Calculate Area-Under-the-Curve (AUC) Metrics for Delay Discounting Data

This function calculates three types of Area-Under-the-Curve (AUC) metrics for delay discounting data: regular AUC (using raw delays), log10 AUC (using logarithmically scaled delays), and ordinal AUC (using ordinally scaled delays). These metrics provide different perspectives on the rate of delay discounting. Metrics are computed separately for each id, so a data frame with several subjects returns one row per subject.

Usage

calc_aucs(dat)

Arguments

dat

A data frame containing delay discounting data. It must include the following columns:

id: Participant or group identifier.
x: Delay values (e.g., in days).
y: Indifference point values (e.g., subjective value of the delayed reward).

Value

A tibble with the following columns:

id: The participant or group identifier.
auc_regular: The regular AUC, calculated using the raw delay values.
auc_log10: The log10 AUC, calculated using logarithmically transformed delay values.
auc_ord: The ordinal AUC, calculated using ordinally scaled delay values.

Examples

# Example data
data <- data.frame(
  id = rep("P1", 6),
  x = c(1, 7, 30, 90, 180, 365),
  y = c(0.8, 0.5, 0.3, 0.2, 0.1, 0.05)
)

# Calculate AUC metrics for a single participant
calc_aucs(data)
#> # A tibble: 1 × 4
#>   id    auc_regular auc_log10 auc_ord
#>   <chr>       <dbl>     <dbl>   <dbl>
#> 1 P1          0.152     0.359   0.305