R/s3oop.R

In jmgirard/mlboot: Bootstrapping for Machine Learning Performance Estimation

## S3 Class Constructors
new_s3_list <- function(x, ..., class) {
  stopifnot(is.list(x))
  stopifnot(is.character(class))
  structure(x, ..., class = class)
}

## S3 Class Methods
#' @export
print.mlboot <- function(x, digits = 3, ...) {
  # Print header
  cat(
    "mlboot Results\n\n",
    "Sample:      \tN=", x$n_total, ", Clusters=", x$n_cluster, "\n",
    "Bootstrap:   \tQuantile, R=", x$n_boot, ", CI=", x$interval, "\n",
    "Metric:      \t", x$metric, ", Null=", x$null, "\n\n",
    sep = ""
  )
  # Print results
  out <- data.frame(
    Estimate = round(x$score_obs, digits),
    Lower.CI = round(x$score_cil, digits),
    Upper.CI = round(x$score_ciu, digits),
    p = sprintf("%.3f", x$pvalue)
  )
  
  out <- dplyr::mutate(out, `p.signif` = sig_star(p))
  
  rownames(out) <- x$score_lab
  
  print.data.frame(out, print.gap = 3L, na.print = "")
  cat("\n")
}

sig_star <- function(p) {
  dplyr::case_when(
    p < .001 ~ "***",
    p < .01 ~ "**",
    p < .05 ~ "*",
    TRUE ~ ""
  )
}

#' @export
tidy.mlboot <- function(x, ...) {

  out <- tibble::tibble(
    term = x$score_lab,
    estimate = x$score_obs,
    lower = x$score_cil,
    upper = x$score_ciu,
    p = x$pvalue
  )
  
  out <- dplyr::mutate(out, p.signif = sig_star(p))
  
  out
}

#' @importFrom generics tidy
#' @export
generics::tidy