rm(list=ls(all=TRUE))  # clear all variables
graphics.off()  # clear all graphics
# Facial Feedback
# Greg Francis
# PSY 626
# 16 September 2020

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

# load the rethinking library
library(rethinking)

# Dummy variable to indicate condition
FFdata$ConditionIndex <-0*FFdata$Trial
FFdata$ConditionIndex[FFdata$Condition =="PenInTeeth"] =1
FFdata$ConditionIndex[FFdata$Condition =="NoPen"] =2
FFdata$ConditionIndex[FFdata$Condition =="PenInLips"] =3

# Other dummy variable approach
FFdata$PenInTeeth <- ifelse(FFdata$Condition =="PenInTeeth", 1, 0)  
FFdata$NoPen <- ifelse(FFdata$Condition =="NoPen", 1, 0)  
FFdata$PenInLips <- ifelse(FFdata$Condition =="PenInLips", 1, 0)  

# Null model: one mean for all conditions
FFmodel0 <- map(
			alist( HappinessRating ~ dnorm(mu, sigma), 
			mu <- a[Participant],
			a[Participant] ~ dnorm(50, 50),
			sigma ~ dunif(0, 100)
			), data= FFdata , control=list(maxit=10000) ) 
cat("Finished FFmodel0\n")
			
# Alternative model 1: different mean for each condition (dumy variable coding)	
FFmodel1 <- map(
			alist( HappinessRating ~ dnorm(mu, sigma), 
			mu <- a[Participant] + b1*PenInTeeth + b3*PenInLips,
			a[Participant] ~ dnorm(50, 50),
			b1 ~ dnorm(0, 20),
			b3 ~ dnorm(0, 20),
			sigma ~ dunif(0, 100)
			), data= FFdata , control=list(maxit=100000) ) 
cat("Finished FFmodel1\n")
			

# Alternative model 2: different means and sds for each condition			
FFmodel2 <- map(
			alist( HappinessRating ~ dnorm(mu, sigma), 
			mu <- a[Participant] + b1*PenInTeeth + b3*PenInLips,
			a[Participant] ~ dnorm(50, 50),
			b1 ~ dnorm(0, 20),
			b3 ~ dnorm(0, 20),
			sigma[ConditionIndex] ~ dunif(0, 100)
			), data= FFdata , control=list(maxit=100000) ) 
cat("Finished FFmodel2\n")
	
print(compare(FFmodel0, FFmodel1, FFmodel2))