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#' Print of \code{survFitTT} object
#'
#' This is the generic \code{print} S3 method for the \code{survFitTT} class.
#' It prints the underlying JAGS model and some information on the Bayesian
#' inference procedure.
#'
#' @param x An object of class \code{survFitTT}
#' @param \dots Further arguments to be passed to generic methods
#'
#' @return print the model text and the Jags Computing information
#'
#' @examples
#' # (1) Load the data
#' data(cadmium1)
#'
#' # (2) Create an object of class 'survData'
#' cadmium1 <- survData(cadmium1)
#'
#' \donttest{
#' # (3) Run the survFitTT function with the log-logistic
#' # binomial model
#' out <- survFitTT(cadmium1, lcx = c(5, 10, 15, 20, 30, 50, 80),
#' quiet = TRUE)
#'
#' # (4) Print the survFitTT object
#' print(out)
#' }
#'
#' @keywords print
#'
#' @export
print.survFitTT <- function(x, ...) {
# print the model text and the Jags Computing information
# for an object of class survFitTT
# M.C.M.C. informations
cat("Model:\n")
print(x$model)
cat("\nComputing information:\n\n")
cat("\n", "Iterations = ", x$n.iter[["start"]], ":",
x$n.iter[["end"]], "\n", sep = "")
cat("Thinning interval =", x$n.thin, "\n")
cat("Number of chains =", x$n.chains, "\n")
cat("Sample size per chain =",
(x$n.iter[["end"]] - x$n.iter[["start"]]) / x$n.thin + 1, "\n")
}
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