R/print.DALSM.R

In DALSM: Nonparametric Double Additive Location-Scale Model (DALSM)

## -----------------------------------
## Philippe LAMBERT (ULiege, Oct 2018
## Email:  p.lambert@uliege.be
## Web: http://www.statsoc.ulg.ac.be
## -----------------------------------
#' Print summary information on a \code{DALSM.object}
#'
#' @description
#' Print summary information on a \code{DALSM.object}.
#'
#' @usage \method{print}{DALSM}(x,...)
#'
#' @param x an object of class \code{\link{DALSM.object}}.
#' @param ... additional generic printing arguments.
#'
#' @details Provides summary measures on the estimation of the regression parameters and additive terms
#' in the location and dispersion submodels corresponding to a \code{\link{DALSM.object}} generated by \code{\link{DALSM}}.
#'
#' @return No returned value (just printed summary).
#'
#' @author Philippe Lambert \email{p.lambert@uliege.be}
#' @references Lambert, P. (2021). Fast Bayesian inference using Laplace approximations
#' in nonparametric double additive location-scale models with right- and
#' interval-censored data.
#' \emph{Computational Statistics and Data Analysis}, 161: 107250.
#' <doi:10.1016/j.csda.2021.107250>
#'
#' @examples
#' require(DALSM)
#' data(DALSM_IncomeData)
#' resp = DALSM_IncomeData[,1:2]
#' fit = DALSM(y=resp,
#'             formula1 = ~twoincomes+s(age)+s(eduyrs),
#'             formula2 = ~twoincomes+s(age)+s(eduyrs),
#'             data = DALSM_IncomeData)
#' print(fit)
#'
#' @seealso \code{\link{plot.DALSM}}, \code{\link{DALSM.object}}, \code{\link{DALSM}}.
#'
#' @export
#'
print.DALSM = function(x,...){
  obj.fit = x
  cat("-----------------------------------------------------------------------\n")
  cat("                Double Additive Location-SCALE Model \n")
  cat("-----------------------------------------------------------------------\n")
  if (obj.fit$REML){
    cat("*** REML-type estimation ***\n")
  }
  cat("Fixed effects for Location:\n")
  print(round(obj.fit$fixed.loc,3))
  J1 = obj.fit$regr1$J
  if (J1 > 0){
    cat("\n",length(obj.fit$ED1[,1])," additive term(s) in Location: Eff.dim / Test No effect\n",sep="")
##    cat("\n",length(obj.fit$ED1[,1])," additive term(s) in Location: Eff.dim / Test No effect or Linearity\n",sep="")
    print(round(obj.fit$ED1[,c(1,4,5,2,3)],3))
  }
  cat("\nFixed effects for Dispersion:\n")
  print(round(obj.fit$fixed.disp,3))
  J2 = obj.fit$regr2$J
  if (J2 > 0){
    cat("\n",length(obj.fit$ED2[,1])," additive term(s) in Dispersion: Eff.dim / Test No effect\n",sep="")
##    cat("\n",length(obj.fit$ED2[,1])," additive term(s) in Dispersion: Eff.dim / Test No effect or Linearity\n",sep="")
    print(round(obj.fit$ED2[,c(1,4,5,2,3)],3))
  }
  ##
  cat("\n")
  if (J1 > 0) cat(obj.fit$K1,"  B-splines per additive component in location\n",sep="")
  if (J2 > 0) cat(obj.fit$K2,"  B-splines per additive component in dispersion\n",sep="")
  if (obj.fit$Normality) cat("Normality assumed for the error density\n")
  else cat(obj.fit$K.error,"  B-splines for the error density on (",round(obj.fit$rmin,2),",",round(obj.fit$rmax,2),")\n",sep="")
  cat("\nTotal sample size:", obj.fit$n,"; Credible level for CI:",1-obj.fit$alpha,"\n")
  cat("Uncensored data: ", obj.fit$n.uncensored, " (", round(100*obj.fit$n.uncensored/obj.fit$n,2)," percents)\n",sep="")
  cat("Interval Censored data: ",obj.fit$n.IC, " (",round(100*sum(obj.fit$n.IC)/obj.fit$n,2), " percents)\n",sep="")
  cat("Right censored data: ",obj.fit$n.RC, " (",round(100*obj.fit$n.RC/obj.fit$n,2)," percents)\n",sep="")
  ## message(sprintf("\nTotal sample size: %s ; Credible level for CI: %s",obj.fit$n,1-obj.fit$alpha))
  ## message(sprintf("Uncensored data: %s (%s percents)",obj.fit$n.uncensored,round(100*obj.fit$n.uncensored/obj.fit$n,2)))
  ## message(sprintf("Interval Censored data: %s (%s percents)",obj.fit$n.IC,round(100*sum(obj.fit$n.IC)/obj.fit$n,2)))
  ## message(sprintf("Right censored data: %s (%s percents)",obj.fit$n.RC,round(100*obj.fit$n.RC/obj.fit$n,2)))
  cat("-----------------------------------------------------------------------\n")
  cat("Convergence status:",obj.fit$converged,"\n")
  cat("Elapsed time: ",round(obj.fit$elapsed.time,1)," seconds  (",obj.fit$iter," iterations)\n",sep="")
  cat("-----------------------------------------------------------------------\n")
}