The goal of discAUC is to provide a solution to easily calculate AUC for delay discounting data. It includes logAUC and ordAUC as published in Borges et al. (2016). It also includes a solution for 0 delays for logAUC.
You can install the released version of discAUC from CRAN with:
install.packages("discAUC")This is a basic example which shows you how to solve a common problem:
library(discAUC)
#Calculate AUC for proportional indiference points for each outcome per subject.
AUC(dat = examp_DD,
x_axis = "delay_months",
indiff = "prop_indiff",
amount = 1,
groupings = c("subject","outcome"))
#> # A tibble: 60 × 3
#> # Groups: subject [15]
#> subject outcome AUC
#> <dbl> <chr> <dbl>
#> 1 -988. $100 Gain 0.359
#> 2 -988. alcohol 0.0953
#> 3 -988. entertainment 0.405
#> 4 -988. food 0.158
#> 5 -2 $100 Gain 0.000278
#> 6 -2 alcohol 0.000278
#> 7 -2 entertainment 0.000278
#> 8 -2 food 0.000278
#> 9 -1 $100 Gain 1
#> 10 -1 alcohol 1
#> # ℹ 50 more rows