print.summary.multisimsum.R

#' @title print.summary.multisimsum
#' @description Print method for `summary.multisimsum` objects
#' @param x An object of class `summary.multisimsum`.
#' @param digits Number of significant digits used for printing.
#'     Defaults to 4.
#' @param mcse Should Monte Carlo standard errors be reported?
#'     If `mcse = FALSE`, confidence intervals based on Monte Carlo standard errors will be reported instead, see [summary.multisimsum()].
#'     If a `NULL` value is passed, only point estimates are printed regardless of whether Monte Carlo standard errors were computed or not.
#'     Defaults to `TRUE`.
#' @param ... Ignored.
#' @export
#'
#' @examples
#' data(frailty)
#' ms <- multisimsum(
#'   data = frailty, par = "par", true = c(
#'     trt = -0.50,
#'     fv = 0.75
#'   ), estvarname = "b", se = "se", methodvar = "model",
#'   by = "fv_dist"
#' )
#' sms <- summary(ms, stats = c("bias", "cover", "mse"))
#' sms
#'
#' # Printing less significant digits:
#' print(sms, digits = 3)
#'
#' # Printing confidence intervals:
#' print(sms, digits = 3, mcse = FALSE)
#'
#' # Printing values only:
#' print(sms, mcse = NULL)
print.summary.multisimsum <- function(x, digits = 4, mcse = TRUE, ...) {
  ### Check arguments
  arg_checks <- checkmate::makeAssertCollection()
  # `digits` must be an integer value greater than or equal to zero
  checkmate::assert_int(x = digits, lower = 0, upper = Inf, add = arg_checks)
  checkmate::assert_logical(x = mcse, len = 1, null.ok = TRUE, add = arg_checks)
  ### Report if there are any errors
  if (!arg_checks$isEmpty()) checkmate::reportAssertions(arg_checks)

  ### Make sure users are not asking for the moon
  if (!x$control$mcse) {
    mcse <- NULL
    message("Monte Carlo Standard Errors were not computed!\nDisplaying point estimates only.")
  }
  if (is.null(mcse)) {
    cat("Values are:\n\tPoint Estimate\n")
  } else if (mcse) {
    cat("Values are:\n\tPoint Estimate (Monte Carlo Standard Error)\n")
  } else {
    cat(paste0("Values are:\n\tPoint Estimate (", sprintf("%.0f%%", 100 * (x$ci_level)), " Confidence Interval based on Monte Carlo Standard Errors)\n"))
  }

  ### Format summary table
  x <- .format(x = x, digits = digits, mcse = mcse)

  ### Make names of the summary table
  names(x$summ)[names(x$summ) == "description"] <- "Performance Measure"
  names(x$summ)[names(x$summ) == "est"] <- "Estimate"

  ### Order data.frame with results
  x$summ <- .order(data = x$summ, by = c("Performance Measure", x$par, x$methodvar, x$by))

  ### If length(methodvar) > 1 then process multiple columns into one...
  if (length(x$methodvar) > 1) {
    x$summ <- .compact_method_columns(data = x$summ, methodvar = x$methodvar)$data
    x$methodvar <- ":methodvar"
  }

  ### Split summary table by parameter
  par_split <- .split_by(data = x$summ, by = x$par)

  ### Create a list of printed stuff to invisibly return
  full_output <- vector(mode = "list", length = length(par_split))
  names(full_output) <- names(par_split)

  ### Loop printing by parameter
  for (i in seq_along(par_split)) {
    cat(paste("\n", ifelse(i > 1, paste(rep("-", times = options()$width), collapse = ""), ""), ifelse(i > 1, "\n", ""), "\nParameter:", names(par_split)[i], "\n"))
    par_split[[i]][[x$par]] <- NULL

    ### If methodvar, put them side by side
    if (!is.null(x$methodvar)) par_split[[i]] <- .bind_methods(data = par_split[[i]], by = x$by, methodvar = x$methodvar)

    ### Split by summary statistics for printing
    par_split[[i]][["Performance Measure"]] <- droplevels(par_split[[i]][["Performance Measure"]])
    output <- .split_by(data = par_split[[i]], by = "Performance Measure")

    for (ii in seq_along(output)) {
      cat(paste0("\n", names(output)[ii], ":\n"))
      output[[ii]][["Performance Measure"]] <- NULL
      print(output[[ii]], row.names = FALSE)
    }

    ### Add to output to return invisibly
    full_output[[i]] <- lapply(X = output, FUN = function(x) {
      row.names(x) <- NULL
      return(x)
    })
  }
  return(invisible(full_output))
}