| Title: | Fits Cubic Bezier Spline Functions to Intertemporal and Risky Choice Data |
|---|---|
| Description: | Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>. |
| Authors: | Sangil Lee [aut, cre] |
| Maintainer: | Sangil Lee <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.5 |
| Built: | 2024-11-27 06:28:14 UTC |
| Source: | CRAN |
Fit either a 1-piece or 2-piece CBS latent utility function to binary intertemporal choice data.
CBS_ITC(choice, Amt1, Delay1, Amt2, Delay2, numpiece, numfit = NULL)CBS_ITC(choice, Amt1, Delay1, Amt2, Delay2, numpiece, numfit = NULL)
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. |
Delay1 |
Vector of positive real numbers. Delay until the reward of choice 1. |
Amt2 |
Vector of positive real numbers. Reward amount of choice 2. |
Delay2 |
Vector of positive real numbers. Delay until 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 |
The input data has n choices (ideally n > 100) between two reward options.
Option 1 is receiving Amt1 in Delay1 and Option 2 is receiving Amt2 in Delay2 (e.g., $40 in 20 days vs. $20 in 3 days).
One of the two options may be immediate (i.e., delay = 0; e.g., $40 in 20 days vs. $20 today).
choice should be 1 if option 1 is chosen, 0 if option 2 is chosen.
A list containing the following:
type: either 'CBS1' or 'CBS2' depending on the number of pieces
LL: log likelihood of the model
numparam: number of total parameters in the model
scale: scaling factor of the logit model
xpos: x coordinates of the fitted CBS function
ypos: y coordinates of the fitted CBS function
AUC: area under the curve of the fitted CBS function. Normalized to be between 0 and 1.
normD : The domain of CBS function runs from 0 to normD. Specifically, this is the constant used to normalize all delays between 0 and 1, since CBS is fitted in a unit square first and then scaled up.
# Fit example ITC data with 2-piece CBS function. # Load example data (included with package). # Each row is a choice between option 1 (Amt at Delay) vs option 2 (20 now). Amount1 = ITCdat$Amt1 Delay1 = ITCdat$Delay1 Amount2 = 20 Delay2 = 0 Choice = ITCdat$Choice # Fit the model out = CBS_ITC(Choice,Amount1,Delay1,Amount2,Delay2,2) # Plot the choices (x = Delay, y = relative amount : 20 / delayed amount) plot(Delay1[Choice==1],20/Amount1[Choice==1],type = 'p',col="blue",xlim=c(0, 180), ylim=c(0, 1)) points(Delay1[Choice==0],20/Amount1[Choice==0],type = 'p',col="red") # Plot the fitted CBS x = 0:out$normD lines(x,CBSfunc(out$xpos,out$ypos,x),col="black")# Fit example ITC data with 2-piece CBS function. # Load example data (included with package). # Each row is a choice between option 1 (Amt at Delay) vs option 2 (20 now). Amount1 = ITCdat$Amt1 Delay1 = ITCdat$Delay1 Amount2 = 20 Delay2 = 0 Choice = ITCdat$Choice # Fit the model out = CBS_ITC(Choice,Amount1,Delay1,Amount2,Delay2,2) # Plot the choices (x = Delay, y = relative amount : 20 / delayed amount) plot(Delay1[Choice==1],20/Amount1[Choice==1],type = 'p',col="blue",xlim=c(0, 180), ylim=c(0, 1)) points(Delay1[Choice==0],20/Amount1[Choice==0],type = 'p',col="red") # Plot the fitted CBS x = 0:out$normD lines(x,CBSfunc(out$xpos,out$ypos,x),col="black")
Fit either a 1-piece or 2-piece CBS latent utility function to binary risky choice data.
CBS_RC(choice, Amt1, Prob1, Amt2, Prob2, numpiece, numfit = NULL)CBS_RC(choice, Amt1, Prob1, Amt2, Prob2, numpiece, numfit = NULL)
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 |
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. $20 for sure).
choice should be 1 if option 1 is chosen, 0 if option 2 is chosen.
A list containing the following:
type: either 'CBS1' or 'CBS2' depending on the number of pieces
LL: log likelihood of the model
numparam: number of total parameters in the model
scale: scaling factor of the logit model
xpos: x coordinates of the fitted CBS function
ypos: y coordinates of the fitted CBS function
AUC: area under the curve of the fitted CBS function. Normalized to be between 0 and 1.
# Fit example Risky choice data with 2-piece CBS function. # Load example data (included with package). # Each row is a choice between option 1 (Amt with prob) vs option 2 (20 for 100%). Amount1 = RCdat$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))# 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))
Calculate either the Area Under the Curve (AUC) of a CBS function, or calculate the y coordinates of CBS function given x.
CBSfunc(xpos, ypos, x = NULL)CBSfunc(xpos, ypos, x = NULL)
xpos |
Vector of real numbers of length 1+3n (n = 1, 2, 3, ...), corresponding to Bezier points' x-coordinates of a CBS function |
ypos |
Vector of real numbers of length 1+3n (n = 1, 2, 3, ...), corresponding to Bezier points' y-coordinates of a CBS function |
x |
Vector of real numbers, corresponding to x-coordinates of a CBS function. Default value is Null. |
If x is provided, return y coordinates corresponding to x. If x is not provided, return AUC.
CBSfunc(c(0,0.3,0.6,1),c(0.5, 0.2, 0.7, 0.9)) CBSfunc(c(0,0.3,0.6,1),c(0.5, 0.2, 0.7, 0.9),seq(0,1,0.1))CBSfunc(c(0,0.3,0.6,1),c(0.5, 0.2, 0.7, 0.9)) CBSfunc(c(0,0.3,0.6,1),c(0.5, 0.2, 0.7, 0.9),seq(0,1,0.1))
A dataset containing one sample participant's 120 binary choices between a delayed monetary option (Amt1 in Delay1) and a immediate monetary option ($20 now).
The immediate monetary option was always '$20 now' across all trials
ITCdatITCdat
A data frame with 120 rows and 3 variables:
Delayed reward amount, in dollars
Delay until the receipt of Amt1, in days
Choice between binary options. Choice==1 means participnat chose the delayed option (i.e., Amt1 in Delay1 days). Choice==0 means participnat chose the immediate option (i.e., $20 now)
Kable, J. W., Caulfield, M. K., Falcone, M., McConnell, M., Bernardo, L., Parthasarathi, T., ... & Diefenbach, P. (2017). No effect of commercial cognitive training on brain activity, choice behavior, or cognitive performance. Journal of Neuroscience, 37(31), 7390-7402.
A dataset containing one sample participant's 120 binary choices between a probabilistic monetary option (Amt1 with Prob1 chance of winning) and a certain monetary option ($20 for sure).
The certain monetary option was always '$20 for sure' across all trials
RCdatRCdat
A data frame with 120 rows and 3 variables:
Probabilistic reward amount, in dollars
Probability of winning Amt1, if it were to be chosen
Choice between binary options. Choice==1 means participnat chose the probabilistic option (i.e., Amt1 with Delay1 chance of winning). Choice==0 means participnat chose the certain option (i.e., $20 for sure)
Kable, J. W., Caulfield, M. K., Falcone, M., McConnell, M., Bernardo, L., Parthasarathi, T., ... & Diefenbach, P. (2017). No effect of commercial cognitive training on brain activity, choice behavior, or cognitive performance. Journal of Neuroscience, 37(31), 7390-7402.