R/sample_conv_rate.R

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

#' Sample Conversion Rate
#'
#' Adds 2 new nested columns to the input_df: `beta_params` and `samples`
#'     `beta_params` in each row should be a tibble of length 2 (\eqn{\alpha}
#'         and \eqn{\beta} parameters)
#'     `samples` in each row should be a tibble of length `n_samples`
#'
#' See update_rules vignette for a mathematical representation.
#' \deqn{conversion_i ~ Bernoulli(\phi)}
#' \deqn{\phi ~ Beta(\alpha, \beta)}
#' Conversion Rate is sampled from a Beta distribution with a Binomial likelihood
#' of an individual converting.
#'
#' @param input_df Dataframe containing option_name (str),
#'     sum_conversions (dbl), and sum_clicks (dbl).
#' @param priors Optional list of priors alpha0 and beta0.
#'     Default \eqn{Beta(1,1)} will be use otherwise.
#' @param n_samples Optional integer value. Defaults to 50,000 samples.
#'
#' @importFrom purrr map map2
#' @importFrom dplyr mutate %>%
#' @importFrom rlang .data
#'
#' @return input_df with 2 new nested columns `beta_params` and `samples`
#'
sample_conv_rate <- function(input_df, priors, n_samples = 5e4){
  input_df %>%
    dplyr::mutate(
      beta_params = purrr::map2(.x = .data$sum_conversions,
                                .y = .data$sum_clicks,
                                ~ update_beta(alpha = .x,
                                              beta = .y - .x,
                                              priors = priors)
                                ),
      samples = purrr::map(.x = .data$beta_params,
                           ~ rbeta(n_samples,
                                   shape1 = .x$alpha,
                                   shape2 = .x$beta)
                           )
    )
}