Check Demand Data for Unsystematic Responding — check_systematic

Modern interface for screening purchase task data using Stein et al. (2015) criteria. Returns a structured object with standardized output vocabulary that is consistent with check_systematic_cp().

Usage

check_systematic_demand(
  data,
  trend_threshold = 0.025,
  bounce_threshold = 0.1,
  max_reversals = 0,
  consecutive_zeros = 2,
  x_var = "x",
  y_var = "y",
  id_var = "id"
)

Arguments

data: Data frame in long format with columns: id, x (price), y (consumption).
trend_threshold: Numeric. Threshold for trend detection (log-log slope). Default 0.025.
bounce_threshold: Numeric. Threshold for bounce proportion. Default 0.10.
max_reversals: Integer. Maximum allowed reversals from zero. Default 0.
consecutive_zeros: Integer. Consecutive zeros required for reversal detection. Default 2 (per Stein et al. 2015).
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".

Value

An object of class beezdemand_systematicity with components:

results: Tibble with one row per subject containing systematicity metrics
type: "demand"
call: The original function call
n_total: Total number of subjects
n_systematic: Number of subjects passing all criteria
n_unsystematic: Number of subjects failing at least one criterion

Details

The results tibble contains standardized columns for both demand and cross-price systematicity checks:

id: Subject identifier
type: "demand" for this function
trend_stat: DeltaQ statistic (log-log slope)
trend_threshold: Threshold used
trend_direction: "down", "up", or "none"
trend_pass: Logical: passed trend criterion
bounce_stat: Bounce proportion
bounce_threshold: Threshold used
bounce_direction: "significant" or "none"
bounce_pass: Logical: passed bounce criterion
reversals: Count of reversals from zero
reversals_pass: Logical: passed reversals criterion
returns: NA for demand (CP-specific)
n_positive: Count of positive values
systematic: Logical: passed all criteria

Examples

# \donttest{
data(apt)
check <- check_systematic_demand(apt)
print(check)
#> 
#> Systematicity Check (demand)
#> ------------------------------ 
#> Total patterns: 10 
#> Systematic: 10 ( 100 %)
#> Unsystematic: 0 ( 0 %)
#> 
#> Use summary() for details, tidy() for per-subject results.
summary(check)
#> 
#> Systematicity Check Summary (demand)
#> ================================================== 
#> 
#> Total patterns: 10 
#> Systematic: 10 ( 100 %)
#> Unsystematic: 0 ( 0 %)
#> 
#> Failures by Criterion:
#> # A tibble: 4 × 3
#>   criterion n_fail pct_fail
#>   <chr>      <int>    <dbl>
#> 1 trend          0        0
#> 2 bounce         0        0
#> 3 reversals      0        0
#> 4 overall        0        0
#> 
tidy(check)
#> # A tibble: 10 × 15
#>    id    type  trend_stat trend_threshold trend_direction trend_pass bounce_stat
#>    <chr> <chr>      <dbl>           <dbl> <chr>           <lgl>            <dbl>
#>  1 19    dema…      0.211           0.025 down            TRUE                 0
#>  2 30    dema…      0.144           0.025 down            TRUE                 0
#>  3 38    dema…      0.788           0.025 down            TRUE                 0
#>  4 60    dema…      0.909           0.025 down            TRUE                 0
#>  5 68    dema…      0.909           0.025 down            TRUE                 0
#>  6 106   dema…      0.818           0.025 down            TRUE                 0
#>  7 113   dema…      0.144           0.025 down            TRUE                 0
#>  8 142   dema…      0.129           0.025 down            TRUE                 0
#>  9 156   dema…      0.862           0.025 down            TRUE                 0
#> 10 188   dema…      0.818           0.025 down            TRUE                 0
#> # ℹ 8 more variables: bounce_threshold <dbl>, bounce_direction <chr>,
#> #   bounce_pass <lgl>, reversals <int>, reversals_pass <lgl>, returns <int>,
#> #   n_positive <int>, systematic <lgl>
# }