R/estimate_value_remaining.R

In grizbayr: Bayesian Inference for A|B and Bandit Marketing Tests

#' Estimate Value Remaining
#'
#' Estimates value remaining or loss (in terms of percent lift, absolute, or relative).
#'
#' @param input_df Dataframe containing option_name (str) and various other columns
#'     depending on the distribution type. See vignette for more details.
#' @param distribution String of the distribution name
#' @param priors Optional list of priors. Defaults will be use otherwise.
#' @param wrt_option string the option loss is calculated with respect to (wrt). If NULL, the best option will be chosen.
#' @param metric string the type of loss.
#'   absolute will be the difference, on the outcome scale. 0 when best = wrt_option
#'   lift will be the (best - wrt_option) / wrt_option, 0 when best = wrt_option
#'   relative_risk will be the ratio best/wrt_option, 1 when best = wrt_option
#' @param threshold The confidence interval specifying what the "worst case scenario should be.
#'     Defaults to 95\%. (optional)
#'
#' @return numeric value remaining at the specified threshold
#' @export
#' @importFrom stats quantile
#'
#' @examples
#' input_df <- tibble::tibble(option_name = c("A", "B", "C"),
#'     sum_clicks = c(1000, 1000, 1000),
#'     sum_conversions = c(100, 120, 110))
#' estimate_value_remaining(input_df, distribution = "conversion_rate")
#' estimate_value_remaining(input_df,
#'     distribution = "conversion_rate",
#'     threshold = 0.99)
#' estimate_value_remaining(input_df,
#'     distribution = "conversion_rate",
#'     wrt_option = "A",
#'     metric = "absolute")
#'
estimate_value_remaining <- function(input_df, distribution, priors = list(),
                                     wrt_option = NULL, metric = "lift", threshold = 0.95){
  validate_input_df(input_df, distribution)

  # Sample from posterior distribution
  posterior_samples <- sample_from_posterior(input_df, distribution, priors)

  # Calculate Loss Distribution
  estimate_loss(posterior_samples = posterior_samples,
                distribution = distribution,
                wrt_option = wrt_option,
                metric = metric) %>%
    stats::quantile(probs = threshold, na.rm = TRUE)
}