README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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# discountr

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The goal of discountr is to provide data analysis tools for discounting studies.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("mncube/discountr")
```
## Example

This is a basic example which shows you how to compute the area under the empirical discounting function using the trapezoid approximation as described
in: 

Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the experimental analysis of behavior, 76 2, 235-43 .
```{r example}
library(discountr)

#Create data frame with successive delays and subjective values
df_disc <- data.frame(delay = sample(1:100, 50, replace = FALSE),
                      value = sample(1:100, 50, replace = TRUE))

#Normalize data in preparation for the auc calculation
#This will ensure that the auc is between 0 and 1
df_disc$delay <- df_disc$delay/max(df_disc$delay)
df_disc$value <- df_disc$value/max(df_disc$value)

#Calculate the area under the curve
output_disc <- trap_auc(df_disc, x = delay, y = value)

#Get the total area under the curve (i.e., sum over trapezoids)
output_disc$total

#Get the data frame containing the area under the curve for each trapezoid
head(output_disc$data)
```

You can also convert the data frame to trapazoidal form and then explicitly provide
the x_lead and y_lead values when computing the area under the empirical discounting function: 
```{r}
#Create data frame with successive delays and subjective values
df_disc_2 <- data.frame(delay = sample(1:100, 50, replace = FALSE),
                      value = sample(1:100, 50, replace = TRUE))

#Normalize data in preparation for auc calculation
df_disc_2$delay <- df_disc_2$delay/max(df_disc_2$delay)
df_disc_2$value <- df_disc_2$value/max(df_disc_2$value)

#Reformat data in preparation to compute the trapezoidal auc
#The resulting data frame will have two new columns:
#delay_lead and value_lead
df_disc_2 <- traper(df_disc_2, x = delay, y = value, rename = TRUE)

#Calculate the area under the curve explicitly defining x_lead and y_lead
output_disc_2<- trap_auc(df_disc_2, x = delay, y = value,
                          x_lead = delay_lead, y_lead = value_lead)

#Get the total area under the curve (i.e., sum over trapezoids)
output_disc_2$total

#Get the data frame containing the area under the curve for each trapezoid
head(output_disc_2$data)
```

The package also contains functions to compute the exponential discounting model (commonly used in economics) and the hyperbolic-like discounting model
(commonly used in behavioral data analysis)
```{r}
#Set up values for models.  In this example assume that rewards are in dollars and delays are in days
A <- 100 #True amount of reward (in dollars for this example)
b <- 1/10 #Discounting rate parameter
X <- 2 #Delay (in days for his example)
s <- 2 #Non-linear scaling factor

#Exponential model (Standard economic account)
discount_exp(A = A, b = b, X = X)

#Hyperbolic-like model (Behavioral model)
discount_hypl(A = A, b = b, X = X, s = s)

#Hyperbolic model (Behavioral model, must use s = 1)
discount_hypl(A = A, b = b, X = X, s = 1)
```