R/print.summary.dsm_varprop.R

In dsm: Density Surface Modelling of Distance Sampling Data

#' Print summary of density surface model variance object
#'
#' See [`summary.dsm_varprop`][summary.dsm_varprop] for information.
#'
#' @param x a summary of `dsm` variance object
#' @param \dots unspecified and unused arguments for S3 consistency
#' @return `NULL`
#' @export
#' @author David L. Miller
#' @seealso [`summary.dsm.var`][summary.dsm.var]
#' @keywords utility
print.summary.dsm_varprop <- function(x, ...){

  cat("Summary of uncertainty in a density surface model calculated\n")
  cat(" by variance propagation.\n")
  cat("\nProbability of detection in fitted model and variance model\n")
  #lapply(x$varprop_diagnostic, function(x){
  #  #cat(attr(x, "model_description"), ":\n")
  #  print(x)
  #})
  if(length(x$varprop_diagnostic) > 1){
    for(i in seq_along(x$varprop_diagnostic)){
      cat("Detection function", i,"\n")
      print(x$varprop_diagnostic[[i]])
    }
  }else{
    print(x$varprop_diagnostic[[1]])
  }

  cat("\n")

  ## calculate the CI around the abundance estimate

    # this doesn't transform N, only se(N)
    # this probably should do the following:
    # lower =
    #  qlnorm(alpha/2, log(x$pred.est) - 0.5*log(x$cv^2+1),
    #         sqrt(log(x$cv^2+1)))
    # upper =
    #  qlnorm(1-alpha/2, log(x$pred.est) - 0.5*log(x$cv^2+1),
    #         sqrt(log(x$cv^2+1)))
  unconditional.cv.square <- x$cv^2
  asymp.ci.c.term <- exp(qnorm(1-x$alpha/2) *
                         sqrt(log(1+unconditional.cv.square)))
  asymp.tot <- c(x$pred.est / asymp.ci.c.term,
                 x$pred.est,
                 x$pred.est * asymp.ci.c.term)

  names(asymp.tot) <- c(paste0(x$alpha/2*100, "%"),
                        "Mean",
                        paste0((1-x$alpha/2)*100,"%"))

  cat("Approximate asymptotic confidence interval:\n")
  print(asymp.tot)
  cat("(Using log-Normal approximation)\n")

  cat("\n")
  cat("Detection function CV          :", paste(round(x$detfct.cv, 4),
                                                collapse=", "), "\n")
  cat("\n")
  cat("Point estimate                 :", x$pred.est,"\n")
  cat("Standard error                 :", sqrt(x$var),"\n")

  cat("Coefficient of variation       :", round(x$cv,4),"\n")
  cat("\n")

  invisible()
}