estimate_lift_vs_baseline: Estimate Lift vs Baseline

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

Estimate Lift vs Baseline

Description

Estimate Lift vs Baseline

Usage

estimate_lift_vs_baseline(
  input_df,
  distribution,
  priors = list(),
  wrt_option,
  metric = "lift",
  threshold = 0.7
)

Arguments

input_df	Dataframe containing option_name (str) and various other columns depending on the distribution type. See vignette for more details.
distribution	String of the distribution name
priors	Optional list of priors. Defaults will be use otherwise.
wrt_option	string the option loss is calculated with respect to (wrt). Required.
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
threshold	Lift percentage threshold between 0 and 1. (0.7 threshold is "at least 70% lift"). Defaults to 0.7.

Value

numeric value remaining at the specified threshold

Examples

input_df <- tibble::tibble(option_name = c("A", "B", "C"),
    sum_clicks = c(1000, 1000, 1000),
    sum_conversions = c(100, 120, 110))
estimate_lift_vs_baseline(input_df, distribution = "conversion_rate", wrt_option = "A")

grizbayr documentation built on Oct. 9, 2023, 5:10 p.m.