rm(list=ls(all=TRUE))  # clear all variables
graphics.off()  # clear all graphics
# Decision Making
# Greg Francis
# PSY 626
# 02 October 202

# load full data file
DMdata<-read.csv(file="DecisionMaking.csv",header=TRUE,stringsAsFactors=FALSE)

# load the rethinking library
library(rethinking)
		



# Different probabilities

DMdata$ConditionIndex <- coerce_index(DMdata$StatementType)  # 2 is Save, 1 is Die

DMmodel1 <- map(
			alist( ChooseA ~ dbinom(1, p), 
			logit(p) <- a[ConditionIndex],
			a[ConditionIndex] ~ dnorm(0, 10)
			), data= DMdata ) 
		
print(precis(DMmodel1))
cat("Finished with DMmodel1\n")

post<-extract.samples(DMmodel1, n= 1000)
dens(logistic(post$a[,1])) 
dens(logistic(post$a[,2])) 

SavePost <- logistic(post$a[,2])
DiePost <- logistic(post$a[,1])

cat("Probability p_Save > p_Die = ", length(SavePost[SavePost> DiePost])/length(SavePost) )



# Null model
DMmodel0 <- map(
			alist( ChooseA ~ dbinom(1, p), 
			logit(p) <- a,
			a ~ dnorm(0, 10)
			), data= DMdata ) 
		
print(precis(DMmodel0))
cat("Finished with DMmodel0\n")

a=coef(DMmodel0)["a"]

cat("Probability of Sure response: ", logistic(a))



# Framing effect model

DMdata$IsSave <- ifelse(DMdata$StatementType=="Save", 1, 0) 

DMmodel2 <- map(
			alist( ChooseA ~ dbinom(1, p), 
			logit(p) <- a + b*IsSave,
			a ~ dnorm(0, 10),
			b ~ dunif(0, 5)
			), DMdata )  
		
print(precis(DMmodel2))
cat("Finished with DMmodel2\n")

cat("Probability of Sure response for Die: ", logistic(coef(DMmodel2)["a"]))
cat("Probability of Sure response for Save: ", logistic(coef(DMmodel2)["a"]+coef(DMmodel2)["b"]))

print(compare(DMmodel0, DMmodel1, DMmodel2))