CBS_RC {CBSr} R Documentation

## CBS_RC

### Description

Fit either a 1-piece or 2-piece CBS latent utility function to binary risky choice data.

### Usage

CBS_RC(choice, Amt1, Prob1, Amt2, Prob2, numpiece, numfit = NULL)


### Arguments

 choice Vector of 0s and 1s. 1 if the choice was option 1, 0 if the choice was option 2. Amt1 Vector of positive real numbers. Reward amount of choice 1. Prob1 Vector of positive real numbers between 0 and 1. Probability of winning the reward of choice 1. Amt2 Vector of positive real numbers. Reward amount of choice 2. Prob2 Vector of positive real numbers between 0 and 1. Probability of winning the reward of choice 2. numpiece Either 1 or 2. Number of CBS pieces to use. numfit Number of model fits to perform from different starting points. If not provided, numfit = 10*numpiece

### Details

The input data has n choices (ideally n > 100) between two reward options. Option 1 is receiving Amt1 with probability Prob1 and Option 2 is receiving Amt2 with probability Prob2 (e.g., $40 with 53% chance vs.$20 with 90% chance). One of the two options may be certain (i.e., prob = 1; e.g., $40 with 53% chance vs.$20 for sure). choice should be 1 if option 1 is chosen, 0 if option 2 is chosen.

### Value

A list containing the following:

• type: either 'CBS1' or 'CBS2' depending on the number of pieces

• LL: log likelihood of the model

• numparam: number of total parameters in the model

• scale: scaling factor of the logit model

• xpos: x coordinates of the fitted CBS function

• ypos: y coordinates of the fitted CBS function

• AUC: area under the curve of the fitted CBS function. Normalized to be between 0 and 1.

### Examples

# Fit example Risky choice data with 2-piece CBS function.
# Load example data (included with package).
# Each row is a choice between option 1 (Amt with prob) vs option 2 (20 for 100%).
Amount1 = RCdat$Amt1 Prob1 = RCdat$Prob1
Amount2 = 20
Prob2 = 1
Choice = RCdat$Choice # Fit the model out = CBS_RC(Choice,Amount1,Prob1,Amount2,Prob2,2) # Plot the choices (x = Delay, y = relative amount : 20 / risky amount) plot(Prob1[Choice==1],20/Amount1[Choice==1],type = 'p',col="blue",xlim=c(0, 1), ylim=c(0, 1)) points(Prob1[Choice==0],20/Amount1[Choice==0],type = 'p',col="red") # Plot the fitted CBS x = seq(0,1,.01) lines(x,CBSfunc(out$xpos,out\$ypos,x))


[Package CBSr version 1.0.5 Index]