code{Hist} and
#' \code{neighborhood}.
#' @param \dots Not used.
#' @author Thomas Gerds <tag@@biostat.ku.dk>
#' @seealso \code{\link{summary.prodlim}}, \code{\link{predict.prodlim}}
#' @keywords survival
#' @export 
"print.prodlim" <- function(x,...) {
    cat("\n")
    cat("Call: ")
    print(x$call)
    cat("\n")
    model <- x$model
    ##   message("Estimation method:")
    if (!(model %in% c("survival","competing.risks"))) stop("Under construction")
    if (model=="survival")
        if (x$cens.type=="intervalCensored"){
            message(switch(x$covariate.type,"NPMLE",
                           "Stratified NPMLE estimator",
                           "Stratified NPMLE estimator",
                           "Stratified NPMLE estimator")," for the",ifelse(x$covariate.type==1," "," conditional "),ifelse(x$reverse==FALSE,"event time ","censoring time "),"survival function")
            message(paste("\nIteration steps:",x$n.iter,"\n"))
            ##   summary(x)
            cat("\n")
        }
        else{
            message(switch(x$covariate.type,"Kaplan-Meier estimator",
                           "Stratified Kaplan-Meier estimator",
                           "Stone-Beran estimator",
                           "Stratified Stone-Beran estimator")," for the",ifelse(x$covariate.type==1," "," conditional "),ifelse(x$reverse==FALSE,"event time ","censoring time "),"survival function")
        }
    cat("\n")
    ##   discrete.predictors <- extract.name.from.special(grep("strata.",names(x$X),value=TRUE),pattern="strata\\.")
    ##   continuous.predictors <- extract.name.from.special(grep("NN.",names(x$X),value=TRUE),pattern="NN\\.")
    discrete.predictors <- x$discrete.predictors
    continuous.predictors <- x$continuous.predictors
    if (!is.null(x$cluster))
        message("\nCluster-correlated data:\n\n cluster variable: ",x$cluster,"\n")
    format.disc <- function(name){
        paste(name," (",
              paste(x$xlevels[[name]],collapse=", ",sep=""),")",
              collapse=", ",sep="")
    }
    message(#"Predictor space:\n\n",
        switch(x$covariate.type,
               "No covariates",{
                   if (length(discrete.predictors)==1){
                       c("Discrete predictor variable: ", format.disc(discrete.predictors))
                   }else{
                       c("Discrete predictor variables:\n", sapply(discrete.predictors,function(x)paste("\n - ",format.disc(x))))
                   }},
               c("Continuous predictors: ",continuous.predictors),
               c("  Discrete predictor variables: ",
                 paste(discrete.predictors,collapse=", "),
                 "\nContinuous predictor variables: ",
                 continuous.predictors)))
    summary(x$model.response,verbose=TRUE)
    if (!is.null(x$na.action)){
        cat("\n",
            length(x$na.action),
            ifelse(length(x$na.action)==1,
                   " observation",
                   " observations")," deleted due to missing values.\n",sep="")
    }
    if (attr(x$model.response,"entry.type")=="leftTruncated")
        message("\nThe data are left truncated (delayed entry). The product-limit estimate uses the convention that
in case of ties, on the same 'day', outcome events occur before censored and censored occur before entry.
Note however that the summary tables include the subjects that enter
at time t in the number at risk at time t.")
}