examp_PD: Probability discounting data

In discAUC: Linear and Non-Linear AUC for Discounting Data

Probability discounting data

Description

Probability discounting data with repeated measures for subjects across unlikely outcomes.

Usage

examp_PD

Format

A data frame with 360 rows and 4 variables:

subject

Subject ID. Positive values are experimentally obtained. -987.987 are median indifference points. -1 and -2 values have indifference points of all 0 and all 1, respectively. These extra data were added for testing and debugging to ensure that AUC calculations will result in 0 when all indifference points are zero and 1 when all indifference points are 1.

prob

Probability of receiving the outcome

outcome

Delayed outcome type (all were scaled to $100)

prop_indiff

Indifference point scaled to the maximum amount of each outcome. The maximum amount was the number of servings of each outcome worth $100.

Details

Note: The PD data shares the same indifference points used in the DD data. The PD data were created by using the DD data and using probabilities instead of delays. The PD was created to demonstrate features of the discAUC package and does not represent real data.

Source

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/jeab.623")}

discAUC documentation built on March 31, 2023, 5:26 p.m.