Package 'IncrementalityTEST'

Analyze an incrementality experiment collection

Description

A high-level workflow that pairs groups, calculates experiment-level differences, and summarizes the overall effect with t and bootstrap confidence intervals.

Usage

analyze_incrementality(
  data,
  metric,
  id_col = "experiment",
  group_col = "group",
  control = "control",
  treatment = "treatment",
  direction = c("treatment-control", "control-treatment"),
  na_action = c("error", "omit"),
  conf_level = 0.95,
  bootstrap_times = 2000L,
  seed = NULL
)

Arguments

data	A data frame containing experiment, group, and metric columns.
metric	Character scalar naming the metric column.
id_col	Character scalar naming the experiment identifier column.
group_col	Character scalar naming the group column.
control	Value identifying the control group.
treatment	Value identifying the treatment group.
direction	Difference direction: "treatment-control" or "control-treatment".
na_action	How to handle missing metric values: "error" or "omit".
conf_level	Confidence level used by both interval estimators.
bootstrap_times	Number of bootstrap replicates.
seed	Optional bootstrap seed.

Value

An object of class incrementality_analysis.

Examples

results <- data.frame(
  experiment = rep(LETTERS[1:4], each = 2),
  group = rep(c("control", "treatment"), 4),
  revenue_per_user = c(10, 11, 8, 8.8, 12, 13, 9, 10.5)
)
analyze_incrementality(
  results, "revenue_per_user",
  bootstrap_times = 199, seed = 42
)

---

Bootstrap confidence interval for a mean effect

Description

Uses ordinary nonparametric resampling of experiment-level differences.

Usage

bootstrap_incrementality(
  x,
  times = 2000L,
  conf_level = 0.95,
  type = "percentile",
  seed = NULL,
  na_rm = TRUE
)

Arguments

x	Numeric vector of differences.
times	Number of bootstrap replicates.
conf_level	Confidence level between 0 and 1.
type	Interval type. Currently percentile intervals are supported.
seed	Optional integer seed. The caller's random-number state is restored after the function returns.
na_rm	Logical; remove missing values?

Value

An object of class incrementality_bootstrap, represented as a list with the estimate, standard error, interval, and bootstrap replicates.

Examples

bootstrap_incrementality(c(0.4, 0.8, 0.1, 0.6), times = 199, seed = 1)

---

Calculate commerce metrics

Description

Calculates revenue per user (RPU), buyer rate (BR), average order value (AOV), and transactions per buyer (TPB). Inputs are recycled using base R rules, so the function works with individual totals or equally sized vectors.

Usage

calculate_metrics(
  nb_transactions,
  nb_users,
  revenue,
  nb_buyers,
  zero_denominator = c("na", "allow")
)

Arguments

nb_transactions	Numeric vector. Number of transactions.
nb_users	Numeric vector. Number of unique users.
revenue	Numeric vector. Revenue.
nb_buyers	Numeric vector. Number of buyers.
zero_denominator	How to handle division by zero: return NA (default) or allow R to return infinite/undefined values.

Value

A data frame with columns RPU, BR, AOV, and TPB.

Examples

calculate_metrics(
  nb_transactions = 11278,
  nb_users = 297073,
  revenue = 279480.4,
  nb_buyers = 10909
)

---

Statistic for the bootstrap (legacy interface)

Description

Statistic for the bootstrap (legacy interface)

Usage

incrementality_func(datadiff, indices)

Arguments

datadiff	Numeric vector of differences.
indices	Resampled indices.

Value

Mean and estimated variance of the mean.

---

Calculate commerce metrics (legacy interface)

Description

This compatibility wrapper returns the same named list as the original package API. New code should generally use calculate_metrics().

Usage

incrementality_metrics(nb_transactions, nb_users, revenue, nb_buyers)

Arguments

nb_transactions	Numeric vector. Number of transactions.
nb_users	Numeric vector. Number of unique users.
revenue	Numeric vector. Revenue.
nb_buyers	Numeric vector. Number of buyers.

Value

A named list containing RPU, BR, AOV, and TPB.

Examples

incrementality_metrics(11278, 297073, 279480.4, 10909)

---

Legacy vector summary helpers

Description

These small helpers are retained for compatibility. They return NA when all values are missing. fnFreqDay() returns the first mode when tied.

Usage

fnFreqDay(x)

fnMax(x)

fnAve(x)

fnSum(x)

Arguments

x

A vector.

Value

fnAve(), fnMax(), and fnSum() return a numeric scalar. fnFreqDay() returns the name of the most frequent value, as a character scalar.

---

Pair experiment groups and calculate metric differences

Description

For every experiment, pairs one control value with one treatment value and computes treatment - control by default. Each experiment must contain exactly one row for each requested group.

Usage

metric_differences(
  data,
  metric,
  id_col = "experiment",
  group_col = "group",
  control = "control",
  treatment = "treatment",
  direction = c("treatment-control", "control-treatment"),
  na_action = c("error", "omit")
)

Arguments

data	A data frame containing experiment, group, and metric columns.
metric	Character scalar naming the metric column.
id_col	Character scalar naming the experiment identifier column.
group_col	Character scalar naming the group column.
control	Value identifying the control group.
treatment	Value identifying the treatment group.
direction	Difference direction: "treatment-control" or "control-treatment".
na_action	How to handle missing metric values: "error" or "omit".

Value

A data frame with the experiment ID, control and treatment values, and a difference column.

Examples

results <- data.frame(
  experiment = rep(c("A", "B", "C"), each = 2),
  group = rep(c("control", "treatment"), 3),
  RPU = c(10, 11, 8, 9.5, 12, 11.5)
)
metric_differences(results, "RPU")

---

Run a bootstrap (legacy interface)

Description

Requires the suggested boot package and returns a boot object.

Usage

res_boot(mydatadiff, statisticfunc = incrementality_func, nb_boot = 2000L)

Arguments

mydatadiff	Numeric vector of differences.
statisticfunc	Statistic function accepted by boot::boot().
nb_boot	Number of bootstrap samples.

Value

An object returned by boot::boot().

---

Student's t confidence interval for a mean effect

Description

Student's t confidence interval for a mean effect

Usage

t_confidence_interval(x, conf_level = 0.95, na_rm = TRUE)

Arguments

x	Numeric vector of experiment-level differences.
conf_level	Confidence level between 0 and 1.
na_rm	Logical; remove missing values?

Value

A one-row data frame with the sample size, mean, standard error, confidence level, and lower and upper confidence limits.

Examples

t_confidence_interval(c(0.4, 0.8, 0.1, 0.6))

---

Student's t confidence interval (legacy interface)

Description

Student's t confidence interval (legacy interface)

Usage

t_test_cf(datawithID, confinter = 0.05)

Arguments

datawithID	A data frame whose second column contains differences.
confinter	Legacy lower-tail alpha value. For example, 0.05 produces a 95 percent two-sided confidence interval.

Value

A list containing the confidence interval, mean, and standard error.

---

Calculate metric differences (legacy interface)

Description

Compatibility wrapper for datasets containing iabtest_id and abt_group, where control is coded 0 and treatment is coded 1. It preserves the original convention of control minus treatment.

Usage

test_metric(mydata, metric)

Arguments

mydata	A data frame containing iabtest_id, abt_group, and the selected metric.
metric	Character scalar naming the metric.

Value

A two-column data frame with iabtest_id and ⁠inc_<metric>⁠.

---