Check Cross-Price Data for Unsystematic Responding

Modern interface for screening cross-price data with standardized output vocabulary aligned with check_systematic_demand().

Usage

check_systematic_cp(
  data,
  trend_threshold = 0.025,
  bounce_threshold_down = 0.1,
  bounce_threshold_up = 0.1,
  bounce_threshold_none = 0.1,
  consecutive_zeros = 2,
  consecutive_nonzeros = 2,
  expected_down = FALSE,
  x_var = "x",
  y_var = "y",
  id_var = "id",
  by = NULL
)

Arguments

data

Data frame with columns: id (optional), x (price), y (consumption).

trend_threshold

Numeric. Threshold for trend detection. Default 0.025.

bounce_threshold_down

Numeric. Bounce threshold for upward trends. Default 0.1.

bounce_threshold_up

Numeric. Bounce threshold for downward trends. Default 0.1.

bounce_threshold_none

Numeric. Bounce threshold when no trend. Default 0.1.

consecutive_zeros

Integer. Zeros for reversal detection. Default 2.

consecutive_nonzeros

Integer. Non-zeros for return detection. Default 2.

expected_down

Logical. Suppress reversal detection if TRUE. Default FALSE.

x_var

Character. Name of the price column. Default "x".

y_var

Character. Name of the consumption column. Default "y".

id_var

Character. Name of the subject identifier column. Default "id".

by

Optional character vector of column names to group by. When supplied, the check is run separately within each unique combination of the by columns. Group columns are prepended to $results. Default NULL (no grouping).

Value

An object of class beezdemand_systematicity with the same structure as check_systematic_demand(), with type = "cp".

Details

If the data contains an id column (or column specified by id_var), each unique ID is checked separately. Otherwise, the entire dataset is treated as a single pattern.

For cross-price data, the wrapper preserves the legacy meaning of check_unsystematic_cp():

• trend_direction and bounce_direction are taken directly from the legacy function outputs.

• trend_pass is set to NA because cross-price systematicity does not use a separate trend “pass/fail” criterion in the same way as purchase-task screening; instead, trend classification determines which bounce rule applies.

• bounce_stat is reported as the proportion relevant to the legacy bounce rule for the detected trend_direction (or expected_down case), computed from the legacy bounce counts and the number of price steps.

Examples

data(etm)
check <- check_systematic_cp(etm)