R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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[Previously saved workspace restored]

> # This is the simplest code for applying the DbS model using the DbS.R
> # library to the Kahneman and Tversky (1979) data. 
> #
> # R CMD BATCH Kahneman_Tversky_1979_example.R should produce example output
> source("DbS.R")
> 
> source("Kahneman_Tversky_1979_data.R")
> 
> # Thresholds for discriminating amounts and probabilities
> thresholds=list(x=10, p=.1)
> 
> # Set the context with samples from the Stewart, Brown, and Chater (2006)
> # distributions of gains, losses, and probabilities
> exchange.rate <- 2.06044 # 1 British pound = 2.14 4 Israeli pounds
> context <- list(
+ 	x=exchange.rate * scan("amounts.csv"),
+ 	p=scan("probs.csv")
+ )
Read 2000 items
Read 2000 items
> 
> # Or uncomment the line below for a uniform distribution for the background
> # context of amounts and probabilities
> # context <- list(x=seq(-6000,6000,1), p=seq(-1,1,.01))
> 
> # Probability of selecting each gamble for each choice
> predictions <- DbS(choices=choices, 
+ 	thresholds=thresholds, 
+ 	context=context, 
+ 	prob.sample.context=.5, 
+ 	prob.sample.amount=.5, 
+ 	self.comparison=F, 
+ 	choice.mechanism="random walk",
+ 	choice.threshold=5
+ )
> predictions
$c1
       g1A        g1B 
0.04753537 0.95246463 

$c2
      g2A       g2B 
0.6704868 0.3295132 

$c3
       g3A        g3B 
0.04320237 0.95679763 

$c4
      g4A       g4B 
0.6733273 0.3266727 

$c7
      g7A       g7B 
0.3177522 0.6822478 

$c8
      g8A       g8B 
0.7031562 0.2968438 

$c3p
        g3pA         g3pB 
0.9992997083 0.0007002917 

$c4p
     g4pA      g4pB 
0.3082821 0.6917179 

$c7p
     g7pA      g7pB 
0.7432663 0.2567337 

$c8p
     g8pA      g8pB 
0.2955077 0.7044923 

$c13
     g13A      g13B 
0.2940626 0.7059374 

$c13p
    g13pA     g13pB 
0.7555496 0.2444504 

$c14
       g14A        g14B 
0.998826764 0.001173236 

$c14p
     g14pA      g14pB 
0.00613019 0.99386981 

$c11
      g11A       g11B 
0.04183664 0.95816336 

$c12
       g12A        g12B 
0.999219661 0.000780339 

> # Probability of selecting the first gamble in each choice
> sapply(predictions,function(z) {z[[1]]})
        c1         c2         c3         c4         c7         c8        c3p 
0.04753537 0.67048676 0.04320237 0.67332725 0.31775223 0.70315620 0.99929971 
       c4p        c7p        c8p        c13       c13p        c14       c14p 
0.30828205 0.74326633 0.29550767 0.29406258 0.75554958 0.99882676 0.00613019 
       c11        c12 
0.04183664 0.99921966 
> 
> proc.time()
   user  system elapsed 
  0.364   0.040   0.528